应用深度学习进行基于前列腺癌转移报告和数据系统指南的晚期前列腺癌盆腔外脏器及转移灶分割

Deep Learning-Based Segmentation of Extra-Pelvic Organs and Metastases in Advanced Prostate Cancer Based on MET-RADS-P

目的探讨应用深度学习进行基于前列腺癌转移报告和数据系统指南的晚期前列腺癌患者盆腔外脏器及转移灶分割的可行性。资料与方法回顾性收集北京大学第一医院2017年1月—2022年1月不同扫描部位(头部、颈部、胸部、腹部)的经临床综合诊断存在转移灶的数据集(头部、颈部、胸部及腹部转移患者分别为68、91、57、263例),用于进行扫描范围的分类模型及不同区域脏器和转移灶的分割模型训练。另收集90例经病理证实为前列腺癌且行全身MRI患者用于模型的外部验证。以手工标注的区域(脑实质、颈椎、肺实质、纵隔、胸椎、肝、腰椎)及转移灶标签为“金标准”,评估模型的分割性能。评价指标包括Dice相似系数(DSC)、体积相似度(VS)。结果在外部验证数据集中,分类模型在头部、颈部、胸部和腹部的符合率分别为100%(90/90)、98.89%(89/90)、96.67%(87/90)和94.44%(85/90);分割模型对不同区域脏器分割的DSC、VS范围分别为(0.86±0.10)~(0.99±0.01)、(0.89±0.10)~(0.99±0.01);分割模型对不同转移灶分割的DSC、VS范围分别为(0.65±0.07)~(0.72±0.13)、(0.74±0.04)~(0.82±0.13)。结论基于深度学习的3D U-Net模型可实现晚期前列腺癌患者的盆腔外区域及转移灶分割。

Purpose To explore the feasibility of the deep learning-based segmentation of extra-pelvic region and metastases in advanced prostate cancer based on metastasis reporting and data system for prostate cancer(MET-RADS-P).Materials and Methods Four datasets(68,91,57 and 263 patients with head,neck,chest and abdomen metastases,respectively)from Jan 2017 to Jan 2022 in Peking University First Hospital were retrospectively collected for the development of the classification model of scanning range and segmentation model of different regions and metastases according to the scanning sites(head,neck,chest and abdomen).In addition,90 patients with prostate cancer confirmed by pathology and underwent whole-body MRI were collected for external validation of the developed model.The manual annotation of the regions and metastases were used as the“reference standard”for the model evaluation.The evaluation indexes included dice similarity coefficient(DSC)and volumetric similarity(VS).Results In the external validation set,the classification accuracy of head,neck,chest and abdomen were 100%(90/90),98.89%(89/90),96.67%(87/90)and 94.44%(85/90),respectively.The range of DSC,VS values of the segmentation model for organs in different regions were(0.86±0.10)-(0.99±0.01),(0.89±0.10)-(0.99±0.01),respectively.The range of DSC,VS values of the segmentation model for metastases in different regions were(0.65±0.07)-(0.72±0.13),(0.74±0.04)-(0.82±0.13),respectively.Conclusion The 3D U-Net model based on deep learning may achieve the segmentation of extra-pelvic region and metastasis in advanced prostate cancer.