Deep Learning-Based Segmentation of Extra-Pelvic Organs and Metastases in Advanced Prostate Cancer Based on MET-RADS-P
10.3969/j.issn.1005-5185.2024.02.012
- VernacularTitle: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
- Author:
Xiang LIU
1
;
Xuelei QUBIE
;
Jingyun WU
;
Pengsheng WU
;
Xiaodong ZHANG
;
Xiaoying WANG
Author Information
1. 北京大学第一医院医学影像科,北京 100034
- Keywords:
Prostatic neoplasms;
Deep learning;
Magnetic resonance imaging;
Neoplasm metastasis;
Pathology,surgical
- From:
Chinese Journal of Medical Imaging
2024;32(2):168-174
- CountryChina
- Language:Chinese
-
Abstract:
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.
