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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Veröffentlicht in:	BMC cancer Jg. 24; H. 1; S. 1523 - 12
Hauptverfasser:	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
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 18.12.2024 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	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-Zugang:	Volltext
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