Embedding machine learning based toxicity models within radiotherapy treatment plan optimization.

Objective.This study addresses radiation-induced toxicity (RIT) challenges in radiotherapy (RT) by developing a personalized treatment planning framework. It leverages patient-specific data and dosimetric information to create an optimization model that limits adverse side effects using constraints learned from historical data.Approach.The study uses the optimization with constraint learning (OCL) framework, incorporating patient-specific factors into the optimization process. It consists of three steps: optimizing the baseline treatment plan using population-wide dosimetric constraints; training a machine learning (ML) model to estimate the patient's RIT for the baseline plan; and adapting the treatment plan to minimize RIT using ML-learned patient-specific constraints. Various predictive models, including classification trees, ensembles of trees, and neural networks, are applied to predict the probability of grade 2+ radiation pneumonitis (RP2+) for non-small cell lung (NSCLC) cancer patients three months post-RT. The methodology is assessed with four high RP2+ risk NSCLC patients, with the goal of optimizing the dose distribution to constrain the RP2+ outcome below a pre-specified threshold. Conventional and OCL-enhanced plans are compared based on dosimetric parameters and predicted RP2+ risk. Sensitivity analysis on risk thresholds and data uncertainty is performed using a toy NSCLC case.Main results.Experiments show the methodology's capacity to directly incorporate all predictive models into RT treatment planning. In the four patients studied, mean lung dose and V20 were reduced by an average of 1.78 Gy and 3.66%, resulting in an average RP2+ risk reduction from 95% to 42%. Notably, this reduction maintains tumor coverage, although in two cases, sparing the lung slightly increased spinal cord max-dose (0.23 and 0.79 Gy).Significance.By integrating patient-specific information into learned constraints, the study significantly reduces adverse side effects like RP2+ without compromising target coverage. This unified framework bridges the gap between predicting toxicities and optimizing treatment plans in personalized RT decision-making.

Optimizing Outpatient Radiation Oncology Consult Workflow by Using Time-Driven Activity-Based Costing: Efficiency and Financial Impacts.

PURPOSE: Clinical efficiency is a key component of value-based health care. Our objective here was to identify workflow inefficiencies by using time-driven activity-based costing (TDABC) and evaluate the implementation of a new clinical workflow in high-volume outpatient radiation oncology clinics. METHODS: Our quality improvement study was conducted with the Departments of GI, Genitourinary (GU), and Thoracic Radiation Oncology at a large academic cancer center and four community network sites. TDABC was used to create process maps and optimize workflow for outpatient consults. Patient encounter metrics were captured with a real-time status function in the electronic medical record. Time metrics were compared using Mann-Whitney U tests. RESULTS: Individual patient encounter data for 1,328 consults before the intervention and 1,234 afterward across all sections were included. The median overall cycle time was reduced by 21% in GI (19 minutes), 18% in GU (16 minutes), and 12% at the community sites (9 minutes). The median financial savings per consult were $52 in US dollars (USD) for the GI, $33 USD for GU, $30 USD for thoracic, and $42 USD for the community sites. Patient satisfaction surveys (from 127 of 228 patients) showed that 99% of patients reported that their providers spent adequate time with them and 91% reported being seen by a care provider in a timely manner. CONCLUSION: TDABC can effectively identify opportunities to improve clinical efficiency. Implementing workflow changes on the basis of our findings led to substantial reductions in overall encounter cycle times across several departments, as well as high patient satisfaction and significant financial savings.

Survival outcomes and toxicity of adjuvant immunotherapy after definitive concurrent chemotherapy with proton beam radiation therapy for patients with inoperable locally advanced non-small cell lung carcinoma.

INTRODUCTION: Adjuvant immunotherapy (IO) following concurrent chemotherapy and photon radiation therapy confers an overall survival (OS) benefit for patients with inoperable locally advanced non-small cell lung carcinoma (LA-NSCLC); however, outcomes of adjuvant IO after concurrent chemotherapy with proton beam therapy (CPBT) are unknown. We investigated OS and toxicity after CPBT with adjuvant IO versus CPBT alone for inoperable LA-NSCLC. MATERIALS AND METHODS: We analyzed 354 patients with LA-NSCLC who were prospectively treated with CPBT with or without adjuvant IO from 2009 to 2021. Optimal variable ratio propensity score matching (PSM) matched CPBT with CPBT + IO patients. Survival was estimated with the Kaplan-Meier method and compared with log-rank tests. Multivariable Cox proportional hazards regression evaluated the effect of IO on disease outcomes. RESULTS: Median age was 70 years; 71 (20%) received CPBT + IO and 283 (80%) received CPBT only. After PSM, 71 CPBT patients were matched with 71 CPBT + IO patients. Three-year survival rates for CPBT + IO vs CPBT were: OS 67% vs 30% (P < 0.001) and PFS 59% vs 35% (P = 0.017). Three-year LRFS (P = 0.137) and DMFS (P = 0.086) did not differ. Receipt of adjuvant IO was a strong predictor of OS (HR 0.40, P = 0.001) and PFS (HR 0.56, P = 0.030), but not LRFS (HR 0.61, P = 0.121) or DMFS (HR 0.61, P = 0.136). There was an increased incidence of grade ≥3 esophagitis in the CPBT-only group (6% CPBT + IO vs 17% CPBT, P = 0.037). CONCLUSION: This study, one of the first to investigate CPBT followed by IO for inoperable LA-NSCLC, showed that IO conferred survival benefits with no increased rates of toxicity.

Evaluation of the Protective Effect of Compound Kushen Injection Against Radiation- induced Pneumonitis in Mice.

Background Radiation-induced lung injury (RILI) via inflammation is a common adverse effect of thoracic radiation that negatively impacts patient quality of life and survival. Compound kushen injection (CKI), a botanical drug treatment, was examined for its ability to reduce RILI, and inflammatory responses and improve survival in mice exposed total lung irradiation (TLI). CKI's specific mechanisms of action were also evaluated. Methods C3H mice underwent TLI and were treated with CKI (2, 4, or 8 mL/kg) intraperitoneally once a day for 8 weeks. The effects of CKI on survival were estimated by Kaplan-Meier survival analysis and compared by log-rank test. RILI damage was evaluated by histopathology and micro-computed tomography (CT). Inflammatory cytokines and cyclooxygenase metabolites were examined by IHC staining, western blot, and ELISA. Results Pre-irradiation treatment with 4 or 8 mL/kg CKI starting 2 weeks before TLI or concurrent treatment with 8 mL/kg CKI were associated with a significantly longer survival compared with TLI vehicle-treated group ( P < 0.05). Micro-CT images evaluations showed that concurrent treatment with 8 mL/kg CKI was associated with significantly lower incidence of RILI ( P < 0.05). Histological evaluations revealed that concurrent TLI treatment of CKI (4 and 8 mL/kg) significantly reduced lung inflammation (p < 0.05). Mechanistic investigation showed that at 72 hours after radiation, TLI plus vehicle mice had significantly elevated serum IL6, IL17A, and TGF-ß levels compared with non-irradiated, age-matched normal mice; in contrast, levels of these cytokines in mice that received TLI plus CKI treatment were lower than those in the TLI plus vehicle-treated mice ( P < 0.05) and similar to the nonirradiated mice. IHC staining showed that the CKI treatment led to a reduction of TGF-ß positive cells in the lung tissues of TLI mice (P < 0.01). The concurrent CKI with TLI treatment group had a significant reduction in COX-2 activity and COX-2 metabolites compared with the TLI vehicle-treated group ( P < 0.05). Conclusions These data suggest that CKI treatment was associated with reduced radiation-induced inflammation in lung tissues, reduced RILI, and improved survival. Further investigation of CKI in human clinical trials as a potential radioprotector against RILI to improve patients' quality of life and survival is warranted.

Prediction of radiation pneumonitis using the effective α/ß of lungs and heart in NSCLC patients treated with proton beam therapy.

PURPOSE: Radiation pneumonitis (RP) remains a major complication in non-small cell lung cancer (NSCLC) patients undergoing radiochemotherapy (RCHT). Traditionally, the mean lung dose (MLD) and the volume of the total lung receiving at least 20 Gy (V20Gy) are used to predict RP in patients treated with normo-fractionated photon therapy. However, other models, including the actual dose-distribution in the lungs using the effective α/ß model or a combination of radiation doses to the lungs and heart, have been proposed for predicting RP. Moreover, the models established for photons may not hold for patients treated with passively-scattered proton therapy (PSPT). Therefore, we here tested and validated novel predictive parameters for RP in NSCLC patient treated with PSPT. METHODS: Data on the occurrence of RP, structure files and dose-volume histogram parameters for lungs and heart of 96 NSCLC patients, treated with PSPT and concurrent chemotherapy, was retrospectively retrieved from prospective clinical studies of two international centers. Data was randomly split into a training set (64 patients) and a validation set (32 patients). Statistical analyses were performed using binomial logistic regression. RESULTS: The biologically effective dose (BED) of the'lungs - GTV' significantly predicted RP ≥ grade 2 in the training-set using both a univariate model (p = 0.019, AUCtrain = 0.72) and a multivariate model in combination with the effective α/ß parameter of the heart (pBED = 0.006, [Formula: see text] = 0.043, AUCtrain = 0.74). However, these results did not hold in the validation-set (AUCval = 0.52 andAUCval = 0.50, respectively). Moreover, these models were found to neither outperform a model built with the MLD (p = 0.015, AUCtrain = 0.73, AUCval = 0.51), nor a multivariate model additionally including the V20Gy of the heart (pMLD = 0.039, pV20Gy,heart = 0.58, AUCtrain = 0.74, AUCval = 0.53). CONCLUSION: Using the effective α/ß parameter of the lungs and heart we achieved similar performance to commonly used models built for photon therapy, such as MLD, in predicting RP ≥ grade 2. Therefore, prediction models developed for photon RCHT still hold for patients treated with PSPT.

Surgery versus radiotherapy for limited-stage small cell esophageal carcinoma: a multicenter, retrospective, cohort study in China (ChiSCEC).

BACKGROUND: There is no standard management for small cell esophageal carcinoma (SCEC). The purpose of this multicenter, retrospective study (ChiSCER) was to investigate the treatment, outcomes, and risk factors impacting survival endpoints in patients with limited-stage SCEC (LS-SCEC). MATERIALS AND METHODS: Consecutive patients with LS-SCEC from 14 institutions between 2000 and 2020 in China were enrolled. Survival curves were constructed using the Kaplan-Meier method and compared using a log-rank test. Univariate and multivariate Cox regression models and propensity score matching (PSM) analysis were adopted in the prognostic analysis. Results were reported as hazard ratio (HR), 95% confidence interval (CI), and P value. Statistical significance was set as P value <0.05 in a two-tailed test. RESULTS: Among 458 LS-SCEC patients, the median age was 63 [interquartile range (IQR), 57-68] years, and 318 (69%) were males. Eighty-four (18%), 167 (36%), and 207 (45%) patients received chemotherapy (CT) alone, CT plus definitive radiotherapy (CT+RT), and CT plus radical surgery (CT+S), respectively. With a median follow-up time of 58.7 (95% CI 48.9-68.6) months, the median overall survival (OS) and 3-year OS rate for all patients 24.3 (95% CI 21.6-27) months and 37.3% (95% CI 32.8-42.5%), respectively. Multivariate analysis indicated that treatment modes, Karnofsky performance status (KPS), TNM stage, and CT cycle were independent prognostic factors for OS ( P <0.05). Compared with CT alone, patients treated with CT+RT (HR 0.57, 95% CI 0.41-0.8, P =0.001) or CT+S (HR 0.59, 95% CI 0.42-0.82, P =0.002) had an improved OS, with no significant survival differences between CT+S and CT+RT groups after multivariate and PSM analyses ( P >0.05). Subgroup analysis indicated that compared with CT+RT, patients with tumor location at lower 1/3 (HR 0.59, 95% CI 0.37-0.93, P =0.03) or tumor length >5 cm (HR 0.52, 95% CI 0.3-0.9, P =0.02) could obtain significant OS benefit from CT+S. Patients with tumor location at middle 1/3 (HR 1.55, 95% CI 1.03-2.36, P =0.04) or tumor length ≤5 cm (HR 1.49, 95% CI 1.02-2.17, P =0.04) favored CT+RT. Distant metastasis accounted for 73.7% of all treatment failures after multidisciplinary treatments. CONCLUSION: Surgery and RT were equally effective local therapies for patients with LS-SCEC. The personalized decision of local therapy should be made after comprehensive considerations on tumor location, length, comorbidities, and organ preservation.

Severe Lymphopenia During Chemoradiation Therapy for Esophageal Cancer: Comprehensive Analysis of Randomized Phase 2B Trial of Proton Beam Therapy Versus Intensity Modulated Radiation Therapy.

PURPOSE: Lymphocytes play an important role in antitumor immunity; however, they are also especially vulnerable to depletion during chemoradiation therapy (CRT). The purpose of this study was to compare the incidence of grade 4 lymphopenia (G4L) between proton beam therapy (PBT) and intensity modulated photon radiation therapy (IMRT) in patients with esophageal cancer treated with CRT in a completed randomized trial and to ascertain patient heterogeneity to G4L risk based on treatment and established prognostic factors. METHODS AND MATERIALS: Between April 2012 and March 2019, a single-institution, open-label, nonblinded, phase 2 randomized trial (NCT01512589) was conducted at the University of Texas MD Anderson Cancer Center. Patients were randomly assigned to IMRT or PBT, either definitively or preoperatively. This secondary analysis of the randomized trial was G4L during concurrent CRT according to Common Terminology Criteria for Adverse Events version 5.0. RESULTS: Among 105 patients evaluable for analysis, 44 patients (42%) experienced G4L at a median of 28 days after the start date of concurrent CRT. Induction chemotherapy (P = .003), baseline absolute lymphocyte count (P < .001), radiation therapy modality (P = .002), and planning treatment volume (P = .033) were found to be significantly associated with G4L. Multivariate classification analysis partitioned patients into 5 subgroups for whom the incidence of G4L was observed in 0%, 14%, 35%, 70%, and 100% of patients. The benefit of PBT over IMRT was most pronounced in patients with an intermediate baseline absolute lymphocyte count and large planning treatment volume (P = .011). CONCLUSIONS: This is the first prospective evidence that limiting dose scatter by PBT significantly reduced the incidence of G4L, especially in the intermediate-risk patients. The implication of this immune-sparing effect of PBT, especially in the context of standard adjuvant immunotherapy, needs further examination in the current phase 3 randomized trials.

Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy.

PURPOSE: The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS: Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses. RESULTS: Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|. CONCLUSIONS: With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.

Deep learning-based automatic segmentation of cardiac substructures for lung cancers.

PURPOSE: Accurate and comprehensive segmentation of cardiac substructures is crucial for minimizing the risk of radiation-induced heart disease in lung cancer radiotherapy. We sought to develop and validate deep learning-based auto-segmentation models for cardiac substructures. MATERIALS AND METHODS: Nineteen cardiac substructures (whole heart, 4 heart chambers, 6 great vessels, 4 valves, and 4 coronary arteries) in 100 patients treated for non-small cell lung cancer were manually delineated by two radiation oncologists. The valves and coronary arteries were delineated as planning risk volumes. An nnU-Net auto-segmentation model was trained, validated, and tested on this dataset with a split ratio of 75:5:20. The auto-segmented contours were evaluated by comparing them with manually drawn contours in terms of Dice similarity coefficient (DSC) and dose metrics extracted from clinical plans. An independent dataset of 42 patients was used for subjective evaluation of the auto-segmentation model by 4 physicians. RESULTS: The average DSCs were 0.95 (+/- 0.01) for the whole heart, 0.91 (+/- 0.02) for 4 chambers, 0.86 (+/- 0.09) for 6 great vessels, 0.81 (+/- 0.09) for 4 valves, and 0.60 (+/- 0.14) for 4 coronary arteries. The average absolute errors in mean/max doses to all substructures were 1.04 (+/- 1.99) Gy and 2.20 (+/- 4.37) Gy. The subjective evaluation revealed that 94% of the auto-segmented contours were clinically acceptable. CONCLUSION: We demonstrated the effectiveness of our nnU-Net model for delineating cardiac substructures, including coronary arteries. Our results indicate that this model has promise for studies regarding radiation dose to cardiac substructures.

Metformin in Conjunction With Stereotactic Radiotherapy for Early-stage Non-small Cell Lung Cancer: Long-term Results of a Prospective Phase II Clinical Trial.

BACKGROUND/AIM: Non-small cell lung cancer (NSCLC) is increasingly detected in early stages and there is interest in improving outcomes with stereotactic body radiotherapy (SBRT). As metformin affects NSCLC signaling pathways, it might alter the metabolism of NSCLC treated with SBRT. This study investigated the long-term outcomes of a phase II clinical trial evaluating metformin in conjunction with SBRT for early-stage NSCLC. PATIENTS AND METHODS: The trial evaluated patients with American Joint Commission on Cancer (AJCC) 7th edition Stage I-II, cT1-T2N0M0 NSCLC who were randomized 6:1 to receive metformin versus placebo in conjunction with SBRT. The outcomes analyzed included local failure (LF), progression-free survival (PFS), overall survival (OS), and Common Terminology Criteria for Adverse Events (CTCAE) version 4 toxicities. RESULTS: There were 14 patients randomized to the metformin arm and one to the placebo. Median follow-up was four years. In the metformin group, the median PFS was 4.65 years [95% confidence interval (CI)=0.31-5.93] and median survival was 4.97 years (95%CI=3.05-4.61). Five year PFS was 27.8% (95%CI=5.3-57.3%) and OS was 46.0% (95%CI=16.0-71.9%). The one patient randomized to placebo was alive and without progression at five years. There were no LFs in the primary SBRT treatment volumes and no CTCAE version 4 Grade ≥3 adverse events. CONCLUSION: Outcomes of SBRT and metformin for early-stage NSCLC were similar to historic controls. These findings along with the results of the NRG-LU001 and OCOG randomized trials do not support the therapeutic use of metformin for NSCLC.

Stereotactic body proton therapy for early stage non-small cell lung cancer - Technical challenges and solutions: The MD Anderson experience.

Our randomized clinical study comparing stereotactic body radiotherapy (SBRT) and stereotactic body proton therapy (SBPT) for early stage non-small cell lung cancer (NSCLC) was closed prematurely owing to poor enrollment, largely because of lack of volumetric imaging and difficulty in obtaining insurance coverage for the SBPT group. In this article, we describe technology improvements in our new proton therapy center, particularly in image guidance with cone beam CT (CBCT) and CT on rail (CTOR), as well as motion management with real-time gated proton therapy (RGPT) and optical surface imaging. In addition, we have a treatment planning system that provides better treatment plan optimization and more accurate dose calculation. We expect to re-start the SBPT program, including for early stage NSCLC as well as for other disease sites soon after starting patient treatment at our new proton therapy center.

Explainable Artificial Intelligence to Identify Dosimetric Predictors of Toxicity in Patients with Locally Advanced Non-Small Cell Lung Cancer: A Secondary Analysis of RTOG 0617.

PURPOSE: Dosimetric predictors of toxicity in patients treated with definitive chemoradiation for locally advanced non-small cell lung cancer are often identified through trial and error. This study used machine learning (ML) and explainable artificial intelligence to empirically characterize dosimetric predictors of toxicity in patients treated as part of a prospective clinical trial. METHODS AND MATERIALS: A secondary analysis of the Radiation Therapy Oncology Group (RTOG) 0617 trial was performed. Multiple ML models were trained to predict grade ≥3 pulmonary, cardiac, and esophageal toxicities using clinical and dosimetric features. Model performance was evaluated using the area under the curve (AUC). The best performing model for each toxicity was explained using the Shapley Additive Explanation (SHAP) framework; SHAP values were used to identify relevant dosimetric thresholds and were converted to odds ratios (ORs) with confidence intervals (CIs) generated using bootstrapping to obtain quantitative measures of risk. Thresholds were validated using logistic regression. RESULTS: The best-performing models for pulmonary, cardiac, and esophageal toxicities, outperforming logistic regression, were extreme gradient boosting (AUC, 0.739), random forest (AUC, 0.706), and naive Bayes (AUC, 0.721), respectively. For pulmonary toxicity, thresholds of a mean dose >18 Gy (OR, 2.467; 95% CI, 1.049-5.800; P = .038) and lung volume receiving ≥20 Gy (V20) > 37% (OR, 2.722; 95% CI, 1.034-7.163; P = .043) were identified. For esophageal toxicity, thresholds of a mean dose >34 Gy (OR, 4.006; 95% CI, 2.183-7.354; P < .001) and V20 > 37% (OR, 3.725; 95% CI, 1.308-10.603; P = .014) were identified. No significant thresholds were identified for cardiac toxicity. CONCLUSIONS: In this data set, ML approaches validated known dosimetric thresholds and outperformed logistic regression at predicting toxicity. Furthermore, using explainable artificial intelligence, clinically useful dosimetric thresholds might be identified and subsequently externally validated.

Stereotactic ablative radiotherapy with or without immunotherapy for early-stage or isolated lung parenchymal recurrent node-negative non-small-cell lung cancer: an open-label, randomised, phase 2 trial.

BACKGROUND: Stereotactic ablative radiotherapy (SABR) is the standard treatment for medically inoperable early-stage non-small-cell lung cancer (NSCLC), but regional or distant relapses, or both, are common. Immunotherapy reduces recurrence and improves survival in people with stage III NSCLC after chemoradiotherapy, but its utility in stage I and II cases is unclear. We therefore conducted a randomised phase 2 trial of SABR alone compared with SABR with immunotherapy (I-SABR) for people with early-stage NSCLC. METHODS: We did an open-label, randomised, phase 2 trial comparing SABR to I-SABR, conducted at three different hospitals in TX, USA. People aged 18 years or older with histologically proven treatment-naive stage IA-IB (tumour size ≤4 cm, N0M0), stage IIA (tumour size ≤5 cm, N0M0), or stage IIB (tumour size >5 cm and ≤7 cm, N0M0) as per the American Joint Committee on Cancer version 8 staging system or isolated parenchymal recurrences (tumour size ≤7 cm) NSCLC (TanyNanyM0 before definitive surgery or chemoradiotherapy) were included in this trial. Participants were randomly assigned (1:1; using the Pocock & Simon method) to receive SABR with or without four cycles of nivolumab (480 mg, once every 4 weeks, with the first dose on the same day as, or within 36 h after, the first SABR fraction). This trial was unmasked. The primary endpoint was 4-year event-free survival (local, regional, or distant recurrence; second primary lung cancer; or death). Analyses were both intention to treat (ITT) and per protocol. This trial is registered with ClinicalTrials.gov (NCT03110978) and is closed to enrolment. FINDINGS: From June 30, 2017, to March 22, 2022, 156 participants were randomly assigned, and 141 participants received assigned therapy. At a median 33 months' follow-up, I-SABR significantly improved 4-year event-free survival from 53% (95% CI 42-67%) with SABR to 77% (66-91%; per-protocol population, hazard ratio [HR] 0·38; 95% CI 0·19-0·75; p=0·0056; ITT population, HR 0·42; 95% CI 0·22-0·80; p=0·0080). There were no grade 3 or higher adverse events associated with SABR. In the I-SABR group, ten participants (15%) had grade 3 immunologial adverse events related to nivolumab; none had grade 3 pneumonitis or grade 4 or higher toxicity. INTERPRETATION: Compared with SABR alone, I-SABR significantly improved event-free survival at 4 years in people with early-stage treatment-naive or lung parenchymal recurrent node-negative NSCLC, with tolerable toxicity. I-SABR could be a treatment option in these participants, but further confirmation from a number of currently accruing phase 3 trials is required. FUNDING: Bristol-Myers Squibb and MD Anderson Cancer Center Alliance, National Cancer Institute at the National Institutes of Health through Cancer Center Core Support Grant and Clinical and Translational Science Award to The University of Texas MD Anderson Cancer Center.

Consensus Quality Measures and Dose Constraints for Lung Cancer From the Veterans Affairs Radiation Oncology Quality Surveillance Program and ASTRO Expert Panel.

PURPOSE: For patients with lung cancer, it is critical to provide evidence-based radiation therapy to ensure high-quality care. The US Department of Veterans Affairs (VA) National Radiation Oncology Program partnered with the American Society for Radiation Oncology (ASTRO) as part of the VA Radiation Oncology Quality Surveillance to develop lung cancer quality metrics and assess quality of care as a pilot program in 2016. This article presents recently updated consensus quality measures and dose-volume histogram (DVH) constraints. METHODS AND MATERIALS: A series of measures and performance standards were reviewed and developed by a Blue-Ribbon Panel of lung cancer experts in conjunction with ASTRO in 2022. As part of this initiative, quality, surveillance, and aspirational metrics were developed for (1) initial consultation and workup; (2) simulation, treatment planning, and treatment delivery; and (3) follow-up. The DVH metrics for target and organ-at-risk treatment planning dose constraints were also reviewed and defined. RESULTS: Altogether, a total of 19 lung cancer quality metrics were developed. There were 121 DVH constraints developed for various fractionation regimens, including ultrahypofractionated (1, 3, 4, or 5 fractions), hypofractionated (10 and 15 fractionations), and conventional fractionation (30-35 fractions). CONCLUSIONS: The devised measures will be implemented for quality surveillance for veterans both inside and outside of the VA system and will provide a resource for lung cancer-specific quality metrics. The recommended DVH constraints serve as a unique, comprehensive resource for evidence- and expert consensus-based constraints across multiple fractionation schemas.

Randomized Phase 2 Placebo-Controlled Trial of Nintedanib for the Treatment of Radiation Pneumonitis.

PURPOSE: Radiation pneumonitis (RP) is the most common dose-limiting toxicity for thoracic radiation therapy. Nintedanib is used for the treatment of idiopathic pulmonary fibrosis, which shares pathophysiological pathways with the subacute phase of RP. Our goal was to investigate the efficacy and safety of nintedanib added to a prednisone taper compared with a prednisone taper alone in reducing pulmonary exacerbations in patients with grade 2 or higher (G2+) RP. METHODS AND MATERIALS: In this phase 2, randomized, double-blinded, placebo-controlled trial, patients with newly diagnosed G2+ RP were randomized 1:1 to nintedanib or placebo in addition to a standard 8-week prednisone taper. The primary endpoint was freedom from pulmonary exacerbations at 1 year. Secondary endpoints included patient-reported outcomes and pulmonary function tests. Kaplan-Meier analysis was used to estimate the probability of freedom from pulmonary exacerbations. The study was closed early due to slow accrual. RESULTS: Thirty-four patients were enrolled between October 2015 and February 2020. Of 30 evaluable patients, 18 were randomized to the experimental Arm A (nintedanib + prednisone taper) and 12 to the control Arm B (placebo + prednisone taper). Freedom from exacerbation at 1 year was 72% (confidence interval, 54%-96%) in Arm A and 40% (confidence interval, 20%-82%) in Arm B (1-sided, P = .037). In Arm A, there were 16 G2+ adverse events possibly or probably related to treatment compared with 5 in the placebo arm. There were 3 deaths during the study period in Arm A due to cardiac failure, progressive respiratory failure, and pulmonary embolism. CONCLUSIONS: There was an improvement in pulmonary exacerbations by the addition of nintedanib to a prednisone taper. Further investigation is warranted for the use of nintedanib for the treatment of RP.

Implementation of Machine Learning Models to Ensure Radiotherapy Quality for Multicenter Clinical Trials: Report from a Phase III Lung Cancer Study.

The outcome of the patient and the success of clinical trials involving RT is dependent on the quality assurance of the RT plans. Knowledge-based Planning (KBP) models using data from a library of high-quality plans have been utilized in radiotherapy to guide treatment. In this study, we report on the use of these machine learning tools to guide the quality assurance of multicenter clinical trial plans. The data from 130 patients submitted to RTOG1308 were included in this study. Fifty patient cases were used to train separate photon and proton models on a commercially available platform based on principal component analysis. Models evaluated 80 patient cases. Statistical comparisons were made between the KBP plans and the original plans submitted for quality evaluation. Both photon and proton KBP plans demonstrate a statistically significant improvement of quality in terms of organ-at-risk (OAR) sparing. Proton KBP plans, a relatively emerging technique, show more improvements compared with photon plans. The KBP proton model is a useful tool for creating proton plans that adhere to protocol requirements. The KBP tool was also shown to be a useful tool for evaluating the quality of RT plans in the multicenter clinical trial setting.

Benchmarking Outcomes for Molecularly Characterized Synchronous Oligometastatic Non-Small-Cell Lung Cancer Reveals EGFR Mutations to Be Associated With Longer Overall Survival.

PURPOSE: Local consolidative therapy (LCT) for patients with synchronous oligometastatic non-small-cell lung cancer is an evolving treatment strategy, but outcomes following LCT stratified by genetic mutations have not been reported. We sought to identify genomic associations with overall survival (OS) and progression-free survival (PFS) for these patients. METHODS: We identified all patients presenting between 2000 and 2017 with stage IV non-small-cell lung cancer and ≤ 3 synchronous metastatic sites. Patients were grouped according to mutational statuses. Primary outcomes included OS and PFS following initial diagnosis. RESULTS: Of 194 included patients, 121 received comprehensive LCT to all sites of disease with either surgery or radiation. TP53 mutations were identified in 40 of 78 (55%), KRAS in 32 of 95 (34%), EGFR in 24 of 109 (22%), and STK11 in nine of 77 (12%). At median follow-up of 96 months, median OS and PFS were 26 (95% CI, 23 to 31) months and 11 (95% CI, 9 to 13) months, respectively. On multivariable analysis, patients with EGFR mutations had lower mortality risk (hazard ratio [HR], 0.53; 95% CI, 0.29 to 0.98; P = .044) compared with wild-type patients, and patients with STK11 mutations had higher risk of progression or mortality (HR, 2.32; 95% CI, 1.12 to 4.79; P = .023) compared with wild-type patients. TP53 and KRAS mutations were not associated with OS or PFS. Among 71 patients with known EGFR mutational status who received comprehensive LCT, EGFR mutations were associated with lower mortality compared with wild-type (HR, 0.45; 95% CI, 0.22 to 0.94; P = .032). CONCLUSION: When compared with wild-type patients, those with EGFR and STK11 mutations had longer OS and shorter PFS, respectively. EGFR mutations were associated with longer OS among oligometastatic patients treated with comprehensive LCT in addition to systemic therapy.

Optimization of mesh generation for geometric accuracy, robustness, and efficiency of biomechanical-model-based deformable image registration.

BACKGROUND: Successful generation of biomechanical-model-based deformable image registration (BM-DIR) relies on user-defined parameters that dictate surface mesh quality. The trial-and-error process to determine the optimal parameters can be labor-intensive and hinder DIR efficiency and clinical workflow. PURPOSE: To identify optimal parameters in surface mesh generation as boundary conditions for a BM-DIR in longitudinal liver and lung CT images to facilitate streamlined image registration processes. METHODS: Contrast-enhanced CT images of 29 colorectal liver cancer patients and end-exhale four-dimensional CT images of 26 locally advanced non-small cell lung cancer patients were collected. Different combinations of parameters that determine the triangle mesh quality (voxel side length and triangle edge length) were investigated. The quality of DIRs generated using these parameters was evaluated with metrics for geometric accuracy, robustness, and efficiency. Metrics for geometric accuracy included target registration error (TRE) of internal vessel bifurcations, dice similar coefficient (DSC), mean distance to agreement (MDA), Hausdorff distance (HD) for organ contours, and number of vertices in the triangle mesh. American Association of Physicists in Medicine Task Group 132 was used to ensure parameters met TRE, DSC, MDA recommendations before the comparison among the parameters. Robustness was evaluated as the success rate of DIR generation, and efficiency was evaluated as the total time to generate boundary conditions and compute finite element analysis. RESULTS: Voxel side length of 0.2 cm and triangle edge length of 3 were found to be the optimal parameters for both liver and lung, with success rate of 1.00 and 0.98 and average DIR computation time of 100 and 143 s, respectively. For this combination, the average TRE, DSC, MDA, and HD were 0.38-0.40, 0.96-0.97, 0.09-0.12, and 0.87-1.17 mm, respectively. CONCLUSION: The optimal parameters were found for the analyzed patients. The decision-making process described in this study serves as a recommendation for BM-DIR algorithms to be used for liver and lung. These parameters can facilitate consistence in the evaluation of published studies and more widespread utilization of BM-DIR in clinical practice.