基于深度学习的原发性全切片图像肾细胞癌患者诊断与生存预测

IF 3.6 3区医学 Q1 PATHOLOGY

Pathology Pub Date : 2024-08-03 DOI:10.1016/j.pathol.2024.05.012

Siteng Chen, Xiyue Wang, Jun Zhang, Liren Jiang, Feng Gao, Jinxi Xiang, Sen Yang, Wei Yang, Junhua Zheng, Xiao Han

引用次数: 0

摘要

探索肾细胞癌（RCC）的新型诊断和预后生物标志物仍然是临床的迫切需求。我们提出了基于深度学习的人工智能策略。研究包括来自多个中心的1752张全切片图像。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Deep learning-based diagnosis and survival prediction of patients with renal cell carcinoma from primary whole slide images

It remains an urgent clinical demand to explore novel diagnostic and prognostic biomarkers for renal cell carcinoma (RCC). We proposed deep learning-based artificial intelligence strategies. The study included 1752 whole slide images from multiple centres.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Pathology 医学-病理学

CiteScore

6.50

自引率

2.20%

发文量

459

审稿时长

54 days

期刊介绍： Published by Elsevier from 2016 Pathology is the official journal of the Royal College of Pathologists of Australasia (RCPA). It is committed to publishing peer-reviewed, original articles related to the science of pathology in its broadest sense, including anatomical pathology, chemical pathology and biochemistry, cytopathology, experimental pathology, forensic pathology and morbid anatomy, genetics, haematology, immunology and immunopathology, microbiology and molecular pathology.