Yin Ping, Liu Ke, Chen Runrong, Liu Yang, Lu Lin, Sun Chao, Liu Ying, Zhang Tianyu, Zhong Junwen, Chen Weidao, Yu Ruize, Wang Dawei, Liu Xia, Hong Nan
Department of Radiology, Peking University People's Hospital, 11 Xizhimen Nandajie, Xicheng District, Beijing, China.
Department of Radiology, Peking University Third Hospital, Beijing, China.
NPJ Precis Oncol. 2025 Aug 15;9(1):286. doi: 10.1038/s41698-025-01077-3.
This study developed an end-to-end deep learning (DL) model using non-enhanced MRI to diagnose benign and malignant pelvic and sacral tumors (PSTs). Retrospective data from 835 patients across four hospitals were employed to train, validate, and test the models. Six diagnostic models with varied input sources were compared. Performance (AUC, accuracy/ACC) and reading times of three radiologists were compared. The proposed Model SEG-CL-NC achieved AUC/ACC of 0.823/0.776 (Internal Test Set 1) and 0.836/0.781 (Internal Test Set 2). In External Dataset Centers 2, 3, and 4, its ACC was 0.714, 0.740, and 0.756, comparable to contrast-enhanced models and radiologists (P > 0.05), while its diagnosis time was significantly shorter than radiologists (P < 0.01). Our results suggested that the proposed Model SEG-CL-NC could achieve comparable performance to contrast-enhanced models and radiologists in diagnosing benign and malignant PSTs, offering an accurate, efficient, and cost-effective tool for clinical practice.
本研究开发了一种使用非增强磁共振成像（MRI）诊断盆腔和骶骨良恶性肿瘤（PSTs）的端到端深度学习（DL）模型。利用来自四家医院的835例患者的回顾性数据对模型进行训练、验证和测试。比较了六种具有不同输入源的诊断模型。比较了三位放射科医生的性能（AUC、准确率/ACC）和阅片时间。所提出的模型SEG-CL-NC在内部测试集1中的AUC/ACC为0.823/0.776，在内部测试集2中的AUC/ACC为0.836/0.781。在外部数据集中心2、3和4中，其ACC分别为0.714、0.740和0.756，与增强扫描模型和放射科医生相当（P>0.05），而其诊断时间明显短于放射科医生（P<0.01）。我们的结果表明，所提出的模型SEG-CL-NC在诊断盆腔和骶骨良恶性PSTs方面可实现与增强扫描模型和放射科医生相当的性能，为临床实践提供了一种准确、高效且经济有效的工具。
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