Evidence map›Paper›PMID 41899865›Full record

ArticleBioengineering (Basel, Switzerland)2026

Uncovering Hidden Prognostic Patterns in Colorectal Cancer Histology Using Unsupervised Learning: A Computational Pathology Study.

Wen-Tong Zhou, Yong Liu, Gang Yu, Kuan-Song Wang, Chao Xu, Jonathan Greenbaum, Chong Wu, Lin-Dong Jiang, Christopher J Papasian, Hong-Mei Xiao and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Wen-Tong ZhouCenter for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410031, China.
Yong LiuCenter for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410031, China.ORCID 0000-0001-9360-1304
Gang YuDepartment of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha 410013, China.
Kuan-Song WangDepartment of Pathology, Xiangya Hospital, School of Basic Medical Science, Central South University, Changsha 410078, China.
Chao XuDepartment of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA.ORCID 0000-0002-3821-6187
Jonathan GreenbaumTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA.
Chong WuDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Lin-Dong JiangTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA.
Christopher J PapasianDepartment of Biomedical Sciences, School of Medicine, University of Missouri-Kansas City, Kansas City, MO 64108, USA.
Hong-Mei XiaoInstitute of Reproductive & Stem Cell Engineering, School of Basic Medical Science, Central South University, Changsha 410000, China.
Hong-Wen DengTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA.ORCID 0000-0002-0387-8818

Funding

Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Katherine Teresa Mills · 2016 to 2026
$25.3M
Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI Chuan Qiu · 2017 to 2026
$24.3M
Intensive Lifestyle Intervention, Metabolomics, and Risk of Frailty Fracture in Overweight or Obese Patients with Type 2 DiabetesR01AG068232 · NIA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI JOHNSON, KAREN C, ZHAO, QI · 2021 to 2025
$3.1M
Jiangwang Educational Endowment NoneNational Key Research and Development Plan of China #2017YFC1001103, #2016YFC1201805National Natural Science Foundation of China #81471453NIA NIH HHS R01 AG068232NIA NIH HHS U19 AG055373NIGMS NIH HHS P20 GM109036
6 · The paper itself

Abstract

Colorectal cancer (CRC) remains a leading cause of cancer mortality globally, yet current histopathological diagnostics capture only limited features. This study aimed to discover subtle, prognostically significant histomorphological patterns in CRC tissues using unsupervised deep learning. We developed a framework integrating convolutional neural networks with deep clustering, trained on 23,341 image patches from 493 patients. We identified 30 distinct histomorphological clusters from CRC tissue images. Through univariate and multivariate survival analyses, three clusters (Cluster13, Cluster19, and Cluster24) were consistently associated with patient prognosis. These clusters were integrated with clinical factors (T stage, N stage, and differentiation degree) to construct a prognostic risk model. Patients stratified into high-risk and low-risk groups based on model predictions showed significant survival differences in both the training set (

Indexed as

colorectal cancerhistomorphological patternspathological imagespatient prognosisunsupervised learning

Identifiers

PMID41899865
PMCPMC13024107

What OpenQuestion holds

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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.