Radiomics nomogram combined with clinical factors for predicting pathological complete response in resectable esophageal squamous cell carcinoma - Lu, Zihao; Li, Yongsen; Hu, Wenxuan; Cao, Yonghao; Lv, Xin; Jia, Xinyu; Shen, Shiyu (Frontiers in Oncology, 2024)
IntroductionPredicting the efficacy of neoadjuvant immunochemotherapy (NICT) for esophageal squamous cell carcinoma (ESSC) prior to surgery can minimize unnecessary surgical interventions and facilitate personalized treatment strategies. Our goal is to develop and validate an image-based radiomic model using preoperative computed tomography (CT) scans and clinical data to predict pathological complete response (pCR) in resectable ESSC following neoadjuvant immunotherapy.MethodsWe retrospectively collected data from patients diagnosed with ESCC at the First Affiliated Hospital of Soochow University between January 2018 and May 2023, who received preoperative neoadjuvant immunochemotherapy. Eligible patients were randomly divided into training and validation sets. Radiomic features extracted from preprocessed CT images were used to develop a radiomic model, incorporating Radiomic score (Rad-score) and clinical factors through multivariate logistic regression analysis. The model’s performance was assessed for calibration, discrimination, and clinical utility in an independent validation cohort.ResultsWe enrolled a total of 105 eligible participants who were randomly divided into two groups: a training set (N=74) and a validation set (N=31). After data dimension reduction and feature selection, we identified 11 radiomic features, which collectively formed the Rad-score. Rad-score had an area under the curve (AUC) of 0.83 (95% CI 0.72-0.93) in the training set and 0.78 (95% CI 0.60-0.95) in the validation set. Multivariate analysis revealed that radiological response and Neutrophil–Lymphocyte Ratio (NLR) were independent predictors of pCR, with p-values of 0.0026 and 0.0414, respectively. We developed and validated a nomogram combining Rad-score and clinical features, achieving AUCs of 0.90 (95% CI 0.82-0.98) in the training set and 0.85 (95% CI 0.70-0.99) in the validation set. The Delong test confirmed the nomogram’s superiority over pure radiomic and clinical models. Decision curve analysis (DCA) and integrated discrimination improvement (IDI) assessment supported the clinical value and superiority of the combined model.ConclusionThe nomogram, which integrates Rad-score and clinical features, offers a precise and reliable method for predicting pCR status in ESCC patients who have undergone neoadjuvant immunochemotherapy. This tool aids in tailoring treatment strategies to individual patients.