Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis

作者：Ying Yu, Gangfeng Li, Weixiong Tan, Xiaoyan Qu, Tao Zhang, Xing-Yi Hou, Yuan-Bo Zhu, Zhi-Ying Ma, Yang Lu, Ya Gao, Mei Yu, Yue Cui, Zhen Zhou, Yang Yang, Lin‐Feng Yan, Guangbin Cui · 发表于：BMC Medical Imaging · 年份：2025 · DOI：10.1186/s12880-025-01596-2 · 被引用次数：10 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment、Advanced Radiotherapy Techniques

OBJECTIVE: By discussing the difference, stability and classification ability of tumor contour extracted by artificial intelligence and doctors, can a more stable method of tumor contour extraction be obtained? METHODS: We propose a novel framework for the automatic segmentation of lung tumor contours and the differential diagnosis of downstream tasks. This framework integrates four key modules: tumor segmentation, extraction of radiomic features, feature selection, and the development of diagnostic models for clinical applications. Using this framework, we conducted a study involving a cohort of 1,429 patients suspected of lung cancer. Four automatic segmentation methods (RNN, UNET, WFCM, and SNAKE) were evaluated against manual segmentation performed by three radiologists with varying levels of expertise. We further studied the consistency of radiomic features extracted from these methods and evaluates their diagnostic performance across three downstream tasks: benign vs. malignant classification, lung adenocarcinoma infiltration, and lung nodule density classification. RESULTS: The Dice coefficient of RNN is the highest among the four automatic segmentation methods (0.803 > 0.751, 0.576, 0.560), and all P < 0.05. In the consistency comparison of the seven contour-extracted radiomic features, that the features extracted by RNN and S1 (the senior radiologist) showed the highest similarity which was higher than the other automatic segmentation methods and doctors with low sen...