Multimodality deep learning radiomics predicts pathological response after neoadjuvant chemoradiotherapy for esophageal squamous cell carcinoma

作者：Yunsong Liu, Yi Wang, Xinyang Hu, Xin Wang, Liyan Xue, Qingsong Pang, Huan Zhang, Zeliang Ma, Heping Deng, Zhaoyang Yang, Xujie Sun, Yu Men, Feng Ye, Kuo Men, Jianjun Qin, Nan Bi, Jing Zhang, Qifeng Wang, Zhouguang Hui · 发表于：Insights into Imaging · 年份：2024 · DOI：10.1186/s13244-024-01851-0 · 被引用次数：20 · 研究领域：Esophageal Cancer Research and Treatment、Radiomics and Machine Learning in Medical Imaging、Pancreatic and Hepatic Oncology Research

OBJECTIVES: This 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 METHODS: Patients 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. RESULTS: The 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. Thi...