Novel technique on deep learning radiomics and its application in the diagnosis of tumor

JIN Shichen; SUN Xiaoming; JIANG Jiehui; ZUO Chuantao

doi:10.19732/j.cnki.2096-6210.2021.06.002

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Novel technique on deep learning radiomics and its application in the diagnosis of tumor

更新时间：2025-12-15

- Novel technique on deep learning radiomics and its application in the diagnosis of tumor
- Vol. 30, Issue 6, Pages: 439-444(2021)
- 作者机构：
  
  1. 上海大学通信与信息工程学院生物医学工程专业,上海,200444
  2. 复旦大学附属华山医院 PET 中心,上海,200235
- 作者简介：
- 基金信息：
- DOI：10.19732/j.cnki.2096-6210.2021.06.002
  CLC：
- Published Online：28 December 2021，
  
  Published：28 December 2021
- 稿件说明：
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金诗晨,孙晓鸣,蒋皆恢,等. 深度学习影像组学新技术及其在肿瘤诊断中的应用[J]. 肿瘤影像学, 2021, 30(6): 439-444 https://doi.

org/10.19732/j.cnki.2096-6210.2021.06.002
金诗晨,孙晓鸣,蒋皆恢,等. 深度学习影像组学新技术及其在肿瘤诊断中的应用[J]. 肿瘤影像学, 2021, 30(6): 439-444 https://doi. DOI： 10.19732/j.cnki.2096-6210.2021.06.002.

org/10.19732/j.cnki.2096-6210.2021.06.002 DOI：

摘要

深度学习影像组学（deep learning radiomics，DLR）是医学影像结合人工智能分析领域的一项新技术，可以解决传统影像组学技术自动化、标准化程度低，特征提取步骤繁琐，耗时耗力的问题，并进一步提升影像组学模型在肿瘤分类诊断与预后预测中的准确性和可靠性。本文首先介绍DLR方法的原理及其工作流程，进而介绍其在肿瘤诊断、分期分型预测、生存预后评估中的研究现状，最后对其进行总结与展望。

Abstract

Deep learning radiomics (DLR) is a novel technique in the field of medical imaging combined with artificial intelligence analysis. It can solve several disadvantages of traditional radiomics

including low degree of automation and standardization

tedious feature extraction steps

and time-consuming and labor-consuming. DLR further improves the accuracy and reliability of radiomic models in tumor diagnosis and prognosis prediction. This paper firstly introduced the principle and workflow of the DLR method; then introduced its applications in tumor diagnosis

staging and typing prediction

and survival prognosis evaluation; and finally made the summary and prospect on DLR.

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