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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Vydáno v:	BMC cancer Ročník 24; číslo 1; s. 1523 - 12
Hlavní autoři:	Zhou, Xuezhi, Dai, Jing, Lu, Yizhan, Zhao, Qingqing, Liu, Yong, Wang, Chang, Zhao, Zongya, Wang, Chong, Gao, Zhixian, Yu, Yi, Zhao, Yandong, Cao, Wuteng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 18.12.2024 BioMed Central Ltd Springer Nature B.V BMC
Témata:	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
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2407 1471-2407
DOI:	10.1186/s12885-024-13292-5