A Phase II Study Evaluating the Effect of Intensity Modulated Postmastectomy Radiation Therapy on Implant Failure Rates in Breast Cancer Patients With Immediate, 2-Stage Implant Reconstruction With an MRI Imaging Correlative Substudy

PURPOSE

Postmastectomy radiation therapy is a mainstay in the adjuvant treatment of node-positive breast cancer, but it poses risks for women with breast reconstruction. Multibeam intensity-modulated radiation therapy improves dose conformality and homogeneity, potentially reducing complications in breast cancer patients with implant-based reconstruction. To investigate this hypothesis, we conducted a single-arm phase 2 clinical trial of breast cancer patients who underwent mastectomy/axillary dissection and prosthesis-based reconstruction.

METHODS AND MATERIALS

The primary endpoint was the rate of implant failure (IF) within 24 months of permanent implant placement, which would be considered an improvement over historical controls if below 16%. IF was defined as removal leading to a flat chest wall or replacement with another reconstruction. Patients were analyzed in 2 cohorts. Cohort 1 (RT-PI) received radiation therapy to the permanent implant. Cohort 2 (RT-TE) received radiation therapy to the TE. IF rates, adverse events, and quality of life were analyzed. Follow-up/postradiation therapy assessments were compared with the baseline/preradiation therapy assessments at 3 to 10 weeks after exchange surgery. A subgroup underwent serial magnetic resonance imaging (MRI) sessions to explore the association between MRI-detected changes and capsular contracture, a known adverse effect of radiation therapy.

RESULTS

Between June 2014 and March 2017, 119 women were enrolled. Cohort 1 included 45 patients, and cohort 2 had 74 patients. Among 100 evaluable participants, 25 experienced IF during the study period. IF occurred in 8/42 (19%) and 17/58 (29%) in cohorts 1 and 2, respectively. Among the IFs, the majority were due to capsular contracture (13), infection (7), exposure (3), and other reasons (2). Morphologic shape features observed in longitudinal MRI images were associated with the development of Baker grade 3 to 4 contractures.

CONCLUSIONS

The rate of IF in reconstructed breast cancer patients treated with intensity-modulated radiation therapy was similar to, but not improved over, that observed with conventional, 3-dimensional-conformal methods. MRI features show promise for predicting capsular contracture but require validation in larger studies.

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.

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.

Artificial intelligence-based automated segmentation and radiotherapy dose mapping for thoracic normal tissues

Background and purpose

Objective assessment of delivered radiotherapy (RT) to thoracic organs requires fast and accurate deformable dose mapping. The aim of this study was to implement and evaluate an artificial intelligence (AI) deformable image registration (DIR) and organ segmentation-based AI dose mapping (AIDA) applied to the esophagus and the heart.

Materials and methods

AIDA metrics were calculated for 72 locally advanced non-small cell lung cancer patients treated with concurrent chemo-RT to 60 Gy in 2 Gy fractions in an automated pipeline. The pipeline steps were: (i) automated rigid alignment and cropping of planning CT to week 1 and week 2 cone-beam CT (CBCT) field-of-views, (ii) AI segmentation on CBCTs, and (iii) AI-DIR-based dose mapping to compute dose metrics. AIDA dose metrics were compared to the planned dose and manual contour dose mapping (manual DA).

Results

AIDA required ∼2 min/patient. Esophagus and heart segmentations were generated with a mean Dice similarity coefficient (DSC) of 0.80±0.15 and 0.94±0.05, a Hausdorff distance at 95th percentile (HD95) of 3.9±3.4 mm and 14.1±8.3 mm, respectively. AIDA heart dose was significantly lower than the planned heart dose (p = 0.04). Larger dose deviations (>=1Gy) were more frequently observed between AIDA and the planned dose (N = 26) than with manual DA (N = 6).

Conclusions

Rapid estimation of RT dose to thoracic tissues from CBCT is feasible with AIDA. AIDA-derived metrics and segmentations were similar to manual DA, thus motivating the use of AIDA for RT applications.

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.

Lesion Dosimetry for [177Lu]Lu-PSMA-617 Radiopharmaceutical Therapy Combined with Stereotactic Body Radiotherapy in Patients with Oligometastatic Castration-Sensitive Prostate Cancer

A single-institution prospective pilot clinical trial was performed to demonstrate the feasibility of combining [177Lu]Lu-PSMA-617 radiopharmaceutical therapy (RPT) with stereotactic body radiotherapy (SBRT) for the treatment of oligometastatic castration-sensitive prostate cancer. Methods: Six patients with 9 prostate-specific membrane antigen (PSMA)-positive oligometastases received 2 cycles of [177Lu]Lu-PSMA-617 RPT followed by SBRT. After the first intravenous infusion of [177Lu]Lu-PSMA-617 (7.46 ± 0.15 GBq), patients underwent SPECT/CT at 3.2 ± 0.5, 23.9 ± 0.4, and 87.4 ± 12.0 h. Voxel-based dosimetry was performed with calibration factors (11.7 counts per second/MBq) and recovery coefficients derived from in-house phantom experiments. Lesions were segmented on baseline PSMA PET/CT (50% SUVmax). After a second cycle of [177Lu]Lu-PSMA-617 (44 ± 3 d; 7.50 ± 0.10 GBq) and an interim PSMA PET/CT scan, SBRT (27 Gy in 3 fractions) was delivered to all PSMA-avid oligometastatic sites, followed by post-PSMA PET/CT. RPT and SBRT voxelwise dose maps were scaled (α/β = 3 Gy; repair half-time, 1.5 h) to calculate the biologically effective dose (BED). Results: All patients completed the combination therapy without complications. No grade 3+ toxicities were noted. The median of the lesion SUVmax as measured on PSMA PET was 16.8 (interquartile range [IQR], 11.6) (baseline), 6.2 (IQR, 2.7) (interim), and 2.9 (IQR, 1.4) (post). PET-derived lesion volumes were 0.4-1.7 cm3 The median lesion-absorbed dose (AD) from the first cycle of [177Lu]Lu-PSMA-617 RPT (ADRPT) was 27.7 Gy (range, 8.3-58.2 Gy; corresponding to 3.7 Gy/GBq, range, 1.1-7.7 Gy/GBq), whereas the median lesion AD from SBRT was 28.1 Gy (range, 26.7-28.8 Gy). Spearman rank correlation, ρ, was 0.90 between the baseline lesion PET SUVmax and SPECT SUVmax (P = 0.005), 0.74 (P = 0.046) between the baseline PET SUVmax and the lesion ADRPT, and -0.81 (P = 0.022) between the lesion ADRPT and the percent change in PET SUVmax (baseline to interim). The median for the lesion BED from RPT and SBRT was 159 Gy (range, 124-219 Gy). ρ between the BED from RPT and SBRT and the percent change in PET SUVmax (baseline to post) was -0.88 (P = 0.007). Two cycles of [177Lu]Lu-PSMA-617 RPT contributed approximately 40% to the maximum BED from RPT and SBRT. Conclusion: Lesional dosimetry in patients with oligometastatic castration-sensitive prostate cancer undergoing [177Lu]Lu-PSMA-617 RPT followed by SBRT is feasible. Combined RPT and SBRT may provide an efficient method to maximize the delivery of meaningful doses to oligometastatic disease while addressing potential microscopic disease reservoirs and limiting the dose exposure to normal tissues.

Multi-institutional analysis of aneuploidy and outcomes to chemoradiation and durvalumab in stage III non-small cell lung cancer

There is a need to identify predictive biomarkers to guide treatment strategies in stage III non-small cell lung cancer (NSCLCs). In this multi-institutional cohort of 197 patients with stage III NSCLC treated with concurrent chemoradiation (cCRT) and durvalumab consolidation, we identify that low tumor aneuploidy is independently associated with prolonged progression-free survival (HR 0.63; p=0.03) and overall survival (HR 0.50; p=0.03). Tumors with high aneuploidy had a significantly greater incidence of distant metastasis and shorter median distant-metastasis free survival (p=0.04 and p=0.048, respectively), but aneuploidy level did not associate with local-regional outcomes. Multiplexed immunofluorescence analysis in a cohort of NSCLC found increased intratumoral CD8-positive, PD-1-positive cells, double-positive PD-1 CD8 cells, and FOXP3-positive T-cell in low aneuploid tumors. Additionally, in a cohort of 101 patients treated with cCRT alone, tumor aneuploidy did not associate with disease outcomes. These data support the need for upfront treatment intensification strategies in stage III NSCLC patients with high aneuploid tumors and suggest that tumor aneuploidy is a promising predictive biomarker.

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.

Determining the Dosimetric Accuracy of Deep Learning-Based Fully Automated Registration-Segmentation Approach for Thoracic Cancer Organs-at-Risk Contouring

PURPOSE/OBJECTIVE(S)

For adaptive radiation therapy (ART), the contours on the planning CT (pCT) are frequently propagated to cone-beam CT (CBCT) via deformable image registration and manually edited, which is observer-dependent and time-consuming. To automate this process, we created a fully automated workflow by combining a deep learning (DL)-based pCT segmentation model with a CT-to-CBCT registration-segmentation DL model. The purpose of our research is to determine how using the proposed workflow's automatically generated contours affects thoracic organs-at-risk sparing (OAR).

MATERIALS/METHODS

Seven patients with locally advanced non-small cell lung cancer who underwent treatment with intensity modulated radiation therapy were included in this study. Each patient's pCT was segmented using a published DL model that has been used for generating thoracic OAR segmentation and radiotherapy planning in the clinic since July of 2020. Next, pCT was deformably registered using a published recurrent deep registration-segmentation method. Whereas the original method's segmentation sub-network was only trained to segment esophagus, the registration sub-network was used to propagate contours for heart, esophagus, and the proximal bronchial tree (PBT). Geometric segmentation accuracy using the Dice Similarity Coefficient (DSC) and the 95th percentile Hausdorff Distance (HD) and dose metrics including the mean esophageal dose (MED) and D90% of the heart (D90) were computed from the total accumulated dose for the first two weeks of treatment.

RESULTS

The esophagus had a high DSC and a low HD (0.93 and 2.85mm) and conversely, the heart had lower accuracy (DSC = 0.85, HD = 22.06mm). PBT showed relatively high performance as well, with DSC of 0.91 and HD of 2.28mm, owing to its proximity to the esophagus. The accumulated MED for manual contour was slightly lower than AI-contours (11.34 vs 11.83 Gy), suggesting reliability of the proposed workflow. The reverse is seen for the D90 of the heart (manual = 1.74 and AI-contour = 1.56 Gy), likely due to the heart not being included in the original DL framework.

CONCLUSION

This study reported preliminary results on the feasibility of using a fully automated and patient-specific workflow for CBCT auto-segmentation in ART, confirming its role as a geometrically and dosimetrically accurate solution for thoracic OARs. However, because it is currently limited to the esophagus, we believe that re-training the algorithm will increase confidence in other OARs such as the heart and lungs.

Clinical Feasibility of Deep Learning-Based CT during Treatment CBCT Tumor Registration-Segmentation in Thoracic Radiotherapy (RT)

PURPOSE/OBJECTIVE(S)

Accurate tumor segmentation on weekly cone-beam computed tomography (CBCT) images is critical for image-guided and adaptive radiation therapy (ART). In thoracic RT, low image contrast, imaging artifact, and geometry and image modality differences from the planning CT (pCT) typically limits accurate tumor segmentation and registration. Here, we explored the clinical feasibility of using 3D recurrent registration-segmentation deep learning (DL) that combines patient-specific anatomic and shape context from higher contrast pCT and planning contours (PACs) for tumor segmentation on during treatment CBCTs.

MATERIALS/METHODS

We included the pCT and CBCTs from six patients with locally advanced non-small cell lung cancer (LA-NSCLC) who had underwent RT. Cases were selected with a primary GTV contoured and labeled separately from the nodal GTV. Using rigidly aligned pCT and CBCT as inputs, DL auto-segmented the GTV on week 1 and 6 CBCTs, and these auto-segmented contours were manually inspected by a radiation oncologist that edited the GTV according to clinical standard quality. The Dice similarity coefficient (DSC), Hausdorff distance (HD95), mean surface distance (MSD), surface DSC (sDSC) and added path length (APL) were used to quantitively compare the DL and the edited GTV.

RESULTS

The primary GTV was in the right lung in five cases, and left lung in one case. Manual adjustments were typically made at the interface of GTV and lung parenchyma with partial inclusion of adjacent vessels. Hypodensities within the GTV were sometimes not segmented in all axial slices resulting in discontinuous components. The quantitative comparison between the edited and DL-generated GTV is shown in Table 1. For week 1, the average DSC and HD95 were 0.87 and 6.94 mm, respectively. The performance for week 6 was slightly lower than week 1, with a DSC of 0.85 and HD95 of 7.22 mm.

CONCLUSION

The agreement with the generated DL GTV and the edited GTV was high in week 1 and decreased somewhat later during the treatment course possibly due to a higher impact of geometric changes in tumor and adjacent structures. The proposed DL algorithm showed reasonable performance throughout the treatment, supporting its potential for use into clinical routine for LA-NSCLC.

Under-Representation for Female Cancers in Commercial Auto-Segmentation Solutions and Open-Source Imaging Datasets

PURPOSE/OBJECTIVE(S)

Auto-segmentations methods to aid radiation therapy (RT) workflows have recently emerged with the increasing availability of commercial solutions for organs at risk (OARs) in addition to open-source imaging datasets that support training for new auto-segmentation algorithms. Here, we explored whether female and male cancer sites are equally represented among these solutions.

MATERIALS/METHODS

Inquiries were sent out to five major RT vendors regarding their currently available auto-segmentation solutions. Additionally, The Cancer Imaging Archive (TCIA) was screened for publicly available imaging datasets pertaining to female and male tumor sites.

RESULTS

The five commercial solutions provided a median of 103 (range: 60-120) OAR auto-segmentations of which the majority concerned the head and neck (45 (24-55)) and thorax (34 (27-43)) and were provided by all vendors (Table). Prostate as a site was also provided by all vendors and included 17 (9-20) auto-segmentations. A total of 23 publicly available TCIA imaging datasets involved the female anatomy (breast: 19; cervix: 2; ovarian: 1; uterus: 1) while 11 imaging datasets involved the male anatomy (prostate). No OARs segmentations were available for the 23 female-specific datasets while 27% of the 11 prostate datasets included segmented OARs. Three vendors and two TCIA datasets provided organs involved in the male sexual function apparatus (neurovascular bundle and penile bulb), whereas nipple or areola segmentations were not available among the commercial solutions for breast or among the TCIA breast datasets. None of the TCIA datasets or any of the five commercial solutions provided OARs for the female pelvis such as organs involved in reproduction (ovaries), sexual health (clitoris, vagina) or the cervix and uterus. Further, auto-segmentations provided for OARs trained exclusively on the male pelvis are likely inadequate for female cancers given the substantial anatomical differences between genders.

CONCLUSION

Commercial auto-segmentation solutions and open-source imaging datasets together include considerably more datasets, tumor sites and consequently more OAR auto-segmentations pertaining to male cancers compared to female cancers. Despite a 1.4 times higher incidence for female cancers (breast: 300,590; female pelvis: 114,810; male cancer: 299,540; Siegel RL et al CA Cancer J Clin 2023), auto-segmentation models are lacking, and this gender disparity is likely to lead to suboptimal care for female-specific cancers.

Reduction of Late Urinary Toxicity from Prostate Cancer Radiotherapy via Intrafractional MV-kV Image Guidance

PURPOSE/OBJECTIVE(S)

Prostate cancer localization during radiotherapy is difficult and introduces positional variability during treatment. Here we evaluate the impact on treatment toxicities of an in-house system that uses MV and kV imaging guidance (MKIG) to track implanted fiducials during stereotactic body radiotherapy (SBRT) for localized prostate cancer.

MATERIALS/METHODS

A 3D MKIG platform that tracks prostate implanted fiducials in real-time was built and clinically translated to replace a prior commercial approach called intrafraction motion review (IMR), which tracks thefiducials in 2D kV view only. MKIG has been shown to correct both superior-inferior and anterior-posterior (AP) motions that are harmful to critical organs and is superior to IMR for intrafractional motion management, which is less sensitive to AP motions. From 2017 to 2019, 150 patients with localized prostate cancer were treated with SBRT to 40 Gy in 5 fractions. During the delivery of volumetric modulated arc therapy, orthogonal MV-kV pairs were simultaneously acquired at every 20° gantry rotation and rigidly registered to the reference image templates created from the planning CT. Calculated 3D translations of implanted fiducials were used to localize the prostate and alert the therapist to interrupt and reposition the prostate when exceeding a 1.5-2 mm threshold. A comparison cohort of 121 prostate patients was treated from 2015 to 2016 with the same prescription dose and treatment technique but instead managed by IMR, where the therapist interrupted treatment based on a 2mm expansion of the fiducial contours superimposed on the kV images. Statistics of intrafractional interruptions, patient shifts, and overall delivery time were collected to evaluate the efficacy of the clinical workflow. The incidence of late grade ≥2 toxicities was analyzed to assess clinical complications. The median follow-up time was 5.5 years (range of 3.6 to 8.0 years).

RESULTS

The MKIG cohort had more interruptions per fraction (1.09 vs. 0.28) and longer average delivery time per fraction (579±205s vs. 357±117s) than IMR. 75% of shifts resulting from MKIG were less than 3mm, compared to 51% in IMR, indicating that MKIG tended to detect and correct smaller deviations (p<0.001). The baseline International Prostate Symptom Score was 7.9 in the MKIG cohort vs. 8.4 in IMR (p = 0.41). The incidence of late grade ≥2 urinary toxicity was lower in the MKIG than IMR cohort: 10.7% vs. 19.8% (p = 0.05). One grade ≥2 rectal toxicity was observed in the IMR cohort but none in MKIG.

CONCLUSION

Wehave demonstrated that MKIG is a clinically practical and effective method for monitoring and correcting prostate positional deviations during SBRT of prostate cancer. MKIG is better suited than 2D IMR to localize the prostate and trigger patient repositioning during treatment. A statistically and clinically significant reduction in urinary toxicity was observed. The potential expansion of MKIG to other clinical sites and translation to other centers should be considered.

A Novel FDG PET and Mean Lung Dose Model to Identify Stage III NSCLC Patients at High Risk of Developing Early Radiation Pneumonitis

PURPOSE/OBJECTIVE(S)

Early radiation pneumonitis (RPEarly) is a primary reason for the premature discontinuation of durvalumab consolidation and can lead to poor survival in patients with stage III non-small cell lung cancer (NSCLC). Currently, there are no RP risk models specifically tested for RPEarly. Here, we tested the applicability of published RP models for predicting RPEarly and explored the value of integrating pretreatment FDG-PET parameters.

MATERIALS/METHODS

The cohort consisted of all 178 LA-NSCLC patients treated with concurrent chemoradiation (cCRT) and durvalumab between May 2017 and December 2021. RPEarly was defined as RP occurring within three months of cCRT completion; late RP (RPLate) was defined as any later occurring RP. The three published RP models analyzed included: 1) Mean lung dose (MLD), 2) MLD, age, pulmonary comorbidity, smoking status, and tumor location, and 3) MLD, age and pulmonary comorbidity. In addition, pretreatment FDG PET-CT scans were used to calculate SUV parameters from auto-segmented normal lung contours: 10th- and 90th percentile (SUVP10, SUVP90), maximum, mean (SUVmean), minimum, and standard deviation. The RP models were fit to RPEarly, RPLate, and RPEarly+Late in the 178 patients. To assess the association between FDG PET parameters and RP unbiasedly, the cohort was then randomly split, but enforcing similar RP rates, into a two-thirds derivation and a one-third validation subset. Model performance was assessed by AUC, p-values and the Hosmer-Lemeshow test (pHL; ideally ∼0.50).

RESULTS

The rates of RPEarly, RPLate, and RPEarly+Late were 12%, 11%, and 23%, respectively (corresponding to 21, 20, and 41 events). Only the MLD model significantly predicted RPEarly (AUC = 0.70; p = 0.04; pHL = 0.84); none of the three models predicted RPLate or RPEarly+Late. Among the FDG PET parameters, SUVP10, SUVP90 and SUVmean predicted RPEarly with similar performance (AUC = 0.69-0.73; p = 0.005-0.01; pHL = 0.68-0.72), and, therefore, bivariate models were built between MLD and each of SUVP10, SUVP90 and SUVmean. Only the MLD + SUVP90 model generalized in the validation subset (AUC = 0.63; p = 0.03; pHL = 0.89) and was thus deemed the final model for RPEarly. A final re-fitting of all model coefficients to the whole cohort indicated improvement over using the published MLD alone model (AUC = 0.75 vs. 0.70; p-value = 0.0006 vs. 0.04; pHL = 0.67 vs. 0.84). Risk of RPEarly is thus estimated as: RPEarly = 1/(1 = e-x); x = -5.79 + (1.57*MLD) + (0.14* SUVP90).

CONCLUSION

Patients at risk for RPEarly can be accurately identified prior to treatment by combining a re-fitted version of the published Mean Lung Dose model and pre-treatment FDG PET SUVP90 of the normal lung. This refined model can be used to identify patients with an exacerbated risk for premature durvalumab discontinuation due to RPEarly and could allow for interventions and/or the generation of "RPEarly sparing" treatment plans to improve overall treatment outcomes.

Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients

BACKGROUND AND PURPOSE

Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria.

MATERIALS AND METHODS

We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria.

RESULTS

The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract.

CONCLUSION

The risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.

Impact of TMB/PD-L1 expression and pneumonitis on chemoradiation and durvalumab response in stage III NSCLC

Although concurrent chemoradiation (CRT) and durvalumab consolidation has become a standard treatment for stage III non-small cell lung cancer (NSCLC), clinicopathologic and genomic factors associated with its efficacy remain poorly characterized. Here, in a multi-institutional retrospective cohort study of 328 patients treated with CRT and durvalumab, we identify that very high PD-L1 tumor proportion score (TPS) expression ( ≥ 90%) and increased tumor mutational burden (TMB) are independently associated with prolonged disease control. Additionally, we identify the impact of pneumonitis and its timing on disease outcomes among patients who discontinue durvalumab: compared to patients who experienced early-onset pneumonitis ( < 3 months) leading to durvalumab discontinuation, patients with late-onset pneumonitis had a significantly longer PFS (12.7 months vs not reached; HR 0.24 [95% CI, 0.10 to 0.58]; P = 0.001) and overall survival (37.2 months vs not reached; HR 0.26 [95% CI, 0.09 to 0.79]; P = 0.017). These findings suggest that opportunities exist to improve outcomes in patients with lower PD-L1 and TMB levels, and those at highest risk for pneumonitis.

Predicting cancer relapse following lung stereotactic radiotherapy: an external validation study using real-world evidence

Purpose

For patients receiving lung stereotactic ablative radiotherapy (SABR), evidence suggests that high peritumor density predicts an increased risk of microscopic disease (MDE) and local-regional failure, but only if there is low or heterogenous incidental dose surrounding the tumor (GTV). A data-mining method (Cox-per-radius) has been developed to investigate this dose-density interaction. We apply the method to predict local relapse (LR) and regional failure (RF) in patients with non-small cell lung cancer.

Methods

199 patients treated in a routine setting were collated from a single institution for training, and 76 patients from an external institution for validation. Three density metrics (mean, 90th percentile, standard deviation (SD)) were studied in 1mm annuli between 0.5cm inside and 2cm outside the GTV boundary. Dose SD and fraction of volume receiving less than 30Gy were studied in annuli 0.5-2cm outside the GTV to describe incidental MDE dosage. Heat-maps were created that correlate with changes in LR and RF rates due to the interaction between dose heterogeneity and density at each distance combination. Regions of significant improvement were studied in Cox proportional hazards models, and explored with and without re-fitting in external data. Correlations between the dose component of the interaction and common dose metrics were reported.

Results

Local relapse occurred at a rate of 6.5% in the training cohort, and 18% in the validation cohort, which included larger and more centrally located tumors. High peritumor density in combination with high dose variability (0.5 - 1.6cm) predicts LR. No interactions predicted RF. The LR interaction improved the predictive ability compared to using clinical variables alone (optimism-adjusted C-index; 0.82 vs 0.76). Re-fitting model coefficients in external data confirmed the importance of this interaction (C-index; 0.86 vs 0.76). Dose variability in the 0.5-1.6 cm annular region strongly correlates with heterogeneity inside the target volume (SD; ρ = 0.53 training, ρ = 0.65 validation).

Conclusion

In these real-world cohorts, the combination of relatively high peritumor density and high dose variability predicts increase in LR, but not RF, following lung SABR. This external validation justifies potential use of the model to increase low-dose CTV margins for high-risk patients.

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 (Plaque_Coro) and coronary arteries and aorta combined (Plaque_Coro+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 Plaque_Coro and Plaque_Coro+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 Plaque_Coro (HR:1.11 (95%CI:1.04-1.19); p = 0.008), and Plaque_Coro+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.

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.

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

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Cardiotoxicity is a common complication of lung cancer radiotherapy.

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Segmentation of cardiac substructures is time-consuming and challenging.

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Deep learning segmentation tools can perform this task in 3D and 4D scans.

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Performance is high when assessed geometrically, dosimetrically and clinically.

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Auto-segmentation tools may accelerate clinical workflows and enable research.

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.

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.

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.

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 (MI_Mean, MPI_Mean), derived from a trismus risk model, were implemented as dose-volume objectives (ECHO₂). Clinical dose-volume criteria were compared between the two scenarios (ECHO₀vs. ECHO₁; ECHO₁vs. 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 MI_Mean and MPI_Mean criteria. The population median MI_Mean and MPI_Mean were considerably lower than those suggested by the trismus model (ECHO₀: MI_Mean = 13 Gy vs. ≤42 Gy; MPI_Mean = 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.

Radiation-Induced Dyspnea in Lung Cancer Patients Treated with Stereotactic Body Radiation Therapy

Simple Summary

Dyspnea is a common symptomatic side-effect of thoracic radiation therapy. The aim of this study is to build a predictive model of any-grade radiation-induced dyspnea within six months after stereotactic body radiation therapy in patients treated for non-small cell lung cancer. The occurrence of pre-treatment chronic obstructive pulmonary disease and higher relative lungs volume receiving more than 15 Gy as well as heart volume were shown to be risk factors for dyspnea. The obtained results encourage further studies on the topic, which could validate the present organ-based findings and explore the voxel-based landscape of radiation dose sensitivity in the development of dyspnea.

Abstract

In this study, we investigated the prognostic factors for radiation-induced dyspnea after hypo-fractionated radiation therapy (RT) in 106 patients treated with Stereotactic Body RT for Non-Small-Cell Lung Cancer (NSCLC). The median prescription dose was 50 Gy (range: 40–54 Gy), delivered in a median of four fractions (range: 3–12). Dyspnea within six months after SBRT was scored according to CTCAE v.4.0. Biologically Effective Dose (α/β = 3 Gy) volume histograms for lungs and heart were extracted. Dosimetric parameters along with patient-specific and treatment-related factors were analyzed, multivariable logistic regression method with Leave-One-Out (LOO) internal validation applied. Model performance was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot parameters. Fifty-seven patients (53.8%) out of 106 developed dyspnea of any grade after SBRT (25/57 grade ≥ 2 cases). A three-variable predictive model including patient comorbidity (COPD), heart volume and the relative lungs volume receiving more than 15 Gy was selected. The model displays an encouraging performance given by a training ROC-AUC = 0.71 [95%CI 0.61–0.80] and a LOO-ROC-AUC = 0.64 [95%CI 0.53–0.74]. Further modeling efforts are needed for dyspnea prediction in hypo-fractionated treatments in order to identify patients at high risk for developing lung toxicity more accurately.

Treatment planning and outcomes effects of reducing the preferred mean esophagus dose for conventionally fractionated non-small cell lung cancer radiotherapy

Based on an analysis of published literature, our department recently lowered the preferred mean esophagus dose (MED) constraint for conventionally fractionated (2 Gy/fraction in approximately 30 fractions) treatment of locally advanced non-small cell lung cancer (LA-NSCLC) with the goal of reducing the incidence of symptomatic acute esophagitis (AE). The goal of the change was to encourage treatment planners to achieve a MED close to 21 Gy while still permitting MED to go up to the previous guideline of 34 Gy in difficult cases. We compared all our suitable LA-NSCLC patients treated with plans from one year before through one year after the constraint change. The primary endpoint for this study was achievability of the new constraint by the planners; the secondary endpoint was reduction in symptomatic AE. Planners were able to achieve the new constraint in statistically significantly more cases during the year following its explicit implementation than in the year before (P = 0.0025). Furthermore, 38% of patients treated after the new constraint developed symptomatic AE during their treatment as opposed to 48% of the patients treated before. This is a clinically desirable endpoint although the observed difference was not statistically significant. A subsequent power calculation suggests that this is due to the relatively small number of patients in the study.

Incidence and Dosimetric Predictors of Radiation-Induced Gastric Bleeding After Chemoradiation for Esophageal and Gastroesophageal Junction Cancer

Purpose

To determine the incidence and predictors of gastric bleeding after chemoradiation for esophageal or gastroesophageal junction cancer.

Methods and Materials

We reviewed patients receiving chemoradiation to at least 41.4 Gy for localized esophageal cancer whose fields included the stomach and who did not undergo surgical resection. The primary endpoint was grade ≥3 gastric hemorrhage (GB3+). Comprehensive stomach dose-volume parameters were collected, and stomach dose-volume histograms were generated for analysis.

Results

A total of 145 patients met our inclusion criteria. Median prescribed dose was 50.4 Gy (range, 41.4-56 Gy). Median stomach Dmax was 53.0 Gy (1.0-62.7 Gy), and median stomach V40, V45, and V50 Gy were 112 cm³ (0-667 cm³), 84 cm³ (0-632 cm³), and 50 cm³ (0-565 cm³), respectively. Two patients (1.4%) developed radiation-induced GB3+. The only dosimetric factor that was significantly different for these patients was a higher stomach Dmax (58.1 and 58.3 Gy) than the cohort median (53 Gy). One of these patients also had cirrhosis, and the other had a history of nonsteroidal anti-inflammatory drug use. Five other patients had GB3+ events associated with documented tumor progression. A Cox proportional hazards model based on stomach Dmax with respect to the development of GB3+ was found to be statistically significant. Time-to-event curves and dose-volume atlases were generated, demonstrating an increased risk of GB3+ only when stomach Dmax was >58 Gy (P < .05).

Conclusions

We observed a low rate of GB3+ events in patients who received chemoradiation to a median dose of 50.4 Gy to volumes that included a significant portion of the stomach. These results suggest that when prescribing 50.4 Gy for esophageal cancer, there is no need to minimize the irradiated gastric volume or dose for the sake of preventing bleeding complications. Limiting stomach maximum doses to <58 Gy may also avoid bleeding, and particular caution should be taken in patients with other risk factors for bleeding, such as cirrhosis.

Early Prediction of Acute Esophagitis for Adaptive Radiation Therapy

PURPOSE

Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change.

METHODS AND MATERIALS

Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1-to-week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MED_Plan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (P_HL).

RESULTS

Univariately, w1→w2 VE10% (P = .004), VE5% (P = .01) and MEex% (P = .02) significantly predicted ≥AE2. A model combining MED_W2 and w1→w2 VE10% had the best performance (AUC = 0.80; P_HL = 0.43), whereas the MED_Plan model had a lower accuracy (AUC = 0.67; P_HL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75).

CONCLUSIONS

A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis.

Predicting spatial esophageal changes in a multimodal longitudinal imaging study via a convolutional recurrent neural network

Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatment plans to reduce radiation-induced esophageal toxicity in an adaptive radiotherapy (ART) workflow. We have developed a novel machine learning framework to predict the patient-specific spatial presentation of the esophagus in the weeks following treatment, using magnetic resonance imaging (MRI)/ cone-beam CT (CBCT) scans acquired earlier in the 6 week radiotherapy course. Our algorithm captures the response patterns of the esophagus to radiation on a patch level, using a convolutional neural network. A recurrence neural network then parses the evolutionary patterns of the selected features in the time series, and produces a predicted esophagus-or-not label for each individual patch over future weeks. Finally, the esophagus is reconstructed, using all the predicted labels. The algorithm is trained and validated by means of ∼ 250 000 patches taken from MRI scans acquired weekly from a variety of patients, and tested using both weekly MRI and CBCT scans under a leave-one-patient-out scheme. In addition, our approach is externally validated using a publicly available dataset (Hugo 2017). Using the first three weekly scans, the algorithm can predict the condition of the esophagus over the succeeding 3 weeks with a Dice coefficient of 0.83 ± 0.04, estimate esophagus volume highly (0.98), correlated with the actual volume, using our institutional MRI/CBCT data. When evaluated using the external weekly CBCT data, the averaged Dice coefficient is 0.89 ± 0.03. Our novel algorithm may prove useful in enabling radiation oncologists to monitor and detect AE in its early stages, and could potentially play an important role in the ART decision-making process.

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 |Dose_DL - Dose_RTOG0617| (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 |Dose_DL - Dose_RTOG0617|; the axial distance between the 2 centers of mass was the strongest predictor (CENT_Axial; median, R² = 0.47; P = 6.1E-62). CENT_Axial agreed with the qualitatively identified inconsistencies in the superior direction. The trial's qualitative heart contouring score was not correlated with |Dose_DL - Dose_RTOG0617| (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.

Safety of thoracic radiotherapy in patients with prior immune-related adverse events from immune checkpoint inhibitors

BACKGROUND

Immune checkpoint inhibitors (ICIs) and thoracic radiotherapy are increasingly used to treat advanced cancers. Despite data indicating exaggerated radiation toxicities in patients with autoimmune disease, the safety of thoracic radiotherapy in patients with prior ICI-associated immune-related adverse events (irAEs) is undefined.

PATIENTS AND METHODS

Patients treated from 2014 to 2020 with ICIs were queried for receipt of corticosteroids and radiotherapy. Patients who received thoracic radiation after symptomatic irAEs were assessed for ≥grade 2 radiation pneumonitis (RP). Characteristics predictive of RP were assessed using logistic regression and response relationships were modeled.

RESULTS

Among 496 assessed patients, 41 with irAE history subsequently treated with thoracic radiotherapy were analyzed. Most irAEs were grade 2 (n = 21) and 3 (n = 19). Median time from irAE onset to radiotherapy was 8.1 months. Most patients received stereotactic body radiation therapy (n = 20) or hypofractionated radiotherapy (n = 18). In total, 25 patients (61%) developed ≥grade 2 RP at a median of 4 months from radiotherapy and 11 months from onset of irAEs. Three months from RP onset, 16 of 24 (67%) assessable patients had persistent symptoms. Among patients with prior ICI pneumonitis (n = 6), five patients (83%) developed ≥grade 2 RP (grade 2, n = 3; grade ≥3, n = 2). The mean lung radiation dose (MLD) predicted for RP (odds ratio: 1.60, P = 0.00002). The relationship between MLD and RP was strong (area under the receiver-operating characteristic curve: 0.85) and showed an exaggerated dose-response. Among patients with an MLD >5 Gy (n = 26), 21 patients (81%) developed ≥grade 2 RP.

CONCLUSION

This is the first study assessing the toxicity of radiotherapy among patients with prior irAEs from ICIs. Patients with prior irAEs were found to be at very high risk for clinically significant and persistent RP from thoracic radiotherapy. Careful consideration should be given to the possibility of an increased risk of RP, and close monitoring is recommended in these patients.

Predictive Modeling of Thoracic Radiotherapy Toxicity and the Potential Role of Serum Alpha-2-Macroglobulin

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

Registering Study Analysis Plans (SAPs) Before Dissecting Your Data—Updating and Standardizing Outcome Modeling

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

Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis

Background and purpose

Radiation dose to the cardio-pulmonary system is critical for radiotherapy-induced mortality in non-small cell lung cancer. Our goal was to automatically segment substructures of the cardio-pulmonary system for use in outcomes analyses for thoracic cancers. We built and validated a multi-label Deep Learning Segmentation (DLS) model for accurate auto-segmentation of twelve cardio-pulmonary substructures.

Materials and methods

The DLS model utilized a convolutional neural network for segmenting substructures from 217 thoracic radiotherapy Computed Tomography (CT) scans. The model was built in the presence of variable image characteristics such as the absence/presence of contrast. We quantitatively evaluated the final model against expert contours for a hold-out dataset of 24 CT scans using Dice Similarity Coefficient (DSC), 95th Percentile of Hausdorff Distance and Dose-volume Histograms (DVH). DLS contours of an additional 25 scans were qualitatively evaluated by a radiation oncologist to determine their clinical acceptability.

Results

The DLS model reduced segmentation time per patient from about one hour to 10 s. Quantitatively, the highest accuracy was observed for the Heart (median DSC = (0.96 (0.95–0.97)). The median DSC for the remaining structures was between 0.81 and 0.93. No statistically significant difference was found between DVH metrics of the auto-generated and manual contours (p-value ⩾ 0.69). The expert judged that, on average, 85% of contours were qualitatively equivalent to state-of-the-art manual contouring.

Conclusion

The cardio-pulmonary DLS model performed well both quantitatively and qualitatively for all structures. This model has been incorporated into an open-source tool for the community to use for treatment planning and clinical outcomes analysis.

Modeling the Impact of Cardiopulmonary Irradiation on Overall Survival in NRG Oncology Trial RTOG 0617

PURPOSE

To quantitatively predict the impact of cardiopulmonary dose on overall survival (OS) after radiotherapy for locally advanced non-small cell lung cancer.

EXPERIMENTAL DESIGN

We used the NRG Oncology/RTOG 0617 dataset. The model building procedure was preregistered on a public website. Patients were split between a training and a set-aside validation subset (N = 306/131). The 191 candidate variables covered disease, patient, treatment, and dose-volume characteristics from multiple cardiopulmonary substructures (atria, lung, pericardium, and ventricles), including the minimum dose to the hottest x% volume (Dx%[Gy]), mean dose of the hottest x% (MOHx%[Gy]), and minimum, mean (Mean[Gy]), and maximum dose. The model building was based on Cox regression and given 191 candidate variables; a Bonferroni-corrected P value threshold of 0.0003 was used to identify predictors. To reduce overreliance on the most highly correlated variables, stepwise multivariable analysis (MVA) was repeated on 1000 bootstrapped replicates. Multivariate sets selected in ≥10% of replicates were fit to the training subset and then averaged to generate a final model. In the validation subset, discrimination was assessed using Harrell c-index, and calibration was tested using risk group stratification.

RESULTS

Four MVA models were identified on bootstrap. The averaged model included atria D45%[Gy], lung Mean[Gy], pericardium MOH55%[Gy], and ventricles MOH5%[Gy]. This model had excellent performance predicting OS in the validation subset (c = 0.89).

CONCLUSIONS

The risk of death due to cardiopulmonary irradiation was accurately modeled, as demonstrated by predictions on the validation subset, and provides guidance on the delivery of safe thoracic radiotherapy.

Radiation pneumonitis in lung cancer patients treated with chemoradiation plus durvalumab

Introduction

Durvalumab after concurrent chemoradiation (cCRT) is now standard of care for unresected stage III non–small cell lung cancer (NSCLC). However, there is limited data on radiation pneumonitis (RP) with this regimen. Therefore, we assessed RP and evaluated previously validated toxicity models in predicting for RP in patients treated with cCRT and durvalumab.

Methods

Patients treated with cCRT and ≥ 1 dose of durvalumab were evaluated to identify cases of ≥ grade 2 RP. The validity of previously published RP models was assessed in this cohort as well a reference cohort treated with cCRT alone. The timing and incidence of RP was compared between cohorts.

Results

In total, 11 (18%) of the 62 patients who received cCRT and durvalumab developed ≥ grade 2 RP a median of 3.4 months after cCRT. The onset of RP among patients treated with cCRT and durvalumab was significantly longer vs the reference cohort (3.4 vs 2.1 months; P = .01). Numerically more patients treated with cCRT and durvalumab developed RP than patients in the reference cohort (18% vs 9%, P = .09). Among patients treated with cCRT and durvalumab, 82% (n = 9) were responsive to treatment with high‐dose glucocorticoids. Previously published RP models widely underestimated the rate of RP in patients treated with cCRT and durvalumab [AUC ~ 0.50; p(Hosmer‐Lemeshow): 0.98‐1.00].

Conclusions

Our data suggest a delayed onset of RP in patients treated with cCRT and durvalumab vs cCRT alone, and for RP to develop in a greater number of patients treated with cCRT and durvalumab. Previously published RP models significantly underestimate the rate of symptomatic RP among patients treated with cCRT and durvalumab.

Library of deep-learning image segmentation and outcomes model-implementations

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

Quantification of accumulated dose and associated anatomical changes of esophagus using weekly Magnetic Resonance Imaging acquired during radiotherapy of locally advanced lung cancer

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MRI is suited for tracking volumetric changes/accumulating doses in the esophagus.

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Introduced medial axis of esophagus to calculate inter-fraction positional uncertainty.

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Planned and accumulated esophagus dose-volume parameter differences are significant.

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Longitudinal expansion of esophagus may link to acute esophagitis.

Background and purpose

Minimizing acute esophagitis (AE) in locally advanced non-small cell lung cancer (LA-NSCLC) is critical given the proximity between the esophagus and the tumor. In this pilot study, we developed a clinical platform for quantification of accumulated doses and volumetric changes of esophagus via weekly Magnetic Resonance Imaging (MRI) for adaptive radiotherapy (RT).

Material and methods

Eleven patients treated via intensity-modulated RT to 60–70 Gy in 2–3 Gy-fractions with concurrent chemotherapy underwent weekly MRIs. Eight patients developed AE grade 2 (AE2), 3–6 weeks after RT started. First, weekly MRI esophagus contours were rigidly propagated to planning CT and the distances between the medial esophageal axes were calculated as positional uncertainties. Then, the weekly MRI were deformably registered to the planning CT and the total dose delivered to esophagus was accumulated. Weekly Maximum Esophagus Expansion (MEex) was calculated using the Jacobian map. Eventually, esophageal dose parameters (Mean Esophagus Dose (MED), V_90% and D_5cc) between the planned and accumulated dose were compared.

Results

Positional esophagus uncertainties were 6.8 ± 1.8 mm across patients. For the entire cohort at the end of RT: the median accumulated MED was significantly higher than the planned dose (24 Gy vs. 21 Gy p = 0.006). The median V_90% and D_5cc were 12.5 cm³ vs. 11.5 cm³ (p = 0.05) and 61 Gy vs. 60 Gy (p = 0.01), for accumulated and planned dose, respectively. The median MEex was 24% and was significantly associated with AE2 (p = 0.008).

Conclusions

MRI is well suited for tracking esophagus volumetric changes and accumulating doses. Longitudinal esophagus expansion could reflect radiation-induced inflammation that may link to AE.

Are unsatisfactory outcomes after concurrent chemoradiotherapy for locally advanced non-small cell lung cancer due to treatment-related immunosuppression?

BACKGROUND AND PURPOSE

We test the hypothesis that unsatisfactory outcomes after concurrent chemoradiotherapy (RT) for locally advanced non-small cell lung cancer (LA-NSCLC) are due to treatment-related immunosuppression.

MATERIALS AND METHODS

White blood cells (WBCs) data were retrospectively collected for all stage IIIA/B LA-NSCLC patients before and after (after RT: two weeks, two months, four months) concurrent chemotherapy and intensity-modulated RT in which patients were treated to a median of 63 Gy (1.8-2.0 Gy/fractions) in 2004-2014 (N = 155). Nine WBC variables were generated from pre-RT normalized absolute number of lymphocytes and neutrophils (L, N) and the N/L thereof. A WBC variable was considered a predictor for overall survival and recurrence (distant/local/nodal/regional) if p ≤ 0.006 (corrected for 9 variables) from Cox regression and competing risk analyses, respectively; both conducted using bootstrap resampling. Finally, a WBC variable predicting any of the outcomes was linearly associated with each of eleven disease/patient/treatment characteristics (p ≤ 0.005; corrected for 11 characteristics).

RESULTS

At the three post-RT time points both L and N significantly decreased (p < 0.0003). Overall survival was associated with N and N/L four months post-RT (p = 0.00001, 0.0003); regional recurrence was associated with L two months post-RT (p < 0.0001). None of the disease/patient/treatment characteristics was significantly associated with any of the three WBC variables that predicted OS or recurrence (lowest p-value: p = 0.006 for tumour stage,).

CONCLUSION

Significantly lower WBC levels after concurrent chemo-RT for LA-NSCLC are associated with worse long-term outcomes. The mechanism behind this treatment-related immunosuppression requires further analysis likely including other characteristics as no statistically significant association was established between any WBC variable and the disease/patient/treatment characteristics.

Dose to the cardio-pulmonary system and treatment-induced electrocardiogram abnormalities in locally advanced non-small cell lung cancer

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ECG abnormalities after chemo-RT in LA-NSCLC are common (35%–67%).

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Non-specific ECG abnormalities are associated with a high superior vena cava dose.

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Reducing cardiopulmonary dose is likely to lead to less radiation-induced cardiac toxicity.

Introduction

High dose radiotherapy (RT) has been associated with unexpectedly short survival times for locally advanced Non-Small Cell Lung Cancer (LA-NSCLC) patients. Here we tested the hypothesis that cardiac substructure dose is associated with electrocardiography (ECG) assessed abnormalities after RT for LA-NSCLC.

Materials and methods

Pre- and post-RT ECGs were analyzed for 155 LA-NSCLC patients treated to a median of 64 Gy in 1.8–2.0 Gy fractions using intensity-modulated RT plus chemotherapy (concurrent/sequential: 64%/36%) between 2004 and 2014. ECG abnormalities were classified as new Arrhythmic, Ischemic/Pericardial, or Non-specific (A_ΔECG, I/P_ΔECG, or NS_ΔECG) events. Abnormalities were modeled as time to ECG events considering death a competing risk, and the variables considered for analysis were fractionation-corrected dose-volume metrics (α/β = 3 Gy) of ten cardio-pulmonary structures (aorta, heart, heart chambers, inferior and superior vena cava, lung, pulmonary artery) and 15 disease, patient and treatment characteristics. Each abnormality was modelled using bootstrapping and a candidate predictor was suggested by a median multiple testing-adjusted p-value ≤0.05 across the 1000 generated samples. Forward-stepwise multivariate analysis was conducted in case of more than one candidate.

Results

At a median of eight months post-RT, the rate of A_ΔECG, I/P_ΔECG, and NS_ΔECG was 66%, 35%, and 67%. Both A_ΔECG and I/P_ΔECG were associated with worse performance status (p = 0.007, 0.03), while a higher superior vena cava minimum dose was associated with NS_ΔECG (p = 0.002).

Conclusion

This study suggests that higher radiation doses to the cardio-pulmonary system lead to non-specific ECG abnormalities. Reducing dose to this system, along with effective tumor control, is likely to decrease radiation-induced cardiac toxicity.