Factors Associated With and Characteristics of Patients Receiving Proton Therapy at the End of Life.

Purpose: To identify the characteristics, indications, and toxicities among patients receiving proton beam therapy (PBT) in the final year of life at an academic medical center. Materials and Methods: A retrospective review of patients who received PBT within the final 12 months of life was performed. Electronic medical records were reviewed for patient and treatment details from 2010 to 2019. Patients were followed from the start of PBT until death or last follow-up. Acute (3 months) toxicities were graded using the Common Terminology Criteria for Adverse Events v5.0. Imaging response was assessed using the Response Evaluation Criteria in Solid Tumors v1.1. The χ2 test was used to evaluate factors associated with palliative treatment. Simple logistic regression was used to evaluate factors associated with toxicity. Results: Bet299 patients were treated at the end of life (EOL) out of 5802 total patients treated with PBT (5.2%). Median age was 68 years (19-94 years), 58% male. The most common cancer was nonsmall cell lung cancer (27%). Patients were treated for symptom palliation alone (11%), durable control (57%), curative intent (16%), local recurrence (14%), or oligometastatic disease (2%). Forty-five percent received reirradiation. Median treatment time was 32 days (1-189 days). Acute toxicity was noted in 85% of the patients (31% G1, 53% G2, 15% G3). Thirteen patients (4%) experienced chronic toxicity. Breast and hematologic malignancy were associated with palliative intent χ2 (1, N = 14) = 17, P = .013; (χ2 (1, N = 14) = 18, P = .009). Conclusion: The number of patients treated with PBT at the EOL was low compared to all comers. Many of these patients received treatment with definitive doses and concurrent systemic therapy. Some patients spent a large portion of their remaining days on treatment. A prognostic indicator may better optimize patient selection for PBT at the EOL.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Proton Pencil-Beam Scanning Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies: Patterns of Practice Survey and Recommendations for Future Development from NRG Oncology and PTCOG.

Stereotactic body radiation therapy (SBRT) and hypofractionation using pencil-beam scanning (PBS) proton therapy (PBSPT) is an attractive option for thoracic malignancies. Combining the advantages of target coverage conformity and critical organ sparing from both PBSPT and SBRT, this new delivery technique has great potential to improve the therapeutic ratio, particularly for tumors near critical organs. Safe and effective implementation of PBSPT SBRT/hypofractionation to treat thoracic malignancies is more challenging than the conventionally-fractionated PBSPT due to concerns of amplified uncertainties at the larger dose per fraction. NRG Oncology and Particle Therapy Cooperative Group (PTCOG) Thoracic Subcommittee surveyed US proton centers to identify practice patterns of thoracic PBSPT SBRT/hypofractionation. From these patterns, we present recommendations for future technical development of proton SBRT/hypofractionation for thoracic treatment. Amongst other points, the recommendations highlight the need for volumetric image guidance and multiple CT-based robust optimization and robustness tools to minimize further the impact of uncertainties associated with respiratory motion. Advances in direct motion analysis techniques are urgently needed to supplement current motion management techniques.

NRG Oncology and PTCOG Patterns of Practice Survey and Consensus Recommendations on Pencil-Beam Scanning Proton Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies.

Stereotactic body radiation therapy (SBRT) and hypofractionation using pencil-beam scanning (PBS) proton therapy (PBSPT) is an attractive option for thoracic malignancies. Combining the advantages of target coverage conformity and critical organ sparing from both PBSPT and SBRT, this new delivery technique has great potential to improve the therapeutic ratio, particularly for tumors near critical organs. Safe and effective implementation of PBSPT SBRT/hypofractionation to treat thoracic malignancies is more challenging than the conventionally fractionated PBSPT because of concerns of amplified uncertainties at the larger dose per fraction. The NRG Oncology and Particle Therapy Cooperative Group Thoracic Subcommittee surveyed proton centers in the United States to identify practice patterns of thoracic PBSPT SBRT/hypofractionation. From these patterns, we present recommendations for future technical development of proton SBRT/hypofractionation for thoracic treatment. Among other points, the recommendations highlight the need for volumetric image guidance and multiple computed tomography-based robust optimization and robustness tools to minimize further the effect of uncertainties associated with respiratory motion. Advances in direct motion analysis techniques are urgently needed to supplement current motion management techniques.

Implementation of a Novel Chart Rounds Application to Facilitate Peer Review in a Virtual Academic Environment.

Purpose: Peer review in the form of chart rounds is a critical component of quality assurance and safety in radiation therapy treatments. Radiation therapy departments have undergone significant changes that impose challenges to meaningful review, including institutional growth and increasing use of virtual environment. We discuss the implementation of a novel chart rounds (NCR) format and application adapted to modern peer review needs at a single high-volume multisite National Cancer Institute designated cancer center. Methods and Materials: A working group was created to improve upon the prior institutional chart rounds format (standard chart rounds or SCR). Using a novel in-house application and format redesign, an NCR was created and implemented to accomplish stated goals. Data regarding the SCR and NCR system were then extracted for review. Results: SCR consisted of 2- 90-minute weekly sessions held to review plans across all disease sites, review of 49 plans per hour on average. NCR uses 1-hour long sessions divided by disease site, enabling additional time to be spent per patient (11 plans per hour on average) and more robust discussion. The NCR application is able to automate a list of plans requiring peer review from the institutional treatment planning system. The novel application incorporates features that enable efficient and accurate review of plans in the virtual setting across multiple sites. A systematic scoring system is integrated into the application to record feedback. Over 5 months of use of the NCR, 1160 plans have been reviewed with 143 scored as requiring minor changes, 32 requiring major changes and 307 with comments. Major changes triggered treatment replan. Feedback from scoring is incorporated into physician workflow to ensure changes are addressed. Conclusion: The presented NCR format and application enables standardized and highly reliable peer review of radiation therapy plans that is robust across a variety of complex planning scenarios and could be implemented globally.

Plunging Into the PACIFIC: Outcomes of Patients With Unresectable KRAS-Mutated Non-Small Cell Lung Cancer Following Definitive Chemoradiation and Durvalumab Consolidation.

BACKGROUND: Immune checkpoint inhibitor (ICI) consolidation following concurrent chemoradiotherapy (CRT) substantially improved progression free survival (PFS) and overall survival (OS) in the PACIFIC trial becoming the standard of care in locally-advanced, unresectable NSCLC. KRAS mutation may influence response to ICI. METHODS: In this single-institution, retrospective analysis, we compared treatment outcomes for patients with unresectable KRAS mutated (KRAS-mt) and wild-type (KRAS-wt) NSCLC treated with CRT between October 2017 and December 2021. Kaplan-Meier analysis was conducted comparing median progression free survival and median overall survival from completion of radiotherapy in all KRAS-mt patients and KRAS-G12C-mutated patients. Outcomes were also compared with and without ICI consolidation. RESULTS: Of 156 patients, 42 (26.9%) were KRAS-mt and 114 (73.1%) were KRAS-wt. Baseline characteristics differed only in histology; KRAS-mt NSCLC more likely to be adenocarcinoma. KRAS-mt patients had worse PFS (median 6.3 vs. 10.7 months, P = .041) but similar OS (median 23.1 vs. 27.3 months, P = .237). KRAS-mt patients were more likely to not receive ICI due to rapid disease progression post-CRT (23.8% vs. 4.4%, P = .007). Among patients who received ICI (n = 114), KRAS-mt was not associated with inferior PFS (8.1 vs. 11.9 months, P = .355) or OS (30.5 vs. 31.7 months, P = .692). KRAS-G12C patients (n = 22) had similar PFS and OS to other KRAS-mt. CONCLUSION: In one of the largest post-CRT KRAS-mt cohort published, KRAS-mt was associated with inferior PFS, largely due to rapid progression prior to ICI consolidation, but did not affect OS. Among those who received ICI consolidation, outcomes were comparable regardless of KRAS-mt status.

Acute hospitalizations after proton therapy versus intensity-modulated radiotherapy for locally advanced non-small cell lung cancer in the durvalumab era.

INTRODUCTION: It was hypothesized that use of proton beam therapy (PBT) in patients with locally advanced non-small cell lung cancer treated with concurrent chemoradiation and consolidative immune checkpoint inhibition is associated with fewer unplanned hospitalizations compared with intensity-modulated radiotherapy (IMRT). METHODS: Patients with locally advanced non-small cell lung cancer treated between October 2017 and December 2021 with concurrent chemoradiation with either IMRT or PBT ± consolidative immune checkpoint inhibition were retrospectively identified. Logistic regression was used to assess the association of radiation therapy technique with 90-day hospitalization and grade 3 (G3+) lymphopenia. Competing risk regression was used to compare G3+ pneumonitis, G3+ esophagitis, and G3+ cardiac events. Kaplan-Meier method was used for progression-free survival and overall survival. Inverse probability treatment weighting was applied to adjust for differences in PBT and IMRT groups. RESULTS: Of 316 patients, 117 (37%) received PBT and 199 (63%) received IMRT. The PBT group was older (p < .001) and had higher Charlson Comorbidity Index scores (p = .02). The PBT group received a lower mean heart dose (p < .0001), left anterior descending artery V15 Gy (p = .001), mean lung dose (p = .008), and effective dose to immune circulating cells (p < .001). On inverse probability treatment weighting analysis, PBT was associated with fewer unplanned hospitalizations (adjusted odds ratio, 0.55; 95% CI, 0.38-0.81; p = .002) and less G3+ lymphopenia (adjusted odds ratio, 0.55; 95% CI, 0.37-0.81; p = .003). There was no difference in other G3+ toxicities, progression-free survival, or overall survival. CONCLUSIONS: PBT is associated with fewer unplanned hospitalizations, lower effective dose to immune circulating cells and less G3+ lymphopenia compared with IMRT. Minimizing dose to lymphocytes may be warranted, but prospective data are needed.

Cardiac radiation dose is associated with inferior survival but not cardiac events in patients with locally advanced non-small cell lung cancer in the era of immune checkpoint inhibitor consolidation.

PURPOSE: We assessed the association of cardiac radiation dose with cardiac events and survival post-chemoradiation therapy (CRT) in patients with locally advanced non-small cell lung cancer (LA-NSCLC) after adoption of modern radiation therapy (RT) techniques, stricter cardiac dose constraints, and immune checkpoint inhibitor (ICI) consolidation. METHODS AND MATERIALS: This single-institution, multi-site retrospective study included 335 patients with LA-NSCLC treated with definitive, concurrent CRT between October 2017 and December 2021. All patients were evaluated for ICI consolidation. Planning dose constraints included heart mean dose < 20 Gy (<10 Gy if feasible) and heart volume receiving ≥ 50 Gy (V50Gy) < 25 %. Twenty-one dosimetric parameters for three different cardiac structures (heart, left anterior descending coronary artery [LAD], and left ventricle) were extracted. Primary endpoint was any major adverse cardiac event (MACE) post-CRT, defined as acute coronary syndrome, heart failure, coronary revascularization, or cardiac-related death. Secondary endpoints were: grade ≥ 3 cardiac events (per CTCAE v5.0), overall survival (OS), lung cancer-specific mortality (LCSM), and other-cause mortality (OCM). RESULTS: Median age was 68 years, 139 (41 %) had baseline coronary heart disease, and 225 (67 %) received ICI consolidation. Proton therapy was used in 117 (35 %) and intensity-modulated RT in 199 (59 %). Median LAD V15Gy was 1.4 % (IQR 0-22) and median heart mean dose was 8.7 Gy (IQR 4.6-14.4). Median follow-up was 3.3 years. Two-year cumulative incidence of MACE was 9.5 % for all patients and 14.3 % for those with baseline coronary heart disease. Two-year cumulative incidence of grade ≥ 3 cardiac events was 20.4 %. No cardiac dosimetric parameter was associated with an increased risk of MACE or grade ≥ 3 cardiac events. On multivariable analysis, cardiac dose (LAD V15Gy and heart mean dose) was associated with worse OS, driven by an association with LCSM but not OCM. CONCLUSIONS: With modern RT techniques, stricter cardiac dose constraints, and ICI consolidation, cardiac dose was associated with LCSM but not OCM or cardiac events in patients with LA-NSCLC.

Pneumonitis Rates Before and After Adoption of Immunotherapy Consolidation in Patients With Locally Advanced Non-Small Cell Lung Cancer Treated With Concurrent Chemoradiation.

PURPOSE: We hypothesized that after adoption of immune checkpoint inhibitor (ICI) consolidation for patients with locally advanced non-small cell lung cancer (LA-NSCLC) receiving concurrent chemoradiation therapy (cCRT), rates of symptomatic pneumonitis would increase, thereby supporting efforts to reduce lung radiation dose. METHODS AND MATERIALS: This single institution, multisite retrospective study included 783 patients with LA-NSCLC treated with definitive cCRT either before introduction of ICI consolidation (pre-ICI era cohort [January 2011-September 2017]; N = 448) or afterward (ICI era cohort [October 2017-December 2021]; N = 335). Primary endpoint was grade ≥2 pneumonitis (G2P) and secondary endpoint was grade ≥3 pneumonitis (G3P), per Common Terminology Criteria for Adverse Events v5.0. Pneumonitis was compared between pre-ICI era and ICI era cohorts using the cumulative incidence function and Gray's test. Inverse probability of treatment weighting (IPTW)-adjusted Fine-Gray models were generated. Logistic models were developed to predict the 1-year probability of G2P as a function of lung dosimetry. RESULTS: G2P was higher in the ICI era than in the pre-ICI era (1-year cumulative incidence 31.4% vs 20.1%; P < .001; IPTW-adjusted multivariable subdistribution hazard ratio, 2.03; 95% confidence interval, 1.53-2.70; P < .001). There was no significant interaction between ICI era treatment and either lung volume receiving ≥20 Gy (V20) or mean lung dose in Fine-Gray regression for G2P; however, the predicted probability of G2P was higher in the ICI era at clinically relevant values of lung V20 (≥24%) and mean lung dose (≥14 Gy). Cut-point analysis revealed a lung V20 threshold of 28% in the ICI era (1-year G2P rate 46.0% above vs 19.8% below; P < .001). Among patients receiving ICI consolidation, lung V5 was not associated with G2P. G3P was not higher in the ICI era (1-year cumulative incidence 7.5% vs 6.0%; P = .39; IPTW-adjusted multivariable subdistribution hazard ratio, 1.12; 95% confidence interval, 0.63-2.01; P = .70). CONCLUSIONS: In patients with LA-NSCLC treated with cCRT, the adoption of ICI consolidation was associated with an increase in G2P but not G3P. With ICI consolidation, stricter lung dose constraints may be warranted.

Deformable lung 4DCT image registration via landmark-driven cycle network.

BACKGROUND: An automated, accurate, and efficient lung four-dimensional computed tomography (4DCT) image registration method is clinically important to quantify respiratory motion for optimal motion management. PURPOSE: The purpose of this work is to develop a weakly supervised deep learning method for 4DCT lung deformable image registration (DIR). METHODS: The landmark-driven cycle network is proposed as a deep learning platform that performs DIR of individual phase datasets in a simulation 4DCT. This proposed network comprises a generator and a discriminator. The generator accepts moving and target CTs as input and outputs the deformation vector fields (DVFs) to match the two CTs. It is optimized during both forward and backward paths to enhance the bi-directionality of DVF generation. Further, the landmarks are used to weakly supervise the generator network. Landmark-driven loss is used to guide the generator's training. The discriminator then judges the realism of the deformed CT to provide extra DVF regularization. RESULTS: We performed four-fold cross-validation on 10 4DCT datasets from the public DIR-Lab dataset and a hold-out test on our clinic dataset, which included 50 4DCT datasets. The DIR-Lab dataset was used to evaluate the performance of the proposed method against other methods in the literature by calculating the DIR-Lab Target Registration Error (TRE). The proposed method outperformed other deep learning-based methods on the DIR-Lab datasets in terms of TRE. Bi-directional and landmark-driven loss were shown to be effective for obtaining high registration accuracy. The mean and standard deviation of TRE for the DIR-Lab datasets was 1.20 ± 0.72 mm and the mean absolute error (MAE) and structural similarity index (SSIM) for our datasets were 32.1 ± 11.6 HU and 0.979 ± 0.011, respectively. CONCLUSION: The landmark-driven cycle network has been validated and tested for automatic deformable image registration of patients' lung 4DCTs with results comparable to or better than competing methods.

Phase 2 Trial of Consolidation Pembrolizumab After Proton Reirradiation for Thoracic Recurrences of Non-Small Cell Lung Cancer.

PURPOSE: Reirradiation (reRT) with proton beam therapy (PBT) may offer a chance of cure while minimizing toxicity for patients with isolated intrathoracic recurrences of non-small cell lung cancer (NSCLC). However, distant failure remains common, necessitating strategies to integrate more effective systemic therapy. METHODS AND MATERIALS: This was a phase 2, single-arm trial (NCT03087760) of consolidation pembrolizumab after PBT reRT for locoregional recurrences of NSCLC. Four to 12 weeks after completion of 60 to 70 Gy PBT reRT, patients without progressive disease received pembrolizumab for up to 12 months. Primary endpoint was progression-free survival (PFS), measured from the start of reRT. Secondary endpoints were overall survival (OS) and National Cancer Institute Common Terminology Criteria for Adverse Events, version 5.0 toxicity. RESULTS: Between 2017 and 2021, 22 patients received PBT reRT. Median interval from prior radiation end to reRT start was 20 months. Most recurrences (91%) were centrally located. Most patients received concurrent chemotherapy (95%) and pencil beam scanning PBT (77%), and 36% had received prior durvalumab. Fifteen patients (68%) initiated consolidation pembrolizumab on trial and received a median of 3 cycles (range, 2-17). Pembrolizumab was discontinued most commonly due to toxicity (n = 5; 2 were pembrolizumab-related), disease progression (n = 4), and completion of 1 year (n = 3). Median follow-up was 38.7 months. Median PFS and OS were 8.8 months (95% CI, 4.2-23.7) and 22.8 months (95% CI, 6.9-not reached), respectively. There was only one isolated in-field failure after reRT. Grade ≥3 toxicities occurred in 10 patients (45%); 2 were pembrolizumab-related. There were 2 grade 5 toxicities, an aorto-esophageal fistula at 6.9 months and hemoptysis at 46.8 months, both probably from reRT. The trial closed early due to widespread adoption of immunotherapy off-protocol. CONCLUSIONS: In the first-ever prospective trial combining PBT reRT with consolidation immunotherapy, PFS was acceptable and OS favorable. Late grade 5 toxicity occurred in 2 of 22 patients. This approach may be considered in selected patients with isolated thoracic recurrences of NSCLC.

Patterns of Failure, Low-Volume Relapse, and Subsequent Ablative Management in Locally Advanced Non-Small Cell Lung Cancer Treated With Definitive Chemoradiation and Consolidation Immune Checkpoint Inhibitors.

PURPOSE: The objective of this study was to describe the patterns of failure, frequency of low-volume relapse (LVR), and candidacy for ablative therapy at time of disease progression (PD) after chemoradiation and consolidative immunotherapy (CRT + ICI) in patients with stage III non-small cell lung cancer. METHODS AND MATERIALS: We identified 229 consecutive patients with stage III non-small cell lung cancer treated with CRT + ICI between October 2017 and December 2021 at a single institution. PD was classified as isolated locoregional failure (LRF), isolated distant failure (DF), or synchronous LRF + DF. Any LRF was subclassified as in-field failure, marginal failure, or out-of-field failure. LVR was defined as 3 or fewer sites of PD in any number of organs. Ablative candidates were defined as having 5 or fewer sites of PD radiographically amenable to high-dose radiation or surgery. Time-to-event data were calculated using cumulative incidence analysis and Kaplan-Meier methods. Multivariable Cox modeling was used to examine the correlations between characteristics of relapse and postprogression survival. RESULTS: Of the 229 patients, 119 (52%) had PD. Of these 119 patients, 20 (21%) had isolated LRF, 28 (24%) had synchronous LRF + DF, and 71 (60%) had isolated DF. Of the 48 patients with any LRF, 28 (58%) had in-field failure, 10 (21%) marginal failure, and 10 (21%) out-of-field failure. The cumulative incidence of LRF and DF was 13% (95% CI, 9.2%-18%) and 32% (95% CI, 26%-38%) at 1 year and 19% (95% CI, 14%-24%) and 39% (95% CI, 33%-46%) at 2 years, respectively. Overall, 64 patients (54%) were considered to have LVR. At time of PD, 60 patients (50%) were eligible for ablative therapy. Patients with LVR had longer median survival versus with high-volume relapse (37.4 vs 15.2 months, P < .001). On multivariable analysis, LVR (hazard ratio, 0.32; 95% CI, 0.18-0.56; P < .001) was associated with improved postprogression survival. CONCLUSIONS: After CRT + ICI, approximately half of patients experience LVR at time of PD and are candidates for ablative therapies. Prospective trials are needed to validate the optimal treatment strategy for LVR.

The effective radiation dose to immune cells predicts lymphopenia and inferior cancer control in locally advanced NSCLC.

PURPOSE: To explore the association of the effective dose to immune cells (EDIC) with disease control, lymphopenia, and toxicity in patients with non-small cell lung cancer (NSCLC) and identify methods to reduce EDIC. METHODS: We abstracted data from all patients with locally advanced NSCLC treated with chemoradiation with or without consolidative immunotherapy over a ten-year period. Associations between EDIC and progression-free survival (PFS) and overall survival (OS) were modeled with Cox proportional hazards and Kaplan-Meier method. Logistic regression was used to model predictors of lymphopenia and higher EDIC. Analyses were performed with EDIC as a continuous and categorical variable. Lymphopenia was graded per CTCAE v5.0. RESULTS: Overall, 786 patients were included (228 of which received consolidative immunotherapy); median EDIC was 4.7 Gy. Patients with EDIC < 4.7 Gy had a longer median PFS (15.3 vs. 9.0 months; p < 0.001) and OS (34.2 vs. 22.4 months; p < 0.001). On multivariable modeling, EDIC correlated with inferior PFS (HR 1.08, 95 % CI 1.01-1.14, p = 0.014) and OS (HR 1.10, 95 % CI 1.04-1.18, p = 0.002). EDIC was predictive of grade 4 lymphopenia (OR 1.16, 95 % CI 1.02-1.33, p = 0.026). EDIC ≥ 4.7 Gy was associated with increased grade 2 + pneumonitis (6-month incidence: 26 % vs 20 %, p = 0.04) and unplanned hospitalizations (90-day incidence: 40 % vs 30 %, p = 0.002). Compared to protons, photon therapy was associated with EDIC ≥ 4.7 Gy (OR 5.26, 95 % CI 3.71-7.69, p < 0.001) in multivariable modeling. CONCLUSIONS: EDIC is associated with inferior disease outcomes, treatment-related toxicity, and the development of severe lymphopenia. Proton therapy is associated with lower EDIC. Further investigations to limit radiation dose to the immune system appear warranted.

Single energy CT-based mass density and relative stopping power estimation for proton therapy using deep learning method.

Background: The number of patients undergoing proton therapy has increased in recent years. Current treatment planning systems (TPS) calculate dose maps using three-dimensional (3D) maps of relative stopping power (RSP) and mass density. The patient-specific maps of RSP and mass density were obtained by translating the CT number (HU) acquired using single-energy computed tomography (SECT) with appropriate conversions and coefficients. The proton dose calculation uncertainty of this approach is 2.5%-3.5% plus 1 mm margin. SECT is the major clinical modality for proton therapy treatment planning. It would be intriguing to enhance proton dose calculation accuracy using a deep learning (DL) approach centered on SECT. Objectives: The purpose of this work is to develop a deep learning method to generate mass density and relative stopping power (RSP) maps based on clinical single-energy CT (SECT) data for proton dose calculation in proton therapy treatment. Methods: Artificial neural networks (ANN), fully convolutional neural networks (FCNN), and residual neural networks (ResNet) were used to learn the correlation between voxel-specific mass density, RSP, and SECT CT number (HU). A stoichiometric calibration method based on SECT data and an empirical model based on dual-energy CT (DECT) images were chosen as reference models to evaluate the performance of deep learning neural networks. SECT images of a CIRS 062M electron density phantom were used as the training dataset for deep learning models. CIRS anthropomorphic M701 and M702 phantoms were used to test the performance of deep learning models. Results: For M701, the mean absolute percentage errors (MAPE) of the mass density map by FCNN are 0.39%, 0.92%, 0.68%, 0.92%, and 1.57% on the brain, spinal cord, soft tissue, bone, and lung, respectively, whereas with the SECT stoichiometric method, they are 0.99%, 2.34%, 1.87%, 2.90%, and 12.96%. For RSP maps, the MAPE of FCNN on M701 are 0.85%, 2.32%, 0.75%, 1.22%, and 1.25%, whereas with the SECT reference model, they are 0.95%, 2.61%, 2.08%, 7.74%, and 8.62%. Conclusion: The results show that deep learning neural networks have the potential to generate accurate voxel-specific material property information, which can be used to improve the accuracy of proton dose calculation. Advances in knowledge: Deep learning-based frameworks are proposed to estimate material mass density and RSP from SECT with improved accuracy compared with conventional methods.

A Novel Inverse Algorithm To Solve the Integrated Optimization of Dose, Dose Rate, and Linear Energy Transfer of Proton FLASH Therapy With Sparse Filters.

PURPOSE: The recently proposed Integrated Physical Optimization Intensity Modulated Proton Therapy (IPO-IMPT) framework allows simultaneous optimization of dose, dose rate, and linear energy transfer (LET) for ultra-high dose rate (FLASH) treatment planning. Finding solutions to IPO-IMPT is difficult because of computational intensiveness. Nevertheless, an inverse solution that simultaneously specifies the geometry of a sparse filter and weights of a proton intensity map is desirable for both clinical and preclinical applications. Such solutions can reduce effective biologic dose to organs at risk in patients with cancer as well as reduce the number of animal irradiations needed to derive extra biologic dose models in preclinical studies. METHODS AND MATERIALS: Unlike the initial forward heuristic, this inverse IPO-IMPT solution includes simultaneous optimization of sparse range compensation, sparse range modulation, and spot intensity. The daunting computational tasks vital to this endeavor were resolved iteratively with a distributed computing framework to enable Simultaneous Intensity and Energy Modulation and Compensation (SIEMAC). SIEMAC was demonstrated on a human patient with central lung cancer and a minipig. RESULTS: SIEMAC simultaneously improves maps of spot intensities and patient-field-specific sparse range compensators and range modulators. For the patient with lung cancer, at our maximum nozzle current of 300 nA, dose rate coverage above 100 Gy/s increased from 57% to 96% in the lung and from 93% to 100% in the heart, and LET coverage above 4 keV/µm dropped from 68% to 9% in the lung and from 26% to <1% in the heart. For a simple minipig plan, the full-width half-maximum of the dose, dose rate, and LET distributions decreased by 30%, 1.6%, and 57%, respectively, again with similar target dose coverage, thus reducing uncertainty in these quantities for preclinical studies. CONCLUSIONS: The inverse solution to IPO-IMPT demonstrated the capability to simultaneously modulate subspot proton energy and intensity distributions for clinical and preclinical studies.

The Inherited KRAS-variant as a Biomarker of Cetuximab Response in NSCLC.

PURPOSE: RTOG 0617 was a phase III randomized trial for patients with unresectable stage IIIA/IIIB non-small cell lung cancer comparing standard-dose (60 Gy) versus high-dose (74 Gy) radiotherapy and chemotherapy, plus or minus cetuximab. Although the study was negative, based on prior evidence that patients with the KRAS-variant, an inherited germline mutation, benefit from cetuximab, we evaluated KRAS-variant patients in RTOG 0617. EXPERIMENTAL DESIGN: From RTOG 0617, 328 of 496 (66%) of patients were included in this analysis. For time-to-event outcomes, stratified log-rank tests and multivariable Cox regression models were used. For binary outcomes, Cochran-Mantel-Haenzel tests and multivariable logistic regression models were used. All statistical tests were two sided, and a P value <0.05 was considered significant. RESULTS: A total of 17.1% (56/328) of patients had the KRAS-variant, and overall survival rates were similar between KRAS-variant and non-variant patients. However, there was a time-dependent effect of cetuximab seen only in KRAS-variant patients-while the hazard of death was higher in cetuximab-treated patients within year 1 [HR = 3.37, 95% confidence interval (CI): 1.13-10.10, P = 0.030], death was lower from year 1 to 4 (HR = 0.33, 95% CI: 0.11-0.97, P = 0.043). In contrast, in non-variant patients, the addition of cetuximab significantly increased local failure (HR = 1.59, 95% CI: 1.11-2.28, P = 0.012). CONCLUSIONS/DISCUSSION: Although an overall survival advantage was not achieved in KRAS-variant patients, there is potential impact of cetuximab for this genetic subset of patients. In contrast, cetuximab seems to harm non-variant patients. These findings further support the importance of genetic patient selection in trials studying the addition of systemic agents to radiotherapy. SIGNIFICANCE: The KRAS-variant is the first functional, inherited miRNA-disrupting variant identified in cancer. Our findings support that cetuximab has a potentially beneficial impact on KRAS-variant patients treated with radiation. The work confirms prior evidence that KRAS-variant patients are a subgroup who are especially sensitive to radiation. These findings further support the potential of this class of variants to enable true treatment personalization, considering the equally important endpoints of response and toxicity.

Multimodal imaging-based material mass density estimation for proton therapy using supervised deep learning.

OBJECTIVE: Mapping CT number to material property dominates the proton range uncertainty. This work aims to develop a physics-constrained deep learning-based multimodal imaging (PDMI) framework to integrate physics, deep learning, MRI, and advanced dual-energy CT (DECT) to derive accurate patient mass density maps. METHODS: Seven tissue substitute MRI phantoms were used for validation including adipose, brain, muscle, liver, skin, spongiosa, 45% hydroxyapatite (HA) bone. MRI images were acquired using T1 weighted Dixon and T2 weighted short tau inversion recovery sequences. Training inputs are from MRI and twin-beam dual-energy images acquired at 120 kVp with gold/tin filters. The feasibility investigation included an empirical model and four residual networks (ResNet) derived from different training inputs and strategies by PDMI framework. PRN-MR-DE and RN-MR-DE denote ResNet (RN) trained with and without a physics constraint (P) using MRI (MR) and DECT (DE) images. PRN-DE stands for RN trained with a physics constraint using only DE images. A retrospective study using institutional patient data was also conducted to investigate the feasibility of the proposed framework. RESULTS: For the tissue surrogate study, PRN-MR-DE, PRN-DE, and RN-MR-DE result in mean mass density errors: -0.72%/2.62%/-3.58% for adipose; -0.03%/-0.61%/-0.18% for muscle; -0.58%/-1.36%/-4.86% for 45% HA bone. The retrospective patient study indicated that PRN-MR-DE predicted the densities of soft tissue and bone within expected intervals based on the literature survey, while PRN-DE generated large density deviations. CONCLUSION: The proposed PDMI framework can generate accurate mass density maps using MRI and DECT images. The supervised learning can further enhance model efficacy, making PRN-MR-DE outperform RN-MR-DE. The patient investigation also shows that the framework can potentially improve proton range uncertainty with accurate patient mass density maps. ADVANCES IN KNOWLEDGE: PDMI framework is proposed for the first time to inform deep learning models by physics insights and leverage the information from MRI to derive accurate mass density maps.

One-step Iterative Estimation of Effective Atomic Number and Electron Density for Dual Energy CT.

Dual-energy computed tomography (DECT) is a promising technology that has shown a number of clinical advantages over conventional X-ray CT, such as improved material identification, artifact suppression, etc. For proton therapy treatment planning, besides material-selective images, maps of effective atomic number (Z) and relative electron density to that of water ($\rho_e$) can also be achieved and further employed to improve stopping power ratio accuracy and reduce range uncertainty. In this work, we propose a one-step iterative estimation method, which employs multi-domain gradient $L_0$-norm minimization, for Z and $\rho_e$ maps reconstruction. The algorithm was implemented on GPU to accelerate the predictive procedure and to support potential real-time adaptive treatment planning. The performance of the proposed method is demonstrated via both phantom and patient studies.

Deep learning-based Fast Volumetric Image Generation for Image-guided Proton FLASH Radiotherapy.

Objective: FLASH radiotherapy leverages ultra-high dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. This may allow dose escalation, toxicity mitigation, or both. To prepare for the ultra-high dose-rate delivery, we aim to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for proton FLASH beam delivery. Approach: The proposed framework comprises four modules, including orthogonal kV x-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a four-dimensional computed tomography (CT) dataset with ten respiratory phases. Leave-phase-out cross-validation was performed to investigate the DL model's robustness for each patient. Main results: The proposed framework reconstructed patients' volumetric anatomy, including tumors and organs at risk from orthogonal x-ray projections. Considering all evaluation metrics, the kV projections with source angles of 135° and 225° yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were 75±22 HU, 19±3.7 dB, 0.938±0.044, and -1.3%±4.1%. Significance: The proposed framework has been demonstrated to reconstruct volumetric images with a high degree of accuracy using two orthogonal x-ray projections. The embedded WET module can be used to detect potential proton beam-specific patient anatomy variations. This framework can rapidly deliver volumetric images to potentially guide proton FLASH therapy treatment delivery systems.

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.