影像组学与深度学习在肾癌精准诊疗中的研究进展
Advancements in radiomics and deep learning for precision diagnosis and treatment of renal cell carcinoma
吕定阳 1双卫兵2
作者信息
- 1. 山西医科大学第一医院 临床医学院,山西 太原 030001
- 2. 山西医科大学第一医院 临床医学院,山西 太原 030001;山西医科大学第一医院 泌尿外科,山西 太原 030001
- 折叠
摘要
肾癌是泌尿系统中常见的恶性肿瘤之一,近年来,随着诊断技术的提高越来越多的肾癌可被早期发现.传统影像技术对肾癌定性定量分析有限,有创穿刺活检因肿瘤的异质性不能表征整个肿瘤.随着人工智能时代的到来,深度学习和影像组学可以挖掘肉眼难以发现的影像学特征,从而分析肿瘤整体的异质性,指导临床诊疗和预后评估.本文将针对深度学习与影像组学技术进行概述,对近年来深度学习技术及影像组学方法在肾癌诊疗中的应用进行总结,以期为肾癌患者的临床诊疗方案提供最新依据.
Abstract
Renal cell carcinoma is one of the common malignant tumors in the urinary system.With the continuous improvement of diagnostic techniques,an increasing number of early-stage renal cell carcinoma are being discovered.Traditional imaging techniques have limited qualitative and quantitative analysis capabilities for renal cell carcinoma,and invasive biopsies cannot characterize the entire tumor due to its heterogeneity.With the advent of the artificial intelligence era,deep learning and radiomics can uncover imaging features that are difficult to discern by the naked eye,thereby analyzing the overall heterogeneity of tumors and guiding clinical diagnosis,treatment,and prognosis assessment.This article provides an overview of deep learning and radiomics techniques,summarizes the recent applications of deep learning and radiomics methods in the diagnosis and treatment of renal cell carcinoma,and aims to provide the latest evidence-based guidance for the clinical management of renal cell carcinoma patients.
关键词
肾癌/影像组学/深度学习/预后评估Key words
Renal cell carcinoma/Radiomics/Deep learning/Prognostic evaluation引用本文复制引用
基金项目
山西省科普宣传专项(202304091001018)
出版年
2024
NETL
NSTL
万方数据