Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence

Background Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progre...

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Bibliographic Details
Published in:BMC cancer Vol. 24; no. 1; pp. 1523 - 12
Main Authors: Zhou, Xuezhi, Dai, Jing, Lu, Yizhan, Zhao, Qingqing, Liu, Yong, Wang, Chang, Zhao, Zongya, Wang, Chong, Gao, Zhixian, Yu, Yi, Zhao, Yandong, Cao, Wuteng
Format: Journal Article
Language:English
Published: London BioMed Central 18.12.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Accuracy
Aged
Algorithms
Artificial Intelligence
Bioinformatics
Biomarkers
Biomarkers, Tumor
Biomedical and Life Sciences
Biomedicine
Cancer
Cancer Research
Clinical outcomes
Colorectal cancer
Datasets
Decision making
Deep learning
Diagnostic imaging
Female
Health aspects
Health Promotion and Disease Prevention
Humans
Male
Medical prognosis
Medicine/Public Health
Methods
Middle Aged
Oncology
Patients
Prognosis
Prognosis prediction
Progression-Free Survival
Rectal cancer
Rectal Neoplasms - mortality
Rectal Neoplasms - pathology
Rectum
Surgical Oncology
Survival
Technology application
Transfer learning
Tumors
Unsupervised learning
Unsupervised Machine Learning
Whole slide image
China
Whole slide image
Prognosis prediction
Rectal cancer
Bioinformatics
Unsupervised learning
ISSN:1471-2407, 1471-2407
Online Access:Get full text
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Summary:Background Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm. Methods A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient’s tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs. Results The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms. Conclusion The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation.
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ISSN:1471-2407
1471-2407
DOI:10.1186/s12885-024-13292-5