BACKGROUND: Obesity is the foremost risk factor in the development of endometrial cancer (EC). However, the impact of obesity on the response to immune checkpoint inhibitors (ICI) in EC remains poorly understood. This retrospective study investigates the association among BMI, body fat distribution, and clinical and molecular characteristics of EC patients treated with ICI. METHODS: We analyzed progression-free survival (PFS) and overall survival (OS) in EC patients treated with ICI, categorized by BMI, fat-mass distribution, and molecular subtypes. Incidence of immune-related adverse events (irAEs) after ICI was also assessed based on BMI status. RESULTS: 524 EC patients were included in the study. Overweight and obese patients exhibited a significantly prolonged PFS and OS compared with normal BMI patients after treatment with ICI. Multivariable Cox’s regression analysis confirmed the independent association of overweight and obesity with improved PFS and OS. Elevated visceral adipose tissue (VAT) was identified as a strong independent predictor for improved PFS to ICI. Associations between obesity and OS/PFS were particularly significant in the copy number–high/ TP53 abnormal (CN-H/ TP53 abn) EC molecular subtype. Finally, obese patients demonstrated a higher irAE rate compared with normal BMI individuals. CONCLUSION: Obesity is associated with improved outcomes to ICI in EC patients and a higher rate of irAEs. This association is more pronounced in the CN-H/ TP53 abn EC molecular subtype. FUNDING: NIH/NCI Cancer Center; MSK Gerstner Physician Scholars Program; National Center for Advancing Translational Sciences (NCATS); Cycle for Survival; Breast Cancer Research Foundation.

Background: Radiomics can provide quantitative descriptors of tumor phenotype, but translation is often limited by feature instability across scanners and protocols. We aimed to develop and internally validate a protocol-specific CT-radiomics model using preoperative imaging to predict 5-year recurrence in patients with stage I lung adenocarcinoma after complete surgical resection. Methods: The retrospective study included 270 patients with completely resected stage I lung adenocarcinoma from January 2010–December 2021, among whom 23 (8.5%) experienced recurrence within five years. Radiomic features were extracted from routine preoperative CT scans. After preprocessing to remove highly constant and highly correlated features, the Synthetic Minority Over-sampling Technique addressed class imbalance in the training set. Recursive Feature Elimination identified the most predictive radiomic features. An XGBoost classifier was trained using optimized hyperparameters identified through RandomizedSearchCV with cross-validation. Model performance was evaluated using the ROC curve and predictive metrics. Results: Five radiomic features differed significantly between recurrence groups (p = 0.007 to <0.001): Shape Sphericity, first-order 90Percentile, GLCM Autocorrelation, GLCM Cluster Shade, and GLDM Large Dependence Low Gray Level Emphasis. The radiomics model showed excellent discriminatory ability with AUC values of 0.99 (95% CI: 0.98–1.00), 0.97 (95% CI: 0.91–1.00), and 0.96 (95% CI: 0.85–1.00) on the training, validation, and test sets, respectively. On the test set, the model achieved sensitivity of 100% (95% CI: 51–100%), specificity of 94% (95% CI: 81–98%), PPV of 67% (95% CI: 30–90%), NPV of 100% (95% CI: 90–100%), and overall accuracy of 95% (95% CI: 83–99%). Conclusions: Under protocol-homogeneous imaging conditions, CT radiomics accurately predicted recurrence in patients with completely resected stage I lung adenocarcinoma. External multi-vendor validation is needed before broader deployment.

Background/Objectives: Distinguishing primary lung squamous cell carcinoma (PLSCC) from metastatic head and neck squamous cell carcinoma (MHNSCC) to the lungs is challenging for pathologists, especially when patients present with a solitary lung nodule. The purpose of this study was to identify CT imaging features that differ between PLSCC from solitary MHNSCC to the lungs, using next-generation sequencing (NGS) and human papillomavirus in situ hybridization analysis as the gold reference standard. Methods: This retrospective, single-institution cross-sectional study included patients with a biopsy-proven PLSCC or solitary MHNSCC from July 2013 to May 2022, who underwent NGS or in situ hybridization, and baseline CT or PET/CT. Each scan was evaluated by at least two radiologists. Nodular, pleural, and ancillary CT features, as well as maximum standardized uptake value (SUVmax) from PET/CTs, were recorded. Associations between imaging features and pathology were examined using either the Wilcoxon rank-sum or Fisher’s exact test. Results: In total, 81 patients were included (median 66 years; 64 male); 36/81 (44%) had PLSCC and 45/81 (56%) had MHNSCC. PLSCC was associated with a larger size (median, 3.3–3.6 cm vs. 1.4–1.6 cm, p < 0.001), and the presence of post obstructive atelectasis (p = 0.002), pleural retraction (p < 0.001), pleural tags (p = 0.02), and pleural surface involvement (p = 0.02). MHNSCC presented as smaller peripheral nodules (p = 0.003) with lower SUVmax (p = 0.01). Conclusions: Several CT imaging features as well as SUVmax from PET/CT were significantly different between PLSCC and solitary MHNSCC and their potential discriminatory ability warrants evaluation in future studies.