Neoadjuvant immunotherapy has emerged as a promising strategy for patients with resectable esophageal cancer (EC), yet the optimal therapeutic approach remains unclear. We performed an updated network meta-analysis to compare the efficacy and safety of current neoadjuvant immunotherapy-based strategies using the most recent prospective randomized controlled trials. Eligible studies published before August 1, 2025 were systematically identified, and a Bayesian network meta-analysis with Markov chain Monte Carlo methods was conducted. The primary endpoints were pathological complete response (pCR) and major pathological response (mPR), while secondary outcomes included objective response rate (ORR), R0 resection rate, grade ≥ 3 treatment-related adverse events (tr-AEs), and survival outcomes. A total of 41 trials involving 4624 patients were included. Neoadjuvant immunotherapy combined with chemoradiotherapy followed by surgery (nICRT+S) achieved the highest pCR rate and demonstrated superior efficacy compared with other neoadjuvant approaches. Neoadjuvant immunotherapy combined with chemotherapy (nICT+S) showed the greatest probability of ranking first in improving mPR and ORR. Importantly, the addition of immunotherapy or radiotherapy did not increase the incidence of grade ≥ 3 tr-AEs, and maintenance immunotherapy after surgery did not confer additional survival benefit based on the currently available evidence with limited follow-up. Overall, these findings indicate that nI(C)RT+S may represent the most effective neoadjuvant strategy for patients with resectable EC, offering improved tumor response without increasing treatment-related toxicity.

Advanced esophageal cancer is characterized by poor prognosis and high recurrence rates, even after receiving standard radical treatments. Current treatment guidelines primarily recommend systemic therapy combined with palliative and supportive care for advanced esophageal cancer, particularly in patients with limited functional status. Despite the recent advances, including the introduction of immunotherapy, overall survival of these patients remains limited. Radiotherapy, traditionally used for palliative purposes, is increasingly being explored as an adjunct to systemic therapies, including chemotherapy and immunotherapy, to improve clinical outcomes. This review aims to examine the current literature on the role of radiotherapy combined with systemic therapies for advanced esophageal cancer. The impact of these combined approaches on overall survival (OS), progression-free survival (PFS), and quality of life was analyzed, focusing on patient selection criteria, optimal treatment strategies, and the timing of radiotherapy administration. Studies have shown that radiotherapy combined with systemic therapy may offer survival benefits, particularly in patients with oligometastasis or limited metastatic disease. Furthermore, the synergistic effects of radiotherapy combined with immunotherapy are promising. However, the impact of such a combination need to be further investigated. Nonetheless, various challenges, including lack of consensus on optimal radiotherapy protocols, appropriate sequencing with systemic treatments, and identification of patient populations most likely to benefit, limit the application of this combination therapy. Therefore, large-scale prospective clinical trials are needed to validate these approaches and refine treatment guidelines for improved prognosis and management of patients with advanced esophageal cancer.

Radiotherapy is a crucial part of cancer treatment that applies to over 50 % of cancer patients. However, its administration could inadvertently damage healthy tissues, such as radiation-induced heart damage (RIHD), when thoracic radiation is implemented. Myocardial fibrosis is a key feature of RIHD that is characterized by excessive extracellular matrix (ECM) protein accumulation, resulting in cardiac stiffness and dysfunction. Macrophage pyroptosis, which is triggered by radiation, leads to the release of inflammatory mediators IL-1 beta and IL-18, which are crucial in inflammatory response and fibrosis. In this study, apoptosis-associated speck-like protein containing a CARD domain (ASC)-overexpressing RAW264.7 cells were exposed to 2 Gy, 4 Gy, and 8 Gy radiation to assess macrophage pyroptosis. Both IL-1 beta and IL-18 levels increased dose-dependently, peaking at 8 Gy. Similarly, LDH activity, which is a pyroptosis indicator, increases dose-dependently. Higher radiation dosages increased ASC specks. NLRP3, cleaved-caspase1 (P20), and GSDMD-N protein levels increased considerably in irradiation groups. Since macrophage pyroptosis promotes inflammation, it was investigated whether irradiated macrophages could cause cardiac fibroblast fibrosis. In co-culture with irradiated macrophages, cardiac fibroblasts showed dose-dependent elevation of fibrotic markers alpha-SMA and Collagen I. Blocking NLRP3-mediated pyroptosis by MCC950 in macrophages and found significant decreases in pyroptotic indicators, fibrosis markers, and Hh pathway activation in co-cultured fibroblasts. The activation of Hedgehog signaling in fibroblasts with Jervine successfully reverses fibrotic alterations caused by macrophage pyroptosis, as evidenced by decreased alpha-SMA, Collagen I, Shh, Smo, and Gli1 levels. These findings emphasize macrophage pyroptosis in radiation-induced cardiac fibrosis and identify NLRP3 and Hh pathway therapeutic targets. Collectively, targeting macrophage pyroptosis and the Hh pathway could offer new therapeutic avenues for preventing myocardial fibrosis in RIHD.

PURPOSE:Neoadjuvant chemoradiotherapy (nCRT) followed by esophagectomy remains standard for locally advanced esophageal squamous cell carcinoma (ESCC). However, accurately predicting pathological complete response (pCR) and treatment outcomes remains challenging. This study aimed to develop and validate a multidimensional deep ensemble learning model (DELRN) using pretreatment CT imaging to predict pCR and stratify prognostic risk in ESCC patients undergoing nCRT. METHODS:In this multicenter, retrospective cohort study, 485 ESCC patients were enrolled from four hospitals (May 2009-August 2023, December 2017-September 2021, May 2014-September 2019, and March 2013-July 2019). Patients were divided into a discovery cohort (n = 194), an internal cohort (n = 49), and three external validation cohorts (n = 242). A multidimensional deep ensemble learning model (DELRN) integrating radiomics and 3D convolutional neural networks was developed based on pretreatment CT images to predict pCR and clinical outcomes. The model's performance was evaluated by discrimination, calibration, and clinical utility. Kaplan-Meier analysis assessed overall survival (OS) and disease-free survival (DFS) at two follow-up centers. RESULTS:The DELRN model demonstrated robust predictive performance for pCR across the discovery, internal, and external validation cohorts, with area under the curve (AUC) values of 0.943 (95 % CI: 0.912-0.973), 0.796 (95 % CI: 0.661-0.930), 0.767 (95 % CI: 0.646-0.887), 0.829 (95 % CI: 0.715-0.942), and 0.782 (95 % CI: 0.664-0.900), respectively, surpassing single-domain radiomics or deep learning models. DELRN effectively stratified patients into high-risk and low-risk groups for OS (log-rank P = 0.018 and 0.0053) and DFS (log-rank P = 0.00042 and 0.035). Multivariate analysis confirmed DELRN as an independent prognostic factor for OS and DFS. CONCLUSION:The DELRN model demonstrated promising clinical potential as an effective, non-invasive tool for predicting nCRT response and treatment outcome in ESCC patients, enabling personalized treatment strategies and improving clinical decision-making with future prospective multicenter validation.

The ADAURA study indicated that adjuvant TKI therapy improves survival in postoperative patients with EGFR-mutated (EGFRm) non-small-cell lung cancer (NSCLC), especially in stage III disease. However, the effect of PORT for stage III (N2) NSCLC with different EGFR statuses remains unclear, which we aimed to investigate in the present study. Between 2006 and 2019, consecutive patients with pN2 non-squamous cell NSCLC (Nsq-NSCLC) after complete resection and adjuvant chemotherapy or EGFR tyrosine kinase inhibitor (TKI) who had detection of EGFR status were retrospectively analyzed. PORT was administered using IMRT at 2 Gy per fraction with a total dose of 50 Gy over 5 weeks. Patients were categorized into 4 groups according to EGFR status and treatment: EGFR wild-type (EGFRwt) PORT group, EGFRwt non-PORT group, EGFRm PORT group, and EGFRm non-PORT group. Propensity score matching (PSM) was used to compensate for differences in baseline characteristics. The Kaplan-Meier method and log-rank test were used to evaluate disease-free survival (DFS), locoregional relapse-free survival (LRFS), and distant metastasis-free survival (DMFS). A total of 566 patients were enrolled: 90 in the EGFRwt PORT group, 154 in the EGFRwt non-PORT group, 111 in the EGFRm PORT group, and 211 in the EGFRm non-PORT group. After PSM, the median DFS in the EGFRwt PORT group versus the EGFRwt non-PORT group were 33.9 versus 17.2 months (HR 0.62, 95

PURPOSE:Neoadjuvant immunochemoradiation therapy (nICRT) is emerging as a promising treatment for resectable esophageal cancer, but comprehensive analyses comparing it with standard neoadjuvant chemoradiation therapy (nCRT) are limited. This meta-analysis aimed to evaluate the efficacy, safety, and survival outcomes of nICRT versus nCRT. METHODS AND MATERIALS:A systematic search of PubMed, Embase, the Cochrane Library, and major conference proceedings up to October 30, 2024, identified studies involving resectable esophageal cancer treated with nICRT or nCRT. Data on pathologic complete response, major pathologic response, treatment-related adverse events, overall survival (OS), and progression-free survival were extracted. A one-stage meta-analysis using reconstructed individual patient data was performed, calculating hazard ratios with 95% CIs. RESULTS:Thirty-seven studies were included, comprising 811 patients treated with nICRT and 1796 with nCRT. nICRT demonstrated significantly longer OS than nCRT (hazard ratio, 0.714; 95% CI, 0.550-0.926; P = .011). The 1-, 2-, and 3-year OS rates were 89.9%, 76.0% and 66.4%, respectively, for nICRT, compared with 85.0%, 66.5%, and 57.3% for nCRT. The pathologic complete response rate was significantly higher in nICRT (50% vs 38%; P = .040) for squamous cell carcinoma. Safety profiles were comparable, with no significant differences in grades 3 and 4 treatment-related adverse events or postoperative complications between the groups. CONCLUSIONS:nICRT showed potential for superior survival compared with standard nCRT in resectable esophageal cancer and showed enhanced pathologic response in squamous cell carcinoma, with a possibly acceptable safety profile. These findings support future trials integrating immunotherapy into neoadjuvant treatment regimens for esophageal cancer.

Background:The PORT-C trial was the first published phase III randomized clinical trial (RCT) to evaluate the role of postoperative radiotherapy (PORT) using intensity-modulated radiation therapy (IMRT)/three-dimensional conformal radiation therapy (3D-CRT) in patients with resected pIIIA-N2 non-small-cell lung cancer (NSCLC). We aimed to assess the long-term outcomes of this RCT. Methods:Patients with pIIIA-N2 NSCLC treated with complete resection followed by four cycles of platinum-based chemotherapy between January 1, 2009 and December 31, 2017 were randomly assigned in a 1:1 ratio to PORT or observation. Radiotherapy was delivered using a 6 MV-X ray linear accelerator via 3D-CRT or IMRT, with 2 Gy per fraction up to 50 Gy over 5 weeks. The primary endpoint was disease-free survival (DFS), analyzed using modified intent-to-treat (mITT). Secondary endpoints included overall survival (OS), locoregional recurrence-free survival (LRFS), distant metastasis-free survival (DMFS), and toxic effects. DFS, OS, LRFS, and DMFS rates were estimated using Kaplan-Meier method, compared using log-rank test, and modeled using Cox proportional hazards method. The patterns of first failures were analyzed using competing risk analyses. This trial is registered with ClinicalTrials.gov, NCT00880971. Findings:Overall, 394 patients were randomly allocated to the PORT (n = 184) or observation (n = 180) arms. The median follow-up time was 87.9 months (interquartile range [IQR] 72.2-113.8). In the mITT analyses, DFS showed no significant difference between the PORT and observation arms (hazards ratio [HR], 0.90; 95% CI, 0.70-1.12; p = 0.39). The 5-year DFS rates in the PORT and observation arms were 36% (95% confidence interval [CI], 28.9%-43.1%) and 31.5% (95% CI, 24.6%-38.4%), respectively; the 5-year OS rates were 64.7% (95% CI, 57.6%-71.8%) and 70.4% (95% CI, 63.5%-77.3%), respectively (p = 0.20). In the per-protocol analyses, 140 and 170 patients were included in the PORT and observation arms, respectively. PORT did not significantly improve DFS (HR, 0.80; 95% CI, 0.61-1.05; p = 0.11) or OS (HR, 1.09; 95% CI, 0.77-1.53; p = 0.63). Most patients died of cancer. However, more deaths due to cardiopulmonary disease were observed in the PORT arm than in the observation arm (6/184, 3.3% vs. 2/180, 1.1%). The first failure of locoregional recurrence (LR)-only was significantly lower in the PORT arm than in the observation arm (10.9%, 95% CI, 6.8%-15.9% vs. 18.9%, 95% CI, 14.0%-26.2%, p = 0.031). Only 1 patient (0.5%) in the PORT arm had grade 3 radiation pneumonitis. No radiotherapy-related grade 4 or 5 AEs were observed. Interpretation:The long-term results of the PORT-C trial indicated no DFS benefit from receiving PORT for patients with pIIIA-N2 NSCLC after complete resection and adjuvant chemotherapy. Funding:National Key R&D Program of China, National Natural Science Foundation of China, Capital's Funds for Health Improvement and Research, Beijing Hope Run Special Fund of Cancer Foundation of China, and Beijing Xisike Clinical Oncology Research Foundation.

ObjectivesThis study aimed to develop and validate a deep-learning radiomics model using CT, T2, and DWI images for predicting pathological complete response (pCR) in patients with esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT).Materials and methodsPatients with ESCC undergoing nCRT followed by surgery were retrospectively enrolled from three institutions and divided into training and testing cohorts. Both traditional and deep-learning radiomics features were extracted from pre-treatment CT, T2, and DWI. Multiple radiomics models were developed, both single modality and integrated, using machine learning algorithms. The models' performance was assessed using receiver operating characteristic curve analysis, with the area under the curve (AUC) as a primary metric, alongside sensitivity and specificity from the cut-off analysis.ResultsThe study involved 151 patients, among whom 63 achieved pCR. The training cohort consisted of 89 patients from Institution 1 (median age 62, 73 males) and the testing cohort included 52 patients from Institution 2 (median age 62, 41 males), and 10 in a clinical trial from Institution 3 (median age 69, 9 males). The integrated model, combining traditional and deep learning radiomics features from CT, T2, and DWI, demonstrated the best performance with an AUC of 0.868 (95% CI: 0.766-0.959), sensitivity of 88% (95% CI: 73.9-100), and specificity of 78.4% (95% CI: 63.6-90.2) in the testing cohort. This model outperformed single-modality models and the clinical model.ConclusionA multimodality deep learning radiomics model, utilizing CT, T2, and DWI images, was developed and validated for accurately predicting pCR of ESCC following nCRT.Critical relevance statementOur research demonstrates the satisfactory predictive value of multimodality deep learning radiomics for the response of nCRT in ESCC and provides a potentially helpful tool for personalized treatment including organ preservation strategy.Key PointsAfter neoadjuvant chemoradiotherapy, patients with ESCC have pCR rates of about 40%.The multimodality deep learning radiomics model, could predict pCR after nCRT with high accuracy.The multimodality radiomics can be helpful in personalized treatment of esophageal cancer.

Background More than 40% of patients with resectable esophageal squamous cell cancer (ESCC) achieve pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT), who have favorable prognosis and may benefit from an organ-preservation strategy. Our study aims to develop and validate a machine learning model based on MR radiomics to accurately predict the pCR of ESCC patients after nCRT. Methods In this retrospective multicenter study, eligible patients with ESCC who underwent baseline MR (T2-weighted imaging) and nCRT plus surgery were enrolled between September 2014 and September 2022 at institution 1 (training set) and between December 2017 and August 2021 at institution 2 (testing set). Models were constructed using machine learning algorithms based on clinical factors and MR radiomics to predict pCR after nCRT. The area under the curve (AUC) and cutoff analysis were used to evaluate model performance. Results A total of 155 patients were enrolled in this study, 82 in the training set and 73 in the testing set. The radiomics model was constructed based on two radiomics features, achieving AUCs of 0.968 (95%CI 0.933–0.992) in the training set and 0.885 (95%CI 0.800-0.958) in the testing set. The cutoff analysis resulted in an accuracy of 82.2% (95%CI 72.6-90.4%), a sensitivity of 75.0% (95%CI 58.3-91.7%), and a specificity of 85.7% (95%CI 75.5-96.0%) in the testing set. Conclusion A machine learning model based on MR radiomics was developed and validated to accurately predict pCR after nCRT in patients with ESCC.

PURPOSE:The therapeutic advantage of postoperative radiation therapy (PORT) for non-small cell lung cancer (NSCLC) has not been shown to benefit overall survival (OS) according to two randomized controlled trials (RCTs), albeit an enhancement in locoregional-free survival was observed. We aimed to evaluate the relative influence of locoregional recurrence (LR) and distant metastasis (DM) on OS for patients with NSCLC after surgery. METHODS:This was a secondary analysis of PORT-C RCT. Patients with pN2 NSCLC undergoing complete resection followed by chemotherapy were included. A dynamic prediction model was developed to evaluate the impact of LR and DM on OS. The endpoint was OS. Age, sex, smoking history, histology, Karnofsky Performance Status, tumor side, T stage, and positive lymph node were baseline factors, whereas LR and DM status were time-dependent covariates. RESULTS:In total, 364 patients were eligible, including 214 and 150 in the non-PORT and PORT groups, respectively. DM significantly decreased OS in both the non-PORT (odds ratio [OR], 4.74; 95 % CI, 2.70-8.30; P < 0.01) and PORT (OR, 5.43; 95 % CI, 2.56-11.48; P < 0.01) groups. LR also significantly impacted OS in the non-PORT (OR, 2.09; 95 % CI, 1.12-3.93; P = 0.02) and the PORT (OR, 3.44; 95 % CI, 1.53-7.75; P < 0.01) groups. Multivariate Cox analysis identified the pT stage, positive lymph nodes, and histology as variables correlated with DM. A nomogram was developed to estimate the risk of DM. PORT did not significantly enhance OS in either the low (HR, 1.42; 95 % CI, 0.63-3.19, P = 0.40) or high-risk (HR, 0.62; 95 % CI, 0.35-1.09, P = 0.10) subgroup but in the medium-risk subgroup (HR, 0.20; 95 % CI, 0.05-0.86, P = 0.02). CONCLUSION:DM and LR significantly impacted OS in patients with NSCLC after surgery. DM emerged as the dominant failure pattern, emphasizing more effective control of DM. PORT was beneficial for patients with a medium risk of DM.

The estimated dose of radiation to immune cells (EDRIC) has been shown to correlate with the overall survival (OS) of patients who receive definitive thoracic radiotherapy. However, the planning target volume (PTV) may be a confounding factor. We assessed the prognostic value of EDRIC for non-small cell lung cancer (NSCLC) in patients who underwent postoperative radiotherapy (PORT) with homogeneous PTV. Patients with NSCLC who underwent PORT between 2004 and 2019 were included. EDRIC was computed as a function of the number of radiation fractions and mean doses to the lungs, heart, and remaining body. The correlations between EDRIC and OS, disease-free survival (DFS), locoregional-free survival (LRFS), and distant metastasis-free survival (DMFS) were analyzed using univariate and multivariate Cox models. Kaplan–Meier analysis was performed to assess the survival difference between low- and high-EDRIC groups. In total, 345 patients were analyzed. The mean EDRIC was 6.26 Gy. Multivariate analysis showed that higher EDRIC was associated with worse outcomes in terms of OS (hazard ratio [HR] 1.207, P = .007), DFS (HR 1.129, P = .015), LRFS (HR 1.211, P = .002), and DMFS (HR 1.131, P = .057). In the low- and high-EDRIC groups, the 3-year OS was 81.2

IntroductionClear cell renal cell carcinoma is the most common type of kidney cancer, but the prediction of prognosis remains a challenge.MethodsWe collected whole-slide histopathological images, corresponding clinical and genetic information from the The Cancer Imaging Archive and The Cancer Genome Atlas databases and randomly divided patients into training (n = 197) and validation (n = 84) cohorts. After feature extraction by CellProfiler, we used 2 different machine learning techniques (Least Absolute Shrinkage and Selector Operation-regularized Cox and Support Vector Machine-Recursive Feature Elimination) and weighted gene co-expression network analysis to select prognosis-related image features and genes, respectively. These features and genes were integrated into a joint model using random forest and used to create a nomogram that combines other predictive indicators.ResultsA total of 4 overlapped features were identified, represented by the computed histopathological risk score in the random forest model, and showed predictive value for overall survival (test set: 1-year area under the curves (AUC) = 0.726, 3-year AUC = 0.727, and 5-year AUC = 0.764). The histopathological-genetic risk score (HGRS) integrating the genetic information computed performed better than the model that used image features only (test set: 1-year AUC = 0.682, 3-year AUC = 0.734, and 5-year AUC = 0.78). The nomogram (gender, stage, and HGRS) achieved the highest net benefit according to decision curve analysis compared to HGRS or clinical model.ConclusionThis study developed a histopathological-genetic-related nomogram by combining histopathological features and clinical predictors, providing a more comprehensive prognostic assessment for clear cell renal cell carcinoma patients.

Purpose: We aimed to integrate MR radiomics and dynamic hematological factors to build a model to predict pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in esophageal squamous cell carcinoma (ESCC). Methods: Patients with ESCC receiving NCRT and esophagectomy between September 2014 and September 2022 were retrospectively included. All patients underwent pre-treatment T2weighted imaging as well as pre-treatment and post-treatment blood tests. Patients were randomly divided to training set and testing set at a ratio of 7:3. Machine learning models were constructed based on MR radiomics and hematological factors to predict pCR, respectively. A nomogram model was developed to integrate MR radiomics and hematological factors. Model performances were evaluated by areas under curves (AUCs), sensitivity, specificity, positive predictive value and negative. Results: A total of 82 patients were included, of whom 39 (47.6 %) achieved pCR. The hematological model built with four hematological factors had an AUC of 0.628 (95%CI 0.391-0.852) in the testing set. Two out of 1106 extracted features were selected to build the radiomics model with an AUC of 0.821 (95%CI 0.641-0.981). The nomogram model integrating hematological factors and MR radiomics had best predictive performance, with an AUC of 0.904 (95%CI 0.770 -1.000) in the testing set. Conclusion: An integrated model using dynamic hematological factors and MR radiomics is constructed to accurately predicted pCR to NCRT in ESCC, which may be potentially useful to assist individualized preservation treatment of the esophagus.

Abstract Background The value of postoperative radiotherapy (PORT) for patients with non-small cell lung cancer (NSCLC) remains controversial. A subset of patients may benefit from PORT. We aimed to identify patients with NSCLC who could benefit from PORT. Methods Patients from cohorts 1 and 2 with pathological Tany N2 M0 NSCLC were included, as well as patients with non-metastatic NSCLC from cohorts 3 to 6. The radiomic prognostic index (RPI) was developed using radiomic texture features extracted from the primary lung nodule in preoperative chest CT scans in cohort 1 and validated in other cohorts. We employed a least absolute shrinkage and selection operator-Cox regularisation model for data dimension reduction, feature selection, and the construction of the RPI. We created a lymph-radiomic prognostic index (LRPI) by combining RPI and positive lymph node number (PLN). We compared the outcomes of patients who received PORT against those who did not in the subgroups determined by the LRPI. Results In total, 228, 1003, 144, 422, 19, and 21 patients were eligible in cohorts 1–6. RPI predicted overall survival (OS) in all six cohorts: cohort 1 (HR = 2.31, 95% CI: 1.18–4.52), cohort 2 (HR = 1.64, 95% CI: 1.26–2.14), cohort 3 (HR = 2.53, 95% CI: 1.45–4.3), cohort 4 (HR = 1.24, 95% CI: 1.01–1.52), cohort 5 (HR = 2.56, 95% CI: 0.73–9.02), cohort 6 (HR = 2.30, 95% CI: 0.53–10.03). LRPI predicted OS (C-index: 0.68, 95% CI: 0.60–0.75) better than the pT stage (C-index: 0.57, 95% CI: 0.50–0.63), pT + PLN (C-index: 0.58, 95% CI: 0.46–0.70), and RPI (C-index: 0.65, 95% CI: 0.54–0.75). The LRPI was used to categorize individuals into three risk groups; patients in the moderate-risk group benefited from PORT (HR = 0.60, 95% CI: 0.40–0.91; p = 0.02), while patients in the low-risk and high-risk groups did not. Conclusions We developed preoperative CT-based radiomic and lymph-radiomic prognostic indexes capable of predicting OS and the benefits of PORT for patients with NSCLC.

INTRODUCTION:Adjuvant radiotherapy is recommended for pT1b esophageal squamous cell cancer (ESCC) after endoscopic submucosal dissection (ESD). However, it is unclear whether additional radiotherapy can improve patient survival. This study aimed to evaluate the efficacy of adjuvant radiotherapy after ESD for pT1b ESCC.METHODS:This was a multicenter, cross-sectional study involving 11 hospitals in China. Between January 2010 and December 2019, patients with T1bN0M0 ESCC treated with or without adjuvant radiotherapy after ESD were included. Survival between groups was compared.RESULTS:Overall, 774 patients were screened, and 161 patients were included. Forty-seven patients (29.2%) received adjuvant radiotherapy after ESD (RT group) and 114 (70.8%) underwent ESD alone (non-RT group). There were no significant differences in overall survival (OS) and disease-free survival (DFS) between the RT and non-RT groups. Lymphovascular invasion (LVI) was the only prognostic factor. In the LVI+ group, adjuvant radiotherapy significantly improved survival (5-year OS: 91.7% vs 59.5%, P = 0.050; 5-year DFS: 92.9% vs 42.6%, P = 0.010). In the LVI- group, adjuvant radiotherapy did not improve survival (5-year OS: 83.5% vs 93.9%, P = 0.148; 5-year DFS: 84.2% vs 84.7%, P = 0.907). The standardized mortality ratios were 1.52 (95% confidence interval 0.04-8.45) in the LVI+ group with radiotherapy and 0.55 (95% confidence interval 0.15-1.42) in the LVI- group without radiotherapy.DISCUSSION:Adjuvant radiotherapy could improve survival in pT1b ESCC with LVI+ other than LVI- after ESD. Selective adjuvant radiotherapy based on LVI status achieved survival rates similar to those of the general population.

Abstract Background Results from Lung ART and PORT-C trials suggest that postoperative radiotherapy (PORT) cannot routinely be recommended as standard treatment in completely resected pIIIA-N2 NSCLC patients, but their effects on the real-world practice of PORT in China remain unclear. Methods A national cross-section survey was conducted by using an online survey service. Participants were voluntarily recruited using a river sampling strategy. A link to the survey was posted on websites of radiation oncologist associations and tweets from public WeChat accounts. The survey collected the real names of participants to ensure that they were board-certified radiation oncologists. Results A total of 484 radiation oncologists were included with a median age of 40 years (IQR, 35–47). A total of 377 (77.9%) participants were male, and 282 (58.1%) had more than 10 years of clinical experience practicing thoracic radiotherapy. Before Lung ART and PORT-C trials were published, 313 (64.7%) respondents recommended PORT, 11 (2.3%) did not recommend it, and 160 (33.1%) reported that they made decisions based on risk factors. After the presentation of two trials, only 42 (8.7%) did not recommend PORT, while 108 (22.3%) recommended it, and 334 (69.0%) made decisions based on risk factors. The five most commonly considered risk factors among these 334 respondents were as follows: nodal extracapsular extension, the highest lymph node (LN) station involved, the number of dissected mediastinal LN stations, the number of positive mediastinal LN stations, and surgical approaches. In addition, the majority of all 484 respondents recommended a total dose of 50 Gy, lung stump + ipsilateral hilus + regions containing positive LNs as the targeted region, lung V20 < 25%, and heart V30 < 40% as dose constraints for PORT. Conclusion Most Chinese radiation oncologists recommended PORT for completely resected IIIA-N2 NSCLC patients based on risk factors, especially status of LN station.

Purpose: Cardiopulmonary toxic effects may reduce the efficacy of postoperative radiation therapy (PORT) in patients with non-small cell lung cancer (NSCLC). However, few studies have examined whether the heart and lung doses affect overall survival (OS). We investigated the correlation of heart and lung doses with OS in patients with NSCLC undergoing PORT.Methods and Materials: This retrospective analysis included 307 patients with NSCLC undergoing PORT. The total dose was 50 Gy. Landmark analyses were performed at 36 months, with hazard ratios (HRs) calculated separately for events occurring up to 36 months (early survival) and after 36 months (long-term survival). Stabilized inverse probability of treatment weighting (sIPTW) was performed to balance the characteristics of the high- and low-dose groups. We performed sensitivity analyses at 24 and 48 months.Results: The median follow-up period was 67.42 months. Heart doses were significantly correlated with long-term survival (HR, 1.14; P = .015) but not with early survival (HR, 0.97; P = .41) or whole survival (HR, 1.02; P = .58). Lung doses were marginally significantly correlated with early survival (HR, 1.03; P = .07) but not with long-term survival (HR, 1.00; P = .85) or whole survival (HR, 1.02; P = .12). Higher heart and lung doses were associated with decreased long-term and early survival, respectively, before and after sIPTW. Landmark analyses at 24 and 48 months showed consistent results. Conclusions: For patients with NSCLC undergoing PORT, a higher heart dose was associated with decreased long-term survival, whereas a higher lung dose was associated with decreased early survival.& COPY; 2023 The Author(s). Published by Elsevier Inc. on behalf of American Society for Radiation Oncology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Background: Radiotherapy-induced esophagitis (RE) diminishes the quality of life and interrupts treatment in patients with non-small cell lung cancer (NSCLC) undergoing postoperative radiotherapy. Dosimetric models showed limited capability in predicting RE. We aimed to develop dosiomic models to predict RE.Methods: Models were trained with a real-world cohort and validated with PORT-C randomized controlled trial cohort. Patients with NSCLC undergoing resection followed by postoperative radiotherapy between 2004 and 2015 were enrolled. The endpoint was grade =2 RE. Esophageal three-dimensional dose distribution features were extracted using handcrafted and convolutional neural network (CNN) methods, screened using an entropy-based method, and selected using minimum redundancy and maximum relevance. Prediction models were built using logistic regression. The areas under the receiver operating characteristic curve (AUC) and precision-recall curve were used to evaluate prediction model performance. A dosimetric model was built for comparison.Results: A total of 190 and 103 patients were enrolled in the training and validation sets, respectively. Using handcrafted and CNN methods, 107 and 4096 features were derived, respectively. Three handcrafted, four CNN-extracted and three dosimetric features were selected. AUCs of training and validation sets were 0.737 and 0.655 for the dosimetric features, 0.730 and 0.724 for handcrafted features, and 0.812 and 0.785 for CNN-extracted features, respectively. Precision-recall curves revealed that CNN-extracted features outperformed dosimetric and handcrafted features.Conclusions: Prediction models may identify patients at high risk of developing RE. Dosiomic models outperformed the dosimetric-feature model in predicting RE. CNN-extracted features were more predictive but less interpretable than handcrafted features.