Evidence map›Paper›PMID 40681867›Full record

ArticleNPJ digital medicine2025

Systematic review and meta-analysis of deep learning for MSI-H in colorectal cancer whole slide images.

Huo Li, Jing Qin, Zhongzhuan Li, Rong Ouyang, Zhixin Chen, Shijiang Huang, Shufen Qin, Qiliang Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Huo Li *Department of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China. 724765917@qq.com.
Jing Qin *Department of General Medicine, Liuzhou People's Hospital, Liuzhou, China.
Zhongzhuan Li *Department of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Rong OuyangDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Zhixin ChenDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Shijiang HuangDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Shufen QinDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
Qiliang HuangDepartment of Gastroenterology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This meta-analysis evaluated diagnostic performance of deep learning (DL) algorithms using whole slide images (WSIs) for detecting microsatellite instability-high (MSI-H) in colorectal cancer (CRC). PubMed, Embase, and Web of Science were searched until January 2025. Nineteen studies comprising 33,383 samples were included. Bivariate random-effects models calculated pooled sensitivity/specificity with 95% CIs. The revised QUADAS-2 tool was used for quality assessment. Pooled patient-based internal validation showed a sensitivity of 0.88 and specificity of 0.86, while external validation revealed higher sensitivity of 0.93 but lower specificity of 0.71. Image-based analysis showed similar accuracy. Meta-regression identified center, reference standard, and tile size as major sources of heterogeneity, with no significant differences observed between internal and external performance. Overall, DL algorithms demonstrate excellent sensitivity in detecting MSI-H; however, their lower specificity in external validation suggests overfitting and highlights the need for algorithm standardization to improve generalizability and clinical utility.

Identifiers

PMID40681867
PMCPMC12274608

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.