MRI影像组学和深度学习在前列腺癌中的研究进展
Research progress of radiomics and deep learning in prostate cancer
刘嘉睿 1吴慧 1刘娜 1高凯华 1杨姣1
作者信息
- 1. 内蒙古医科大学附属医院影像科,呼和浩特 010050
- 折叠
摘要
前列腺癌(prostate cancer,Pca)是男性泌尿生殖系统最常见的恶性肿瘤之一,其发病率逐年上升,因此Pca早期诊断、病理分级、风险分层和预后评估对患者诊疗计划的制订至关重要.近年来影像组学和深度学习(deep learning,DL)在Pca的研究中取得了显著进展,为精准医疗的实现提供了重要工具.本文系统综述了这两项技术在Pca图像分割、诊断、格里森分级、包膜外侵犯及转移预测、预后评估以及治疗决策中的应用和潜力,并对现有研究的成果、局限性以及未来的改进措施和发展方向进行总结,以期为Pca患者提供更加精准、个性化的诊疗方案,提高治疗效果和生活质量.
Abstract
Prostate cancer(Pca)is one of the most common malignant tumors of male genitourinary system,and its incidence rate is increasing year by year.Therefore,early diagnosis,pathological classification,risk stratification and prognosis evaluation of Pca are crucial to the formulation of patient diagnosis and treatment plans.Radiomics and deep learning(DL)have made significant progress in Pca research,providing important tools for the realization of precision medicine in recent years.This article systematically reviews the applications and potential of these two techniques in Pca image segmentation,diagnosis,Gleason grading,prediction of extracapsular extension and metastasis,prognosis evaluation,and treatment decision-making.It also summarizes the achievements,limitations,and future improvement measures and development directions of current research,aiming to provide more precise and personalized diagnosis and treatment plans for Pca patients,thereby improving treatment effectiveness and quality of life.
关键词
前列腺癌/影像组学/深度学习/磁共振成像/诊断/预后评估Key words
prostate cancer/radiomics/deep learning/magnetic resonance imaging/diagnosis/prognostic evaluation引用本文复制引用
基金项目
内蒙古医科大学附属医院重点实验室开放基金(2022NYFYSY006)
出版年
2024
国家科技期刊平台
NSTL
万方数据