An 18F-FDG PET/CT and Mean Lung Dose Model to Predict Early Radiation Pneumonitis in Stage III Non-Small Cell Lung Cancer Patients Treated with Chemoradiation and Immunotherapy.

Radiation pneumonitis (RP) that develops early (i.e., within 3 mo) (RPEarly) after completion of concurrent chemoradiation (cCRT) leads to treatment discontinuation and poorer survival for patients with stage III non-small cell lung cancer. Since no RPEarly risk model exists, we explored whether published RP models and pretreatment 18F-FDG PET/CT-derived features predict RPEarly Methods: One hundred sixty patients with stage III non-small cell lung cancer treated with cCRT and consolidative immunotherapy were analyzed for RPEarly Three published RP models that included the mean lung dose (MLD) and patient characteristics were examined. Pretreatment 18F-FDG PET/CT normal-lung SUV featured included the following: 10th percentile of SUV (SUVP10), 90th percentile of SUV (SUVP90), SUVmax, SUVmean, minimum SUV, and SD. Associations between models/features and RPEarly were assessed using area under the receiver-operating characteristic curve (AUC), P values, and the Hosmer-Lemeshow test (pHL). The cohort was randomly split, with similar RPEarly rates, into a 70%/30% derivation/internal validation subset. Results: Twenty (13%) patients developed RPEarly Predictors for RPEarly were MLD alone (AUC, 0.72; P = 0.02; pHL, 0.87), SUVP10, SUVP90, and SUVmean (AUC, 0.70-0.74; P = 0.003-0.006; pHL, 0.67-0.70). The combined MLD and SUVP90 model generalized in the validation subset and was deemed the final RPEarly model (RPEarly risk = 1/[1+e(- x )]; x = -6.08 + [0.17 × MLD] + [1.63 × SUVP90]). The final model refitted in the 160 patients indicated improvement over the published MLD-alone model (AUC, 0.77 vs. 0.72; P = 0.0001 vs. 0.02; pHL, 0.65 vs. 0.87). Conclusion: Patients at risk for RPEarly can be detected with high certainty by combining the normal lung's MLD and pretreatment 18F-FDG PET/CT SUVP90 This refined model can be used to identify patients at an elevated risk for premature immunotherapy discontinuation due to RPEarly and could allow for interventions to improve treatment outcomes.

The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Clinical and Dosimetric Risk Factors Associated With Radiation-Induced Lung Toxicities After Multiple Courses of Lung Stereotactic Body Radiation Therapy.

Purpose: Data are limited on radiation-induced lung toxicities (RILT) after multiple courses of lung stereotactic body radiation therapy (SBRT). We herein analyze a large cohort of patients to explore the clinical and dosimetric risk factors associated with RILT in such settings. Methods and Materials: A single institutional database of patients treated with multiple courses of lung SBRT between January 2014 and December 2019 was analyzed. Grade 2 or higher (G2+) RILT after the last course of SBRT was the primary endpoint. Composite plans were generated with advanced algorithms including deformable registration and equivalent dose adjustment. Logistic regression analyses were performed to examine correlations between patient or treatment factors including dosimetry and G2+ RILT. Risk stratification of patients and lung constraints based on acceptable normal tissue complication probability were calculated based on risk factors identified. Results: Among 110 eligible patients (56 female and 54 male), there were 64 synchronous (58.2%; defined as 2 courses of SBRT delivered within 30 days) and 46 metachronous (41.8%) courses of SBRT. The composite median lung V20, lung V5, and mean lung dose were 9.9% (interquartile range [IQR], 7.3%-12.4%), 32.2% (IQR, 25.5%-40.1%), and 7.0 Gy (IQR, 5.5 Gy-8.6 Gy), respectively. With a median follow-up of 21.1 months, 30 patients (27.3%) experienced G2+ RILT. Five patients (4.5%) developed G3 RILT, and 1 patient (0.9%) developed G4 RILT, and no patients developed G5 RILT. On multivariable regression analysis, female sex (odds ratio [OR], 4.35; 95% CI, 1.49%-14.3%; P = .01), synchronous SBRT (OR, 8.78; 95% CI, 2.27%-47.8%; P = .004), prior G2+ RILT (OR, 29.8; 95% CI, 2.93%-437%; P = .007) and higher composite lung V20 (OR, 1.18; 95% CI, 1.02%-1.38%; P = .030) were associated with significantly higher likelihood of G2+ RILT. Conclusions: Our data suggest an acceptable incidence of G2+ RILT after multiple courses of lung SBRT. Female sex, synchronous SBRT, prior G2+ RILT, and higher composite lung V20 may be risk factors for G2+ RILT.

Exploring published and novel pre-treatment CT and PET radiomics to stratify risk of progression among early-stage non-small cell lung cancer patients treated with stereotactic radiation.

PURPOSE: Disease progression after definitive stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) occurs in 20-40% of patients. Here, we explored published and novel pre-treatment CT and PET radiomics features to identify patients at risk of progression. MATERIALS/METHODS: Published CT and PET features were identified and explored along with 15 other CT and PET features in 408 consecutively treated early-stage NSCLC patients having CT and PET < 3 months pre-SBRT (training/set-aside validation subsets: n = 286/122). Features were associated with progression-free survival (PFS) using bootstrapped Cox regression (Bonferroni-corrected univariate predictor: p ≤ 0.002) and only non-strongly correlated predictors were retained (|Rs|<0.70) in forward-stepwise multivariate analysis. RESULTS: Tumor diameter and SUVmax were the two most frequently reported features associated with progression/survival (in 6/20 and 10/20 identified studies). These two features and 12 of the 15 additional features (CT: 6; PET: 6) were candidate PFS predictors. A re-fitted model including diameter and SUVmax presented with the best performance (c-index: 0.78; log-rank p-value < 0.0001). A model built with the two best additional features (CTspiculation1 and SUVentropy) had a c-index of 0.75 (log-rank p-value < 0.0001). CONCLUSIONS: A re-fitted pre-treatment model using the two most frequently published features - tumor diameter and SUVmax - successfully stratified early-stage NSCLC patients by PFS after receiving SBRT.

Simulating the Potential of Model-Based Individualized Prescriptions for Ultracentral Lung Tumors.

Purpose: The use of stereotactic body radiation therapy for ultracentral lung tumors is limited by increased toxicity. We hypothesized that using published normal tissue complication probability (NTCP) and tumor control probability (TCP) models could improve the therapeutic ratio between tumor control and toxicity. A proposed model-based approach was applied to virtually replan early-stage non-small cell lung cancer (NSCLC) tumors. Methods and Materials: The analysis included 63 patients with ultracentral NSCLC tumors treated at our center between 2008 and 2017. Along with current clinical constraints, additional NTCP model-based criteria, including for grade 3+ radiation pneumonitis (RP3+) and grade 2+ esophagitis, were implemented using 4 different fractionation schemes. Scaled dose distributions resulting in the highest TCP without violating constraints were selected (optimal plan [Planopt]). Planopt predictions were compared with the observed local control and toxicities. Results: The observed 2-year local control rate was 72% (95% CI, 57%-88%) compared with 87% (range, 6%-93%) for Planopt TCP. Thirty-nine patients had Planopt with TCP > 80%, and 14 patients had Planopt TCP < 50%. The Planopt NTCPs for RP3+ were reduced by nearly half compared with patients' observed RP3+. The RP3+ NTCP was the most frequent reason for TCP of Planopt < 80% (14/24 patients), followed by grade 2+ esophagitis NTCP (5/24 patients) due to larger tumors (>40 cc vs ≤40 cc; P = .002) or a shorter tumor to esophagus distance (≥5 cm vs <5 cm; P < .001). Conclusions: We demonstrated the potential for model-based prescriptions to yield higher TCP while respecting NTCP for patients with ultracentral NSCLC. Individualizing treatments based on NTCP- and TCP-driven simulations halved the predicted relative to the observed rates of RP3+. Our simulations also identified patients whose TCP could not be improved without violating NTCP due to larger tumors or a near tumor to esophagus proximity.

ROE (Radiotherapy Outcomes Estimator): An open-source tool for optimizing radiotherapy prescriptions.

BACKGROUND AND OBJECTIVES: Radiotherapy prescriptions currently derive from population-wide guidelines established through large clinical trials. We provide an open-source software tool for patient-specific prescription determination using personalized dose-response curves. METHODS: We developed ROE, a plugin to the Computational Environment for Radiotherapy Research to visualize predicted tumor control and normal tissue complication simultaneously, as a function of prescription dose. ROE can be used natively with MATLAB and is additionally made accessible in GNU Octave and Python, eliminating the need for commercial licenses. It provides a curated library of published and validated predictive models and incorporates clinical restrictions on normal tissue outcomes. ROE additionally provides batch-mode tools to evaluate and select among different fractionation schemes and analyze radiotherapy outcomes across patient cohorts. CONCLUSION: ROE is an open-source, GPL-copyrighted tool for interactive exploration of the dose-response relationship to aid in radiotherapy planning. We demonstrate its potential clinical relevance in (1) improving patient awareness by quantifying the risks and benefits of a given treatment protocol (2) assessing the potential for dose escalation across patient cohorts and (3) estimating accrual rates of new protocols.

Association of Breast Cancer Odds with Background Parenchymal Enhancement Quantified Using a Fully Automated Method at MRI: The IMAGINE Study.

Background Background parenchymal enhancement (BPE) at breast MRI has been associated with increased breast cancer risk in several independent studies. However, variability of subjective BPE assessments have precluded its use in clinical practice. Purpose To examine the association between fully objective measures of BPE at MRI and odds of breast cancer. Materials and Methods This prospective case-control study included patients who underwent a bilateral breast MRI examination and were receiving care at one of three centers in the United States from November 2010 to July 2017. Breast volume, fibroglandular tissue (FGT) volume, and BPE were quantified using fully automated software. Fat volume was defined as breast volume minus FGT volume. BPE extent was defined as the proportion of FGT voxels with enhancement of 20% or more. Spearman rank correlation between quantitative BPE extent and Breast Imaging Reporting and Data System (BI-RADS) BPE categories assigned by an experienced board-certified breast radiologist was estimated. With use of multivariable logistic regression, breast cancer case-control status was regressed on tertiles (low, moderate, and high) of BPE, FGT volume, and fat volume, with adjustment for covariates. Results In total, 536 case participants with breast cancer (median age, 48 years [IQR, 43-55 years]) and 940 cancer-free controls (median age, 46 years [IQR, 38-55 years]) were included. BPE extent was positively associated with BI-RADS BPE (rs = 0.54; P < .001). Compared with low BPE extent (range, 2.9%-34.2%), high BPE extent (range, 50.7%-97.3%) was associated with increased odds of breast cancer (odds ratio [OR], 1.74 [95% CI: 1.23, 2.46]; P for trend = .002) in a multivariable model also including FGT volume (OR, 1.39 [95% CI: 0.97, 1.98]) and fat volume (OR, 1.46 [95% CI: 1.04, 2.06]). The association of high BPE extent with increased odds of breast cancer was similar for premenopausal and postmenopausal women (ORs, 1.75 and 1.83, respectively; interaction P = .73). Conclusion Objectively measured BPE at breast MRI is associated with increased breast cancer odds for both premenopausal and postmenopausal women. Clinical trial registration no. NCT02301767 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bokacheva in this issue.

Interpretable Machine Learning for Choosing Radiation Dose-volume Constraints on Cardio-pulmonary Substructures Associated with Overall Survival in NRG Oncology RTOG 0617.

PURPOSE: Our objective was to use interpretable machine learning for choosing dose-volume constraints on cardiopulmonary substructures (CPSs) associated with overall survival (OS) in radiation therapy for locally advanced non-small cell lung cancer. METHODS AND MATERIALS: A total of 428 patients with non-small cell lung cancer were randomly divided into training/validation/test subsets (n = 230/149/49) in Radiation Therapy Oncology Group 0617. Manual or automated contouring was performed to segment CPSs, including heart, atria, ventricles, aorta, left/right ventricle/atrium (LV+RV+LA+RA), inferior/superior vena cava, pulmonary artery, and pericardium. Peri (pericardium-heart), rest (heart-[LV+RV+LA+RA]), clinical target volume (CTV), and lungs-CTV contours were also obtained. Dose-volume histogram features were extracted, including minimum/mean dose to the hottest x% volume (Dx%[Gy]/MOHx%[Gy]), minimum/mean/maximum dose, percent volume receiving at least xGy (VxGy[%]), and overlapping volume of each CPS with planning target volume (PTV_Voverlap[%]). Clinical parameters were collected from the National Clinical Trials Network/Community oncology research program data archive. Feature selection was performed using a series of multiblock sparse partial least squares regression, stability selection supervised principal component analysis, and Boruta. Explainable boosting machine (EBM) was trained using a conditional survival distribution-based approach for imputing censored data, treating survival analysis as a regression problem. Harrell's C-index was used to evaluate OS discrimination performance of EBM, Cox proportional hazards (CPH), random survival forest, extreme gradient boosting survival embeddings, and CPH deep neural network (DeepSurv) models in the test set. Dose-volume constraints were selected using the binary change point detection algorithm in Shapley additive explanations-based partial dependence functions. RESULTS: Selected features included LA_V60Gy(%), pericardium_D30%(Gy), lungs-CTV_PTV_Voverlap(%), RA_V55Gy(%), and received_cons_chemo. All models ranked LA_V60Gy(%) as the most important feature. EBM achieved the best performance for predicting OS, followed by extreme gradient boosting survival embeddings, random survival forest, DeepSurv, and CPH (C-index = 0.653, 0.646, 0.642, 0.638, and 0.632). EBM global explanations suggested that LA_V60Gy(%) < 25.6, lungs-CTV_PTV_Voverlap(%) < 1.1, pericardium_D30%(Gy) < 18.9, RA_V55Gy(%) < 19.5, and received_cons_chemo = 'Yes' for improved OS. CONCLUSIONS: EBM can be used to discriminate OS while also guiding dose-volume constraint selection for optimal management of cardiac toxicity in lung cancer radiation therapy.

Association of cardiac calcium burden with overall survival after radiotherapy for non-small cell lung cancer.

Background and purpose: Coronary calcifications are associated with coronary artery disease in patients undergoing radiotherapy (RT) for non-small cell lung cancer (NSCLC). We quantified calcifications in the coronary arteries and aorta and investigated their relationship with overall survival (OS) in patients treated with definitive RT (Def-RT) or post-operative RT (PORT). Materials and methods: We analyzed 263 NSCLC patients treated from 2004 to 2017. Calcium burden was ascertained with a Hounsfield unit (HU) cutoff of > 130 in addition to a deep learning (DL) plaque estimator. The HU cutoff volumes were defined for coronary arteries (PlaqueCoro) and coronary arteries and aorta combined (PlaqueCoro+Ao), while the DL estimator ranged from 0 (no plaque) to 3 (high plaque). Patient and treatment characteristics were explored for association with OS. Results: The median PlaqueCoro and PlaqueCoro+Ao was 0.75 cm3 and 0.87 cm3 in the Def-RT group and 0.03 cm3 and 0.52 cm3 in the PORT group. The median DL estimator was 2 in both cohorts. In Def-RT, large PlaqueCoro (HR:1.11 (95%CI:1.04-1.19); p = 0.008), and PlaqueCoro+Ao (HR:1.06 (95%CI:1.02-1.11); p = 0.03), and poor Karnofsky Performance Status (HR: 0.97 (95%CI: 0.94-0.99); p = 0.03) were associated with worse OS. No relationship was identified between the plaque volumes and OS in PORT, or between the DL plaque estimator and OS in either Def-RT or PORT. Conclusions: Coronary artery calcification assessed from RT planning CT scans was significantly associated with OS in patients who underwent Def-RT for NSCLC. This HU thresholding method can be straightforwardly implemented such that the role of calcifications can be further explored.

Death without progression as an endpoint to describe cardiac radiation effects in locally advanced non-small cell lung cancer.

Background and purpose: Prior studies have examined associations of cardiovascular substructure dose with overall survival (OS) or cardiac events after chemoradiotherapy (CRT) for non-small cell lung cancer (NSCLC). Herein, we investigate an alternative endpoint, death without cancer progression (DWP), which is potentially more specific than OS and more sensitive than cardiac events for understanding CRT toxicity. Materials and methods: We retrospectively reviewed records of 187 patients with locally advanced or oligometastatic NSCLC treated with definitive CRT from 2008 to 2016 at a single institution. Dosimetric parameters to the heart, lung, and ten cardiovascular substructures were extracted. Charlson Comorbidity Index (CCI), excluding NSCLC diagnosis, was used to stratify patients into CCI low (0-2; n = 66), CCI intermediate (3-4; n = 78), and CCI high (≥5; n = 43) groups. Primary endpoint was DWP, modeled with competing risk regression. Secondary endpoints included OS. An external cohort consisted of 140 patients from another institution. Results: Median follow-up was 7.3 years for survivors. Death occurred in 143 patients (76.5 %), including death after progression in 118 (63.1 %) and DWP in 25 (13.4 %). On multivariable analysis, increasing CCI stratum and mean heart dose were associated with DWP. For mean heart dose ≥ 10 Gy vs < 10 Gy, DWP was higher (5-year rate, 16.9 % vs 6.7 %, p = 0.04) and OS worse (median, 22.9 vs 34.1 months, p < 0.001). Ventricle (left, right, and bilateral) and pericardial but not atrial substructure dose were associated with DWP, whereas all three were inversely associated with OS. Cutpoint analysis identified right ventricle mean dose ≥ 5.5 Gy as a predictor of DWP. In the external cohort, we confirmed an association of ventricle, but not atrial, dose with DWP. Conclusion: Cardiovascular substructure dose showed distinct associations with DWP. Future cardiotoxicity studies in NSCLC could consider DWP as an endpoint.

External validation of pulmonary radiotherapy toxicity models for ultracentral lung tumors.

Introduction: Pulmonary toxicity is dose-limiting in stereotactic body radiation therapy (SBRT) for tumors that abut the proximal bronchial tree (PBT), esophagus, or other mediastinal structures. In this work we explored published models of pulmonary toxicity following SBRT for such ultracentral tumors in an independent cohort of patients. Methods: The PubMed database was searched for pulmonary toxicity models. Identified models were tested in a cohort of patients with ultracentral lung tumors treated between 2008 and 2017 at one large center (N = 88). This cohort included 60 % primary and 40 % metastatic tumors treated to 45 Gy in 5 fractions (fx), 50 Gy in 5 fx, 60 Gy in 8 fx, or 60 Gy in 15 fx prescribed as 100 % dose to PTV. Results: Seven published NTCP models from two studies were identified. The NTCP models utilized PBT max point dose (Dmax), D0.2 cm3, V65, V100, and V130. Within the independent cohort, the ≥ grade 3 toxicity and grade 5 toxicity rates were 18 % and 7-10 %, respectively, and the Dmax models best described pulmonary toxicity. The Dmax to 0.1 cm3 model was better calibrated and had increased steepness compared to the Dmax model. A re-planning study minimizing PBT 0.1 cm3 to below 122 Gy in EQD23 (for a 10 % ≥grade 3 pulmonary toxicity) was demonstrated to be completely feasible in 4/6 patients, and dose to PBT 0.1 cm3 was considerably lowered in all six patients. Conclusions: Pulmonary toxicity models were identified from two studies and explored within an independent ultracentral lung tumor cohort. A modified Dmax to 0.1 cm3 PBT model displayed the best performance. This model could be utilized as a starting point for rationally constructed airways constraints in ultracentral patients treated with SBRT or hypofractionation.

CT and cone-beam CT of ablative radiation therapy for pancreatic cancer with expert organ-at-risk contours.

We describe a dataset from patients who received ablative radiation therapy for locally advanced pancreatic cancer (LAPC), consisting of computed tomography (CT) and cone-beam CT (CBCT) images with physician-drawn organ-at-risk (OAR) contours. The image datasets (one CT for treatment planning and two CBCT scans at the time of treatment per patient) were collected from 40 patients. All scans were acquired with the patient in the treatment position and in a deep inspiration breath-hold state. Six radiation oncologists delineated the gastrointestinal OARs consisting of small bowel, stomach and duodenum, such that the same physician delineated all image sets belonging to the same patient. Two trained medical physicists further edited the contours to ensure adherence to delineation guidelines. The image and contour files are available in DICOM format and are publicly available from The Cancer Imaging Archive ( https://doi.org/10.7937/TCIA.ESHQ-4D90 , Version 2). The dataset can serve as a criterion standard for evaluating the accuracy and reliability of deformable image registration and auto-segmentation algorithms, as well as a training set for deep-learning-based methods.

Validation of an established deep learning auto-segmentation tool for cardiac substructures in 4D radiotherapy planning scans.

Background: Emerging data suggest that dose-sparing several key cardiac regions is prognostically beneficial in lung cancer radiotherapy. The cardiac substructures are challenging to contour due to their complex geometry, poor soft tissue definition on computed tomography (CT) and cardiorespiratory motion artefact. A neural network was previously trained to generate the cardiac substructures using three-dimensional radiotherapy planning CT scans (3D-CT). In this study, the performance of that tool on the average intensity projection from four-dimensional (4D) CT scans (4D-AVE), now commonly used in lung radiotherapy, was evaluated. Materials and Methods: The 4D-AVE of n=20 patients completing radiotherapy for lung cancer 2015-2020 underwent manual and automated cardiac substructure segmentation. Manual and automated substructures were compared geometrically and dosimetrically. Two senior clinicians also qualitatively assessed the auto-segmentation tool's output. Results: Geometric comparison of the automated and manual segmentations exhibited high levels of similarity across parameters, including volume difference (11.8% overall) and Dice similarity coefficient (0.85 overall), and were consistent with 3D-CT performance. Differences in mean (median 0.2 Gy, range -1.6-0.3 Gy) and maximum (median 0.4 Gy, range -2.2-0.9 Gy) doses to substructures were generally small. Nearly all structures (99.5 %) were deemed to be appropriate for clinical use without further editing. Conclusions: Cardiac substructure auto-segmentation using a deep learning-based tool trained on a 3D-CT dataset was feasible on the 4D-AVE scan, meaning this tool is suitable for use on 4D-CT radiotherapy planning scans. Application of this tool would increase the practicality of routine clinical cardiac substructure delineation, and enable further cardiac radiation effects research.

Prospectively-validated deep learning model for segmenting swallowing and chewing structures in CT.

Objective.Delineating swallowing and chewing structures aids in radiotherapy (RT) treatment planning to limit dysphagia, trismus, and speech dysfunction. We aim to develop an accurate and efficient method to automate this process.Approach.CT scans of 242 head and neck (H&N) cancer patients acquired from 2004 to 2009 at our institution were used to develop auto-segmentation models for the masseters, medial pterygoids, larynx, and pharyngeal constrictor muscle using DeepLabV3+. A cascaded framework was used, wherein models were trained sequentially to spatially constrain each structure group based on prior segmentations. Additionally, an ensemble of models, combining contextual information from axial, coronal, and sagittal views was used to improve segmentation accuracy. Prospective evaluation was conducted by measuring the amount of manual editing required in 91 H&N CT scans acquired February-May 2021.Main results. Medians and inter-quartile ranges of Dice similarity coefficients (DSC) computed on the retrospective testing set (N = 24) were 0.87 (0.85-0.89) for the masseters, 0.80 (0.79-0.81) for the medial pterygoids, 0.81 (0.79-0.84) for the larynx, and 0.69 (0.67-0.71) for the constrictor. Auto-segmentations, when compared to two sets of manual segmentations in 10 randomly selected scans, showed better agreement (DSC) with each observer than inter-observer DSC. Prospective analysis showed most manual modifications needed for clinical use were minor, suggesting auto-contouring could increase clinical efficiency. Trained segmentation models are available for research use upon request viahttps://github.com/cerr/CERR/wiki/Auto-Segmentation-models.Significance.We developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT and demonstrated its potential for use in treatment planning to limit complications post-RT. To the best of our knowledge, this is the only prospectively-validated deep learning-based model for segmenting chewing and swallowing structures in CT. Segmentation models have been made open-source to facilitate reproducibility and multi-institutional research.

Pre-treatment immune status predicts disease control in NSCLCs treated with chemoradiation and durvalumab.

BACKGROUND: The impact of peripheral blood immune measures and radiation-induced lymphopenia on outcomes in non-small cell lung cancer (NSCLC) patients treated with concurrent chemoradiation (cCRT) and immune check point inhibition (ICI) has yet to be fully defined. METHODS: Stage III NSCLC patients treated with cCRT and ≥1 dose of durvalumab across a cancer center were examined. Peripheral blood counts were assessed pre-cCRT, during cCRT and at the start of ICI. These measures and risk-scores from two published models estimating radiation dose to immune-bearing organs were tested for association with disease control. RESULTS: We assessed 113 patients treated with cCRT and a median of 8.5 months of durvalumab. Median PFS was 29 months (95% CI 18-35 months). A lower pre-cCRT ALC (HR: 0.51 (95% CI: 0.32-0.82), p = 0.02) and a higher pre-cCRT ANC (HR: 1.14 (1.06-1.23), p = 0.005) were associated with poor PFS. Neither ALC nadir, ALC at ICI start, ANC at ICI start or the normalized change in ALC from pre-cCRT to nadir were significantly associated with PFS (p = 0.07-0.49). Also, risk scores from the two radiation-dose models were not associated with PFS (p = 0.14, p = 0.21) but were so with the ALC Nadir (p = 0.001, p = 0.002). A higher pre-cCRT NLR was the strongest predictor for PFS (HR: 1.09 (1.05-1.14), p = 0.0001). The 12-month PFS in patients with the bottom vs. top NLR tertile was 84% vs 46% (p = 0.000004). CONCLUSIONS: Baseline differences in peripheral immune cell populations are associated with disease outcomes in NSCLC patients treated with cCRT and ICI.

Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy.

BACKGROUND AND PURPOSE: Reducing trismus in radiotherapy for head and neck cancer (HNC) is important. Automated deep learning (DL) segmentation and automated planning was used to introduce new and rarely segmented masticatory structures to study if trismus risk could be decreased. MATERIALS AND METHODS: Auto-segmentation was based on purpose-built DL, and automated planning used our in-house system, ECHO. Treatment plans for ten HNC patients, treated with 2 Gy × 35 fractions, were optimized (ECHO0). Six manually segmented OARs were replaced with DL auto-segmentations and the plans re-optimized (ECHO1). In a third set of plans, mean doses for auto-segmented ipsilateral masseter and medial pterygoid (MIMean, MPIMean), derived from a trismus risk model, were implemented as dose-volume objectives (ECHO2). Clinical dose-volume criteria were compared between the two scenarios (ECHO0 vs. ECHO1; ECHO1 vs. ECHO2; Wilcoxon signed-rank test; significance: p < 0.01). RESULTS: Small systematic differences were observed between the doses to the six auto-segmented OARs and their manual counterparts (median: ECHO1 = 6.2 (range: 0.4, 21) Gy vs. ECHO0 = 6.6 (range: 0.3, 22) Gy; p = 0.007), and the ECHO1 plans provided improved normal tissue sparing across a larger dose-volume range. Only in the ECHO2 plans, all patients fulfilled both MIMean and MPIMean criteria. The population median MIMean and MPIMean were considerably lower than those suggested by the trismus model (ECHO0: MIMean = 13 Gy vs. ≤42 Gy; MPIMean = 29 Gy vs. ≤68 Gy). CONCLUSIONS: Automated treatment planning can efficiently incorporate new structures from DL auto-segmentation, which results in trismus risk sparing without deteriorating treatment plan quality. Auto-planning and deep learning auto-segmentation together provide a powerful platform to further improve treatment planning.

Quality control of radiomic features using 3D-printed CT phantoms.

Purpose: The lack of standardization in quantitative radiomic measures of tumors seen on computed tomography (CT) scans is generally recognized as an unresolved issue. To develop reliable clinical applications, radiomics must be robust across different CT scan modes, protocols, software, and systems. We demonstrate how custom-designed phantoms, imprinted with human-derived patterns, can provide a straightforward approach to validating longitudinally stable radiomic signature values in a clinical setting. Approach: Described herein is a prototype process to design an anatomically informed 3D-printed radiomic phantom. We used a multimaterial, ultra-high-resolution 3D printer with voxel printing capabilities. Multiple tissue regions of interest (ROIs), from four pancreas tumors, one lung tumor, and a liver background, were extracted from digital imaging and communication in medicine (DICOM) CT exam files and were merged together to develop a multipurpose, circular radiomic phantom (18 cm diameter and 4 cm width). The phantom was scanned 30 times using standard clinical CT protocols to test repeatability. Features that have been found to be prognostic for various diseases were then investigated for their repeatability and reproducibility across different CT scan modes. Results: The structural similarity index between the segment used from the patients' DICOM image and the phantom CT scan was 0.71. The coefficient variation for all assessed radiomic features was < 1.0 % across 30 repeat scans of the phantom. The percent deviation (pDV) from the baseline value, which was the mean feature value determined from repeat scans, increased with the application of the lung convolution kernel, changes to the voxel size, and increases in the image noise. Gray level co-occurrence features, contrast, dissimilarity, and entropy were particularly affected by different scan modes, presenting with pDV > ± 15 % . Conclusions: Previously discovered prognostic and popular radiomic features are variable in practice and need to be interpreted with caution or excluded from clinical implementation. Voxel-based 3D printing can reproduce tissue morphology seen on CT exams. We believe that this is a flexible, yet practical, way to design custom phantoms to validate and compare radiomic metrics longitudinally, over time, and across systems.

Reproducibility of radiomic features using network analysis and its application in Wasserstein k-means clustering.

Purpose: The goal of this study is to develop innovative methods for identifying radiomic features that are reproducible over varying image acquisition settings. Approach: We propose a regularized partial correlation network to identify reliable and reproducible radiomic features. This approach was tested on two radiomic feature sets generated using two different reconstruction methods on computed tomography (CT) scans from a cohort of 47 lung cancer patients. The largest common network component between the two networks was tested on phantom data consisting of five cancer samples. To further investigate whether radiomic features found can identify phenotypes, we propose a k -means clustering algorithm coupled with the optimal mass transport theory. This approach following the regularized partial correlation network analysis was tested on CT scans from 77 head and neck squamous cell carcinoma (HNSCC) patients in the Cancer Imaging Archive (TCIA) and validated using an independent dataset. Results: A set of common radiomic features was found in relatively large network components between the resultant two partial correlation networks resulting from a cohort of lung cancer patients. The reliability and reproducibility of those radiomic features were further validated on phantom data using the Wasserstein distance. Further analysis using the network-based Wasserstein k -means algorithm on the TCIA HNSCC data showed that the resulting clusters separate tumor subsites as well as HPV status, and this was validated on an independent dataset. Conclusion: We showed that a network-based analysis enables identifying reproducible radiomic features and use of the selected set of features can enhance clustering results.

An Incognizant Glitch-pinnacle Polyliner Dissociation: A Case Report.

Introduction: Liner dissociation of the pinnacle total hip arthroplasty system is a rare but documented complication. Although a few reports are published internationally, to the best of our knowledge no cases have been documented from India so far. Case Report: A 31-year-old male presented with failed femoral head fracture fixation for which total hip replacement was done. Postoperatively at 18 months, he was diagnosed with pinnacle liner dissociation and liner exchange was performed. Conclusion: This report aims to raise awareness about the incidence of pinnacle liner dissociation.

Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

PURPOSE: Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses were higher than reported due to inconsistent and insufficient heart segmentation. We tested our hypothesis by comparing doses between deep-learning (DL) segmented hearts and trial hearts. METHODS AND MATERIALS: The RTOG 0617 data were downloaded from The Cancer Imaging Archive; the 442 patients with trial hearts and dose distributions were included. All hearts were resegmented using our DL pipeline and quality assured to meet the requirements for clinical implementation. Dose (V5%, V30%, and mean heart dose) was compared between the 2 sets of hearts (Wilcoxon signed-rank test). Each dose metric was associated with overall survival (Cox proportional hazards). Lastly, 18 volume similarity metrics were assessed for the hearts and correlated with |DoseDL - DoseRTOG0617| (linear regression; significance: P ≤ .0028; corrected for 18 tests). RESULTS: Dose metrics were significantly higher for DL hearts compared with trial hearts (eg, mean heart dose: 15 Gy vs 12 Gy; P = 5.8E-16). All 3 DL heart dose metrics were stronger overall survival predictors than those of the trial hearts (median, P = 2.8E-5 vs 2.0E-4). Thirteen similarity metrics explained |DoseDL - DoseRTOG0617|; the axial distance between the 2 centers of mass was the strongest predictor (CENTAxial; median, R2 = 0.47; P = 6.1E-62). CENTAxial agreed with the qualitatively identified inconsistencies in the superior direction. The trial's qualitative heart contouring score was not correlated with |DoseDL - DoseRTOG0617| (median, R2 = 0.01; P = .02) or with any of the similarity metrics (median, Rs = 0.13 [range, -0.22 to 0.31]). CONCLUSIONS: Using a coherent heart definition, as enabled through our open-source DL algorithm, the trial heart doses in RTOG 0617 were found to be significantly higher than previously reported, which may have led to an even more rapid mortality accumulation. Auto-segmentation is likely to reduce contouring and dose inconsistencies and increase the quality of clinical RT trials.