Evidence map›Paper›PMID 34323050›Full record

ArticleSichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition2021

[Identifying Molecular Subtypes of Whole-Slide Image in Colorectal Cancer via Deep Learning].

Jun Liao, Xiao-Bing Feng, Yu-Hong Wang, Ling-Chuan Guo

Abstract read
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In one paragraph

Article in Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Article
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

4 authors.

Jun LiaoSchool of Science, China Pharmaceutical University, Nanjing 211198, China.
Xiao-Bing FengSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.
Yu-Hong WangDepartment of Pathology, the First Affiliated Hospital of Soochow University, Suzhou 215000, China.
Ling-Chuan GuoDepartment of Pathology, the First Affiliated Hospital of Soochow University, Suzhou 215000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo establish an artificial intelligence-assisted diagnosis system for molecular subtyping of colorectal cancer (CRC).

methods812 whole-slide images (WSIs) of 422 patients were selected from the database of The Cancer Genome Atlas (TCGA) and were put into the training set (75%) and the test set (25%). The slides were stored in the www.paiwsit.com database. We preprocessed and segmented the slides based on the labelling results of experienced pathologists to generate a training set of more than 4 million labeled samples. Finally, deep learning models were adopted for training.

resultsAfter training with several convolutional neural network models, we tested the performance of the trained deep learning model on the test set of 203 WSIs from 110 patients, and our model achieved an accuracy of 53.04% at patch-level and 51.72% at slide-level, while the accuracy of CMS2 (one of a consensus of four subtypes for CRC) at slide-level was as high as 75.00%.

conclusionThis study is of great significance to the promotion of colorectal cancer screening and precision treatment.

Indexed as

Colorectal NeoplasmsDeep LearningArtificial IntelligenceHumansNeural Networks, ComputerAssisted diagnosisColorectal cancerDeep learningMolecular subtypesWhole-slide image

Identifiers

PMID34323050
PMCPMC10409381

What OpenQuestion holds

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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.