Research progress in radiomics and deep learning for early prediction and efficacy evaluation in colorectal cancer liver metastases
10.12354/j.issn.1000-8179.2024.20231333
- VernacularTitle:影像组学与深度学习在结直肠癌肝转移早期预测及疗效评估中的研究进展
- Author:
Zhuofu LI
1
;
Zhaoxiang YE
Author Information
1. 天津医科大学肿瘤医院放射科,国家恶性肿瘤临床医学研究中心,天津市恶性肿瘤临床医学研究中心,天津市"肿瘤防治"重点实验室,天津市消化系统肿瘤重点实验室(天津市 300060)
- Keywords:
radiomics;
deep learning(DL);
colorectal cancer liver metastases(CCLM);
early prediction;
efficacy evaluation
- From:
Chinese Journal of Clinical Oncology
2024;51(1):36-40
- CountryChina
- Language:Chinese
-
Abstract:
Radiomics-based early prediction and treatment efficacy evaluation is critical for personalized treatment strategies in patients with colorectal cancer liver metastases(CCLM).Owing to the high artificial intelligence(AI)participation,repeatability,and reliable perform-ance,deep learning(DL)based on convolutional neural networks enhances the predictive efficacy of the models,enabling its potential clinic-al application more promising.Subsequent to the gradual construction of a multimodal fusion model and multicenter large sample database,radiomics and DL will become increasingly essential in the management of CCLM.This review focuses on the main steps of radiomics and DL,and summarizes the value of its application in early state prediction and treatment efficacy evaluation of different treatment modalities in CCLM,we also look forward to the potential of its in-depth application in the clinical management of CCLM.
