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Complete methodological description of the Voxel Forecast modeling framework and benchmarking simulations.

Background Clinical medical physics duties include routine tasks, special procedures, and development projects. It can be challenging to distribute the effort equitably across all team members, especially in large clinics or systems where physicists cover multiple sites. The purpose of this work is to study an equitable workload distribution system in radiotherapy physics that addresses the complex and dynamic nature of effort assignment. Methods We formed a working group that defined all relevant clinical tasks and estimated the total time spent per task. Estimates used data from the oncology information system, a survey of physicists, and group consensus. We introduced a quantitative workload unit, “equivalent workday” (eWD), as a common unit for effort. The sum of all eWD values adjusted for each physicist's clinical full‐time equivalent yields a “normalized total effort” (nTE) metric for each physicist, that is, the fraction of the total effort assigned to that physicist. We implemented this system in clinical operation. During a trial period of 9 months, we made adjustments to include tasks previously unaccounted for and refined the system. The workload distribution of eight physicists over 12 months was compared before and after implementation of the nTE system. Results Prior to implementation, differences in workload of up to 50% existed between individual physicists (nTE range of 10.0%–15.0%). During the trial period, additional categories were added to account for leave and clinical projects that had previously been assigned informally. In the 1‐year period after implementation, the individual workload differences were within 5% (nTE range of 12.3%–12.8%). Conclusion We developed a system to equitably distribute workload and demonstrated improvements in the equity of workload. A quantitative approach to workload distribution improves both transparency and accountability. While the system was motivated by the complexities within an academic medical center, it may be generally applicable for other clinics.

Context: Cancer Radiomics is an emerging field in medical imaging and refers to the process of converting routine radiological images that are typically qualitatively interpreted to quantifiable descriptions of the tumor phenotypes and when combined with statistical analytics can improve the accuracy of clinical outcome prediction models. However, to understand the radiomic features and their correlation to molecular changes in the tumor, first, there is a need for the development of robust image analysis methods, software tools and statistical prediction models which is often limited in low- and middle-income countries (LMIC). Aims: The aim is to build a framework for machine learning of radiomic features of planning computed tomography (CT) and positron emission tomography (PET) using open source radiomics and data analytics platforms to make it widely accessible to clinical groups. The framework is tested in a small cohort to predict local disease failure following radiation treatment for head-and-neck cancer (HNC). The predictors were also compared with the existing Aerts HNC radiomics signature. Settings and Design: Retrospective analysis of patients with locally advanced HNC between 2017 and 2018 and 31 patients with both pre- and post-radiation CT and evaluation PET were selected. Subjects and Methods: Tumor volumes were delineated on baseline PET using the semi-automatic adaptive-threshold algorithm and propagated to CT; PyRadiomics features (total of 110 under shape/intensity/texture classes) were extracted. Two feature-selection methods were tested for model stability. Models were built based on least absolute shrinkage and selection operator-logistic and Ridge regression of the top pretreatment radiomic features and compared to Aerts' HNC-signature. Average model performance across all internal validation test folds was summarized by the area under the receiver operator curve (ROC). Results: Both feature selection methods selected CT features MCC (GLCM), SumEntropy (GLCM) and Sphericity (Shape) that could predict the binary failure status in the cross-validated group and achieved an AUC >0.7. However, models using Aerts' signature features (Energy, Compactness, GLRLM-GrayLevelNonUniformity and GrayLevelNonUniformity-HLH wavelet) could not achieve a clear separation between outcomes (AUC = 0.51–0.54). Conclusions: Radiomics pipeline included open-source workflows which makes it adoptable in LMIC countries. Additional independent validation of data is crucial for the implementation of radiomic models for clinical risk stratification.

The best survival for patients with unresectable, locally advanced NSCLC is currently achieved through concurrent chemoradiation followed by durvalumab for a year. Despite the best standard of care treatment, the majority of patients still develop disease recurrence, which could be distant and/or local. Trials continue to try and improve outcomes for patients with unresectable NSCLC, typically through treatment intensification, with the addition of more systemic agents, or more radiation dose to the tumor. Although RTOG 0617 showed that uniform dose escalation across an unselected population of patients undergoing chemoradiation is not beneficial, efforts continue to select patients and tumor subsets that are likely to benefit from dose escalation. This review describes some of the ongoing therapeutic trials in unresectable NSCLC, with an emphasis on quantitative imaging and precision radiation dose escalation.

Importance: Acute COVID19 related myocardial, pulmonary and vascular pathology, and how these relate to each other, remains unclear. No studies have used complementary imaging techniques, including molecular imaging, to elucidate this. Objective: We used multimodality imaging and biochemical sampling in vivo to identify the pathobiology of acute COVID19. Design, Setting and Participants: Consecutive patients presenting with acute COVID19 were recruited during hospital admission in a prospective cross sectional study. Imaging involved computed tomography coronary angiography (CTCA - identified coronary disease), cardiac 2deoxy2[fluorine18]fluoroDglucose positron emission tomography/computed tomography (18F FDG PET/CT identified vascular, cardiac and pulmonary inflammatory cell infiltration) and cardiac magnetic resonance (CMR identified myocardial disease), alongside biomarker sampling. Results: Of 33 patients (median age 51 years, 94% male), 24 (73%) had respiratory symptoms, with the remainder having non-specific viral symptoms. Nine patients (35%, n=9/25) had CMR defined myocarditis. 53% (n=5/8) of these patients had myocardial inflammatory cell infiltration. Two patients (5%) had elevated troponin levels. Cardiac troponin concentrations were not significantly higher in patients with myocarditis (8.4ng/L [IQR 4.0, 55.3] vs 3.5ng/L [2.5, 5.5], p=0.07) or myocardial cell infiltration (4.4ng/L [3.4, 8.3] vs 3.5ng/L [2.8, 7.2], p=0.89). No patients had obstructive coronary artery disease or vasculitis. Pulmonary inflammation and consolidation (percentage of total lung volume) was 17% (IQR 5, 31%) and 11% (7, 18%) respectively. Neither were associated with presence of myocarditis. Conclusions and relevance: Myocarditis was present in a third patients with acute COVID-19, and the majority had inflammatory cell infiltration. Pneumonitis was ubiquitous, but this inflammation was not associated with myocarditis. The mechanism of cardiac pathology is non-ischaemic, and not due to a vasculitic process.

BackgroundSpinal cord dose limits are critically important for the safe practice of spine stereotactic body radiotherapy (SBRT). However, the effect of inherent spinal cord motion on cord dose in SBRT is unknown.ObjectiveTo assess the effects of cord motion on spinal cord dose in SBRT.MethodsDynamic balanced fast field echo (BFFE) magnetic resonance imaging (MRI) was obtained in 21 spine metastasis patients treated with SBRT. Planning computed tomography (CT), conventional static T2-weighted MRI, BFFE MRI, and dose planning data were coregistered. Spinal cord from the dynamic BFFE images (corddyn) was compared with the T2-weighted MRI (cordstat) to analyze motion of corddyn beyond the cordstat (Dice coefficient, Jaccard index), and beyond cordstat with added planning organ at risk volume (PRV) margins. Cord dose was compared between cordstat, and corddyn (Wilcoxon signed-rank test).ResultsDice coefficient (0.70-0.95, median 0.87) and Jaccard index (0.54-0.90, median 0.77) demonstrated motion of corddyn beyond cordstat. In 62% of the patients (13/21), the dose to corddyn exceeded that of cordstat by 0.6% to 13.8% (median 4.3%). The corddyn spatially excursed outside the 1-mm PRV margin of cordstat in 9 patients (43%); among these dose to corddyn exceeded dose to cordstat>+1-mm PRV margin in 78% of the patients (7/9). Corddyn did not excurse outside the 1.5-mm or 2-mm PRV cord cordstat margin.ConclusionSpinal cord motion may contribute to increases in radiation dose to the cord from SBRT for spine metastasis. A PRV margin of at least 1.5 to 2mm surrounding the cord should be strongly considered to account for inherent spinal cord motion.

Medical imaging provides quantitative and spatial information to evaluate treatment response in the management of patients with non-small cell lung cancer (NSCLC). High throughput extraction of radiomic features on these images can potentially phenotype tumors non-invasively and support risk stratification based on survival outcome prediction. The prognostic value of radiomics from different imaging modalities and time points prior to and during chemoradiation therapy of NSCLC, relative to conventional imaging biomarker or delta radiomics models, remains uncharacterized. We investigated the utility of multitask learning of multi-time point radiomic features, as opposed to single-task learning, for improving survival outcome prediction relative to conventional clinical imaging feature model benchmarks. Survival outcomes were prospectively collected for 45 patients with unresectable NSCLC enrolled on the FLARE-RT phase II trial of risk-adaptive chemoradiation and optional consolidation PD-L1 checkpoint blockade (NCT02773238). FDG-PET, CT, and perfusion SPECT imaging pretreatment and week 3 mid-treatment was performed and 110 IBSI-compliant pyradiomics shape-/intensity-/texture-based features from the metabolic tumor volume were extracted. Outcome modeling consisted of a fused Laplacian sparse group LASSO with component-wise gradient boosting survival regression in a multitask learning framework. Testing performance under stratified 10-fold cross-validation was evaluated for multitask learning radiomics of different imaging modalities and time points. Multitask learning models were benchmarked against conventional clinical imaging and delta radiomics models and evaluated with the concordance index (c-index) and index of prediction accuracy (IPA). FDG-PET radiomics had higher prognostic value for overall survival in test folds (c-index 0.71 [0.67, 0.75]) than CT radiomics (c-index 0.64 [0.60, 0.71]) or perfusion SPECT radiomics (c-index 0.60 [0.57, 0.63]). Multitask learning of pre-/mid-treatment FDG-PET radiomics (c-index 0.71 [0.67, 0.75]) outperformed benchmark clinical imaging (c-index 0.65 [0.59, 0.71]) and FDG-PET delta radiomics (c-index 0.52 [0.48, 0.58]) models. Similarly, the IPA for multitask learning FDG-PET radiomics (30%) was higher than clinical imaging (26%) and delta radiomics (15%) models. Radiomics models performed consistently under different voxel resampling conditions. Multitask learning radiomics for outcome modeling provides a clinical decision support platform that leverages longitudinal imaging information. This framework can reveal the relative importance of different imaging modalities and time points when designing risk-adaptive cancer treatment strategies.

Purpose of the report We evaluated the reliability of 18F-FDG PET imaging biomarkers to classify early response status across observers, scanners, and reconstruction algorithms in support of biologically adaptive radiation therapy for locally advanced non-small cell lung cancer. Patients and methods Thirty-one patients with unresectable locally advanced non-small cell lung cancer were prospectively enrolled on a phase 2 trial (NCT02773238) and underwent 18F-FDG PET on GE Discovery STE (DSTE) or GE Discovery MI (DMI) PET/CT systems at baseline and during the third week external beam radiation therapy regimens. All PET scans were reconstructed using OSEM; GE-DMI scans were also reconstructed with BSREM-TOF (block sequential regularized expectation maximization reconstruction algorithm incorporating time of flight). Primary tumors were contoured by 3 observers using semiautomatic gradient-based segmentation. SUVmax, SUVmean, SUVpeak, MTV (metabolic tumor volume), and total lesion glycolysis were correlated with midtherapy multidisciplinary clinical response assessment. Dice similarity of contours and response classification areas under the curve were evaluated across observers, scanners, and reconstruction algorithms. LASSO logistic regression models were trained on DSTE PET patient data and independently tested on DMI PET patient data. Results Interobserver variability of PET contours was low for both OSEM and BSREM-TOF reconstructions; intraobserver variability between reconstructions was slightly higher. ΔSUVpeak was the most robust response predictor across observers and image reconstructions. LASSO models consistently selected ΔSUVpeak and ΔMTV as response predictors. Response classification models achieved high cross-validated performance on the DSTE cohort and more variable testing performance on the DMI cohort. Conclusions The variability FDG PET lesion contours and imaging biomarkers was relatively low across observers, scanners, and reconstructions. Objective midtreatment PET response assessment may lead to improved precision of biologically adaptive radiation therapy.

Cost-effectiveness analysis (CEA) in medicine is a form of economic study that compares the relative value of medical technologies and health care services. It helps decision makers to form...

Purpose: Pencil beam (PB) analytical algorithms have been the standard of care for proton therapy dose calculations. The introduction of Monte Carlo (MC) algorithms may provide more robust and accurate planning and can improve therapeutic benefit. We conducted a dosimetric analysis to quantify the differences between MC and PB algorithms in the clinical setting of dose-painted nasopharyngeal cancer intensity-modulated proton radiotherapy. Patients and Methods: Plans of 14 patients treated with PB analytical algorithm optimized and calculated (PBPB) were retrospectively analyzed. The PBPB plans were recalculated using MC to generate PBMC plans and, finally, reoptimized and recalculated with MC to generate MCMC plans. The plans were compared across several dosimetric endpoints and correlated with documented toxicity. Robustness of the planning scenarios (PBPB, PBMC, MCMC) in the presence of setup and range uncertainties was compared. Results: A median decrease of up to 5 Gy (P < .05) was observed in coverage of planning target volume high-risk, intermediate-risk, and low-risk volumes when PB plans were recalculated using the MC algorithm. This loss in coverage was regained by reoptimizing with MC, albeit with a slightly higher dose to normal tissues but within the standard tolerance limits. The robustness of both PB and MC plans remained similar in the presence of setup and range uncertainties. The MC-calculated mean dose to the oral avoidance structure, along with changes in global maximum dose between PB and MC dosimetry, may be associated with acute toxicity-related events. Conclusion: Retrospective analyses of plan dosimetry quantified a loss of coverage with PB that could be recovered under MC optimization. MC optimization should be performed for the complex dosimetry in patients with nasopharyngeal carcinoma before plan acceptance and should also be used in correlative studies of proton dosimetry with clinical endpoints.

PURPOSE/OBJECTIVE(S) We hypothesized that FDG PET imaging during chemoradiation for unresectable non-small cell lung cancer (NSCLC) is prognostic for survival, and that PET response is correlated with systemic immune response to treatment, as characterized by peripheral immunologic correlates and tumor PD-L1 status. A prognostic test that could predict patient outcome would help better select patients who need additional therapies, and spare toxicity in patients who do not. MATERIALS/METHODS A total of 45 patients with AJCCv7 stage IIB-IIIB NSCLC enrolled on the phase II FLARE-RT trial and received platinum-doublet chemotherapy concurrent with 6 weeks of radiation (NCT02773238). FDG PET imaging was performed prior to treatment start and after 24 Gy of radiation (week 3). PET response status was prospectively defined by multifactor radiomics. PET responders received 60 Gy in 30 fractions, while non-responders received concomitant boosts to 74 Gy in 30 fractions. 22/45 patients received durvalumab post-chemoradiation. Peripheral blood was drawn synchronously with PET imaging, from which germline DNA sequencing, T-cell receptor (TCR) sequencing, and plasma cytokines analysis were performed. Overall survival (OS), progression-free survival (PFS), and locoregional control (LRC) were estimated by Kaplan-Meier. Unsupervised learning of germline DNA sequencing SNP genotypes was performed with hierarchical clustering and summarized with cumulative frequency distributions of rank ordered SNPs. Genotype associations to OS, PFS, and PET response status were characterized by logistic regression with Firth penalization for estimating odds ratios (OR). TCR diversity metric trends were estimated by Spearman rank correlation and pairwise changes by Wilcoxon signed rank testing. TCR diversity metric associations to OS and PFS were assessed by Permutation testing of Harrell's c-index. RESULTS Median follow-up was 18.8 months, 1-year OS 82%, 1-year PFS 53%, and 1-year LRC 88%. Higher mid-treatment PET total lesion glycolysis was detrimental to OS (1-yr 87% vs. 63%, P 50%) classified as PET responders and 4/5 patients with negative PD-L1 TPS (< 1%) classified as PET non-responders. Germline DNA alterations in immunologic pathways had an outsized effect on OS and PET response; of the top 30 SNPs ranked by association with PET response status (P < 0.016), a plurality (13/30) came from immunologic pathways. Higher TCR richness, higher clone distribution slope, and smaller decline in clone distribution slope was associated with improved OS (P = 0.018-0.05); clone distribution slope was correlated with PET response (P = 0.031). CONCLUSION Mid-chemoradiation PET imaging is prognostic for survival; PET response may be linked to tumor and peripheral immunologic biomarkers.

Purpose: We conducted a prospective pilot study to evaluate safety and feasibility of TraceIT, a resorbable radiopaque hydrogel, to improve image guidance for bladder cancer radiation therapy (RT). Methods and Materials: Patients with muscle invasive bladder cancer receiving definitive RT were eligible. TraceIT was injected intravesically around the tumor bed during maximal transurethral resection of bladder tumor. The primary endpoint was the difference between radiation treatment planning margin on daily cone beam computed tomography based on alignment to TraceIT versus standard-of-care pelvic bone anatomy. The Van Herk margin formula was used to determine the optimal planning target volume margin. TraceIT visibility, recurrence rates, and survival were estimated by Kaplan-Meier method. Toxicity was measured by Common Terminology Criteria for Adverse Events version 4.03. Results: The trial was fully accrued and 15 patients were analyzed. TraceIT was injected in 4 sites/patient (range, 4-6). Overall, 94% (95% confidence interval [CI], 90%-98%) of injection sites were radiographically visible at RT initiation versus 71% (95% CI, 62%-81%) at RT completion. The median duration of radiographic visibility for injection sites was 106 days (95% CI, 104-113). Most patients were treated with a standard split-course approach with initial pelvic radiation fields, then midcourse repeat transurethral resection of bladder tumor followed by bladder tumor bed boost fields, and 14/15 received concurrent chemotherapy. Alignment to fiducials could allow for reduced planning target volume margins (0.67 vs 1.56 cm) for the initial phase of RT, but not for the boost (1.01 vs 0.96 cm). This allowed for improved target coverage (D95% 80%-83% to 91%-94%) for 2 patients retrospectively planned with both volumetric-modulated arc therapy and 3-dimensional conformal RT. At median follow-up of 22 months, no acute or late complications attributable to TraceIT placement occurred. No patients required salvage cystectomy. Conclusions: TraceIT intravesical fiducial placement is safe and feasible and may facilitate tumor bed delineation and targeting in patients undergoing RT for localized muscle invasive bladder cancer. Improved image guided treatment may facilitate strategies to improve local control and minimize toxicity.

Purpose: We sought to examine the prognostic value of fluorodeoxyglucose-positron emission tomography (PET) imaging during chemoradiation for unresectable non-small cell lung cancer for survival and hypothesized that tumor PET response is correlated with peripheral T-cell function. Methods and Materials: Forty-five patients with American Joint Committee on Cancer version 7 stage IIB-IIIB non-small cell lung cancer enrolled in a phase II trial and received platinum-doublet chemotherapy concurrent with 6 weeks of radiation (NCT02773238). Fluorodeoxyglucose-PET was performed before treatment start and after 24 Gy of radiation (week 3). PET response status was prospectively defined by multifactorial radiologic interpretation. PET responders received 60 Gy in 30 fractions, while nonresponders received concomitant boosts to 74 Gy in 30 fractions. Peripheral blood was drawn synchronously with PET imaging, from which germline DNA sequencing, T-cell receptor sequencing, and plasma cytokine analysis were performed. Results: Median follow-up was 18.8 months, 1-year overall survival (OS) 82%, 1-year progression-free survival 53%, and 1-year locoregional control 88%. Higher midtreatment PET total lesion glycolysis was detrimental to OS (1 year 87% vs 63%, P < .001), progression-free survival (1 year 60% vs 26%, P = .044), and locoregional control (1 year 94% vs 65%, P = .012), even after adjustment for clinical/treatment factors. Twenty-nine of 45 patients (64%) were classified as PET responders based on a priori definition. Higher tumor programmed death-ligand 1 expression was correlated with response on PET (P = .017). Higher T-cell receptor richness and clone distribution slope were associated with improved OS (P = .018-0.035); clone distribution slope was correlated with PET response (P = .031). Conclusions: Midchemoradiation PET imaging is prognostic for survival; PET response may be linked to tumor and peripheral T-cell biomarkers.

We investigated the sensitivity of regional tumor response prediction to variability in voxel clustering techniques, imaging features, and machine learning algorithms in 25 patients with locally advanced non-small cell lung cancer (LA-NSCLC) enrolled on the FLARE-RT clinical trial. Metabolic tumor volumes (MTV) from pre-chemoradiation (PETpre) and mid-chemoradiation fluorodeoxyglucose-positron emission tomography (FDG PET) images (PETmid) were subdivided into K-means or hierarchical voxel clusters by standardized uptake values (SUV) and 3D-positions. MTV cluster separability was evaluated by CH index, and morphologic changes were captured by Dice similarity and centroid Euclidean distance. PETpre conventional features included SUVmean, MTV/MTV cluster size, and mean radiation dose. PETpre radiomics consisted of 41 intensity histogram and 3D texture features (PET Oncology Radiomics Test Suite) extracted from MTV or MTV clusters. Machine learning models (multiple linear regression, support vector regression, logistic regression, support vector machines) of conventional features or radiomic features were constructed to predict PETmid response. Leave-one-out-cross-validated root-mean-squared-error (RMSE) for continuous response regression (ΔSUVmean) and area-under-receiver-operating-characteristic-curve (AUC) for binary response classification were calculated. K-means MTV 2-clusters (MTVhi, MTVlo) achieved maximum CH index separability (Friedman p < 0.001). Between PETpre and PETmid, MTV cluster pairs overlapped (Dice 0.70-0.87) and migrated 0.6-1.1 cm. PETmid ΔSUVmean response prediction was superior in MTV and MTVlo (RMSE = 0.17-0.21) compared to MTVhi (RMSE = 0.42-0.52, Friedman p < 0.001). PETmid ΔSUVmean response class prediction performance trended higher in MTVlo (AUC = 0.83-0.88) compared to MTVhi (AUC = 0.44-0.58, Friedman p = 0.052). Models were more sensitive to MTV/MTV cluster regions (Friedman p = 0.026) than feature sets/algorithms (Wilcoxon signed-rank p = 0.36). Top-ranked radiomic features included GLZSM-LZHGE (large-zone-high-SUV), GTSDM-CP (cluster-prominence), GTSDM-CS (cluster-shade) and NGTDM-CNT (contrast). Top-ranked features were consistent between MTVhi and MTVlo cluster pairs but varied between MTVhi-MTVlo clusters, reflecting distinct regional radiomic phenotypes. Variability in tumor voxel cluster response prediction can inform robust radiomic target definition for risk-adaptive chemoradiation in patients with LA-NSCLC. FLARE-RT trial: NCT02773238.

Purpose This study aims to investigate the use of machine learning models for delivery error prediction in proton pencil beam scanning (PBS) delivery. Methods A dataset of planned and delivered PBS spot parameters was generated from a set of 20 prostate patient treatments. Planned spot parameters (spot position, MU and energy) were extracted from the treatment planning system (TPS) for each beam. Delivered spot parameters were extracted from irradiation log-files for each beam delivery following treatment. The dataset was used as a training dataset for three machine learning models which were trained to predict delivered spot parameters based on planned parameters. K-fold cross validation was employed for hyper-parameter tuning and model selection where the mean absolute error (MAE) was used as the model evaluation metric. The model with lowest MAE was then selected to generate a predicted dose distribution for a test prostate patient within a commercial TPS. Results Analysis of the spot position delivery error between planned and delivered values resulted in standard deviations of 0.39 mm and 0.44 mm for x and y spot positions respectively. Prediction error standard deviation values of spot positions using the selected model were 0.22 mm and 0.11 mm for x and y spot positions respectively. Finally, a three-way comparison of dose distributions and DVH values for select OARs indicates that the random-forest-predicted dose distribution within the test prostate patient was in closer agreement to the delivered dose distribution than the planned distribution. Conclusions PBS delivery error can be accurately predicted using machine learning techniques.

PURPOSE Mitigating radiation-induced liver disease (RILD) is an ongoing need in patients with hepatocellular carcinoma. We hypothesize that [99mTc]-sulfur colloid (SC) single photon emission computed tomography (SPECT)/computed tomography (CT) scans can provide global functional liver metrics and functional liver dosimetric parameters that are predictive of hepatotoxicity risk in patients with primary liver cancer with cirrhosis. MATERIALS AND METHODS We retrospectively reviewed 47 patients (n = 26 proton, n = 21 stereotactic body radiation therapy) with Child-Pugh (CP)-A (62%) or CP-B (38%) cirrhosis who underwent SC SPECT/CT scans for radiation therapy planning. SC SPECT scans were mined for image intensity threshold-based functional liver volumes (FLV), mean liver-spleen uptake ratio (L/Smean), and total liver function (TLF = FLV*L/Smean). Radiation therapy doses were voxel-wise converted to the biologically equivalent dose (EQD2a/b=3) and relative biological effectiveness (GyRBE). Normal liver (liver minus gross tumor volume [GTV]) and FLV mean doses, absolute and relative dose-volumes (VGy[cc], VGy[%]), and relative dose-function histogram quantiles in 10 GyEQD2 increments were calculated. Logistic regression was performed for correlation to CP score increase of 2 or higher (CP+2). Cox regression was performed for correlation to RILD-specific survival (RILD-SS) and overall survival. RESULTS The strongest predictors of RILD-SS were FLV V20 and liver-GTV F20. FLV mean dose, but not CT-derived anatomic mean dose, was predictive of RILD-SS. TLF and L/Smean were the only parameters that were associated with CP+2 after adjusting for baseline CP score. Optimal cutoffs to mitigate risk RILD-SS were identified: FLV mean dose 0.75, TLF >0.60, tumor volume

Charged particle therapy with proton beam therapy (PBT) and carbon ion radiotherapy (CIRT) has emerged as a promising radiation modality to minimize radiation hepatotoxicity while maintaining high rates of tumor local control. Both PBT and CIRT deposit the majority of their dose at the Bragg peak with little to no exit dose, resulting in superior sparing of normal liver tissue. CIRT has an additional biological advantage of increased relative biological effectiveness, which may allow for increased hypofractionation regimens. Retrospective and prospective studies have demonstrated encouragingly high rates of local control and overall survival and low rates of hepatotoxicity with PBT and CIRT. Ongoing randomized trials will evaluate the value of PBT over photons and other standard liver-directed therapies and future randomized trials are needed to assess the value of CIRT over PBT.

Purpose Normal liver-sparing with proton beam therapy (PBT) allows for dose escalation in the treatment of liver malignancies, but it may result in high doses to the chest wall (CW). CW toxicity (CWT) data after PBT for liver malignancies are limited, with most published reports describing toxicity after a combination of hypofractionated proton and photon radiation therapy. We examined the incidence and associated factors for CWT after hypofractionated PBT for liver malignancies. Methods and materials We retrospectively reviewed the charts of 37 consecutive patients with liver malignancies (30 hepatocellular carcinoma, 6 intrahepatic cholangiocarcinoma, and 1 metastasis) treated with hypofractionated PBT. CWT was scored using Common Terminology Criteria for Adverse Events, version 4. Receiver-operating characteristic curves were used to identify patient and dosimetric factors associated with CWT and to determine optimal dose-volume histogram parameters/cutoffs. Cox regression univariate analysis was used to associate factors to time-dependent onset of CWT. Results Thirty-nine liver lesions were treated with a median dose of 60 GyE (range, 35-67.5) in 15 fractions (range, 13-20). Median follow-up was 11 months (range, 2-44). Grade ≥2 and 3 CW pain occurred in 7 (19%) and 4 (11%) patients, respectively. Median time to onset of pain was 6 months (range, 1-14). No patients had radiographic rib fracture. On univariate analysis, CW equivalent 2 Gy dose with an α/β = 3 Gy (EQD2 α/β=3 ), V57 >20 cm 3 (hazard ratio [HR], 2.7; P = .004), V63 >17 cm 3 (HR, 2.7; P = .003), and V78 >8 cm 3 (HR, 2.6; P = .003) had the strongest association with grade ≥2 CW pain, as did tumor dose of >75 Gy EQD2 α/β=10 (HR, 8.7; P = .03). No other patient factors were associated with CWT. Conclusions CWT after hypofractionated PBT for liver malignancies is clinically relevant. For a 15-fraction regimen, V47 >20 cm 3 , V50 >17 cm 3 , and V58 >8 cm 3 were associated with higher rates of CWT. Further investigation of PBT techniques to reduce CW dose are warranted.

Background Our purpose was to define the most clinically relevant "nonclassic" radiation-induced liver disease (RILD) endpoints in cirrhotic patients receiving stereotactic body radiation therapy or proton beam therapy for primary liver cancer. Methods and materials We retrospectively collected pretreatment, detailed toxicity (≤6 months posttreatment), and outcomes data from 48 patients. Deaths were examined for association with RILD. Univariate and multivariate Cox models defined significant predictors of overall survival (OS)/RILD-specific survival (RILD-SS). Results With median follow-up of 13 months, 23 patients (48%) had an increase in Child-Pugh (CP) score (≥2, 25%) and 3 (6%) had ≥G3 transaminase elevation. Of 18 deaths, 6 were potentially ascribed to RILD. Univariate analysis showed that CP score increases of ≥1 and ≥2 and CP class change predicted OS, as did ≥G3 aspartate transaminase (AST) elevation and ≥1 Common Terminology Criteria for Adverse Events (CTCAE) AST toxicity grade change. On multivariate analysis, CP score increase of ≥2 and ≥1 CTCAE AST toxicity grade change were the strongest independent nonclassic RILD predictors of OS. For RILD-SS, CP score increases of ≥2, ≥grade 3 CTCAE alanine transaminase, and ≥grade 2 bilirubin elevations were predictive. Conclusions Increased CP score ≥2 strongly predicts for both OS and RILD-SS and should be reported in future studies along with transaminase elevations, which are also predictive of outcomes.

The accuracy of dose calculation is vital to the quality of care for patients undergoing proton beam therapy (PBT). Currently, the dose calculation algorithms available in commercial treatment planning systems (TPS) in PBT are classified into two classes: pencil beam (PB) and Monte-Carlo (MC) algorithms. PB algorithms are still regarded as the standard of practice in PBT, but they are analytical approximations whereas MC algorithms use random sampling of interaction cross-sections that represent the underlying physics to simulate individual particles trajectories. This article provides a brief review of PB and MC dose calculation algorithms employed in commercial treatment planning systems and their performance comparison in phantoms through simulations and measurements. Deficiencies of PB algorithms are first highlighted by a simplified simulation demonstrating the transport of a single sub-spot of proton beam that is incident at an oblique angle in a water phantom. Next, more typical cases of clinical beams in water phantom are presented and compared to measurements. The inability of PB to correctly predict the range and subsequently distal fall-off is emphasized. Through the presented examples, it is shown how dose errors as high as 30% can result with use of a PB algorithm. These dose errors can be minimized to clinically acceptable levels of less than 5%, if MC algorithm is employed in TPS. As a final illustration, comparison between PB and MC algorithm is made for a clinical beam that is use to deliver uniform dose to a target in a lung section of an anthropomorphic phantom. It is shown that MC algorithm is able to correctly predict the dose at all depths and matched with measurements. For PB algorithm, there is an increasing mismatch with the measured doses with increasing tissue heterogeneity. The findings of this article provide a foundation for the second article of this series to compare MC vs. PB based lung cancer treatment planning.

Background Robust approaches to quantify tumor heterogeneity are needed to provide early decision support for precise individualized therapy. Purpose To conduct a technical exploration of longitudinal changes in tumor heterogeneity patterns on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI) and FDG positron emission tomography / computed tomography (PET/CT), and their association to radiation therapy (RT) response in cervical cancer. Study Type Prospective observational study with longitudinal MRI and PET/CT pre-RT, early-RT (2 weeks), and mid-RT (5 weeks). Population Twenty-one FIGO IB2-IVA cervical cancer patients receiving definitive external beam RT and brachytherapy. Field Strength/Sequence 1.5T, precontrast axial T1-weighted, axial and sagittal T2-weighted, sagittal DWI (multi-b values), sagittal DCE MRI (

RaySearch Americas Inc. (NY) has introduced a commercial Monte Carlo dose algorithm (RS-MC) for routine clinical use in proton spot scanning. In this report, we provide a validation of this algorithm against phantom measurements and simulations in the GATE software package. We also compared the performance of the RayStation analytical algorithm (RS-PBA) against the RS-MC algorithm. A beam model (G-MC) for a spot scanning gantry at our proton center was implemented in the GATE software package. The model was validated against measurements in a water phantom and was used for benchmarking the RS-MC. Validation of the RS-MC was performed in a water phantom by measuring depth doses and profiles for three spread-out Bragg peak (SOBP) beams with normal incidence, an SOBP with oblique incidence, and an SOBP with a range shifter and large air gap. The RS-MC was also validated against measurements and simulations in heterogeneous phantoms created by placing lung or bone slabs in a water phantom. Lateral dose profiles near the distal end of the beam were measured with a microDiamond detector and compared to the G-MC simulations, RS-MC and RS-PBA. Finally, the RS-MC and RS-PBA were validated against measured dose distributions in an Alderson-Rando (AR) phantom. Measurements were made using Gafchromic film in the AR phantom and compared to doses using the RS-PBA and RS-MC algorithms. For SOBP depth doses in a water phantom, all three algorithms matched the measurements to within ±3% at all points and a range within 1 mm. The RS-PBA algorithm showed up to a 10% difference in dose at the entrance for the beam with a range shifter and >30 cm air gap, while the RS-MC and G-MC were always within 3% of the measurement. For an oblique beam incident at 45°, the RS-PBA algorithm showed up to 6% local dose differences and broadening of distal fall-off by 5 mm. Both the RS-MC and G-MC accurately predicted the depth dose to within ±3% and distal fall-off to within 2 mm. In an anthropomorphic phantom, the gamma index (dose tolerance = 3%, distance-to-agreement = 3 mm) was greater than 90% for six out of seven planes using the RS-MC, and three out seven for the RS-PBA. The RS-MC algorithm demonstrated improved dosimetric accuracy over the RS-PBA in the presence of homogenous, heterogeneous and anthropomorphic phantoms. The computation performance of the RS-MC was similar to the RS-PBA algorithm. For complex disease sites like breast, head and neck, and lung cancer, the RS-MC algorithm will provide significantly more accurate treatment planning.

Purpose: The results from Radiation Therapy Oncology Group (RTOG) 0617, a dose escalation trial that compared treatment with 60 Gy versus 74 Gy for patients with stage III non-small cell lung cancer (NSCLC), suggested that in these patients, the heart dose from radiation therapy correlates with survival. In particular, the study noted that patients with a high heart V5 and V30 had a poorer overall survival; however, the exact cause of this correlation is not known. We hypothesize that heart dose may be a surrogate for mediastinal nodal involvement, which has prognostic value in NSCLC. This study evaluates the relationship between heart dose and involvement of mediastinal lymph nodes in patients with stage III NSCLC treated with radiation therapy. Methods and materials: A total of 56 patients were identified and treated with definitive radiation therapy from 2007 to 2014. The heart was recontoured for every patient by a single physician, per the RTOG 1106 contouring atlas. We assessed lymph node station involvement using pretreatment data, and nodal coverage was confirmed on plan review. Results: Mean heart dose was found to be significantly higher in patients with multinodal station and level 7 involvement. On Spearman's rank correlation, level 7 was significantly associated with all heart parameters tested (P < .001). Patients who had 2 or more lymph node stations involved were found to have significantly higher heart doses for all parameters tested when compared with those who had only one station involved or no nodal involvement. Conclusions: Our findings suggest that heart dose may be a surrogate for other prognostic factors in stage III NSCLC rather than an independent predictor of outcome.

To design and apply a framework for predicting symptomatic radiation pneumonitis in patients undergoing thoracic radiation, using both pretreatment anatomic and perfused lung dose–volume parameters. Radiation treatment planning CT scans were coregistered with pretreatment [99mTc]MAA perfusion SPECT/CT scans of 20 patients who underwent definitive thoracic radiation. Clinical radiation pneumonitis was defined as grade ≥ 2 (CTCAE v4 grading system). Anatomic lung dose–volume parameters were collected from the treatment planning scans. Perfusion dose–volume parameters were calculated from pretreatment SPECT/CT scans. Equivalent doses in 2 Gy per fraction were calculated in the lung to account for differences in treatment regimens and spatial variations in lung dose (EQD2lung). Anatomic lung dosimetric parameters (MLD) and functional lung dosimetric parameters (pMLD70%) were identified as candidate predictors of grade ≥ 2 radiation pneumonitis (AUC > 0.93, p 13.6 GyEQD2lung, 19.3 Gy for patients receiving 60 Gy in 30 fractions) developed radiation pneumonitis, but all individuals who also had high mean dose to perfused lung (pMLD70% > 13.3 GyEQD2) developed radiation pneumonitis. The preliminary application of this framework revealed differences between anatomic and perfused lung dosimetry in this limited patient cohort. The addition of perfused lung parameters may help risk stratify patients for radiation pneumonitis, especially in treatment plans with high anatomic mean lung dose. Further investigations are warranted.

The ability of respiratory-correlated fan beam CT (4DCT) and respiratory-correlated cone beam CT (4DCBCT) to accurately estimate tumor volume is critical to accurate dosimetry and treatment verification for lung stereotactic body radiation therapy (SBRT) and other motion-managed therapies. However, it is known that 4DCT and 4DCBCT differ in aspects of image acquisition and reconstruction that may lead to discrepancies between the two modalities. To evaluate quantitative differences between 4DCT and 4DCBCT imaging under respiratory motion, we performed a phantom study in the ground truth setting. A programmable respiratory motion phantom was used to simulate the 1D S-I position of a known-size lesion. Ten sinusoidal and twenty patient-specific breathing waveforms were applied to drive lesion motion during the 4DCT and 4DCBCT acquisitions. The difference in lesion volume acquired between the two imaging modalities was as high as 34.4% and 18.4% for sinusoidal and patient-specific breathing motions, respectively. When compared to the true volume, 4DCT measurement often underestimated the lesion size whereas 4DCBCT overestimated the lesion volume in most of the cases. 4DCBCT gave more accurate recovery of the volume than 4DCT for most settings tested in this study. These findings may be helpful for improving the definition of internal target and planning target volume margins, and extracting quantitative information from on-board treatment verification imaging.

OBJECTIVE. In this article, we describe the concepts of ventilation-perfusion planar, SPECT, and SPECT/CT and outline the advantages of integrated ventilation-perfusion SPECT/CT over planar imaging. We present an overview of the traditional and new applications of ventilation-perfusion scintigraphy. CONCLUSION. SPECT/CT has improved the diagnostic accuracy of ventilation-perfusion imaging and opened the door for a new spectrum of applications.

Objective: The effect of functional lung avoidance planning on radiation dose-dependent changes in regional lung perfusion is unknown. We characterized dose-perfusion response on longitudinal perfusion single photon emission computed tomography (SPECT)/CT in two cohorts of lung cancer patients treated with and without functional lung avoidance techniques. Methods: The study included 28 primary lung cancer patients: 20 from interventional (NCT02773238) (FLARE-RT) and eight from observational (NCT01982123) (LUNG-RT) clinical trials. FLARE-RT treatment plans included perfused lung dose constraints while LUNG-RT plans adhered to clinical standards. Pre- and 3 month post-treatment macro-aggregated albumin (MAA) SPECT/CT scans were rigidly co-registered to planning four-dimensional CT scans. Tumour-subtracted lung dose was converted to EQD2 and sorted into 5 Gy bins. Mean dose and percent change between pre/post-RT MAA-SPECT uptake (%ΔPERF), normalized to total tumour-subtracted lung uptake, were calculated in each binned dose region. Perfusion frequency histograms of pre/post-RT MAA-SPECT were analyzed. Dose–response data were parameterized by sigmoid logistic functions to estimate maximum perfusion increase (%ΔPERFmaxincrease), maximum perfusion decrease (%ΔPERFmaxdecrease), dose midpoint (Dmid), and dose-response slope (k). Results: Differences in MAA perfusion frequency distribution shape between time points were observed in 11/20 (55%) FLARE-RT and 2/8 (25%) LUNG-RT patients (p < 0.05). FLARE-RT dose response was characterized by >10% perfusion increase in the 0–5 Gy dose bin for 8/20 patients (%ΔPERFmaxincrease = 10–40%), which was not observed in any LUNG-RT patients (p = 0.03). The dose midpoint Dmid at which relative perfusion declined by 50% trended higher in FLARE-RT compared to LUNG-RT cohorts (35 GyEQD2 vs 21 GyEQD2, p = 0.09), while the dose-response slope k was similar between FLARE-RT and LUNG-RT cohorts (3.1–3.2, p = 0.86). Conclusion: Functional lung avoidance planning may promote increased post-treatment perfusion in low dose regions for select patients, though inter-patient variability remains high in unbalanced cohorts. These preliminary findings form testable hypotheses that warrant subsequent validation in larger cohorts within randomized or case-matched control investigations. Advances in knowledge: This novel preliminary study reports differences in dose-response relationships between patients receiving functional lung avoidance radiation therapy (FLARE-RT) and those receiving conventionally planned radiation therapy (LUNG-RT). Following further validation and testing of these effects in larger patient populations, individualized estimation of regional lung perfusion dose-response may help refine future risk-adaptive strategies to minimize lung function deficits and toxicity incidence.

The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is 18F-fluorodeoxyglucose ([ 18F]FDG), a glucose analog with demonstrated efficacy in cancer diagnosis and staging. However, diagnosis and RT planning are different endeavors with unique requirements, and very little literature is available for guiding physicists and clinicians in the utilization of [ 18F]FDG-PET in RT. The two goals of this report are to educate and provide recommendations. The report provides background and education on current PET imaging systems, PET tracers, intensity quantification, and current utilization in RT (staging, segmentation, image registration, treatment planning, and therapy response assessment). Recommendations are provided on acceptance testing, annual and monthly quality assurance, scanning protocols to ensure consistency between interpatient scans and intrapatient longitudinal scans, reporting of patient and scan parameters in literature, requirements for incorporation of [ 18F]FDG-PET in treatment planning systems, and image registration. The recommendations provided here are minimum requirements and are not meant to cover all aspects of the use of [ 18F]FDG-PET for RT.

Rationale and Objectives To explore the diagnostic value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intensity histogram metrics, relative to time intensity curve (TIC)-derived metrics, in patients with suspected lung cancer. Materials and Methods This retrospective study enrolled 49 patients with suspected lung cancer on routine CT imaging who underwent DCE-MRI scans and had final histopathologic diagnosis. Three TIC-derived metrics (maximum enhancement ratio, peak time [Tmax] and slope) and eight intensity histogram metrics (volume, integral, maximum, minimum, median, coefficient of variation [CoV], skewness, and kurtosis) were extracted from DCE-MRI images. TIC-derived and intensity histogram metrics were compared between benignity versus malignancy using the Wilcoxon rank-sum test. Associations between imaging metrics and malignancy risk were assessed by univariate and multivariate logistic regression odds ratios (ORs). Results There were 33 malignant lesions and 16 benign lesions based on histopathology. Lower CoV (OR = 0.2 per 1-SD increase, p = 0.0006), lower Tmax (OR = 0.4 per 1-SD increase, p = 0.005), and steeper slope (OR = 2.4 per 1-SD increase, p = 0.010) were significantly associated with increased risk of malignancy. Under multivariate analysis, CoV was significantly independently associated with malignancy likelihood after accounting for either Tmax (OR = 0.3 per 1-SD increase, p = 0.007) or slope (OR = 0.3 per 1-SD increase, p = 0.011). Conclusion This initial study found that DCE-MRI CoV was independently associated with malignancy in patients with suspected lung cancer. CoV has the potential to help diagnose indeterminate pulmonary lesions and may complement TIC-derived DCE-MRI metrics. Further studies are warranted to validate the diagnostic value of DCE-MRI intensity histogram analysis.

The conversion of images, such as those from positron emission tomography (PET) and x-ray computed tomography (CT), into mineable high-dimensional data is known as radiomics. Radiomics is motivated by the concept that biomedical images contain information that reflects underlying physiology and molecular biology, and that these relationships can be revealed via quantitative image analyses. Thus an understanding of quantitative PET/CT accuracy, and how variations in acquisition and image reconstruction parameters influence imaging features in the absence of true underlying biologic effects, is essential to PET/CT radiomics. In this chapter, we first provide an overview of quantitative PET/CT, with emphasis on uncertainties and their clinical impact on quantitative precision and accuracy. We highlight ongoing efforts towards PET/CT standardization and harmonization as part of quantitative imaging biomarker quality assurance programs. With a basis in quantitative PET/CT, we introduce PET/CT radiomics, list additional sources of radiomic data processing uncertainty, propose guidelines to improve transparent reporting of PET/CT radiomic features, and emphasize the growing need for robust omics data analytics to help ensure reproducibility and clinical translation of scientific findings. Lastly, we summarize future directions beyond PET/CT radiomics, including PET/MR radiomics, non-FDG-PET radiomics, normal tissue function PET radiomics, and deep learning of PET/CT images.

Purpose: Prediction of spatially variant response to cancer therapies can inform risk-adaptive management within precision oncology. We developed the “Voxel Forecast” multiscale regression framework for predicting spatially variant tumor response to chemoradiotherapy on fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging. Experimental Design: Twenty-five patients with locally advanced non–small cell lung cancer, enrolled on the FLARE-RT phase II trial (NCT02773238), underwent FDG PET/CT imaging prior to (PETpre) and during week 3 (PETmid) of concurrent chemoradiotherapy. Voxel Forecast was designed to predict tumor voxel standardized uptake value (SUV) on PETmid from baseline patient-level and voxel-level covariates using a custom generalized least squares (GLS) algorithm. Matérn covariance matrices were fit to patient- specific empirical variograms of distance-dependent intervoxel correlation. Regression coefficients from variogram-based weights and corresponding standard errors were estimated using the jackknife technique. The framework was validated using statistical simulations of known spatially variant tumor response. Mean absolute prediction errors (MAEs) of Voxel Forecast models were calculated under leave-one-patient-out cross-validation. Results: Patient-level forecasts resulted in tumor voxel SUV MAE on PETmid of 1.5 g/mL while combined patient- and voxel-level forecasts achieved lower MAE of 1.0 g/mL (P < 0.0001). PETpre voxel SUV was the most important predictor of PETmid voxel SUV. Patients with a greater percentage of under-responding tumor voxels were classified as PETmid nonresponders (P = 0.030) with worse overall survival prognosis (P < 0.001). Conclusions: Voxel Forecast multiscale regression provides a statistical framework to predict voxel-wise response patterns during therapy. Voxel Forecast can be extended to predict spatially variant response on multimodal quantitative imaging and may eventually guide optimized spatial–temporal dose distributions for precision cancer therapy.

Purpose There are limited clinical data on scanning-beam proton therapy (SPT) in treating locally advanced lung cancer, as most published studies have used passive-scatter technology. There is increasing interest in whether the dosimetric advantages of SPT compared with photon therapy can translate into superior clinical outcomes. We present our experience of SPT and photon intensity modulated radiation therapy (IMRT) with clinical dosimetry and outcomes in patients with stage III lung cancer. Methods and Materials Patients with stage III lung cancer treated at our center between 2013 and May 2018 were identified in compliance with our institutional review board (64 patients = 34 SPT + 30 IMRT). Most proton patients were treated with pencil beam scanning (28 of 34), and 6 of 34 were treated with uniform scanning. Fisher exact test, χ2 test, and Mann-Whitney test were used to compare groups. All tests were 2-sided. Results Patient characteristics were similar between the IMRT and SPT patients, except for worse lung function in the IMRT group. Mean dose to lung, heart, and esophagus was lower in the SPT group, with most benefit in the low-dose region (lungs, 9.7 Gy vs 15.7 Gy for SPT vs IMRT, respectively [P = .004]; heart, 7 Gy vs 14 Gy [P = .001]; esophagus, 28.2 Gy vs 30.9 Gy [P = .023]). Esophagitis and dermatitis grades were not different between the 2 groups. Grade 2+ pneumonitis was 21% in the SPT group and 40% in the IMRT group (P = .107). Changes in blood counts were not different between the 2 groups. Overall survival and progression-free survival were not different between SPT and IMRT (median overall survival, 41.6 vs 30.7 months, respectively [P = .52]; median progression-free survival, 19.5 vs 14.6 months [P = .50]). Conclusions We report our experience with SPT and IMRT in stage III lung cancer. Our cohort of patients treated with SPT had lower doses to normal organs (lungs, heart, esophagus) than our IMRT cohort. There was no statistically significant difference in toxicity rates or survival, although there may have been a trend toward lower rates of pneumonitis.

The use of positron emission tomography (PET) in radiation therapy (RT) is rapidly increasing in the areas of staging, segmentation, treatment planning, and response assessment. The most common radiotracer is

457 Background: Precision image-guided radiotherapy (RT) for patients (pts) with muscle-invasive bladder cancer (MIBC) is limited by daily anatomic variation and difficulty visualizing the tumor bed. We hypothesized that TraceIT, a radiopaque resorbable hydrogel, can be injected around the tumor bed to improve image guidance. We present the results of our pilot trial (NCT03125226) evaluating safety/feasibility of TraceIT in pts undergoing definitive RT. Methods: Eligibility included MIBC with plan to receive definitive RT +/- chemo. Fifteen patients were available for analysis (11 enrolled on trial from 2017-2018, plus an additional 4 received TraceIT off study). TraceIT was injected around the circumference of the tumor bed during pre-radiation maximal re-transurethral resection of bladder tumor (TURBT) for all pts, and again during the mid-radiation TURBT to improve visibility in n = 8 pts. The primary endpoint was assessment of interfraction motion on daily cone-beam CT (CBCTs) based on alignment to fiducial vs standard-of-care pelvic bone anatomy. Van Herk (VH) margin equation was used to determine the planning target volume margin optimized for the clinical target volume receiving at least 95%-prescription dose in 90% of pts. Recurrence rates and survival were estimated by Kaplan Meier. Toxicity was measured by CTCAE v4.03. Results: Patients underwent RT to a median total dose of 63.5 Gy (range 35.75-66.6), typically to an initial small pelvis field follow by a tumor boost. 14/15 received chemo with RT. Median TraceIT volume was 0.5cc (range 0.3-0.75) per injection site for 4 (range 4-6) sites per patient for total volume of 2cc (range 2-3). All pts demonstrated 100% visibility of TraceIT on initial simulation CT and day 1 CBCT. TraceIT visibility declined slightly over time, with 91.5% of patients having visible TraceIT by the end of the initial RT phase (usually week 5) and 82.5% by end of the boost phase. For the initial phase, alignment to fiducials over bone anatomy allowed for reduced VH margins (0.67cm vs 1.56cm). For the boost phase, the VH margin was similar between fiducial and bone alignment (1.01cm vs 0.96cm). There have been no acute or late complications from TraceIT placement. There were no grade grade (G) 4/5 toxicities; three pts had acute G 3 events, and three pts experienced late G 3 toxicity: two related to hematuria and one from ureteral stenosis. There were no late G≥2 GI toxicities. No patients have undergone cystectomy. At a median follow-up of 22 months, 2-yr OS was 79.1% and 2-yr PFS was 75.4%. Conclusions: TraceIT is safe and feasible for use in image-guided RT. TraceIT can increase the precision of RT by facilitating accurate target delineation of the bladder tumor bed and daily motion management which may allow for smaller radiation treatment margins to be used to reduce toxicity and to potentially facilitate safe dose escalation to tumor, which can improve tumor control. Clinical trial information: NCT03125226.

PURPOSE: In this report, we present the commissioning and validation results for a commercial proton pencil beam scanning RayStation treatment planning system. MATERIALS AND METHODS: The commissioning data requirements are (1) integrated depth dose curves, (2) spot profiles, (3) absolute dose/monitor unit calibration, and (4) virtual source position. An 8-cm parallel plate chamber was used to measure the integrated depth dose curves by scanning a beam composed of a single spot in a water phantom. The spot profiles were measured at 5 different planes using a 2-dimensional scintillation detector. The absolute dose/monitor unit calibration was based on dose measurements in single-layer fields of size 10 × 10 cm(2). The virtual-source position was calculated from the change in spot spacing with the distance from the isocenter. The beam model validation consisted of a comparison against commissioning data as well as a new set of verification measurements. For end-to-end testing, a series of phantom plans were created. These plans were measured at 1 to 3 depths using a 2-dimensional ion chamber array and evaluated for gamma index using the 3% and 3 mm criteria. RESULTS: The maximum deviation for spot sigma measured versus calculated was −0.2 mm. The point-dose measurements for single-layer beams were within ± 3%, except for the largest field size (29 × 29 cm(2)) and the highest energy (226 MeV). The point doses in the spread-out Bragg peak plans showed a trend in which differences > 3% were seen for ranges > 30 cm, field sizes > 15 × 15 cm(2), and depths > 25 cm. For end-to-end testing, 34 planes corresponding to 13 beams were analyzed for gamma index with a minimum pass rate of 92.8%. CONCLUSION: The acceptable verification results and successful end-to-end testing ensured that all components of the treatment planning system were functional and the system was ready for clinical use.

Purpose The aim of this study was to review institutional outcomes for advanced thyroid cancers treated with fast neutron radiation therapy (FNRT) and photon radiation therapy (RT). Methods and materials In all, 62 consecutive patients were analyzed. Fifty-nine had stage IV disease. Twenty-three were treated with FNRT and 39 with photon RT. Median follow-up was 14 months. The primary endpoint was overall survival (OS). Results There was no significant difference in median OS between FNRT and photon RT (26 vs 16 months; P = .49). Patients with well-differentiated histologies had superior median OS with photon RT (17 vs 69 months; P = .04). There was a nonsignificant trend toward improved OS with FNRT for medullary and anaplastic histologies. Conclusions Outcomes in this study are in line with historical results. There is an apparent detriment in OS with FNRT for well-differentiated histologies and a trend toward improved OS with medullary and anaplastic histologies that warrants further investigation.