Application of deep learning technology in diagnosis of renal cell carcinoma

JIAO Panpan; YANG Rui; ZHENG Qingyuan; WANG Lei; CHEN Zhiyuan; LIU Xiuheng

doi:10.14188/j.1671-8852.2023.0287

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Application of deep learning technology in diagnosis of renal cell carcinoma

更新时间：2026-11-05

- Application of deep learning technology in diagnosis of renal cell carcinoma
- Medical Journal of Wuhan University Vol. 46, Issue 3, Pages: 321-326(2025)
- 作者机构：
  
  武汉大学人民医院泌尿外科湖北武汉 430060
- 作者简介：
- 基金信息：
- DOI：10.14188/j.1671-8852.2023.0287
  CLC： R692.9
- Received：08 May 2023，
  
  Published：25 March 2025
- 稿件说明：
移动端阅览
焦攀攀,杨瑞,郑庆源等.深度学习技术在肾癌诊断中的应用[J].武汉大学学报（医学版）,2025,46(03):321-326.

JIAO Panpan,YANG Rui,ZHENG Qingyuan,et al.Application of deep learning technology in diagnosis of renal cell carcinoma[J].Medical Journal of Wuhan University,2025,46(03):321-326.
焦攀攀,杨瑞,郑庆源等.深度学习技术在肾癌诊断中的应用[J].武汉大学学报（医学版）,2025,46(03):321-326. DOI： 10.14188/j.1671-8852.2023.0287.

JIAO Panpan,YANG Rui,ZHENG Qingyuan,et al.Application of deep learning technology in diagnosis of renal cell carcinoma[J].Medical Journal of Wuhan University,2025,46(03):321-326. DOI： 10.14188/j.1671-8852.2023.0287.

摘要

传统影像学和组织病理学检查虽在肾癌的诊断中发挥了重要作用，但存在诸多弊端。深度学习技术通过有效提取图像特征，可在CT和MRI图像上实现对肾癌的初步鉴别和良恶性判断。在组织病理学检查上，深度学习技术可以准确预测病理学切片上肾癌的分型并进行预后预测。深度学习技术在肾癌的诊疗中展现出了巨大的潜力，但仍然需要多中心、大样本的高质量研究才能服务于临床工作。

Abstract

Traditional radiology and pathological examinations are pivotal in diagnosing renal cancer

yet they have their limitations. Through the efficient extraction of image features

deep learning technology facilitates the initial differentiation and malignancy assessment of renal cancer on CT and MRI images. In pathological examinations

deep learning accurately predicts the classification of renal cancer on histopathological slides and offers prognostic insights. While deep learning technology shows significant promise in the diagnosis and treatment of renal cancer

its full potential can be realized through high⁃quality

multicenter studies with large sample sizes to effectively benefit clinical practice.

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references

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