Diagnostic value of artificial intelligence-based pathology diagnosis system in lymphatic metastasis of gastric cancer.

IF 2.5 3区医学 Q3 ONCOLOGY

Oncology Pub Date : 2024-12-02 DOI:10.1159/000542852

Tiantian Zhao, Qiong Wu, Chenglou Zhu, Mingxu Da, Hong Ma

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引用次数: 0

Abstract

Introduction Gastric cancer remains one of the leading causes of cancer-related mortality worldwide, with lymph node metastasis (LNM) being an independent prognostic factor. However, there are still challenges in the pathological diagnosis of LNM in gastric cancer (GC). The aim of this meta-analysis is to systematically evaluate the accuracy of artificial intelligence (AI) in detecting LNM in GC from whole-slide pathological images.. Methods As of March 24, 2024, a comprehensive search for studies on the pathological diagnosis of GC LNM AI was performed in the databases of PubMed, Web of Science, Cochrane Library, and CNKI. Meta-analysis of the included data was performed using Meta-Disc 1.4, Review Manager 5.4, and Stata SE 17.0 software to calculate diagnostic metrics such as overall sensitivity and specificity. The overall diagnostic performance of the AI was assessed. Meta-regression analysis explored sources of heterogeneity. Results A total of 7 articles involving 1,669 GC patients were included. The analysis showed that AI had a sensitivity of 0.90 (95% CI: 0.84-0.94) and a specificity of 0.95 (95% CI: 0.91-0.98) for the diagnosis of GC LNM, with significant heterogeneity across studies. The area under the curve was 0.97, indicating an excellent diagnostic value. Meta-regression analysis showed that the sample size and the number of study centers contributed to the heterogeneity. Conclusion AI for diagnosing LNM in GC from whole-slide pathological images demonstrates high accuracy, offering significant clinical implications for improving diagnosis and treatment strategies.

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来源期刊

Oncology 医学-肿瘤学

CiteScore

6.00

自引率

2.90%

发文量

审稿时长

6-12 weeks

期刊介绍： Although laboratory and clinical cancer research need to be closely linked, observations at the basic level often remain removed from medical applications. This journal works to accelerate the translation of experimental results into the clinic, and back again into the laboratory for further investigation. The fundamental purpose of this effort is to advance clinically-relevant knowledge of cancer, and improve the outcome of prevention, diagnosis and treatment of malignant disease. The journal publishes significant clinical studies from cancer programs around the world, along with important translational laboratory findings, mini-reviews (invited and submitted) and in-depth discussions of evolving and controversial topics in the oncology arena. A unique feature of the journal is a new section which focuses on rapid peer-review and subsequent publication of short reports of phase 1 and phase 2 clinical cancer trials, with a goal of insuring that high-quality clinical cancer research quickly enters the public domain, regardless of the trial’s ultimate conclusions regarding efficacy or toxicity.