Search

Purpose This study investigated the prognostic potential of intra-treatment PET radiomics data in patients undergoing definitive (chemo)radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic expression of OPC tumors after 20 Gy is associated with recurrence free survival (RFS). Materials and methods Sixty-four patients undergoing definitive (chemo)radiation for OPC were prospectively enrolled on an IRB-approved study. Investigational 18 F-FDG-PET/CT images were acquired prior to treatment and two weeks (20 Gy) into a seven-week course of therapy. Fifty-five quantitative radiomic features were extracted from the primary tumor as potential biomarkers of early metabolic response. An unsupervised data clustering algorithm was used to partition patients into clusters based only on their radiomic expression. Clustering results were naively compared to residual disease and/or subsequent recurrence and used to derive Kaplan-Meier estimators of RFS. To test whether radiomic expression provides prognostic value beyond conventional clinical features associated with head and neck cancer, multivariable Cox proportional hazards modeling was used to adjust radiomic clusters for T and N stage, HPV status, and change in tumor volume. Results While pre-treatment radiomics were not prognostic, intra-treatment radiomic expression was intrinsically associated with both residual/recurrent disease (p = 0.0256, ꭕ2 test) and RFS (HR = 7.53, 95% CI = 2.54 - 22.3; p = 0.0201). On univariate Cox analysis, radiomic cluster was associated with RFS (unadjusted HR = 2.70; 95% CI = 1.26 - 5.76; p = 0.0104) and maintained significance after adjustment for T, N staging, HPV status, and change in tumor volume after 20 Gy (adjusted HR = 2.69; 95% CI = 1.03 - 7.04; p = 0.0442). The particular radiomic characteristics associated with outcomes suggest that metabolic spatial heterogeneity after 20 Gy portends complete and durable therapeutic response. This finding is independent of baseline metabolic imaging characteristics and clinical features of head and neck cancer, thus providing prognostic advantages over existing approaches. Conclusions Our data illustrate the prognostic value of intra-treatment metabolic image interrogation, which may potentially guide adaptive therapy strategies for OPC patients and serve as a blueprint for other disease sites. The quality of our study is strengthened by its prospective image acquisition protocol, homogenous patient cohort, relatively long patient follow-up times, and unsupervised clustering formalism that is less prone to hyper-parameter tuning and over-fitting compared to supervised learning.

Background To develop and evaluate a robust deformable image registration (DIR) enhancement method based on radial basis function (RBF) expansion. Methods To improve DIR accuracy using sparsely available measured displacements, it is crucial to estimate the motion correlation between the voxels. In the proposed method, we chose to derive this correlation from the initial displacement vector fields (DVFs), and represent it in the form of RBF expansion coefficients of the voxels. The method consists of three steps: (I) convert an initial DVF to a coefficient matrix comprising expansion coefficients of the Wendland's RBF; (II) modify the coefficient matrix under the guidance of sparely distributed landmarks to generate the post-enhancement coefficient matrix; and (III) convert the post-enhancement coefficient matrix to the post-enhancement DVF. The method was tested on five DIR algorithms using a digital phantom. 3D registration errors were calculated for comparisons between the pre-/post-enhancement DVFs and the ground-truth DVFs. Effects of the number and locations of landmarks on DIR enhancement were evaluated. Results After applying the DIR enhancement method, the 3D registration errors per voxel (unit: mm) were reduced from pre-enhancement to post-enhancement by 1.3 (2.4 to 1.1, 54.2%), 0.0 (0.9 to 0.9, 0.0%), 6.1 (8.2 to 2.1, 74.4%), 3.2 (4.7 to 1.5, 68.1%), and 1.7 (2.9 to 1.2, 58.6%) for the five tested DIR algorithms respectively. The average DIR error reduction was 2.5±2.3 mm (percentage error reduction: 51.1%±29.1%). 3D registration errors decreased inverse-exponentially as the number of landmarks increased, and were insensitive to the landmarks' locations in relation to the down-sampling DVF grids. Conclusions We demonstrated the feasibility of a robust RBF-based method for enhancing DIR accuracy using sparsely distributed landmarks. This method has been shown robust and effective in reducing DVF errors using different numbers and distributions of landmarks for various DIR algorithms.

Purpose To develop a spatiotemporal-constrained sorting technique for motion-robust 4 dimensional-magnetic resonance imaging. Methods and Materials This sorting method implemented 2 new approaches for 4-dimensional imaging: (1) an optimized sparse k-space acquisition trajectory with self-gating signal derivation, and (2) a retrospective k-space sorting for reconstruction using a novel spatiotemporal-constrained strategy to minimize breathing variation–induced motion artifacts. Such sorting was regularized by a spatiotemporal index. Volumetric reconstruction was implemented iteratively with a secnd-order total generalized variation penalty. The proposed method was evaluated and compared with the conventional phase-sorting and amplitude-sorting methods in 2 studies. In a computer simulation study, 6 abdominal motion scenarios, including 2 cosine and 4 patient breathing motion patterns, were studied. Reconstruction accuracy was evaluated quantitatively in reference to the ground truth by average image relative error (IRE) in 10 phases and target Dice similarity coefficients (DSCs) in end-of-exhalation/inhalation phases. In addition, the proposed method was evaluated using a custom-made motion phantom. Reconstruction accuracy was evaluated by motion range measurement and image quality comparison in both fast and slow breathing motions. Results In the simulation study, stitching motion artifacts in restricted images were lessened using the proposed method compared with those using the conventional methods. The average IRE and target DSC (end-of-exhalation/inhalation) were 0.031 and 0.95/0.94, respectively, suggesting better motion reconstruction accuracy than the phase-sorted method (IRE, 0.057; DSC, 0.89/0.89) and the amplitude-sorted method (IRE, 0.048; DSC, 0.91/0.88). In the phantom study, the moving target reconstructed by the proposed method demonstrated better rendering with less edge blurring. With fast breathing motion, the range measured using the proposed method was more accurate than that of the phase-sorted method and was comparable to the result of amplitude-sorted method and ground truths. Conclusions Preliminary results suggested that the proposed sorting technique could reconstruct high-quality images and accurate motion estimation with reduced artifacts in 4 dimensional-magnetic resonance imaging.

Purpose. We have previously reported an artificial intelligence (AI) agent that automatically generates intensity-modulated radiation therapy (IMRT) plans via fluence map prediction, by-passing inverse planning. This AI agent achieved clinically comparable quality for prostate cases, but its performance on head-and-neck patients leaves room for improvement. This study aims to collect insights of the deep-learning-based (DL-based) fluence map prediction model by systematically analyzing its prediction errors. Methods. From the modeling perspective, the DL model’s output is the fluence maps of IMRT plans. However, from the clinical planning perspective, the plan quality evaluation should be based on the clinical dosimetric criteria such as dose-volume histograms. To account for the complex and non-intuitive relationships between fluence map prediction errors and the corresponding dose distribution changes, we propose a novel error analysis approach that systematically examines plan dosimetric changes that are induced by varying amounts of fluence prediction errors. We investigated four decomposition modes of model prediction errors. The two spatial domain decompositions are based on fluence intensity and fluence gradient. The two frequency domain decompositions are based on Fourier-space banded frequency rings and Fourier-space truncated low-frequency disks. The decomposed error was analyzed for its impact on the resulting plans’ dosimetric metrics. The analysis was conducted on 15 test cases spared from the 200 training and 16 validation cases used to train the model. Results. Most planning target volume metrics were significantly correlated with most error decompositions. The Fourier space disk radii had the largest Spearman’s coefficients. The low-frequency region within a disk of ∼20% Fourier space contained most of errors that impact overall plan quality. Conclusions. This study demonstrates the feasibility of using fluence map prediction error analysis to understand the AI agent’s performance. Such insights will help fine-tune the DL models in architecture design and loss function selection.

Spectral clustering has become one of the most popular clustering methods due to its superior performance compared to the traditional clustering methods. However, the performance of spectral clustering would be limited by complex data, such as a huge number of samples and high dimensionality. To address this problem, some existing methods apply deep learning to learn the lower-dimensional representations, spectral clustering is then applied to the representations. Different from the existing methods that separate the two stages of feature representation learning and spectral clustering, in this paper, we propose Spectral Clustering Joint Deep Embedding (SCJDE), a method that simultaneously learns the feature representations and the spectral embedding of spectral clustering via a deep autoencoder. Moreover, a sparsity constraint is imposed to generate better spectral embedding for spectral clustering. Finally, $k$ -means is performed on the spectral embedding to obtain clustering result. The proposed method can learn a good spectral embedding for spectral clustering by deep learning to obtain better clustering results. The experimental results on both synthetic and real-world datasets demonstrate the effectiveness of the proposed method.

This article has been retracted: please see Elsevier Policy on Article Withdrawal (http://www.elsevier.com/locate/withdrawalpolicy). This abstract has been retracted at the request of the authors. After submitting this abstract the authors realized that the core concept, the encoder-decoder design, was also being used by other investigators at their institution for similar radiation therapy applications. As a result the abstract had not been discussed with, nor approved by, all the relevant investigators before submission. International Journal of Radiation Oncology, Biology, Physics, 108, (2020) S128-S128, https://doi.org/10.1016/j.ijrobp.2020.07.854.

Background To develop a high-efficiency pulmonary nodule computer-aided detection (CAD) method for localization and diameter estimation. Methods The developed CAD method centralizes a novel convolutional neural network (CNN) algorithm, You Only Look Once (YOLO) v3, as a deep learning approach. This method is featured by two distinct properties: (I) an automatic multi-scale feature extractor for nodule feature screening, and (II) a feature-based bounding box generator for nodule localization and diameter estimation. Two independent studies were performed to train and evaluate this CAD method. One study comprised of a computer simulation that utilized computer-based ground truth. In this study, 300 CT scans were simulated by Cardiac-torso (XCAT) digital phantom. Spherical nodules of various sizes (i.e., 3-10 mm in diameter) were randomly implanted within the lung region of the simulated images-the second study utilized human-based ground truth in patients. The CAD method was developed by CT scans sourced from the LIDC-IDRI database. CT scans with slice thickness above 2.5 mm were excluded, leaving 888 CT images for analysis. A 10-fold cross-validation procedure was implemented in both studies to evaluate network hyper-parameterization and generalization. The overall accuracy of the CAD method was evaluated by the detection sensitivities, in response to average false positives (FPs) per image. In the patient study, the detection accuracy was further compared against 9 recently published CAD studies using free-receiver response operating characteristic (FROC) curve analysis. Localization and diameter estimation accuracies were quantified by the mean and standard error between the predicted value and ground truth. Results The average results among the 10 cross-validation folds in both studies demonstrated the CAD method achieved high detection accuracy. The sensitivity was 99.3% (FPs =1), and improved to 100% (FPs =4) in the simulation study. The corresponding sensitivities were 90.0% and 95.4% in the patient study, displaying superiority over several conventional and CNN-based lung nodule CAD methods in the FROC curve analysis. Nodule localization and diameter estimation errors were less than 1 mm in both studies. The developed CAD method achieved high computational efficiency: it yields nodule-specific quantitative values (i.e., number, existence confidence, central coordinates, and diameter) within 0.1 s for 2D CT slice inputs. Conclusions The reported results suggest that the developed lung pulmonary nodule CAD method possesses high accuracies of nodule localization and diameter estimation. The high computational efficiency enables its potential clinical application in the future.

Purpose: To develop an Artificial Intelligence (AI) agent for fully-automated rapid head and neck (H&N) IMRT plan generation without time-consuming inverse planning.$$$$ Methods: This AI agent was trained using a conditional Generative Adversarial Network architecture. The generator, PyraNet, is a novel Deep Learning network that implements 28 classic ResNet blocks in pyramid-like concatenations. The discriminator is a customized 4-layer DenseNet. The AI agent first generates customized 2D projections at 9 template beam angles from 3D CT volume and structures of a patient. These projections are then stacked as 4D inputs of PyraNet, from which 9 radiation fluence maps are generated simultaneously. Finally, the predicted fluence maps are imported into a commercial treatment planning system (TPS) for plan integrity checks. The AI agent was built and tested upon 231 oropharyngeal plans from a TPS plan library. Only the primary plans in the sequential boost regime were studied. A customized Harr wavelet loss was adopted for fluence map comparison. Isodose distributions in test AI plans and TPS plans were qualitatively evaluated. Key dosimetric metrics were statistically compared.$$$$ Results: All test AI plans were successfully generated. Isodose gradients outside of PTV in AI plans were comparable with TPS plans. After PTV coverage normalization, $D_{mean}$ of parotids and oral cavity in AI plans and TPS plans were comparable without statistical significance. AI plans achieved comparable $D_{max}$ at 0.01cc of brainstem and cord+5mm without clinically relevant differences, but body $D_{max}$ was higher than the TPS plan results. The AI agent needs ~3s per case to predict fluence maps.$$$$ Conclusions: The developed AI agent can generate H&N IMRT plans with satisfying dosimetry quality. With rapid and fully automated implementation, it holds great potential for clinical applications., Withdraw for internal review

Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a difficult and time-consuming task. In this study, we aim to develop a novel deep learning framework to generate clinical-quality plans by direct prediction of fluence maps from patient anatomy using convolutional neural networks (CNNs). Materials and Methods: Our proposed framework utilizes two CNNs to predict intensity-modulated radiation therapy fluence maps and generate deliverable plans: (1) Field-dose CNN predicts field-dose distributions in the region of interest using planning images and structure contours; (2) a fluence map CNN predicts the final fluence map per beam using the predicted field dose projected onto the beam's eye view. The predicted fluence maps were subsequently imported into the treatment planning system for leaf sequencing and final dose calculation (model-predicted plans). One hundred patients previously treated with pancreas SBRT were included in this retrospective study, and they were split into 85 training cases and 15 test cases. For each network, 10% of training data were randomly selected for model validation. Nine-beam benchmark plans with standardized target prescription and organ-at-risk constraints were planned by experienced clinical physicists and used as the gold standard to train the model. Model-predicted plans were compared with benchmark plans in terms of dosimetric endpoints, fluence map deliverability, and total monitor units. Results: The average time for fluence-map prediction per patient was 7.1 s. Comparing model-predicted plans with benchmark plans, target mean dose, maximum dose (0.1 cc), and D95% absolute differences in percentages of prescription were 0.1, 3.9, and 2.1%, respectively; organ-at-risk mean dose and maximum dose (0.1 cc) absolute differences were 0.2 and 4.4%, respectively. The predicted plans had fluence map gamma indices (97.69 ± 0.96% vs. 98.14 ± 0.74%) and total monitor units (2,122 ± 281 vs. 2,265 ± 373) that were comparable to the benchmark plans. Conclusions: We develop a novel deep learning framework for pancreas SBRT planning, which predicts a fluence map for each beam and can, therefore, bypass the lengthy inverse optimization process. The proposed framework could potentially change the paradigm of treatment planning by harnessing the power of deep learning to generate clinically deliverable plans in seconds.

Purpose to develop digital phantoms for characterizing inconsistencies among radiomics extraction methods based on three radiomics toolboxes: CERR (Computational Environment for Radiological Research), IBEX (imaging biomarker explorer), and an in-house radiomics platform. Materials and methods we developed a series of digital bar phantoms for characterizing intensity and texture features and a series of heteromorphic sphere phantoms for characterizing shape features. The bar phantoms consisted of n equal-width bars (n = 2, 4, 8, or 64). The voxel values of the bars were evenly distributed between 1 and 64. Starting from a perfect sphere, the heteromorphic sphere phantoms were constructed by stochastically attaching smaller spheres to the phantom surface over 5500 iterations. We compared 61 features typically extracted from three radiomics toolboxes: (1) CERR (2) IBEX (3) in-house toolbox. The degree of inconsistency was quantified by concordance correlation coefficient (CCC) and Pearson correlation coefficient (PCC). Sources of discrepancies were characterized based on differences in mathematical definition, pre-processing, and calculation methods. Results For the intensity and texture features, only 53%, 45%, 55% features demonstrated perfect reproducibility (CCC = 1) between in-house/CERR, in-house/IBEX, and CERR/IBEX comparisons, while 71%, 61%, 61% features reached CCC > 0.8 and 25%, 39%, 39% features were with CCC 0.95. For shape features, the toolboxes produced similar (CCC > 0.98) volume yet inconsistent surface area, leading to inconsistencies in other shape features. However, all toolboxes resulted in PCC > 0.8 for all shape features except for compactness 1, where inconsistent mathematical definitions were observed. Discrepancies were characterized in pre-processing and calculation implementations from both type of phantoms. Conclusions Inconsistencies among radiomics extraction toolboxes can be accurately identified using the developed digital phantoms. The inconsistencies demonstrate the significance of implementing quality assurance (QA) of radiomics extraction for reproducible and generalizable radiomic studies. Digital phantoms are therefore very useful tools for QA.

Purpose On-board MRI can provide superb soft tissue contrast for improving liver SBRT localization. However, the availability of on-board MRI in clinics is extremely limited. On the contrary, on-board kV imaging systems are widely available on radiotherapy machines, but its capability to localize tumors in soft tissue is limited due to its poor soft tissue contrast. This study aims to explore the feasibility of using an on-board kV imaging system and patient prior knowledge to generate on-board four-dimensional (4D)-MRI for target localization in liver SBRT. Methods Prior 4D MRI volumes were separated into end of expiration (EOE) phase (MRIprior ) and all other phases. MRIprior was used to generate a synthetic CT at EOE phase (sCTprior ). On-board 4D MRI at each respiratory phase was considered a deformation of MRIprior . The deformation field map (DFM) was estimated by matching DRRs of the deformed sCTprior to on-board kV projections using a motion modeling and free-form deformation optimization algorithm. The on-board 4D MRI method was evaluated using both XCAT simulation and real patient data. The accuracy of the estimated on-board 4D MRI was quantitatively evaluated using Volume Percent Difference (VPD), Volume Dice Coefficient (VDC), and Center of Mass Shift (COMS). Effects of scan angle and number of projections were also evaluated. Results In the XCAT study, VPD/VDC/COMS among all XCAT scenarios were 10.16 ± 1.31%/0.95 ± 0.01/0.88 ± 0.15 mm using orthogonal-view 30° scan angles with 102 projections. The on-board 4D MRI method was robust against the various scan angles and projection numbers evaluated. In the patient study, estimated on-board 4D MRI was generated successfully when compared to the "reference on-board 4D MRI" for the liver patient case. Conclusions A method was developed to generate on-board 4D MRI using prior 4D MRI and on-board limited kV projections. Preliminary results demonstrated the potential for MRI-based image guidance for liver SBRT using only a kV imaging system on a conventional LINAC.

Purpose/objective(s) With many deep learning (DL) models being developed for clinical applications, it is important to understand their behavior and clinical consequence. This study aims to collect insights of the relationship between fluence map prediction error and its dosimetric impacts in a DL-based AI agent for H&N IMRT planning. Materials/methods An AI agent has been implemented to generate IMRT plans via fluence map prediction, bypassing inverse optimization. While the prostate IMRT plans generated by the agent were comparable to clinical plans in quality, the application into H&N patients exhibited large variations in the plan quality due to higher anatomy complexity. As the DL model's output is fluence maps of an IMRT plan, standard error analyses were focused on the differences between the predicted and ground truth fluence maps, i.e., prediction error. However, the ultimate plan evaluation is based on clinical criteria such as DVHs and dose distributions. Therefore, the AI agent's performance in clinics is subjected to complex and non-intuitive relationships between fluence map prediction error and corresponding dose distribution changes, and warrants thorough investigation. In this study, a series of tests were designed to collect insights of the impact of DL model performance on plan's dosimetric quality. The fluence map prediction error was analyzed for its dosimetric effects using five error decomposition modes:1) ground truth fluence intensity bands in 5 threshold levels, 2) predicted fluence intensity bands in 5 threshold levels, 3) ground truth fluence gradient bands (high and low), 4) Fourier space bands (frequency bands) in 8 threshold levels, and 5) Fourier space circles (below certain frequency) in 8 threshold levels. The DL model was trained with 216 cases and tested with 15 additional cases. PTV and OAR dosimetric metrics were analyzed by Spearman's rank tests (P = 0.05). Results Most PTV-related metrics were significantly correlated with the error components. Among the different decomposition modes, the Fourier space circle radii have large Spearman's coefficients with PTV metrics, suggesting that they were best able to extract error components that reveal plan quality impacts. The low-frequency error within a Fourier space circle of radius = 32 pixels (20% of Fourier space) had the most significant impact on overall plan quality and PTV heterogeneity. Conclusion The fluence map prediction error analysis is critical to evaluate the AI agent performance. Such insight will help with fine-tuning the DL models in architecture design and loss function selection.

PURPOSE/OBJECTIVE(S) To compare dosimetric results of two beam arrangement strategies and their robustness to setup uncertainties in fractionated thoracic spine Stereotactic Body Radiotherapy (SBRT) MATERIALS/METHODS: Fifteen patients who received fractionated thoracic spine SBRT were retrospectively studied. All patients received simulation CT scans in body vacuum bag immobilization and multiparametric MRI exams. Clinical target volumes (CTVs) included single vertebral bodies with possible paraspinal space inclusion. Planning target volumes (PTVs) were expanded from CTVs with a 2mm margin but were cropped from spinal cord defined by MRI with a 2mm margin. Two different beam arrangement strategies of volumetric modulated arc therapy planning were studied: 1) 5 full arcs (FA) (360° each arc) with different collimator angles; and 2) 6 partial arcs (PA) (90° each arc) divided into two groups (3 arcs in each group) covering patient left-posterior-oblique (LPO) and right-posterior-oblique (RPO) regions, respectively, with orthogonal collimator angles. Both plans (PlanFA and PlanPA) were calculated as 24 Gy in 3 fractions using 6xFFF photon energy and a high-definition MLC model. During the inverse optimization of each plan for same patient, a same set of dose-volume constraints and optimization settings was used to exhaust parameter space search. Key dosimetric results of PTV as well as dose-volume parameters of relevant organs-at-risk (OARs) including spinal cord, esophagus, heart, lung and liver were evaluated. Dosimetric impact of on-board patient setup uncertainties to both plans were also simulated. All comparison results were analyzed by Wilcoxon signed-rank tests when the statistical power was sufficient. RESULTS Both PlanFA and PlanPA achieved satisfactory spatial dose distribution. After PTV coverage normalization, PlanPA had better PTV dose uniformity (P = 0.026) and PlanFA had better dose fall-off gradient outside PTV. PlanPA had slightly better (18.1 ± 0.8 Gy vs 18.3 ± 0.9 Gy) cord max dose (D0.035cc) results (P = 0.213) and better cord low dose sparing V12 Gy (P = 0.013) results. PlanPA also achieved lower max dose (D0.035cc) of esophagus (P = 0.003) and heart, and improved low dose sparing (V5 Gy) of lung (P = 0.002) and liver. In plan parameter comparisons, PlanFA demonstrated stronger beam modulation effect (P < 0.001) but PlanPA had smaller MLC apertures (P < 0.001). In the simulated on-board scenarios with setup uncertainties, while both PlanFA and PlanPA had similar cord max dose increases with simulated pitch and/or roll in setup (< 0.1 Gy differences), PlanPA had minimal cord max dose increase (P < 0.001) with simulated anterior body weight loss. CONCLUSION For thoracic spine SBRT plans, the beam arrangement in PlanPA might be favored dosimetrically with better OAR sparing results and could be less sensitive to certain patient uncertainties, while the PlanFA could be acceptable with satisfactory dosimetry results. AUTHOR DISCLOSURE C. Wang: None. Y. Xie: None. Z. Hu: None. F. Yin: Research Grant; Varian Medical Systems. Teaching and mentoring graduate students. Administration of graduate program activities; Duke Kunshan University. Board of Directors Member at Large Members; AAPM. organize activities of the SANTRO; SANTRO.Z. Reitman: None. Y. Cui: None.

Purpose: To develop a 4-dimensional computerized magnetic resonance imaging phantom with image textures extracted from real patient scans for liver motion studies. Methods: The proposed phantom was developed based on the current version of 4-dimensional extended cardiac-torso computerized phantom and a clinical magnetic resonance scan. Initially, the extended cardiac-torso phantom was voxelized in abdominal–chest region at the end of exhalation phase. Structures/tissues were classified into 4 categories: (1) Seven key textured organs, including liver, gallbladder, spleen, stomach, heart, kidneys, and pancreas, were mapped from a clinical T1-weighted liver magnetic resonance scan using deformable registration. (2) Large textured soft tissue volumes were simulated via an iterative pattern generation method using the same magnetic resonance scan. (3) Lung and intestine structures were generated by assigning uniform intensity with proper noise modeling. (4) Bony structures were generated by assigning the magnetic resonance values. A spherical hypointensity tumor was inserted into the liver. Other respiratory phases of the 4-dimensional phantom were generated using the backward deformation vector fields exported by the extended cardiac-torso program, except that bony structures were generated separately for each phase. A weighted image filtering process was utilized to improve the overall tissue smoothness at each phase. Results: Three 4-dimensional series with different motion amplitudes were generated. The developed motion phantom produced good illustrations of abdominal–chest region with anatomical structures in key organs and texture patterns in large soft tissue volumes. In a standard series, the tumor volume was measured as 13.90 ± 0.11 cm3 in a respiratory cycle and the tumor’s maximum center-of-mass shift was 2.95 cm/1.84 cm on superior–inferior/anterior–posterior directions. The organ motion during the respiratory cycle was well rendered. The developed motion phantom has the flexibility of motion pattern variation, organ geometry variation, and tumor modeling variation. Conclusions: A 4-D computerized phantom was developed and could be used to produce image series with synthetic magnetic resonance textures for magnetic resonance imaging research of liver motion.

Purpose To develop a deep learning-based AI agent, DDD-PIOP (Dose-Distribution-Driven PET Image Outcome Prediction), for predicting 18FDG-PET image outcomes of oropharyngeal cancer (OPC) in response to intensity-modulated radiation therapy (IMRT). Methods DDD-PIOP uses pre-radiotherapy 18FDG-PET/CT images and the planned spatial dose distribution as the inputs, and it predicts the 18FDG-PET image outcomes in response to the planned IMRT delivery. This AI agent centralizes a customized convolutional neural network (CNN) as a deep learning approach, and it incorporates a few designs to enhance prediction accuracy. 66 OPC patients who received IMRT treatment on a sequential boost regime (2 Gy/daily fraction) were studied for DDD-PIOP development. 61 patients were used for AI agent training/validation, and the remaining five were used as independent tests. To evaluate the developed AI agent's performance, the predicted mean standardized uptake values (SUVs) of gross tumor volume (GTV) and clinical target volume (CTV) were compared with the ground truth values. Overall SUV distribution accuracy was evaluated by gamma test passing rates under different criteria. Results The developed DDD-PIOP successfully generated 18FDG-PET image outcome predictions for five test patients. The predicted mean SUV values of GTV/CTV were 3.50/1.41, which were close to the ground-truth values of 3.57/1.51. In 2D-based gamma tests, the average passing rate was 92.1% using 5%/10 mm criteria, which was improved to 95.9%/93.2% when focusing on GTV/CTV regions. 3D gamma test passing rates were 98.7% using 5%/10 mm criteria, and the corresponding GTV/CTV results were 99.8%/99.4%. Conclusion The reported results suggest that the developed AI agent DDD-PIOP successfully predicted 18FDG-PET image outcomes with high quantitative accuracy. The generated voxel-based image outcome predictions could be used for treatment planning optimization prior to radiation delivery for the best individual-based outcome.

Purpose To develop a tradeoff hyperplane model to facilitate tradeoff decision-making before inverse planning. Methods and Materials We propose a model-based approach to determine the tradeoff hyperplanes that allow physicians to navigate the clinically viable space of plans with best achievable dose–volume parameters before planning. For a given case, a case reference set (CRS) is selected using a novel anatomic similarity metric from a large reference plan pool. Then, a regression model is built on the CRS to estimate the expected dose–volume histograms (DVHs) for the current case. This model also predicts the DVHs for all CRS cases and captures the variation from the corresponding DVHs in the clinical plans. Finally, these DVH variations are analyzed using the principal component analysis to determine the tradeoff hyperplane for the current case. To evaluate the effectiveness of the proposed approach, 244 head and neck cases were randomly partitioned into reference (214) and validation (30) sets. A tradeoff hyperplane was built for each validation case and evenly sampled for 12 tradeoff predictions. Each prediction yielded a tradeoff plan. The root-mean-square errors of the predicted and the realized plan DVHs were computed for prediction achievability evaluation. Results The tradeoff hyperplane with 3 principal directions accounts for 57.8% ± 3.6% of variations in the validation cases, suggesting the hyperplanes capture a significant portion of the clinical tradeoff space. The average root-mean-square errors in 3 tradeoff directions are 5.23 ± 2.46, 5.20 ± 2.52, and 5.19 ± 2.49, compared with 4.96 ± 2.48 of the knowledge-based planning predictions, indicating that the tradeoff predictions are comparably achievable. Conclusions Clinically relevant tradeoffs can be effectively extracted from existing plans and characterized by a tradeoff hyperplane model. The hyperplane allows physicians and planners to explore the best clinically achievable plans with different organ-at-risk sparing goals before inverse planning and is a natural extension of the current knowledge-based planning framework.

Pancreas stereotactic body radiotherapy treatment planning requires planners to make sequential, time consuming interactions with the treatment planning system (TPS) to reach the optimal dose distribution. We seek to develop a reinforcement learning (RL)-based planning bot to systematically address complex tradeoffs and achieve high plan quality consistently and efficiently. The focus of pancreas SBRT planning is finding a balance between organs-at-risk sparing and planning target volume (PTV) coverage. Planners evaluate dose distributions and make planning adjustments to optimize PTV coverage while adhering to OAR dose constraints. We have formulated such interactions between the planner and the TPS into a finite-horizon RL model. First, planning status features are evaluated based on human planner experience and defined as planning states. Second, planning actions are defined to represent steps that planners would commonly implement to address different planning needs. Finally, we have derived a reward system based on an objective function guided by physician-assigned constraints. The planning bot trained itself with 48 plans augmented from 16 previously treated patients and generated plans for 24 cases in a separate validation set. All 24 bot-generated plans achieve similar PTV coverages compared to clinical plans while satisfying all clinical planning constraints. Moreover, the knowledge learned by the bot can be visualized and interpreted as consistent with human planning knowledge, and the knowledge maps learned in separate training sessions are consistent, indicating reproducibility of the learning process., Comment: 13 pages, 5 figures, and a table. Submitted to International Journal of Radiation Oncology Biology Physics

Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence–based treatment planning applications, such as deep learning–based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence–based treatment planning are discussed for future works.

Aim: The aim of the study is to develop a geometrically adaptive and statistically robust plan quality inference method. Methods and Materials: We propose a knowledge-based plan quality inference method that references to similar plans in the historical database for patient-specific plan quality evaluation. First, a novel plan similarity metric with high-dimension geometrical difference quantification is utilized to retrieve similar plans. Subsequently, dosimetric statistical inferences are obtained from the selected similar plans. Two plan quality metrics—dosimetric result probability and dose deviation index—are proposed to quantify plan quality among prior similar plans. To evaluate the performance of the proposed method, we exported 927 clinically approved head and neck treatment plans. Eight organs at risk, including brain stem, cord, larynx, mandible, pharynx, oral cavity, left parotid and right parotid, were analyzed. Twelve suboptimal plans identified by dosimetric result probability were replanned to validate the capability of the proposed methods in identifying inferior plans. Results: After replanning, left and right parotid median doses are reduced by 31.7% and 18.2%, respectively; 83% of these cases would not be identified as suboptimal without the proposed similarity plan selection. Analysis of population plan quality reveals that average parotid sparing has been improving significantly over time (21.7% dosimetric result probability reduction from year 2006-2007 to year 2016-2017). Notably, the increasing dose sparing over time in retrospective plan quality analysis is strongly correlated with the increasing dose prescription ratios to the 2 planning targets, revealing the collective trend in planning conventions. Conclusions: The proposed similar plan retrieval and analysis methodology has been proven to be predictive of the current plan quality. Therefore, the proposed workflow can potentially be applied in the clinics as a real-time plan quality assurance tool. The proposed metrics can also serve the purpose of plan quality analytics in finding connections and historical trends in the clinical treatment planning workflow.

The radiologic appearance of locally advanced lung cancer may be linked to molecular changes of the disease during treatment, but characteristics of this phenomenon are poorly understood. Radiomics, liquid biopsy of cell-free DNA (cfDNA), and next-generation sequencing of circulating tumor DNA (ctDNA) encode tumor-specific radiogenomic expression patterns that can be probed to study this problem. Preliminary findings are reported from a radiogenomic analysis of CT imaging, cfDNA, and ctDNA in 24 patients (median age, 64 years; range, 49–74 years) with stage III lung cancer undergoing chemoradiation on a prospective pilot study (NCT00921739) between September 2009 and September 2014. Unsupervised clustering of radiomic signatures resulted in two clusters that were associated with ctDNA TP53 mutations (P = .03) and changes in cfDNA concentration after 2 weeks of chemoradiation (P = .02). The radiomic features dissimilarity (hazard ratio [HR] = 0.56; P = .05), joint entropy (HR = 0.56; P = .04), sum entropy (HR = 0.53; P = .02), and normalized inverse difference (HR = 1.77; P = .05) were associated with overall survival. These results suggest heterogeneous and low-attenuating disease without a detectable ctDNA TP53 mutation was associated with early surges of cfDNA concentration in response to therapy and a generally better prognosis. Keywords: CT-Quantitative, Radiation Therapy, Lung, Computer Applications-3D, Oncology, Tumor Response, Outcomes Analysis Clinical trial registration no. NCT00921739 Supplemental material is available for this article. © RSNA, 2021

The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Field Fluence Prediction (AIP-SFFP), for automated prostate IMRT planning with real-time planning efficiency. The following method was adopted: AIP-SFFP generates a prostate IMRT plan through predictions of fluence maps using patient anatomy. No inverse planning is required. AIP-SFFP is centered on a custom-built deep learning (DL) neural network for fluence map prediction. Predictions are imported to a commercial treatment-planning system for dose calculation and plan generation. AIP-SFFP was demonstrated for prostate IMRT simultaneously-integrated-boost planning (58.8 Gy/70 Gy to PTV58.8 Gy/PTV70 Gy in 28 fx, PTV = Planning Target Volume). Training data was generated from 106 patients using a knowledge-based planning (KBP) plan generator. Two types of 2D projection images were designed to represent structures' sizes and locations, and a total of eight projections were utilized to describe targets and organs-at-risk. Projections at nine template beam angles were stacked as inputs for artificial intelligence (AI) training. 14 patients were used as independent tests. The generated test plans were compared with the plans from the KBP training plan generator and clinic practice. The following results were obtained: After normalization (PTV70 Gy V70 Gy = 95%), all 14 AI plans met institutional criteria. The coverage of PTV58.8 Gy in the AI plans was comparable to KBP and clinic plans without statistical significance. The whole body (BODY) D1cc and rectum D0.1cc of AI plans were slightly higher (

PURPOSE To accelerate volumetric cine MRI (VC-MRI) using undersampled 2D-cine MRI to provide real-time 3D guidance for gating/target tracking in radiotherapy. METHODS 4D-MRI is acquired during patient simulation. One phase of the prior 4D-MRI is selected as the prior images, designated as MRIprior. The on-board VC-MRI at each time-step is considered a deformation of the MRIprior. The deformation field map is represented as a linear combination of the motion components extracted by principal component analysis from the prior 4D-MRI. The weighting coefficients of the motion components are solved by matching the corresponding 2D-slice of the VC-MRI with the on-board undersampled 2D-cine MRI acquired. Undersampled Cartesian and radial k-space acquisition strategies were investigated. The effects of k-space sampling percentage (SP) and distribution, tumor sizes and noise on the VC-MRI estimation were studied. The VC-MRI estimation was evaluated using XCAT simulation of lung cancer patients and data from liver cancer patients. Volume percent difference (VPD) and Center of Mass Shift (COMS) of the tumor volumes and tumor tracking errors were calculated. RESULTS For XCAT, VPD/COMS were 11.93 ± 2.37%/0.90 ± 0.27 mm and 11.53 ± 1.47%/0.85 ± 0.20 mm among all scenarios with Cartesian sampling (SP = 10%) and radial sampling (21 spokes, SP = 5.2%), respectively. When tumor size decreased, higher sampling rate achieved more accurate VC-MRI than lower sampling rate. VC-MRI was robust against noise levels up to SNR = 20. For patient data, the tumor tracking errors in superior-inferior, anterior-posterior and lateral (LAT) directions were 0.46 ± 0.20 mm, 0.56 ± 0.17 mm and 0.23 ± 0.16 mm, respectively, for Cartesian-based sampling with SP = 20% and 0.60 ± 0.19 mm, 0.56 ± 0.22 mm and 0.42 ± 0.15 mm, respectively, for radial-based sampling with SP = 8% (32 spokes). CONCLUSIONS It is feasible to estimate VC-MRI from a single undersampled on-board 2D cine MRI. Phantom and patient studies showed that the temporal resolution of VC-MRI can potentially be improved by 5-10 times using a 2D cine image acquired with 10-20% k-space sampling.

A Tm 3+ /Ho 3+ :NaGd(MoO 4 ) 2 crystal was grown by the Czochralski method. The XRD results matched standard data from JCPDS file, which accorded with scheelite structure with an I41/a space group. Thermal properties of crystal were analyzed by using the TG–DSC curve. The melting point and specific heat are 1182 °C and 0.5 J/g K at 300 K, respectively. The Spectral properties of the Tm 3+ /Ho 3+ :NaGd(MoO 4 ) 2 crystal were investigated, including room temperature absorption spectrum and fluorescence spectrum. The absorption cross-section of Tm 3+ at 796 nm is 4.33 × 10 −20 cm 2 with pertinent full widths at half maximum (FWHM) of 10 nm. The intensity parameters, spontaneous emission probability, fluorescence branching ratios and radiative lifetimes were calculated by Judd–Ofelt theory. The emission cross-sections are 1.47 × 10 −20 cm 2 and 1.36 × 10 −20 cm 2 for Tm 3+ at 1850 nm and Ho 3+ at 2000 nm respectively. The lifetime of 5 I 7 → 5 I 8 (Ho 3+ ) was 4.263 ms.

PURPOSE:This study aimed to investigate the effectiveness of using delta-radiomics to predict overall survival (OS) for patients with recurrent malignant gliomas treated by concurrent stereotactic radiosurgery and bevacizumab, and to investigate the effectiveness of machine learning methods for delta-radiomics feature selection and building classification models. METHODS:The pre-treatment, one-week post-treatment, and two-month post-treatment T1 and T2 fluid-attenuated inversion recovery (FLAIR) MRI were acquired. 61 radiomic features (intensity histogram-based, morphological, and texture features) were extracted from the gross tumor volume in each image. Delta-radiomics were calculated between the pre-treatment and post-treatment features. Univariate Cox regression and 3 multivariate machine learning methods (L1-regularized logistic regression [L1-LR], random forest [RF] or neural networks [NN]) were used to select a reduced number of features, and 7 machine learning methods (L1-LR, L2-LR, RF, NN, kernel support vector machine [KSVM], linear support vector machine [LSVM], or naïve bayes [NB]) was used to build classification models for predicting OS. The performances of the total 21 model combinations built based on single-time-point radiomics (pre-treatment, one-week post-treatment, and two-month post-treatment) and delta-radiomics were evaluated by the area under the receiver operating characteristic curve (AUC). RESULTS:For a small cohort of 12 patients, delta-radiomics resulted in significantly higher AUC than pre-treatment radiomics (p-value

This study compares the shutter-speed (SS) and the Tofts models as used in assessing therapeutic response in a longitudinal DCE-MRI experiment. Sixteen nu/nu mice with implanted colorectal adenocarcinoma cell line (LS-174T) were randomly assigned into treatment/control groups (n = 8/group) and received bevacizumab/saline twice weekly (Day1/Day4/Day8). All mice were scanned at one pre- (Day0) and two post-treatment (Day2/Day9) time points using a high spatiotemporal resolution DCE-MRI pulse sequence. The CA extravasation rate constant [Formula: see text] from the Tofts/SS model and the mean intracellular water residence time [Formula: see text] from the SS model were analyzed. A biological subvolume (BV) within the tumor was identified based on the [Formula: see text] intensity distribution, and the SS model parameters within the BV ([Formula: see text] and [Formula: see text]) were analyzed. It is found that [Formula: see text] and [Formula: see text] have a similar spatial distribution in the tumor volume. The Bayesian information criterion results show that the SS model was a better fit for all scans. At Day9, the treatment group had significantly higher tumor mean [Formula: see text] (p = 0.021), [Formula: see text] (p = 0.021) and [Formula: see text] (p = 0.045). When BV from transcytolemmal water exchange analysis was adopted, the treatment group had higher mean [Formula: see text] at both Day2 (p = 0.038) and Day9 (p = 0.007). Additionally, at Day9, the treatment group had higher mean [Formula: see text] (p = 0.045) and higher [Formula: see text] spatial heterogeneity indices (Rényi dimensions) d 1 (p = 0.010) and d 2 (p = 0.021). When mean [Formula: see text] and its coefficient of variation (CV) were used to separate treatment/control group samples using supporting vector machine, the accuracy of treatment/control classification was 68.8% at Day2 and 87.5% at Day9; in contrast, the Day2/Day9 accuracy were 62.5%/87.5% using tumor mean [Formula: see text] and its CV and were 50.0%/87.5% using tumor mean [Formula: see text] and its CV, respectively. These results suggest that the SS model parameters outperformed the Tofts model parameters in terms of capturing bevacizumab therapeutic effect in this longitudinal experiment.

Purpose: To investigate the feasibility of using undersampled k-space data and an iterative image reconstruction method with total generalized variation penalty in the quantitative pharmacokinetic analysis for clinical brain dynamic contrast-enhanced magnetic resonance imaging. Methods: Eight brain dynamic contrast-enhanced magnetic resonance imaging scans were retrospectively studied. Two k-space sparse sampling strategies were designed to achieve a simulated image acquisition acceleration factor of 4. They are (1) a golden ratio–optimized 32-ray radial sampling profile and (2) a Cartesian-based random sampling profile with spatiotemporal-regularized sampling density constraints. The undersampled data were reconstructed to yield images using the investigated reconstruction technique. In quantitative pharmacokinetic analysis on a voxel-by-voxel basis, the rate constant Ktrans in the extended Tofts model and blood flow FB and blood volume VB from the 2-compartment exchange model were analyzed. Finally, the quantitative pharmacokinetic parameters calculated from the undersampled data were compared with the corresponding calculated values from the fully sampled data. To quantify each parameter’s accuracy calculated using the undersampled data, error in volume mean, total relative error, and cross-correlation were calculated. Results: The pharmacokinetic parameter maps generated from the undersampled data appeared comparable to the ones generated from the original full sampling data. Within the region of interest, most derived error in volume mean values in the region of interest was about 5% or lower, and the average error in volume mean of all parameter maps generated through either sampling strategy was about 3.54%. The average total relative error value of all parameter maps in region of interest was about 0.115, and the average cross-correlation of all parameter maps in region of interest was about 0.962. All investigated pharmacokinetic parameters had no significant differences between the result from original data and the reduced sampling data. Conclusion: With sparsely sampled k-space data in simulation of accelerated acquisition by a factor of 4, the investigated dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic parameters can accurately estimate the total generalized variation-based iterative image reconstruction method for reliable clinical application.

Purpose: To develop a dynamic fractal signature dissimilarity (FSD) method as a novel image texture analysis technique for the quantification of tumor heterogeneity information for better therapeutic response assessment with dynamic contrast-enhanced (DCE)-MRI. Methods: A small animal antiangiogenesis drug treatment experiment was used to demonstrate the proposed method. Sixteen LS-174T implanted mice were randomly assigned into treatment and control groups (n = 8/group). All mice received bevacizumab (treatment) or saline (control) three times in two weeks, and one pretreatment and two post-treatment DCE-MRI scans were performed. In the proposed dynamic FSD method, a dynamic FSD curve was generated to characterize the heterogeneity evolution during the contrast agent uptake, and the area under FSD curve (AUCFSD) and the maximum enhancement (MEFSD) were selected as representative parameters. As for comparison, the pharmacokinetic parameter Ktrans map and area under MR intensity enhancement curve AUCMR map were calculated. Besides the tumor’s mean value and coefficient of variation, the kurtosis, skewness, and classic Renyi dimensions d1 and d2 of Ktrans and AUCMR maps were evaluated for heterogeneity assessment for comparison. For post-treatment scans, the Mann–Whitney U-test was used to assess the differences of the investigated parameters between treatment/control groups. The support vector machine (SVM) was applied to classify treatment/control groups using the investigated parameters at each post-treatment scan day. Results: The tumor mean Ktrans and its heterogeneity measurements d1 and d2 values showed significant differences between treatment/control groups in the second post-treatment scan. In contrast, the relative values (in reference to the pretreatment value) of AUCFSD and MEFSD in both post-treatment scans showed significant differences between treatment/control groups. When using AUCFSD and MEFSD as SVM input for treatment/control classification, the achieved accuracies were 93.8% and 93.8% at first and second post-treatment scan days, respectively. In comparison, the classification accuracies using d1 and d2 of Ktrans map were 87.5% and 100% at first and second post-treatment scan days, respectively. Conclusions: As quantitative metrics of tumor contrast agent uptake heterogeneity, the selected parameters from the dynamic FSD method accurately captured the therapeutic response in the experiment. The potential application of the proposed method is promising, and its addition to the existing DCE-MRI techniques could improve DCE-MRI performance in early assessment of treatment response.

Purpose: To optimize collimator setting to improve dosimetric quality of pancreas volumetric modulated arc therapy plan for stereotactic body radiation therapy. Materials and Methods: Fifty-five volumetric modulated arc therapy cases in stereotactic body radiation therapy of pancreas were retrospectively included in this study with internal review board approval. Different from the routine practice of initializing collimator settings with a template, the proposed algorithm simultaneously optimizes the collimator angles and jaw positions that are customized to the patient geometry. Specifically, this algorithm includes 2 key steps: (1) an iterative optimization algorithm via simulated annealing that generates a set of potential collimator settings from 39 cases with pancreas stereotactic body radiation therapy, and (2) a multi-leaf collimator modulation scoring system that makes the final decision of the optimal collimator settings (collimator angles and jaw positions) based on organs at risk sparing criteria. For validation, the other 16 cases with pancreas stereotactic body radiation therapy were analyzed. Two plans were generated for each validation case, with one plan optimized using the proposed algorithm ( Planopt) and the other plan with the template setting ( Planconv). Each plan was optimized with 2 full arcs and the same set of constraints for the same case. Dosimetric results were analyzed and compared, including target dose coverage, conformity, organs at risk maximum dose, and modulation complexity score. All results were tested by Wilcoxon signed rank tests, and the statistical significance level was set to .05. Results: Both plan groups had comparable target dose coverage and mean doses of all organs at risk. However, organs at risk (stomach, duodenum, large/small bowel) maximum dose sparing ( D0.1 cc and D0.03 cc) was improved in Planopt compared to Planconv. Planopt also showed lower modulation complexity score, which suggests better capability of handling complex shape and sparing organs at risk . Conclusions: The proposed collimator settings optimization algorithm successfully improved dosimetric performance for dual-arc pancreas volumetric modulated arc therapy plans in stereotactic body radiation therapy of pancreas. This algorithm has the capability of immediate clinical application.

The purpose of this research was to study the sensitivity of Computed Tomography (CT) radiomic features to motion blurring and signal-to-noise ratios (SNR), and investigate its downstream effect regarding the classification of non-small cell lung cancer (NSCLC) histology. Forty-three radiomic features were considered and classified into one of four categories: Morphological, Intensity, Fine Texture, and Coarse Texture. First, a series of simulations were used to study feature-sensitivity to changes in spatial-temporal resolution. A dynamic digital phantom was used to generate images with different breathing amplitudes and SNR, from which features were extracted and characterized relative to initial simulation conditions. Stage I NSCLC patients were then retrospectively identified, from which three different acquisition-specific feature-spaces were generated based on free-breathing (FB), average-intensity-projection (AIP), and end-of-exhalation (EOE) CT images. These feature-spaces were derived to cover a wide range of spatial-temporal tradeoff. Normalized percent differences and concordance correlation coefficients (CCC) were used to assess the variability between the 3D and 4D acquisition techniques. Subsequently, three corresponding acquisition-specific logistic regression models were developed to classify lung tumor histology. Classification performance was compared between the different data-dependent models. Simulation results demonstrated strong linear dependences (p 0.95) between respiratory motion and morphological features, as well as between SNR and texture features. The feature Short Run Emphasis was found to be particularly stable to both motion blurring and changes in SNR. When comparing FB-to-EOE, 37% of features demonstrated high CCC agreement (CCC 0.8), compared to only 30% for FB-to-AIP. In classifying tumor histology, EoE images achieved an average AUC, Accuracy, Sensitivity, and Specificity of [Formula: see text], respectively. FB images achieved respective values of [Formula: see text], and AIP images achieved respective values of [Formula: see text]. Several radiomic features have been identified as being relatively robust to spatial-temporal variations based on both simulation data and patient data. In general, features that were sensitive to motion blurring were not necessarily the same features that were sensitive to changes in SNR. Our modeling results suggest that the EoE phase of a 4DCT acquisition may provide useful radiomic information, particularly for features that are highly sensitive to respiratory motion.

Single-dose preoperative stereotactic body radiotherapy is a novel radiotherapy technique for the early-stage breast cancer, and the treatment response pattern of this technique needs to be investigated on a quantitative basis. In this work, dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging were used to study the treatment response pattern in a unique cohort of patients with early-stage breast cancer treated with preoperative radiation. Fifteen female qualified patients received single-dose preoperative radiotherapy with 1 of the 3 prescription doses: 15 Gy, 18 Gy, and 21 Gy. Magnetic resonance imaging scans including both diffusion-weighted magnetic resonance imaging and dynamic contrast-enhanced magnetic resonance imaging were acquired before radiotherapy for planning and after radiotherapy but before surgical resection. In diffusion-weighted magnetic resonance imaging, the regional averaged apparent diffusion coefficient was calculated. In dynamic contrast-enhanced magnetic resonance imaging, quantitative parameters Ktrans and ve were evaluated using the standard Tofts model based on the average contrast agent concentration within the region of interest, and the semiquantitative initial area under the concentration curve (iAUC6min) was also recorded. These parameters’ relative changes after radiotherapy were calculated for gross tumor volume, clinical target volume, and planning target volume. The initial results showed that after radiotherapy, initial area under the concentration curve significantly increased in planning target volume ( P < .006) and clinical target volume ( P < .006), and ve significantly increased in planning target volume ( P < .05) and clinical target volume ( P < .05). Statistical studies suggested that linear correlations between treatment dose and the observed parameter changes exist in most examined tests, and among these tests, the change in gross tumor volume regional averaged apparent diffusion coefficient ( P < .012) and between treatment dose and planning target volume Ktrans ( P < .029) were found to be statistically significant. Although it is still preliminary, this pilot study may be useful to provide insights for future works.

Purpose Women with biologically favorable early-stage breast cancer are increasingly treated with accelerated partial breast radiation (PBI). However, treatment-related morbidities have been linked to the large postoperative treatment volumes required for external beam PBI. Relative to external beam delivery, alternative PBI techniques require equipment that is not universally available. To address these issues, we designed a phase 1 trial utilizing widely available technology to 1) evaluate the safety of a single radiation treatment delivered preoperatively to the small-volume, intact breast tumor and 2) identify imaging and genomic markers of radiation response. Methods and Materials Women aged ≥55 years with clinically node-negative, estrogen receptor–positive, and/or progesterone receptor–positive HER2−, T1 invasive carcinomas, or low- to intermediate-grade in situ disease ≤2 cm were enrolled (n=32). Intensity modulated radiation therapy was used to deliver 15 Gy (n=8), 18 Gy (n=8), or 21 Gy (n=16) to the tumor with a 1.5-cm margin. Lumpectomy was performed within 10 days. Paired pre- and postradiation magnetic resonance images and patient tumor samples were analyzed. Results No dose-limiting toxicity was observed. At a median follow-up of 23 months, there have been no recurrences. Physician-rated cosmetic outcomes were good/excellent, and chronic toxicities were grade 1 to 2 (fibrosis, hyperpigmentation) in patients receiving preoperative radiation only. Evidence of dose-dependent changes in vascular permeability, cell density, and expression of genes regulating immunity and cell death were seen in response to radiation. Conclusions Preoperative single-dose radiation therapy to intact breast tumors is well tolerated. Radiation response is marked by early indicators of cell death in this biologically favorable patient cohort. This study represents a first step toward a novel partial breast radiation approach. Preoperative radiation should be tested in future clinical trials because it has the potential to challenge the current treatment paradigm and provide a path forward to identify radiation response biomarkers.

We investigate the broadband anti-Stokes emission (BASE) from Ybsup3+/sup-doped crystals with a laser diode (LD) pumping at 940 nm. Our experiment reveals that Ybsup3+/sup-doped crystals with random cracks are able to generate bright BASE at room temperature and atmospheric pressure. By examining the various characteristics of the crystals and the emitted light, we supply a theory for interpreting the underlying physics for this variety of BASE. In particular, we take into consideration the effects of energy migration, avalanche process, and charge-transfer luminescence. This represents the first time, to the best of our knowledge, that BASE was obtained from Ybsup3+/sup-doped bulk crystals with a high optical-optical efficiency.

Recent developments in magnetic resonance (MR) functional quantitative imaging have made it a potentially powerful tool to assess treatment response in radiation therapy. With its abilities to capture functional information on underlying tissue characteristics, MR functional quantitative imaging can be valuable in assessing treatment response and as such to optimize therapeutic outcome. Various MR quantitative imaging techniques, including diffusion weighted imaging, diffusion tensor imaging, MR spectroscopy and dynamic contrast enhanced imaging, have been investigated and found useful for assessment of radiotherapy. However, various aspects including data reproducibility, interpretation of biomarkers, image quality and data analysis impose challenges on applications of MR functional quantitative imaging in radiotherapy assessment. All of these challenging issues shall be addressed to help us understand whether MR functional quantitative imaging is truly beneficial and contributes to future development of radiotherapy. It is evident that individualized therapy is the future direction of patient care. MR functional quantitative imaging might serves as an indispensable tool towards this promising direction.

As a noninvasive functional imaging technique, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is being used in oncology to measure properties of tumor microvascular structure and permeability. Studies have shown that parameters derived from certain pharmacokinetic models can be used as imaging biomarkers for tumor treatment response. The use of DCE-MRI for quantitative and objective assessment of radiation therapy has been explored in a variety of methods and tumor types. However, due to the complexity in imaging technology and divergent outcomes from different pharmacokinetic approaches, the method of using DCE-MRI in treatment assessment has yet to be standardized, especially for breast cancer. This article reviews the basic principles of breast DCE-MRI and recent studies using DCE-MRI in treatment assessment. Technical and clinical considerations are emphasized with specific attention to assessment of radiation treatment response.