中山大学学报（自然科学版）（中英文）2025，Vol.64Issue(3)：156-164,9.DOI:10.13471/j.cnki.acta.snus.ZR20240309
基于深度学习的多亚型腹膜后软组织肉瘤诊断
Deep learning-based diagnosis of multi-subtype retroperitoneal soft tissue sarcomas
摘要
Abstract
In the absence of additional auxiliary tests and relying solely on histopathological images,small-volume biopsy samples of retroperitoneal soft tissue tumors often lead to interobserver variability,impacting the overall diagnostic accuracy of disease subtypes.To address this issue,157 whole-slide images(WSIs)were collected from multiple centers,encompassing five disease categories:dedifferentiated liposarcoma(DDLP),leiomyosarcoma(LMS),malignant peripheral nerve sheath tumor(MPNST),undifferentiated pleomorphic sarcoma(UPS),and well-differentiated liposarcoma(WDLP).Based on these WSIs,two model ensemble methods were proposed:one based on single-scale images and the other on multi-scale images.Deep learning models,such as ResNet18,EfficientNet B7,and EfficientNet V2,were trained on the collected data.Results showed that both ensemble methods achieved high classification accuracy,with the best model achieving an overall accuracy of 82.27%in patch-level analysis and 80.95%in WSI-level analysis.Therefore,the proposed methods can effectively assist pathologists in the clinical diagnosis of retroperitoneal soft tissue tumors.关键词
腹膜后软组织肉瘤/深度学习/全切片图像/亚型诊断Key words
retroperitoneal soft tissue sarcomas/deep learning/whole slide images/subtyping分类
信息技术与安全科学引用本文复制引用
姜永军,李红玲,阮萍,陈路,李艳春,胡庆,谢功勋,孟云鹤..基于深度学习的多亚型腹膜后软组织肉瘤诊断[J].中山大学学报（自然科学版）（中英文）,2025,64(3):156-164,9.基金项目
国家自然科学基金(61673390) （61673390）
