Data Sheet 1_Application of machine learning based on habitat imaging and vision transformer to predict treatment response of locally advanced esophageal squamous cell carcinoma following neoadjuvant chemoimmunotherapy: a multi-center study.docx

ObjectiveCurrent medical examinations and biomarkers struggle to assess the efficacy of chemoimmunotherapy (nICT) for locally advanced esophageal squamous cell carcinoma (ESCC). This study aimed to develop a machine learning model integrating habitat imaging and deep learning (DL) to predict the treatment response of ESCC patients to nICT.
MethodsThe study retrospectively collected 309 ESCC patients from 6 medical centers, divided into training and external validation cohorts. For habitat imaging analysis, intratumoral subregions were clustered using the K-means clustering method. DL features from intratumoral and peritumoral subregions were extracted by Vision Transformer (ViT) respectively and then subjected to feature selection. Subsequently, 11 machine learning models were constructed for predictive model. The model’s performance was evaluated using the area under the curve (AUC), decision curve analysis (DCA), calibration curve, and accuracy.
ResultsA total of 18 DL features were selected. The model of ExtraTrees, which was optimal, demonstrated superior performance with AUCs of 0.917 in training cohort and 0.831 in external validation cohort. Similarly, ExtraTrees showed good predictive capabilities in patients undergoing 2 cycles of nICT with AUC of 0.862 in validation cohort. This model also showed good calibration for prediction probability and satisfied clinical value on DCAs. Finally, the SHapley Additive exPlanations method elucidated the model’s precise predictions.
ConclusionThe ExtraTrees model leveraging habitat imaging and ViT offered a non-invasive and accurate method to predict pathological response to nICT, guiding personalized treatment strategies, and decreasing the risk of immune-related adverse effects.

研究背景 目前临床检查与生物标志物难以评估化疗免疫治疗（chemoimmunotherapy, nICT）对局部晚期食管鳞状细胞癌（locally advanced esophageal squamous cell carcinoma, ESCC）的疗效。本研究旨在构建一种整合栖息地成像与深度学习（deep learning, DL）的机器学习模型，以预测食管鳞状细胞癌患者对化疗免疫治疗的治疗响应。
研究方法 本研究回顾性收集了来自6个医疗中心的309例食管鳞状细胞癌患者，将其划分为训练队列与外部验证队列。针对栖息地成像分析，采用K-means聚类方法对肿瘤内亚区域进行聚类。分别通过视觉Transformer（Vision Transformer, ViT）提取肿瘤内及肿瘤周围亚区域的深度学习特征，并开展特征选择。随后构建了11种机器学习模型用于预测建模。采用曲线下面积（area under the curve, AUC）、决策曲线分析（decision curve analysis, DCA）、校准曲线以及准确率对模型性能进行评估。
研究结果 最终共筛选得到18个深度学习特征。表现最优的极端随机树（ExtraTrees）模型在训练队列与外部验证队列中的曲线下面积分别为0.917与0.831，展现出优异的预测性能。同样，该模型在接受2个周期化疗免疫治疗的患者亚组中也表现出良好的预测能力，验证队列中的曲线下面积达0.862。此外，该模型的预测概率校准效果良好，且在决策曲线分析中具备可观的临床应用价值。最后，通过夏普利可加解释法（SHapley Additive exPlanations, SHAP）阐明了该模型的精准预测逻辑。
研究结论 本研究构建的整合栖息地成像与视觉Transformer的极端随机树模型，可为局部晚期食管鳞状细胞癌患者化疗免疫治疗的病理响应预测提供一种无创且精准的方法，有助于指导个体化治疗策略的制定，并降低免疫相关不良反应的发生风险。