Preoperative prediction of the Lauren classification in gastric cancer using automated nnU-Net and radiomics: a multicenter study

作者：Bo Cao, Jun Hu, Haige Li, Xuebing Liu, Chang Rong, Shuai Li, Xuedan He, Xiaomin Zheng, Kaicai Liu, Chuanbin Wang, Wei Guo, Xingwang Wu · 发表于：Insights into Imaging · 年份：2025 · DOI：10.1186/s13244-025-01923-9 · 被引用次数：8 · 研究领域：Gastric Cancer Management and Outcomes、Radiomics and Machine Learning in Medical Imaging、Gastrointestinal Tumor Research and Treatment

OBJECTIVES: To develop and validate a deep learning model based on nnU-Net combined with radiomics to achieve autosegmentation of gastric cancer (GC) and preoperative prediction via the Lauren classification. METHODS: Patients with a pathological diagnosis of GC were retrospectively enrolled in three medical centers. The nnU-Net autosegmentation model was developed using manually segmented datasets and evaluated by the Dice similarity coefficient (DSC). The CT images were processed by the nnU-Net model to obtain autosegmentation results and extract radiomic features. The least absolute shrinkage and selection operator (LASSO) method selects optimal features for calculating the Radscore and constructing a radiomic model. Clinical characteristics and the Radscore were integrated to construct a combined model. Model performance was evaluated via the receiver operating characteristic (ROC) curve. RESULTS: A total of 433 GC patients were divided into the training set, internal validation set, external test set-1, and external test set-2. The nnU-Net model achieved a DSC of 0.79 in the test set. The areas under the curve (AUCs) of the internal validation set, external test set-1, and external test set-2 were 0.84, 0.83, and 0.81, respectively, for the radiomic model; and 0.81, 0.81, and 0.82, respectively, for the combined model. The AUCs of the radiomic and combined models showed no statistically significant difference (p > 0.05). The radiomic model was selected as the optimal mod...