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Purpose: To quantitatively predict the impact of cardiopulmonary dose on overall survival (OS) after radiotherapy for locally advanced non-small cell lung cancer., Experimental Design: We used the NRG Oncology/RTOG 0617 dataset. The model building procedure was preregistered on a public website. Patients were split between a training and a set-aside validation subset ( N = 306/131). The 191 candidate variables covered disease, patient, treatment, and dose-volume characteristics from multiple cardiopulmonary substructures (atria, lung, pericardium, and ventricles), including the minimum dose to the hottest x% volume (Dx%[Gy]), mean dose of the hottest x% (MOHx%[Gy]), and minimum, mean (Mean[Gy]), and maximum dose. The model building was based on Cox regression and given 191 candidate variables; a Bonferroni-corrected P value threshold of 0.0003 was used to identify predictors. To reduce overreliance on the most highly correlated variables, stepwise multivariable analysis (MVA) was repeated on 1000 bootstrapped replicates. Multivariate sets selected in ≥10% of replicates were fit to the training subset and then averaged to generate a final model. In the validation subset, discrimination was assessed using Harrell c -index, and calibration was tested using risk group stratification., Results: Four MVA models were identified on bootstrap. The averaged model included atria D45%[Gy], lung Mean[Gy], pericardium MOH55%[Gy], and ventricles MOH5%[Gy]. This model had excellent performance predicting OS in the validation subset ( c = 0.89)., Conclusions: The risk of death due to cardiopulmonary irradiation was accurately modeled, as demonstrated by predictions on the validation subset, and provides guidance on the delivery of safe thoracic radiotherapy., (©2020 American Association for Cancer Research.)

Background: To investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis using multifactorial predictive models. Materials and Methods: Baseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearman's correlation (Rs) test was used to correlate clinical and dosimetric variables with toxicities. Toxicity prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1,000 bootstrapped datasets. Results: Grade ≥2 esophagitis and pneumonitis developed in 61 (23.6%) and 36 (14.0%) patients, respectively. The median A2M level was 191 mg/dL (range: 94-511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant negative univariate correlation between baseline A2M levels and esophagitis (Rs = -0.18/ p = 0.003) and between A2M and smoking status (Rs = 0.13/ p = 0.04). Further significant parameters for grade ≥2 esophagitis included age (Rs = -0.32/ p < 0.0001), chemotherapy use (Rs = 0.56/ p < 0.0001), dose per fraction (Rs = -0.57/ p < 0.0001), total dose (Rs = 0.35/ p < 0.0001), and several other dosimetric variables with Rs > 0.5 ( p < 0.0001). The only significant non-dosimetric parameter for grade ≥2 pneumonitis was sex (Rs = -0.32/ p = 0.037) with higher risk for women. For pneumonitis D15 (lung) (Rs = 0.19/ p = 0.006) and D45 (heart) (Rs = 0.16/ p = 0.016) had the highest correlation. LASSO models applied on the validation data were statistically significant and resulted in areas under the receiver operating characteristic curve of 0.84 (esophagitis) and 0.78 (pneumonitis). Multivariate predictive models did not require A2M to reach maximum predictive power. Conclusion: This is the first study showing a likely association of higher baseline A2M values with lower risk of radiation esophagitis and with smoking status. However, the baseline A2M level was not a significant risk factor for radiation pneumonitis., (Copyright © 2020 von Reibnitz, Yorke, Oh, Apte, Yang, Pham, Thor, Wu, Fleisher, Gelb, Deasy and Rimner.)

Purpose: To present a fully automated treatment planning process for proton therapy including beam angle selection using a novel Bayesian optimization approach and previously developed constrained hierarchical fluence optimization method., Methods: We adapted our in-house automated intensity modulated radiation therapy (IMRT) treatment planning system, which is based on constrained hierarchical optimization and referred to as ECHO (expedited constrained hierarchical optimization), for proton therapy. To couple this to beam angle selection, we propose using a novel Bayesian approach. By integrating ECHO with this Bayesian beam selection approach, we obtain a fully automated treatment planning framework including beam angle selection. Bayesian optimization is a global optimization technique which only needs to search a small fraction of the search space for slowly varying objective functions (i.e., smooth functions). Expedited constrained hierarchical optimization is run for some initial beam angle candidates and the resultant treatment plan for each beam configuration is rated using a clinically relevant treatment score function. Bayesian optimization iteratively predicts the treatment score for not-yet-evaluated candidates to find the best candidate to be optimized next with ECHO. We tested this technique on five head-and-neck (HN) patients with two coplanar beams. In addition, tests were performed with two noncoplanar and three coplanar beams for two patients., Results: For the two coplanar configurations, the Bayesian optimization found the optimal beam configuration after running ECHO for, at most, 4% of all potential configurations (23 iterations) for all patients (range: 2%-4%). Compared with the beam configurations chosen by the planner, the optimal configurations reduced the mandible maximum dose by 6.6 Gy and high dose to the unspecified normal tissues by 3.8 Gy, on average. For the two noncoplanar and three coplanar beam configurations, the algorithm converged after 45 iterations (examining <1% of all potential configurations)., Conclusions: A fully automated and efficient treatment planning process for proton therapy, including beam angle optimization was developed. The algorithm automatically generates high-quality plans with optimal beam angle configuration by combining Bayesian optimization and ECHO. As the Bayesian optimization is capable of handling complex nonconvex functions, the treatment score function which is used in the algorithm to evaluate the dose distribution corresponding to each beam configuration can contain any clinically relevant metric., (© 2020 American Association of Physicists in Medicine.)

We present a technique to construct a simplification of a feature network which can be used for interactive data exploration, biological hypothesis generation, and the detection of communities or modules of cofunctional features. These are modules of features that are not necessarily correlated, but nevertheless exhibit common function in their network context as measured by similarity of relationships with neighboring features. In the case of genetic networks, traditional pathway analyses tend to assume that, ideally, all genes in a module exhibit very similar function, independent of relationships with other genes. The proposed technique explicitly relaxes this assumption by employing the comparison of relational profiles. For example, two genes which always activate a third gene are grouped together even if they never do so concurrently. They have common, but not identical, function. The comparison is driven by an average of a certain computationally efficient comparison metric between Gaussian mixture models. The method has its basis in the local connection structure of the network and the collection of joint distributions of the data associated with nodal neighborhoods. It is benchmarked on networks with known community structures. As the main application, we analyzed the gene regulatory network in lung adenocarcinoma, finding a cofunctional module of genes including the pregnancy-specific glycoproteins (PSGs). About 20% of patients with lung, breast, uterus, and colon cancer in The Cancer Genome Atlas (TCGA) have an elevated PSG+ signature, with associated poor group prognosis. In conjunction with previous results relating PSGs to tolerance in the immune system, these findings implicate the PSGs in a potential immune tolerance mechanism of cancers., Competing Interests: Competing interest statement: J.O.D. is a shareholder in PAIGE.AI. The authors have applied for patent protection related to this publication., (Copyright © 2020 the Author(s). Published by PNAS.)

Purpose: To assess the performance and optimize the MR image quality when using a custom-built flexible radiofrequency (RF) spine coil array fitted between the immobilization device and the patient for spine radiotherapy treatment planning., Methods: A 32 channel flexible custom-designed receive-only coil array has been developed for spine radiotherapy simulation for a 3 T Philips MR scanner. Coil signal-to-noise performance and interactions with standard vendor hardware were assessed. In four volunteers, immobilization molds were created with a dummy version of the array within the mold, and subjects were scanned using the custom array in the mold. Phantoms and normal volunteers were scanned with both the custom spine coil array and the vendor's FDA-approved in-table posterior coil array to compare performance., Results: The superior-inferior field of view for the custom spine array was ~30 cm encompassing at least 10 vertebrae. A noise correlation matrix showed at least 25 dB isolation between all coil elements. Signal-to-noise ratio (SNR) calculated on a phantom scan at the depth of the spinal cord was a factor of 3 higher with the form-fit spine array as compared to the vendor's posterior coil array. The body coil B 1 transmit map was equivalent with and without the spine array in place demonstrating that the elements are decoupled from the body coil. Volunteer imaging showed improved SNR as compared to the vendor's posterior coil array. The custom array permitted a high degree of acceleration making possible the acquisition of isotropic high-resolution 1.1 × 1.1 × 1.1 mm 3 three-dimensional data set over a 30-cm section of the spine in less than 5 min., Conclusion: The custom-designed form-fitting flexible spine coil array provided enhanced SNR and increased acceleration compared to the vendor's posterior array. Future studies will assess MR-based spinal cord imaging with the custom coil in comparison to CT myelogram., (© 2020 American Association of Physicists in Medicine.)

Purpose: We present a method for fully automated generation of high quality robust proton treatment plans using hierarchical optimization. To fill the gap between the two common extreme robust optimization approaches, that is, stochastic and worst-case, a robust optimization approach based on the p-norm function is used whereby a single parameter, p , can be used to control the level of robustness in an intuitive way., Methods: A fully automated approach to treatment planning using Expedited Constrained Hierarchical Optimization (ECHO) is implemented in our clinic for photon treatments. ECHO strictly enforces critical (inviolable) clinical criteria as hard constraints and improves the desirable clinical criteria sequentially, as much as is feasible. We extend our in-house developed ECHO codes for proton therapy and integrate it with a new approach for robust optimization. Multiple scenarios accounting for both setup and range uncertainties are included (13scenarios), and the maximum/mean/dose-volume constraints on organs-at-risk (OARs) and target are fulfilled in all scenarios. We combine the objective functions of the individual scenarios using the p-norm function. The p-norm with a parameter p = 1 or p = ∞ result in the stochastic or the worst-case approach, respectively; an intermediate robustness level is obtained by employing p -values in-between. While the worst-case approach only focuses on the worst-case scenario(s), the p-norm approach with a large p value ( p ≈ 20 ) resembles the worst-case approach without completely neglecting other scenarios. The proposed approach is evaluated on three head-and-neck (HN) patients and one water phantom with different parameters, p ∈ 1 , 2 , 5 , 10 , 20 . The results are compared against the stochastic approach (p-norm approach with p = 1 ) and the worst-case approach, as well as the nonrobust approach (optimized solely on the nominal scenario)., Results: The proposed algorithm successfully generates automated robust proton plans on all cases. As opposed to the nonrobust plans, the robust plans have narrower dose volume histogram (DVH) bands across all 13 scenarios, and meet all hard constraints (i.e., maximum/mean/dose-volume constraints) on OARs and the target for all scenarios. The spread in the objective function values is largest for the stochastic approach ( p = 1 ) and decreases with increasing p toward the worst-case approach. Compared to the worst-case approach, the p-norm approach results in DVH bands for clinical target volume (CTV) which are closer to the prescription dose at a negligible cost in the DVH for the worst scenario, thereby improving the overall plan quality. On average, going from the worst-case approach to the p-norm approach with p = 20 , the median objective function value across all the scenarios is improved by 15% while the objective function value for the worst scenario is only degraded by 3%., Conclusion: An automated treatment planning approach for proton therapy is developed, including robustness, dose-volume constraints, and the ability to control the robustness level using the p-norm parameter p , to fit the priorities deemed most important., (© 2020 American Association of Physicists in Medicine.)

Introduction: Durvalumab after concurrent chemoradiation (cCRT) is now standard of care for unresected stage III non-small cell lung cancer (NSCLC). However, there is limited data on radiation pneumonitis (RP) with this regimen. Therefore, we assessed RP and evaluated previously validated toxicity models in predicting for RP in patients treated with cCRT and durvalumab., Methods: Patients treated with cCRT and ≥ 1 dose of durvalumab were evaluated to identify cases of ≥ grade 2 RP. The validity of previously published RP models was assessed in this cohort as well a reference cohort treated with cCRT alone. The timing and incidence of RP was compared between cohorts., Results: In total, 11 (18%) of the 62 patients who received cCRT and durvalumab developed ≥ grade 2 RP a median of 3.4 months after cCRT. The onset of RP among patients treated with cCRT and durvalumab was significantly longer vs the reference cohort (3.4 vs 2.1 months; P = .01). Numerically more patients treated with cCRT and durvalumab developed RP than patients in the reference cohort (18% vs 9%, P = .09). Among patients treated with cCRT and durvalumab, 82% (n = 9) were responsive to treatment with high-dose glucocorticoids. Previously published RP models widely underestimated the rate of RP in patients treated with cCRT and durvalumab [AUC ~ 0.50; p(Hosmer-Lemeshow): 0.98-1.00]., Conclusions: Our data suggest a delayed onset of RP in patients treated with cCRT and durvalumab vs cCRT alone, and for RP to develop in a greater number of patients treated with cCRT and durvalumab. Previously published RP models significantly underestimate the rate of symptomatic RP among patients treated with cCRT and durvalumab., (© 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Public preregistration of study analysis plans (SAPs) is widely recognized for clinical trials, but adopted to a much lesser extent in observational studies. Registration of SAPs prior to analysis is encouraged to not only increase transparency and exactness but also to avoid positive finding bias and better standardize outcome modeling. Efforts to generally standardize outcome modeling, which can be based on clinical trial and/or observational data, have recently spurred. We suggest a three-step SAP concept in which investigators are encouraged to (1) Design the SAP and circulate it among the co-investigators, (2) Log the SAP with a public repository, which recognizes the SAP with a digital object identifier (DOI), and (3) Cite (using the DOI), briefly summarize and motivate any deviations from the SAP in the associated manuscript. More specifically, the SAP should include the scope (brief data and study description, co-investigators, hypotheses, primary outcome measure, study title), in addition to step-by-step details of the analysis (handling of missing data, resampling, defined significance level, statistical function, validation, and variables and parameterization)., (Copyright © 2020 Thor, Oh, Apte and Deasy.)

Background and Purpose: Radiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardio-pulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures., Materials and Methods: The DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability., Results: The DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95-0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value ⩾ 0.69). The expert judged that, on average, 85% of contours were qualitatively equivalent to state-of-the-art manual contouring., Conclusion: The cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Authors.)

We developed and tested the feasibility of computational fluid modeling (CFM) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for quantitative estimation of interstitial fluid pressure (IFP) and velocity (IFV) in patients with head and neck (HN) cancer with locoregional lymph node metastases. Twenty-two patients with HN cancer, with 38 lymph nodes, underwent pretreatment standard MRI, including DCE-MRI, on a 3-Tesla scanner. CFM simulation was performed with the finite element method in COMSOL Multiphysics software. The model consisted of a partial differential equation (PDE) module to generate 3D parametric IFP and IFV maps, using the Darcy equation and K t r a n s values (min -1 , estimated from the extended Tofts model) to reflect fluid influx into tissue from the capillary microvasculature. The Spearman correlation (ρ) was calculated between total tumor volumes and CFM estimates of mean tumor IFP and IFV. CFM-estimated tumor IFP and IFV mean ± standard deviation for the neck nodal metastases were 1.73 ± 0.39 (kPa) and 1.82 ± 0.9 × (10 -7 m/s), respectively. High IFP estimates corresponds to very low IFV throughout the tumor core, but IFV rises rapidly near the tumor boundary where the drop in IFP is precipitous. A significant correlation was found between pretreatment total tumor volume and CFM estimates of mean tumor IFP (ρ = 0.50, P = 0.004). Future studies can validate these initial findings in larger patients with HN cancer cohorts using CFM of the tumor in concert with DCE characterization, which holds promise in radiation oncology and drug-therapy clinical trials., (© 2020 The Authors. Published by Grapho Publications, LLC.)

Background: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classifies breast cancer pCR post-NAC on MRI prior to surgery., Methods: This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre- and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response. Automated radiomics analysis of pre- and post-NAC breast MRI involved image segmentation, radiomics feature extraction, feature pre-filtering, and classifier building through recursive feature elimination random forest (RFE-RF) machine learning. The RFE-RF classifier was trained with nested five-fold cross-validation using (a) radiomics only (model 1) and (b) radiomics and molecular subtype (model 2). Class imbalance was addressed using the synthetic minority oversampling technique., Results: Two hundred seventy-three women with 278 invasive breast cancers were included; the training set consisted of 222 cancers (61 pCR, 161 no-pCR; mean age 51.8 years, SD 11.8), and the independent test set consisted of 56 cancers (13 pCR, 43 no-pCR; mean age 51.3 years, SD 11.8). There was no significant difference in pCR or molecular subtype between the training and test sets. Model 1 achieved a cross-validation AUROC of 0.72 (95% CI 0.64, 0.79) and a similarly accurate (P = 0.1) AUROC of 0.83 (95% CI 0.71, 0.94) in both the training and test sets. Model 2 achieved a cross-validation AUROC of 0.80 (95% CI 0.72, 0.87) and a similar (P = 0.9) AUROC of 0.78 (95% CI 0.62, 0.94) in both the training and test sets., Conclusions: This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC.

Background: We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data., Methods: We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic ( P  < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided., Results: Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association ( P  < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant ( P  < 5.00 × 10 -8 ) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P  = .01) and marginally improved with clinical variables (area under the curve = 0.60, P  = .005). Single nucleotide polymorphisms found to be associated ( P  < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P  = .03), cognitive disorders ( P  = 1.51 × 10 -12 ), and synaptic transmission ( P  = 6.28 × 10 -8 )., Conclusions: Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

An open-source library of implementations for deep-learning-based image segmentation and outcomes models based on radiotherapy and radiomics is presented. As oncology treatment planning becomes increasingly driven by automation, such a library of model implementations is crucial to (i) validate existing models on datasets collected at different institutions, (ii) automate segmentation, (iii) create ensembles for improving performance and (iv) incorporate validated models in the clinical workflow. Inclusion of deep-learning-based image segmentation and outcomes models in the same library provides a fully automated and reproduceable pipeline to estimate prognosis. The library was developed with the Computational Environment for Radiological Research (CERR) software platform. Centralizing model implementations in CERR builds upon its rich set of radiotherapy and radiomics tools and caters to the world-wide user base. CERR provides well-validated feature extraction pipelines for radiotherapy dosimetry and radiomics with fine control over the calculation settings, allowing users to select appropriate parameters used in model derivation. Models for automatic image segmentation are distributed via containers, allowing them to be deployed with a variety of scientific computing architectures. The library includes implementations of popular DVH-based models outlined in the Quantitative Analysis of Normal Tissue Effects in the Clinic effort and recently published literature. Radiomics models include features from the Image Biomarker Standardization Initiative and application-specific features found to be relevant across multiple sites and image modalities. The library is distributed as a module within CERR at https://www.github.com/cerr/CERR under the GNU-GPL copyleft with additional restrictions on clinical and commercial use and provision to dual license in future., (Copyright © 2020. Published by Elsevier Ltd.)

The Wasserstein distance is a powerful metric based on the theory of optimal mass transport. It gives a natural measure of the distance between two distributions with a wide range of applications. In contrast to a number of the common divergences on distributions such as Kullback-Leibler or Jensen-Shannon, it is (weakly) continuous, and thus ideal for analyzing corrupted and noisy data. Until recently, however, no kernel methods for dealing with nonlinear data have been proposed via the Wasserstein distance. In this work, we develop a novel method to compute the L 2 -Wasserstein distance in reproducing kernel Hilbert spaces (RKHS) called kernel L 2 -Wasserstein distance, which is implemented using the kernel trick. The latter is a general method in machine learning employed to handle data in a nonlinear manner. We evaluate the proposed approach in identifying computed tomography (CT) slices with dental artifacts in head and neck cancer, performing unsupervised hierarchical clustering on the resulting Wasserstein distance matrix that is computed on imaging texture features extracted from each CT slice. We further compare the performance of kernel Wasserstein distance with alternatives including kernel Kullback-Leibler divergence we previously developed. Our experiments show that the kernel approach outperforms classical non-kernel approaches in identifying CT slices with artifacts., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

There is now strong clinical and preclinical evidence that lymphocytes, for example, CD8 + T cells, are key effectors of immunotherapy and that irradiation of large blood vessels, the heart, and lymphoid organs (including nodes, spleen, bones containing bone marrow, and thymus in children) causes transient or persistent lymphopenia. Furthermore, there is extensive clinical evidence, across multiple cancer sites and treatment modalities, that lymphopenia correlates strongly with decreased overall survival. At the moment, we lack quantitative evidence to establish the relationship between dose-volume and dose-rate to critical normal structures and lymphopenia. Therefore, we propose that data should be systematically recorded to characterise a possible quantitative relationship. This might enable us to improve the efficacy of radiotherapy and develop strategies to predict and prevent treatment-related lymphopenia. In anticipation of more quantitative data, we recommend the application of the principle of As Low As Reasonably Achievable to lymphocyte-rich regions for radiotherapy treatment planning to reduce the radiation doses to these structures, thus moving toward "Lymphocyte-Sparing Radiotherapy.", (Copyright © 2019. Published by Elsevier Inc.)

Tissue survival responses to ionizing radiation are nonlinear with dose, rather yielding tissue-specific descending curves that impede straightforward analysis of biologic effects. Apoptotic cell death often occurs at low doses, while at clinically relevant intermediate doses, double-strand break misrepair yields mitotic death that determines outcome. As researchers frequently use a single low dose for experimentation, such strategies may inaccurately depict inherent tissue responses. Cutting edge radiobiology has adopted full dose survival profiling and devised mathematical algorithms to fit curves to observed data to generate highly reproducible numerical data that accurately define clinically relevant inherent radiosensitivities. Here, we established a protocol for irradiating organoids that delivers radiation profiles simulating the organ of origin. This technique yielded highly similar dose-survival curves of small and large intestinal crypts in vivo and their cognate organoids analyzed by the single-hit multi-target (SHMT) algorithm, outcomes reflecting the inherent radiation profile of their respective Lgr5 + stem cell populations. As this technological advance is quantitative, it will be useful for accurate evaluation of intestinal (patho)physiology and drug screening. SIGNIFICANCE: These findings establish standards for irradiating organoids that deliver radiation profiles that phenocopy the organ of origin. See related commentary by Muschel et al., p. 927 ., (©2019 American Association for Cancer Research.)

The purpose of this study was to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Women's Environmental Cancer and Radiation Epidemiology (WECARE) Study who were at increased risk of RCBC because they were ≤ 40 years of age at first diagnosis of breast cancer and received a scatter radiation dose > 1 Gy to the contralateral breast. A previously reported algorithm, preconditioned random forest regression, was applied to predict the risk of developing RCBC. The resulting model produced an area under the curve (AUC) of 0.62 (p = 0.04) on hold-out validation data. The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent manner. The key connected genes and proteins that are identified in this analysis were previously identified as relevant to breast cancer, radiation response, or both. In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC., Competing Interests: P.C. is a stockholder in AMGEN; J.O.D. has research contracts with Varian Medical Systems and Philips and is a shareholder in Paige.AI. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Purpose: Dose-volume constraints (DVCs) continue to be common features in intensity-modulated radiation therapy (IMRT) prescriptions, but they are non-convex and difficult to incorporate. We propose computationally efficient methods to incorporate dose-volume constraints (DVCs) into automated IMRT planning., Methods: We propose a two-phase approach: in phase-1, we solve a convex approximation with DVCs. Although this convex approximation does not guarantee DVC satisfaction, it provides crucial initial information about voxels likely to receive doses below DVC thresholds. Subsequently, phase-2 solves an optimization problem with maximum dose constraints imposed on those subthreshold voxels. We further categorize DVCs into hard- and soft-DVCs, where hard-DVCs are strictly enforced by the optimization and soft-DVCs are encouraged in the objective function. We tested this approach in our automated treatment planning system which is based on hierarchical constrained optimization. Performance is demonstrated on a series of paraspinal, lung, oligometastasis, and prostate cases as well as a small paraspinal case for which we can computationally afford to obtain a ground-truth by solving a non-convex optimization problem., Results: The proposed algorithm successfully meets all the hard-DVCs while increasing the overall computational time of the baseline planning process (without DVCs) by 20%, 10%, and 11% for paraspinal, oligometastasis, and prostate cases, respectively. For a soft-DVC applied to the lung case, the dose-volume histogram curve moves toward the desired direction and the computational time is increased by 11%. For a low-resolution paraspinal case, the ground-truth solution process using mixed-integer programming methods required 15 h while the proposed algorithm converges in only 2 min with a proximal solution., Conclusions: A computationally tractable algorithm to handle hard- and soft-DVCs is developed which is capable of satisfying DVCs without any parameter tweaking. Although the algorithm is demonstrated in our in-house developed automated treatment planning system, it can potentially be used in any constrained optimization framework., (© 2019 American Association of Physicists in Medicine.)

Purpose: To evaluate pix2pix and CycleGAN and to assess the effects of multiple combination strategies on accuracy for patch-based synthetic computed tomography (sCT) generation for magnetic resonance (MR)-only treatment planning in head and neck (HN) cancer patients., Materials and Methods: Twenty-three deformably registered pairs of CT and mDixon FFE MR datasets from HN cancer patients treated at our institution were retrospectively analyzed to evaluate patch-based sCT accuracy via the pix2pix and CycleGAN models. To test effects of overlapping sCT patches on estimations, we (a) trained the models for three orthogonal views to observe the effects of spatial context, (b) we increased effective set size by using per-epoch data augmentation, and (c) we evaluated the performance of three different approaches for combining overlapping Hounsfield unit (HU) estimations for varied patch overlap parameters. Twelve of twenty-three cases corresponded to a curated dataset previously used for atlas-based sCT generation and were used for training with leave-two-out cross-validation. Eight cases were used for independent testing and included previously unseen image features such as fused vertebrae, a small protruding bone, and tumors large enough to deform normal body contours. We analyzed the impact of MR image preprocessing including histogram standardization and intensity clipping on sCT generation accuracy. Effects of mDixon contrast (in-phase vs water) differences were tested with three additional cases. The sCT generation accuracy was evaluated using mean absolute error (MAE) and mean error (ME) in HU between the plan CT and sCT images. Dosimetric accuracy was evaluated for all clinically relevant structures in the independent testing set and digitally reconstructed radiographs (DRRs) were evaluated with respect to the plan CT images., Results: The cross-validated MAEs for the whole-HN region using pix2pix and CycleGAN were 66.9 ± 7.3 vs 82.3 ± 6.4 HU, respectively. On the independent testing set with additional artifacts and previously unseen image features, whole-HN region MAEs were 94.0 ± 10.6 and 102.9 ± 14.7 HU for pix2pix and CycleGAN, respectively. For patients with different tissue contrast (water mDixon MR images), the MAEs increased to 122.1 ± 6.3 and 132.8 ± 5.5 HU for pix2pix and CycleGAN, respectively. Our results suggest that combining overlapping sCT estimations at each voxel reduced both MAE and ME compared to single-view non-overlapping patch results. Absolute percent mean/max dose errors were 2% or less for the PTV and all clinically relevant structures in our independent testing set, including structures with image artifacts. Quantitative DRR comparison between planning CTs and sCTs showed agreement of bony region positions to <1 mm., Conclusions: The dosimetric and MAE based accuracy, along with the similarity between DRRs from sCTs, indicate that pix2pix and CycleGAN are promising methods for MR-only treatment planning for HN cancer. Our methods investigated for overlapping patch-based HU estimations also indicate that combining transformation estimations of overlapping patches is a potential method to reduce generation errors while also providing a tool to potentially estimate the MR to CT aleatoric model transformation uncertainty. However, because of small patient sample sizes, further studies are required., (© 2019 American Association of Physicists in Medicine.)

Background and Purpose: We test the hypothesis that unsatisfactory outcomes after concurrent chemoradiotherapy (RT) for locally advanced non-small cell lung cancer (LA-NSCLC) are due to treatment-related immunosuppression., Materials and Methods: White blood cells (WBCs) data were retrospectively collected for all stage IIIA/B LA-NSCLC patients before and after (after RT: two weeks, two months, four months) concurrent chemotherapy and intensity-modulated RT in which patients were treated to a median of 63 Gy (1.8-2.0 Gy/fractions) in 2004-2014 (N = 155). Nine WBC variables were generated from pre-RT normalized absolute number of lymphocytes and neutrophils (L, N) and the N/L thereof. A WBC variable was considered a predictor for overall survival and recurrence (distant/local/nodal/regional) if p ≤ 0.006 (corrected for 9 variables) from Cox regression and competing risk analyses, respectively; both conducted using bootstrap resampling. Finally, a WBC variable predicting any of the outcomes was linearly associated with each of eleven disease/patient/treatment characteristics (p ≤ 0.005; corrected for 11 characteristics)., Results: At the three post-RT time points both L and N significantly decreased (p < 0.0003). Overall survival was associated with N and N/L four months post-RT (p = 0.00001, 0.0003); regional recurrence was associated with L two months post-RT (p < 0.0001). None of the disease/patient/treatment characteristics was significantly associated with any of the three WBC variables that predicted OS or recurrence (lowest p-value: p = 0.006 for tumour stage,)., Conclusion: Significantly lower WBC levels after concurrent chemo-RT for LA-NSCLC are associated with worse long-term outcomes. The mechanism behind this treatment-related immunosuppression requires further analysis likely including other characteristics as no statistically significant association was established between any WBC variable and the disease/patient/treatment characteristics., Competing Interests: Declaration of Competing Interest None. AR reports relevant grants outside the submitted work from Varian Medical Systems, Boehringer Ingelheim, Pfizer, and AztraZeneca, personal fees from AztraZeneca, Merck, Research to Practice, and Cybrexa, as well as non-financial support from Philips/Elekta., (Copyright © 2019. Published by Elsevier B.V.)

Purpose: Conventional radiotherapy for large lung tumors is given over several weeks, during which the tumor typically regresses in a highly nonuniform and variable manner. Adaptive radiotherapy would ideally follow these shape changes, but we need an accurate method to extrapolate tumor shape changes. We propose a computationally efficient algorithm to quantitate tumor surface shape changes that makes minimal assumptions, identifies fixed points, and can be used to predict future tumor geometrical response., Methods: A novel combination of nonrigid iterative closest point (ICP) and local shape-preserving map algorithms, LDeform, is developed to enable visualization, prediction, and categorization of both diffeomorphic and nondiffeomorphic tumor deformations during an extended course of radiotherapy., Results: We tested and validated our technique on 31 longitudinal CT/MRI subjects, with five to nine time points each. Based on this tumor deformation analysis, regions of local growth, shrinkage, and anchoring are identified and tracked across multiple time points. This categorization in turn represents a rational biomarker of local response. Results demonstrate useful predictive power, with an averaged Dice coefficient and surface mean-squared error of 0.85 and 2.8 mm, respectively, over all images., Conclusions: We conclude that the LDeform algorithm can facilitate the adaptive decision-making process during lung cancer radiotherapy., (© 2019 American Association of Physicists in Medicine.)

Unbalanced optimal mass transport (OMT) seeks to remove the conservation of mass constraint by adding a source term to the standard continuity equation in the Benamou-Brenier formulation of OMT. In this study, we show how the unbalanced case fits into the vector-valued OMT framework simply by adding an auxiliary source layer and taking the flow between the source layer and the original layer(s) as the source term. This allows for unbalanced models both in the scalar and vector-valued density settings. The results are demonstrated on a number of synthetic and real vector-valued data sets.

Purpose: We report on the clinical performance of a fully automated approach to treatment planning based on a Pareto optimal, constrained hierarchical optimization algorithm, named Expedited Constrained Hierarchical Optimization (ECHO)., Methods and Materials: From April 2017 to October 2018, ECHO produced 640 treated plans for 523 patients who underwent stereotactic body radiation therapy (RT) for paraspinal and other metastatic tumors. A total of 182 plans were for 24 Gy in a single fraction, 387 plans were for 27 Gy in 3 fractions, and the remainder were for other prescriptions or fractionations. Of the plans, 84.5% were for paraspinal tumors, with 69, 302, and 170 in the cervical, thoracic, and lumbosacral spine, respectively. For each case, after contouring, a template plan using 9 intensity modulated RT fields based on disease site and tumor location was sent to ECHO through an application program interface plug-in from the treatment planning system. ECHO returned a plan that satisfied all critical structure hard constraints with optimal target volume coverage and the lowest achievable normal tissue doses. Upon ECHO completion, the planner received an e-mail indicating the plan was ready for review. The plan was accepted if all clinical criteria were met. Otherwise, a limited number of parameters could be adjusted for another ECHO run., Results: The median planning target volume size was 84.3 cm 3 (range, 6.9-633.2). The median time to produce 1 ECHO plan was 63.5 minutes (range, 11-340 minutes) and was largely dependent on the field sizes. Of the cases, 79.7% required 1 run to produce a clinically accepted plan, 13.3% required 1 additional run with minimal parameter adjustments, and 7.0% required ≥2 additional runs with significant parameter modifications. All plans met or bettered the institutional clinical criteria., Conclusions: We successfully implemented automated stereotactic body RT paraspinal and other metastatic tumors planning. ECHO produced high-quality plans, improved planning efficiency and robustness, and enabled expedited treatment planning at our clinic., (© 2019 The Author(s).)

Purpose: Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows., Method: Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and underwent DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ( Φ F ) and backward flux ( Φ B ). OMT-derived Φ F was compared with the volume transfer constant for CA, K trans , derived from the Extended Tofts Model (ETM)., Results: The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 ± 0.0091 ( min - 1 ) whereas the mean advective component was 0.0052 ± 0.0086 ( min - 1 ) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67% to 18.76% and 12.76% to 30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ( Φ F ) is comparable with the ETM-derived K trans ., Conclusions: This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC., (© 2019 International Society for Magnetic Resonance in Medicine.)

Purpose: Manual delineation of head and neck (H&N) organ-at-risk (OAR) structures for radiation therapy planning is time consuming and highly variable. Therefore, we developed a dynamic multiatlas selection-based approach for fast and reproducible segmentation., Methods: Our approach dynamically selects and weights the appropriate number of atlases for weighted label fusion and generates segmentations and consensus maps indicating voxel-wise agreement between different atlases. Atlases were selected for a target as those exceeding an alignment weight called dynamic atlas attention index. Alignment weights were computed at the image level and called global weighted voting (GWV) or at the structure level and called structure weighted voting (SWV) by using a normalized metric computed as the sum of squared distances of computed tomography (CT)-radiodensity and modality-independent neighborhood descriptors (extracting edge information). Performance comparisons were performed using 77 H&N CT images from an internal Memorial Sloan-Kettering Cancer Center dataset (N = 45) and an external dataset (N = 32) using Dice similarity coefficient (DSC), Hausdorff distance (HD), 95th percentile of HD, median of maximum surface distance, and volume ratio error against expert delineation. Pairwise DSC accuracy comparisons of proposed (GWV, SWV) vs single best atlas (BA) or majority voting (MV) methods were performed using Wilcoxon rank-sum tests., Results: Both SWV and GWV methods produced significantly better segmentation accuracy than BA (P < 0.001) and MV (P < 0.001) for all OARs within both datasets. SWV generated the most accurate segmentations with DSC of: 0.88 for oral cavity, 0.85 for mandible, 0.84 for cord, 0.76 for brainstem and parotids, 0.71 for larynx, and 0.60 for submandibular glands. SWV's accuracy exceeded GWV's for submandibular glands (DSC = 0.60 vs 0.52, P = 0.019)., Conclusions: The contributed SWV and GWV methods generated more accurate automated segmentations than the other two multiatlas-based segmentation techniques. The consensus maps could be combined with segmentations to visualize voxel-wise consensus between atlases within OARs during manual review., (© 2019 American Association of Physicists in Medicine.)

Pulmonary perfusion with dynamic contrast-enhanced (DCE-) MRI is typically assessed using a single-input tracer kinetic model. Preliminary studies based on perfusion CT are indicating that dual-input perfusion modeling of lung tumors may be clinically valuable as lung tumors have a dual blood supply from the pulmonary and aortic system. This study aimed to investigate the feasibility of fitting dual-input tracer kinetic models to DCE-MRI datasets of thoracic malignancies, including malignant pleural mesothelioma (MPM) and nonsmall cell lung cancer (NSCLC), by comparing them to single-input (pulmonary or systemic arterial input) tracer kinetic models for the voxel-level analysis within the tumor with respect to goodness-of-fit statistics. Fifteen patients (five MPM, ten NSCLC) underwent DCE-MRI prior to radiotherapy. DCE-MRI data were analyzed using five different single- or dual-input tracer kinetic models: Tofts-Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH) and distributed parameter (DP) models. The pulmonary blood flow (BF), blood volume (BV), mean transit time (MTT), permeability-surface area product (PS), fractional interstitial volume (v I ), and volume transfer constant (K Trans ) were calculated for both single- and dual-input models. The pulmonary arterial flow fraction (γ), pulmonary arterial blood flow (BF PA ) and systemic arterial blood flow (BF A ) were additionally calculated for only dual-input models. The competing models were ranked and their Akaike weights were calculated for each voxel according to corrected Akaike information criterion (cAIC). The optimal model was chosen based on the lowest cAIC value. In both types of tumors, all five dual-input models yielded lower cAIC values than their corresponding single-input models. The 2CX model was the best-fitted model and most optimal in describing tracer kinetic behavior to assess microvascular properties in both MPM and NSCLC. The dual-input 2CX-model-derived BF A was the most significant parameter in differentiating adenocarcinoma from squamous cell carcinoma histology for NSCLC patients., (© 2019 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: Accurate tumor segmentation is a requirement for magnetic resonance (MR)-based radiotherapy. Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmentation approach that augments training data using pseudo MR images produced by transforming expert-segmented CT images was developed., Methods: Eighty-one T2-weighted MRI scans from 28 patients with non-small cell lung cancers (nine with pretreatment and weekly MRI and the remainder with pre-treatment MRI scans) were analyzed. Cross-modality model encoding the transformation of CT to pseudo MR images resembling T2w MRI was learned as a generative adversarial deep learning network. This model was used to translate 377 expert segmented non-small cell lung cancer CT scans from the Cancer Imaging Archive into pseudo MRI that served as additional training set. This method was benchmarked against shallow learning using random forest, standard data augmentation, and three state-of-the art adversarial learning-based cross-modality data (pseudo MR) augmentation methods. Segmentation accuracy was computed using Dice similarity coefficient (DSC), Hausdorff distance metrics, and volume ratio., Results: The proposed approach produced the lowest statistical variability in the intensity distribution between pseudo and T2w MR images measured as Kullback-Leibler divergence of 0.069. This method produced the highest segmentation accuracy with a DSC of (0.75 ± 0.12) and the lowest Hausdorff distance of (9.36 mm ± 6.00 mm) on the test dataset using a U-Net structure. This approach produced highly similar estimations of tumor growth as an expert (P = 0.37)., Conclusions: A novel deep learning MR segmentation was developed that overcomes the limitation of learning robust models from small datasets by leveraging learned cross-modality information using a model that explicitly incorporates knowledge of tumors in modality translation to augment segmentation training. The results show the feasibility of the approach and the corresponding improvement over the state-of-the-art methods., (© 2019 American Association of Physicists in Medicine.)

Background and Purpose: Magnetic resonance (MR) only radiation therapy for prostate treatment provides superior contrast for defining targets and organs-at-risk (OARs). This study aims to develop a deep learning model to leverage this advantage to automate the contouring process., Materials and Methods: Six structures (bladder, rectum, urethra, penile bulb, rectal spacer, prostate and seminal vesicles) were contoured and reviewed by a radiation oncologist on axial T2-weighted MR image sets from 50 patients, which constituted expert delineations. The data was split into a 40/10 training and validation set to train a two-dimensional fully convolutional neural network, DeepLabV3+, using transfer learning. The T2-weighted image sets were pre-processed to 2D false color images to leverage pre-trained (from natural images) convolutional layers' weights. Independent testing was performed on an additional 50 patient's MR scans. Performance comparison was done against a U-Net deep learning method. Algorithms were evaluated using volumetric Dice similarity coefficient (VDSC) and surface Dice similarity coefficient (SDSC)., Results: When comparing VDSC, DeepLabV3+ significantly outperformed U-Net for all structures except urethra ( P < 0.001). Average VDSC was 0.93 ± 0.04 (bladder), 0.83 ± 0.06 (prostate and seminal vesicles [CTV]), 0.74 ± 0.13 (penile bulb), 0.82 ± 0.05 (rectum), 0.69 ± 0.10 (urethra), and 0.81 ± 0.1 (rectal spacer). Average SDSC was 0.92 ± 0.1 (bladder), 0.85 ± 0.11 (prostate and seminal vesicles [CTV]), 0.80 ± 0.22 (penile bulb), 0.87 ± 0.07 (rectum), 0.85 ± 0.25 (urethra), and 0.83 ± 0.26 (rectal spacer)., Conclusion: A deep learning-based model produced contours that show promise to streamline an MR-only planning workflow in treating prostate cancer.

Purpose: To predict the spatial and temporal trajectories of lung tumor during radiotherapy monitored under a longitudinal magnetic resonance imaging (MRI) study via a deep learning algorithm for facilitating adaptive radiotherapy (ART)., Methods: We monitored 10 lung cancer patients by acquiring weekly MRI-T2w scans over a course of radiotherapy. Under an ART workflow, we developed a predictive neural network (P-net) to predict the spatial distributions of tumors in the coming weeks utilizing images acquired earlier in the course. The three-step P-net consisted of a convolutional neural network to extract relevant features of the tumor and its environment, followed by a recurrence neural network constructed with gated recurrent units to analyze trajectories of tumor evolution in response to radiotherapy, and finally an attention model to weight the importance of weekly observations and produce the predictions. The performance of P-net was measured with Dice and root mean square surface distance (RMSSD) between the algorithm-predicted and experts-contoured tumors under a leave-one-out scheme., Results: Tumor shrinkage was 60% ± 27% (mean ± standard deviation) by the end of radiotherapy across nine patients. Using images from the first three weeks, P-net predicted tumors on future weeks (4, 5, 6) with a Dice and RMSSD of (0.78 ± 0.22, 0.69 ± 0.24, 0.69 ± 0.26), and (2.1 ± 1.1 mm, 2.3 ± 0.8 mm, 2.6 ± 1.4 mm), respectively., Conclusion: The proposed deep learning algorithm can capture and predict spatial and temporal patterns of tumor regression in a longitudinal imaging study. It closely follows the clinical workflow, and could facilitate the decision-making of ART. A prospective study including more patients is warranted., (© 2019 American Association of Physicists in Medicine.)

Background: Proton dose distributions are sensitive to range uncertainties, resulting in margins added to ensure adequate tumor control probability (TCP). We investigated the required margin and dose shape needed to ensure adequate TCP, for representative tumor cell distributions in the clinical target volume (CTV). Material and methods: A mechanistic tumor response model, validated for lung tumors, was used to estimate TCP. The tumor cell distribution ( ρ ) was assumed to decrease exponentially in the CTV with decay parameter λ toward the outer border ( x CTV max ). It was investigated if a gradual dose fall-off could reduce the dose to normal tissues outside the CTV, while achieving adequate TCP. For various values of x CTV max and λ , we derived adequate uniform dose margins ( m ), coupled to linear dose fall-off regions ( Δ x , Δ x nom = Δ x - 0.9 cm), that ensured TCP > TCP limit , while delivering the least mean dose outside the CTV. To account for variabilities in patients and tumor types, variable probabilities ( p ) of finding tumor cells in the non-GTV part of the CTV for a given patient were also tested. Dose from a single beam or two opposing beams was simulated under the influence of a typical stopping power ratio uncertainty of 3.5%. Results: For large λ and x CTV max , a dose distribution with a shallower dose fall-off ( Δ x > 0 ) was advantageous, and m could be smaller than x CTV max . In the case of small x CTV max values, however, a conventional dose distribution ( Δ x = 0 ) would generally perform better. For no CTV, m = 0.4 cm in the case of two opposing beams, while it was 0.7 cm for a single beam, however, for two opposing beams Δ x = 1.2 cm ( Δ x nom = 0.3 cm), while it was zero for a single beam. Conclusion: The details of the underlying cancer cell distribution characteristics do impact the adequate dose arrangements, and for opposing beams a non-conventional dose distribution shape is often advantageous.

Many biological datasets are high-dimensional yet manifest an underlying order. In this paper, we describe an unsupervised data analysis methodology that operates in the setting of a multivariate dataset and a network which expresses influence between the variables of the given set. The technique involves network geometry employing the Wasserstein distance, global spectral analysis in the form of diffusion maps, and topological data analysis using the Mapper algorithm. The prototypical application is to gene expression profiles obtained from RNA-Seq experiments on a collection of tissue samples, considering only genes whose protein products participate in a known pathway or network of interest. Employing the technique, we discern several coherent states or signatures displayed by the gene expression profiles of the sarcomas in the Cancer Genome Atlas along the TP53 (p53) signaling network. The signatures substantially recover the leiomyosarcoma, dedifferentiated liposarcoma (DDLPS), and synovial sarcoma histological subtype diagnoses, and they also include a new signature defined by activation and inactivation of about a dozen genes, including activation of serine endopeptidase inhibitor SERPINE1 and inactivation of TP53-family tumor suppressor gene TP73.

Introduction: High dose radiotherapy (RT) has been associated with unexpectedly short survival times for locally advanced Non-Small Cell Lung Cancer (LA-NSCLC) patients. Here we tested the hypothesis that cardiac substructure dose is associated with electrocardiography (ECG) assessed abnormalities after RT for LA-NSCLC., Materials and Methods: Pre- and post-RT ECGs were analyzed for 155 LA-NSCLC patients treated to a median of 64 Gy in 1.8-2.0 Gy fractions using intensity-modulated RT plus chemotherapy (concurrent/sequential: 64%/36%) between 2004 and 2014. ECG abnormalities were classified as new Arrhythmic, Ischemic/Pericardial, or Non-specific ( A ΔECG , I/P ΔECG , or NS ΔECG ) events. Abnormalities were modeled as time to ECG events considering death a competing risk, and the variables considered for analysis were fractionation-corrected dose-volume metrics (α/β = 3 Gy) of ten cardio-pulmonary structures (aorta, heart, heart chambers, inferior and superior vena cava, lung, pulmonary artery) and 15 disease, patient and treatment characteristics. Each abnormality was modelled using bootstrapping and a candidate predictor was suggested by a median multiple testing-adjusted p-value ≤0.05 across the 1000 generated samples. Forward-stepwise multivariate analysis was conducted in case of more than one candidate., Results: At a median of eight months post-RT, the rate of A ΔECG , I/P ΔECG , and NS ΔECG was 66%, 35%, and 67%. Both A ΔECG and I/P ΔECG were associated with worse performance status (p = 0.007, 0.03), while a higher superior vena cava minimum dose was associated with NS ΔECG (p = 0.002)., Conclusion: This study suggests that higher radiation doses to the cardio-pulmonary system lead to non-specific ECG abnormalities. Reducing dose to this system, along with effective tumor control, is likely to decrease radiation-induced cardiac toxicity., Competing Interests: None. AR reports relevant grants outside the submitted work from Varian Medical Systems, Boehringer Ingelheim, Pfizer, and AztraZeneca, personal fees from AztraZeneca, Merck, Research to Practice, and Cybrexa, as well as non-financial support from Philips/Elekta.

We introduce a classification of breast tumors into seven classes which are more clearly defined by interpretable mRNA signatures along the PAM50 gene set than the five traditional PAM50 intrinsic subtypes. Each intrinsic subtype is partially concordant with one of our classes, and the two additional classes correspond to division of the classes concordant with the Luminal B and the Normal intrinsic subtypes along expression of the Her2 gene group. Our Normal class shows similarity with the myoepithelial mammary cell phenotype, including TP63 expression (specificity: 80.8% and sensitivity: 82.8%), and exhibits the best overall survival (89.6% at 5 years). Though Luminal A tumors are traditionally considered the least aggressive, our analysis shows that only the Luminal A tumors which are now classified as myoepithelial have this phenotype, while tumors in our luminal class (concordant with Luminal A) may be more aggressive than previously thought. We also find that patients with basal tumors surviving to 48 months exhibit favorable continued survival rates when certain markers for B lymphocytes are present and poor survival rates when they are absent, which is consistent with recent findings., Competing Interests: Competing interestsThe authors declare no competing interests.

Recent advances in radiomics have enhanced the value of medical imaging in various aspects of clinical practice, but a crucial component that remains to be investigated further is the robustness of quantitative features to imaging variations and across multiple institutions. In the case of MRI, signal intensity values vary according to the acquisition parameters used, yet no consensus exists on which preprocessing techniques are favorable in reducing scanner-dependent variability of image-based features. Hence, the purpose of this study was to assess the impact of common image preprocessing methods on the scanner dependence of MRI radiomic features in multi-institutional glioblastoma multiforme (GBM) datasets. Two independent GBM cohorts were analyzed: 50 cases from the TCGA-GBM dataset and 111 cases acquired in our institution, and each case consisted of 3 MRI sequences viz. FLAIR, T1-weighted, and T1-weighted post-contrast. Five image preprocessing techniques were examined: 8-bit global rescaling, 8-bit local rescaling, bias field correction, histogram standardization, and isotropic resampling. A total of 420 features divided into eight categories representing texture, shape, edge, and intensity histogram were extracted. Two distinct imaging parameters were considered: scanner manufacturer and scanner magnetic field strength. Wilcoxon tests identified features robust to the considered acquisition parameters under the selected image preprocessing techniques. A machine learning-based strategy was implemented to measure the covariate shift between the analyzed datasets using features computed using the aforementioned preprocessing methods. Finally, radiomic scores (rad-scores) were constructed by identifying features relevant to patients' overall survival after eliminating those impacted by scanner variability. These were then evaluated for their prognostic significance through Kaplan-Meier and Cox hazards regression analyses. Our results demonstrate that overall, histogram standardization contributes the most in reducing radiomic feature variability as it is the technique to reduce the covariate shift for three feature categories and successfully discriminate patients into groups of different survival risks.

Late disease recurrence (more than 5 years after initial diagnosis) represents a clinical challenge in the treatment and management of estrogen receptor-positive breast cancer (BC). An international workshop was convened in Toronto, Canada, in February 2018 to review the current understanding of late recurrence and to identify critical issues that require future study. The underlying biological causes of late recurrence are complex, with the processes governing cancer cell dormancy, including immunosurveillance, cell proliferation, angiogenesis, and cellular stemness, being integral to disease progression. These critical processes are described herein as well as their role in influencing risk of recurrence. Moreover, observational and interventional clinical trials are proposed, with a focus on methods to identify patients at risk of recurrence and possible strategies to combat this in patients with estrogen receptor-positive BC. Because the problem of late BC recurrence of great importance, recent advances in disease detection and patient monitoring should be incorporated into novel clinical trials to evaluate approaches to enhance patient management. Indeed, future research on these issues is planned and will offer new options for effective late recurrence treatment and prevention strategies., (© The Author(s) 2019. Published by Oxford University Press.)

Disease recurrence (locoregional, distant) exerts a significant clinical impact on the survival of estrogen receptor-positive breast cancer patients. Many of these recurrences occur late, more than 5 years after original diagnosis, and represent a major obstacle to the effective treatment of this disease. Indeed, methods to identify patients at risk of late recurrence and therapeutic strategies designed to avert or treat these recurrences are lacking. Therefore, an international workshop was convened in Toronto, Canada, in February 2018 to review the current understanding of late recurrence and to identify critical issues that require future study. In this article, the major issues surrounding late recurrence are defined and current approaches that may be applicable to this challenge are discussed. Specifically, diagnostic tests with potential utility in late-recurrence prediction are described as well as a variety of patient-related factors that may influence recurrence risk. Clinical and therapeutic approaches are also reviewed, with a focus on patient surveillance and the implementation of extended endocrine therapy in the context of late-recurrence prevention. Understanding and treating late recurrence in estrogen receptor-positive breast cancer is a major unmet clinical need. A concerted effort of basic and clinical research is required to confront late recurrence and improve disease management and patient survival., (© The Author(s) 2019. Published by Oxford University Press.)

Thoracic radiation therapy (RT) is often associated with lung side effects, whose etiology is still controversial. Our aim was to explore correlations between local dose in the thoracic anatomy and the radiation-induced lung damage (RILD). To this end, we designed a robust scheme for voxel-based analysis (VBA) to explore dose patterns associated with RILD in non-small-cell lung cancer (NSCLC) patients receiving stereotactic body RT (SBRT). We analyzed 106 NSCLC SBRT patients (median prescription dose: 50 Gy; range: [40-54] Gy) in 4 fractions (range: [3-5]) with clinical and dosimetric records suitable for the analysis. The incidence of acute G1 RILD (RTOG grade  ⩾  1) was 68%. Each planning CT and dose map was spatially normalized to a common anatomical reference using a B-spline inter-patient registration algorithm after masking the gross tumor volume. The tumor-subtracted dose maps were converted into biologically effective dose maps (α/β  =  3 Gy). VBA was performed according to a non-parametric permutation test accounting for multiple comparison, based on a cluster analysis method. The underlying general linear model of RILD was designed to include dose maps and each non-dosimetric variable significantly correlated with RILD. The clusters of voxels with dose differences significantly correlated with RILD at a given p -level (S p  ) were generated. The only non-dosimetric variable significantly correlated with RILD was the chronic obstructive pulmonary disease (p   =  0.034). Patients with G1 RILD received significantly (p   ⩽  0.05) higher doses in two voxel clusters S 0.05 in the lower-left lung (14 cm 3 ) and in an area (64 cm 3 ) largely included within the ventricles. The applied VBA represents a powerful tool to probe the dose susceptibility of inhomogeneous organs in clinical radiobiology studies. The identified subregions with dose differences associated with G1 RILD in both the heart and lower lungs endorse a trend of previously reported hypotheses on lung toxicity radiobiology.

Purpose: Most studies examining accelerated partial breast irradiation (APBI) have used twice-daily fractionation. Cosmesis with this approach has produced mixed results, and the optimal fractionation scheme remains unknown. We sought to evaluate the safety and efficacy of APBI with a total dose of 40 Gy in 10 daily fractions., Methods and Materials: Between 2010 and 2014, we prospectively enrolled 106 patients to receive APBI after lumpectomy for invasive or in situ node-negative breast cancer. Radiation was administered via 3-dimensional conformal techniques., Results: The median age was 62 years (range, 39-85), and all patients underwent APBI per protocol. With a median follow-up of 58 months, we evaluated patient-reported local toxicity and recurrence outcomes. Of 106 patients, 16 (15%) experienced grade ≥2 skin toxicity. The most common significant toxicities were acute cutaneous changes at 4 to 9 weeks after radiation therapy, including grade 2 erythema in 2 patients (1.8%) and skin color changes in 4 patients (3.8%). Only 2 instances of grade 3 toxicity were reported, including 1 patient with acute moist desquamation after radiation therapy and another with fibrosis at 2 years. Planning target volume and breast V 20 were significantly predictive of skin/subcutaneous toxicity, with evidence that limiting breast V 20 to <45% may improve tolerability. Overall, 3 breast cancer recurrences arose: 1 local recurrence in the original quadrant (3 years after APBI), 1 in a different ipsilateral quadrant (5 years after APBI), and 1 with distant disease 2 years after APBI., Conclusions: In an appropriately selected group of patients with early stage breast cancer, APBI to a dose of 40 Gy in 10 daily fractions was well tolerated, with most patients (99%) reporting excellent/good cosmesis. Planning target volume and breast V 20 should be carefully constrained to limit local morbidity. Longer follow-up will be needed to establish efficacy and subsequent local recurrence rates., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Purpose: To develop and implement a fully automated approach to intensity modulated radiation therapy (IMRT) treatment planning., Method: The optimization algorithm is developed based on a hierarchical constrained optimization technique and is referred internally at our institution as expedited constrained hierarchical optimization (ECHO). Beamlet contributions to regions-of-interest are precomputed and captured in the influence matrix. Planning goals are of two classes: hard constraints that are strictly enforced from the first step (e.g., maximum dose to spinal cord), and desirable goals that are sequentially introduced in three constrained optimization problems (better planning target volume (PTV) coverage, lower organ at risk (OAR) doses, and smoother fluence map). After solving the optimization problems using external commercial optimization engines, the optimal fluence map is imported into an FDA-approved treatment planning system (TPS) for leaf sequencing and accurate full dose calculation. The dose-discrepancy between the optimization and TPS dose calculation is then calculated and incorporated into optimization by a novel dose correction loop technique using Lagrange multipliers. The correction loop incorporates the leaf sequencing and scattering effects into optimization to improve the plan quality and reduce the calculation time. The resultant optimal fluence map is again imported into TPS for leaf sequencing and final dose calculation for plan evaluation and delivery. The workflow is automated using application program interface (API) scripting, requiring user interaction solely to prepare the contours and beam arrangement prior to launching the ECHO plug-in from the TPS. For each site, parameters and objective functions are chosen to represent clinical priorities. The first site chosen for clinical implementation was metastatic paraspinal lesions treated with stereotactic body radiotherapy (SBRT). As a first step, 75 ECHO paraspinal plans were generated retrospectively and compared with clinically treated plans generated by planners using VMAT (volumetric modulated arc therapy) with 4 to 6 partial arcs. Subsequently, clinical deployment began in April, 2017., Results: In retrospective study, ECHO plans were found to be dosimetrically superior with respect to tumor coverage, plan conformity, and OAR sparing. For example, the average PTV D95%, cord and esophagus max doses, and Paddick Conformity Index were improved, respectively, by 1%, 6%, 14%, and 15%, at a negligible 3% cost of the average skin D10cc dose., Conclusion: Hierarchical constrained optimization is a powerful and flexible tool for automated IMRT treatment planning. The dosimetric correction step accurately accounts for detailed dosimetric multileaf collimator and scattering effects. The system produces high-quality, Pareto optimal plans and avoids the time-consuming trial-and-error planning process., (© 2019 American Association of Physicists in Medicine.)

Purpose/objective: Hypofractionated radiotherapy (HRT) regimens for prostate cancer are emerging, but tolerance doses for late adverse events are scarce. The purpose of this study is to define dose-volume predictors for late gastrointestinal and genitourinary (GI and GU) toxicities after HRT in the multi-center NRG Oncology/RTOG 0415 low-risk prostate cancer trial (N = 521)., Material/methods: Treatment in the studied HRT arm was delivered as 70 Gy at 2.5 Gy/fraction with 3D-CRT/IMRT (N = 108/413). At a median follow-up of 5.9 years, the crude late ≥Grade 2 GI and GU toxicities were 19% and 29%, respectively. For modeling, the complete HRT cohort was randomly split into training and validation (70% and 30%; preserved toxicity rates). Within training, dose-response modeling was based on dose-volume cut-points (EQD2Gy; bladder/rectum: α/β = 6 Gy/3Gy), age, acute ≥Grade 2 toxicity, and treatment technique using univariate and multivariate logistic regression on bootstrapping (UVA and MVA). Candidate predictors were determined at p ≤ 0.05, and the selected MVA models were explored on validation where model generalizability was judged if the area under the receiver-operating curve in validation (AUC validation ) was within AUC training  ± SD with p ≤ 0.05, and with an Hosmer-Lemeshow p-value (p HL ) > 0.05., Results: Three candidate predictors were suggested for late GI toxicity: the minimum dose to the hottest 5% rectal volume (D5%[Gy]), the absolute rectal volume <35 Gy, and acute GI toxicity (AUC = 0.59-0.63; p = 0.02-0.04). The two generalizable MVA models, i.e., D5%[Gy] with or without acute GI toxicity (AUC validation  = 0.64, 0.65; p = 0.01, 0.03; p HL  = 0.45-0.56), suggest that reducing late GI toxicity from 20% to 10% would require reducing D5%[Gy] from ≤65 Gy to ≤62 Gy (logistic function argument: 17+(0.24D5%[Gy])). Acute GU toxicity showed only a trend to predict late GU toxicity (AUC training  = 0.57; p = 0.07)., Conclusion: Late GI toxicity, following moderate HRT for low-risk prostate cancer, increases with higher doses to small rectal volumes. This work provides quantitative evidence that limiting small rectal dose 'hotspots' in clinical practice of such HRT regimens is likely to further reduce the associated rates of GI toxicity., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Purpose: This study examined tumor growth delay resulting from partial irradiation in preclinical mouse models., Methods and Materials: We investigated 67NR murine orthotopic breast tumors in both immunocompetent and nude mice. Treatment was delivered to 50% or 100% of the tumor using a 2 × 2 cm collimator on a microirradiator. Radiation response was modulated by treatment with anti-CD8 and anti-intercellular adhesion molecule (anti-ICAM) antibodies. Similar experiments were performed using the less immunogenic Lewis lung carcinoma mouse model. Tumor growth delay and γ-H2AX phosphorylation were measured, and immune response was assessed by immunofluorescence and flow cytometry at 1 and 7 days after radiation therapy. Tumor expression of cellular adhesion molecules was also measured at different times after radiation therapy., Results: Partial irradiation led to tumor responses similar to those of fully exposed tumors in immunocompetent mice, but not in nude mice. After a single dose of 10 Gy, infiltration of CD8 + T cells was observed along with increased expression of ICAM. The response to 10 Gy in hemi-irradiated tumors was abrogated by treatment with either anti-CD8 or anti-ICAM antibodies. Similar responses were obtained in the less immunogenic Lewis lung carcinoma mouse model delivering 15 Gy to half the tumor volume. Treatment with FTY720, a compound that inhibits T-cell egress from lymph nodes, did not affect tumor response at the time of CD8 + T cells infiltration in the nonirradiated area of the tumor. This result indicated that the most likely source of these cells is the irradiated portion of the hemi-irradiated tumors. In addition, a significant abscopal effect was observed after partial irradiation with a single dose of 10 Gy in the 67NR model., Conclusions: In these models, radiation controls tumor growth both directly through cell killing and indirectly through immune activation. This outcome raises the possibility that this effect could be induced in the clinic., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: The purpose of this study was to enhance the correlation between external and internal respiratory motions by dynamically determining and correcting the patient-specific phase shift between external and internal respiratory waveforms acquired concurrently during respiratory-correlated 4-dimensional magnetic resonance imaging scans., Methods and Materials: Internal-navigator and external-bellows waveforms were acquired simultaneously during 6- to 15-minute respiratory-correlated 4-dimensional magnetic resonance imaging scans in 10 healthy participants under an institutional review board-approved protocol. The navigator was placed at the right lung-diaphragm interface, and the bellows were placed ∼5 cm inferior to the sternum. Three segments of each respiratory waveform, at the beginning, middle, and end of a scan, were analyzed. Three phase-domain methods were employed to estimate the phase shift, including analytical signal analysis, phase-space oval fitting, and principal component analysis. A robust strategy for estimating the phase shift was realized by combining these methods in a weighted average and by eliminating outliers (>2 σ) caused by breathing irregularities. Whether phase-shift correction affects the external-internal correlation was evaluated. The cross-correlation between the 2 waveforms in the time domain provided an independent check of the correlation enhancement., Results: Phase-shift correction significantly enhanced the external-internal correlation in all participants across the entire 6- to 15-minute scans. On average, the correlation increased from 0.45 ± 0.28 to 0.85 ± 0.15 for the combined method. The combined method exhibited a 99.5% success rate and revealed that the phase of the external waveform leads that of the internal waveform in all 10 participants by 57 o ± 17 o (1.6 ± 0.5 bins) on average. Seven participants exhibited highly reproducible phase shifts over time, evidenced by standard deviations (σ) < 4 o , whereas 8 o  < σ < 12 o in the remaining 3 participants. Regardless, phase-shift correction significantly improved the correlation in all participants., Conclusions: Correcting the phase shift estimated by the phase-domain methods provides a new approach for enhancing the correlation between external and internal respiratory motions. This strategy holds promise for improving the accuracy of respiratory-gated radiation therapy.

Background: Glioblastoma (GBM) is the most common malignant central nervous system tumor, and MGMT promoter hypermethylation in this tumor has been shown to be associated with better prognosis. We evaluated the capacity of radiomics features to add complementary information to MGMT status, to improve the ability to predict prognosis., Methods: 159 patients with untreated GBM were included in this study and divided into training and independent test sets. 286 radiomics features were extracted from the magnetic resonance images acquired prior to any treatments. A least absolute shrinkage selection operator (LASSO) selection followed by Kaplan-Meier analysis was used to determine the prognostic value of radiomics features to predict overall survival (OS). The combination of MGMT status with radiomics was also investigated and all results were validated on the independent test set., Results: LASSO analysis identified 8 out of the 286 radiomic features to be relevant which were then used for determining association to OS. One feature (edge descriptor) remained significant on the external validation cohort after multiple testing (p=0.04) and the combination with MGMT identified a group of patients with the best prognosis with a survival probability of 0.61 after 43 months (p=0.0005)., Conclusion: Our results suggest that combining radiomics with MGMT is more accurate in stratifying patients into groups of different survival risks when compared to with using these predictors in isolation. We identified two subgroups within patients who have methylated MGMT : one with a similar survival to unmethylated MGMT patients and the other with a significantly longer OS., Competing Interests: CONFLICTS OF INTEREST The authors declare no competing interests.

Purpose: To investigate the potential of an atlas-based approach in generation of synthetic CT for pelvis anatomy., Methods: Twenty-three matched pairs of computed tomography (CT) and magnetic resonance imaging (MRI) scans were selected from a pool of prostate cancer patients. All MR scans were preprocessed to reduce scanner- and patient-induced intensity inhomogeneities and to standardize their intensity histograms. Ten (training dataset) of 23 pairs were then utilized to construct the coregistered CT-MR atlas. The synthetic CT for a new patient is generated by appropriately weighting the deformed atlas of CT-MR onto the new patient MRI. The training dataset was used as an atlas to generate the synthetic CT for the rest of the patients (test dataset). The mean absolute error (MAE) between the deformed planning CT and synthetic CT was computed over the entire CT image, bone, fat, and muscle tissues. The original treatment plans were also recomputed on the new synthetic CTs and dose-volume histogram metrics were compared. The results were compared with a commercially available synthetic CT Software (MRCAT) that is routinely used in our clinic., Results: MAE errors (±SD) between the deformed planning CT and our proposed synthetic CTs in the test dataset were 47 ± 5, 116 ± 12, 36 ± 6, and 47 ± 5 HU for the entire image, bone, fat, and muscle tissues respectively. The MAEs were 65 ± 5, 172 ± 9, 43 ± 7, and 42 ± 4 HU for the corresponding tissues in MRCAT CT. The dosimetric comparison showed consistent results for all plans using our synthetic CT, deformed planning CT and MRCAT CT., Conclusion: We investigated the potential of a multiatlas approach to generate synthetic CT images for the pelvis. Our results demonstrate excellent results in terms of HU value assignment compared to the original CT and dosimetric consistency., (© 2018 Memorial Sloan Kettering Cancer Center. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Volumetric lung tumor segmentation and accurate longitudinal tracking of tumor volume changes from computed tomography images are essential for monitoring tumor response to therapy. Hence, we developed two multiple resolution residually connected network (MRRN) formulations called incremental-MRRN and dense-MRRN. Our networks simultaneously combine features across multiple image resolution and feature levels through residual connections to detect and segment the lung tumors. We evaluated our method on a total of 1210 non-small cell (NSCLC) lung tumors and nodules from three data sets consisting of 377 tumors from the open-source Cancer Imaging Archive (TCIA), 304 advanced stage NSCLC treated with anti- PD-1 checkpoint immunotherapy from internal institution MSKCC data set, and 529 lung nodules from the Lung Image Database Consortium (LIDC). The algorithm was trained using 377 tumors from the TCIA data set and validated on the MSKCC and tested on LIDC data sets. The segmentation accuracy compared to expert delineations was evaluated by computing the dice similarity coefficient, Hausdorff distances, sensitivity, and precision metrics. Our best performing incremental-MRRN method produced the highest DSC of 0.74 ± 0.13 for TCIA, 0.75±0.12 for MSKCC, and 0.68±0.23 for the LIDC data sets. There was no significant difference in the estimations of volumetric tumor changes computed using the incremental-MRRN method compared with the expert segmentation. In summary, we have developed a multi-scale CNN approach for volumetrically segmenting lung tumors which enables accurate, automated identification of and serial measurement of tumor volumes in the lung.

Purpose: To compare single-shot echo-planar (SS-EPI)-based and turbo spin-echo (SS-TSE)-based diffusion-weighted imaging (DWI) in Non-Small Cell Lung Cancer (NSCLC) patients and to characterize the distributions of apparent diffusion coefficient (ADC) values generated by the two techniques., Methods: Ten NSCLC patients were enrolled in a prospective IRB-approved study to compare and optimize DWI using EPI and TSE-based techniques for radiotherapy planning. The imaging protocol included axial T2w, EPI-based DWI and TSE-based DWI on a 3 T Philips scanner. Both EPI-based and TSE-based DWI sequences used three b values (0, 400, and 800 s/mm 2 ). The acquisition times for EPI-based and TSE-based DWI were 5 and 8 min, respectively. DW-MR images were manually coregistered with axial T2w images, and tumor volume contoured on T2w images were mapped onto the DWI scans. A pixel-by-pixel fit of tumor ADC was calculated based on monoexponential signal behavior. Tumor ADC mean, standard deviation, kurtosis, and skewness were calculated and compared between EPI and TSE-based DWI. Image distortion and ADC values between the two techniques were also quantified using fieldmap analysis and a NIST traceable ice-water diffusion phantom, respectively., Results: The mean ADC for EPI and TSE-based DWI were 1.282 ± 0.42 × 10 -3 and 1.211 ± 0.31 × 10 -3  mm 2 /s. The average skewness and kurtosis were 0.14 ± 0.4 and 2.43 ± 0.40 for DWI-EPI and -0.06 ± 0.69 and 2.89 ± 0.62 for DWI-TSE. Fieldmap analysis showed a mean distortion of 13.72 ± 8.12 mm for GTV for DWI-EPI and 0.61 ± 0.4 mm for DWI-TSE. ADC values obtained using the diffusion phantom for the two techniques were within 0.03 × 10 -3  mm 2 /s with respect to each other as well as the established values., Conclusions: Diffusion-weighted turbo spin-echo shows better geometrical accuracy compared to DWI-EPI. Mean ADC values were similar with both acquisitions but the shape of the histograms was different based on the skewness and kurtosis values. The impact of differences in respiratory technique on ADC values requires further investigation., (© 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

Purpose: The aims of this study were to systematically review tolerance doses for late distinct gastrointestinal (GI), genitourinary (GU), and sexual dysfunction (SD) symptoms after external beam radiation therapy (EBRT) alone and treatments involving brachytherapy (BT) for prostate cancer after Quantitative Analysis of Normal Tissue Effects in the Clinic (QUANTEC) and ultimately to perform quantitative syntheses of identified dose/volume tolerances represented by dose-volume histogram (DVH) thresholds, that is, statistically significant (P ≤ .05) cutoff points between symptomatic and asymptomatic patients in a certain study., Methods and Materials: PubMed was scrutinized for full-text articles in English after QUANTEC (January 1, 2010). The inclusion criteria were randomized controlled trials, case-control studies, or cohort studies with tolerance doses for late distinct symptoms ≥3 months after primary radiation therapy for prostate cancer (N > 30). All DVH thresholds were converted into equivalent doses in 2-Gy fractions (EQD2 α/β ) and were fitted with a linear or linear-quadratic function (goodness of fit, R 2 ). The review was registered on PROSPERO (CRD42016042464)., Results: From 33 identified studies, which included 36 to 746 patients per symptom domain, the majority of dose/volume tolerances were derived for GI toxicity after EBRT alone (GI, 97 thresholds; GU, 8 thresholds; SD, 1 threshold). For 5 symptoms (defecation urgency, diarrhea, fecal incontinence, proctitis, and rectal bleeding), relationships between dose/volume tolerances across studies (R 2  = 0.93 [0.82-1.00]), and across symptoms, leading to a curve for overall GI toxicity (R 2  = 0.98), could be determined. For these symptoms, mainly rectal thresholds were found throughout low and high doses (10 Gy ≤ equivalent dose in 2-Gy fractions using α/β = 3Gy (EQD2 3 ) ≤ 50 Gy and 55 Gy ≤ EQD2 3  ≤ 78 Gy, respectively). For BT with or without EBRT, dose/volume tolerances were also mainly identified for GI toxicity (GI, 14 thresholds; GU, 4 thresholds; SD, 2 thresholds) with the largest number of DVH thresholds concerning rectal bleeding (5 thresholds)., Conclusions: Updated dose/volume tolerances after QUANTEC were found for 17 GI, GU, or SD symptoms. A DVH curve described the relationship between dose/volume tolerances across 5 GI symptoms after EBRT alone. Restricting treatments for EBRT alone using the lower boundaries of this curve is likely to limit overall GI toxicity, but this should be explored prospectively. Dose/volume tolerances for GU and SD toxicity after EBRT alone and after BT with or without EBRT were scarce and support further research including data-sharing initiatives to untangle the dose/volume relationships for these symptoms., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: To cross-validate and expand a predictive atlas that can estimate geometric patterns of lung tumor shrinkage during radiation therapy using data from 2 independent institutions and to model its integration into adaptive radiation therapy (ART) for enhanced dose escalation., Methods and Materials: Data from 22 patients at a collaborating institution were obtained to cross-validate an atlas, originally created with 12 patients, for predicting patterns of tumor shrinkage during radiation therapy. Subsequently, the atlas was expanded by integrating all 34 patients. Each study patient was selected via a leave-one-out scheme and was matched with a subgroup of the remaining 33 patients based on similarity measures of tumor volume and surroundings. The spatial distribution of residual tumor was estimated by thresholding the superimposed shrinkage patterns in the subgroup. A Bayesian method was also developed to recalibrate the prediction using the tumor observed on the midcourse images. Finally, in a retrospective predictive treatment planning (PTP) study, at the initial planning stage, the predicted residual tumors were escalated to the highest achievable dose while maintaining the original prescription dose to the remainder of the tumor. The PTP approach was compared isotoxically to ART that replans with midcourse imaging and to PTP-ART with the recalibrated prediction., Results: Predictive accuracy (true positive plus true negative ratios based on predicted and actual residual tumor) were comparable across institutions, 0.71 versus 0.73, and improved to 0.74 with an expanded atlas including 2 institutions. Recalibration further improved accuracy to 0.76. PTP increased the mean dose to the actual residual tumor by an averaged 6.3Gy compared to ART., Conclusion: A predictive atlas found to perform well across institutions and benefit from more diversified shrinkage patterns and tumor locations. Elevating tumoricidal dose to the predicted residual tumor throughout the entire treatment course could improve the efficacy and efficiency of treatment compared to ART with midcourse replanning., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: To identify imaged regions in which dose is associated with radiation-induced trismus after head and neck cancer radiation therapy (HNRT) using a novel image-based data mining (IBDM) framework., Methods and Materials: A cohort of 86 HNRT patients were analyzed for region identification. Trismus was characterized as a continuous variable by the maximum incisor-to-incisor opening distance (MID) at 6 months after radiation therapy. Patient anatomies and dose distributions were spatially normalized to a common frame of reference using deformable image registration. IBDM was used to identify clusters of voxels associated with MID (P ≤ .05 based on permutation testing). The result was externally tested on a cohort of 35 patients with head and neck cancer. Internally, we also performed a dose-volume histogram-based analysis by comparing the magnitude of the correlation between MID and the mean dose for the IBDM-identified cluster in comparison with 5 delineated masticatory structures., Results: A single cluster was identified with the IBDM approach (P < .01), partially overlapping with the ipsilateral masseter. The dose-volume histogram-based analysis confirmed that the IBDM cluster had the strongest association with MID, followed by the ipsilateral masseter and the ipsilateral medial pterygoid (Spearman's rank correlation coefficients: R s = -0.36, -0.35, -0.32; P = .001, .001, .002, respectively). External validation confirmed an association between mean dose to the IBDM cluster and MID (R s = -0.45; P = .007)., Conclusions: IBDM bypasses the common assumption that dose patterns within structures are unimportant. Our novel IBDM approach for continuous outcome variables successfully identified a cluster of voxels that are highly associated with trismus, overlapping partially with the ipsilateral masseter. Tests on an external validation cohort showed an even stronger correlation with trismus. These results support use of the region in HNRT treatment planning to potentially reduce trismus., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: To correlate semiquantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18 F-fluorodeoxyglucose positron emission tomography ( 18 F-FDG-PET) for non-small cell lung cancer (NSCLC)., Methods and Materials: Twenty-four NSCLC patients who underwent pretreatment 18 F-FDG-PET and DCE-MRI were analyzed. The maximum standardized uptake value (SUVmax) was measured from 18F-FDG-PET. Dynamic contrast-enhanced MRI was obtained on a 3T MRI scanner using 4-dimensional T1-weighted high-resolution imaging with a volume excitation sequence. The DCE-MRI parameters, consisting of mean, median, standard deviation (SD), and median absolute deviation (MAD) of peak enhancement, time to peak (TTP), time to half peak (TTHP), wash-in slope (WIS), wash-out slope (WOS), initial gradient, wash-out gradient, signal enhancement ratio, and initial area under the relative signal enhancement curve taken up to 30, 60, 90, 120, 150, and 180 seconds, TTP, and TTHP (IAUCtthp), were calculated for each lesion. Univariate analysis (UVA) was performed using Spearman correlation. A linear regression model to predict SUVmax from DCE-MRI parameters was developed by multivariate analysis (MVA) using least absolute shrinkage selection operator in combination with leave-one-out cross-validation (LOOCV)., Results: In UVA, mean(WOS) (ρ = -0.456, P = .025), mean(IAUCtthp) (ρ = -0.439, P = .032), median(IAUCtthp) (ρ = -0.543, P = .006), and MAD(IAUCtthp) (ρ = -0.557, P = .005) were statistically significant; all these parameters were negatively correlated with SUVmax. In MVA, a linear combination of SD(WIS), SD(TTP), MAD(TTHP), and MAD(IAUCtthp) was statistically significant for predicting SUVmax (LOOCV-based adjusted R 2  = 0.298, P = .0006). A decrease in SD(WIS), MAD(TTHP), and MAD(IAUCtthp) and an increase in SD(TTP) were associated with a significant increase in SUVmax., Conclusion: An association was found between SUVmax, the SD, and MAD of DCE-MRI metrics derived during contrast uptake in NSCLC, reflecting that intratumoral heterogeneity in wash-in contrast kinetics is associated with tumor metabolism. Although MAD(IAUCtthp) was a significant feature in both UVA and MVA, the LASSO-based multivariate regression model yielded better predictability of SUVmax than a univariate regression model using MAD(IAUCtthp). This study will facilitate understanding of the complex relationship between tumor vascularization and metabolism and eventually help in guiding targeted therapy., (Copyright © 2018 Elsevier Inc. All rights reserved.)