Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning
Abstract
Objective The aim of this study was to develop quantitative feature-based models from histopathological images to distinguish hepatocellular carcinoma (HCC) from adjacent normal tissue and predict the prognosis of HCC patients after surgical resection.Methods A fully automated pipeline was constructed using computational approaches to analyze the quantitative features of histopathological slides of HCC patients, in which the features were extracted from the hematoxylin and eosin (H&E)-stained whole-slide images of HCC patients from The Cancer Genome Atlas and tissue microarray images from West China Hospital. The extracted features were used to train the statistical models that classify tissue slides and predict patients' survival outcomes by machine-learning methods.Results A total of 1733 quantitative image features were extracted from each histopathological slide. The diagnostic classifier based on 31 features was able to successfully distinguish HCC from adjacent normal tissues in both the test [area under the receiver operating characteristic curve (AUC) 0.988] and external validation sets (AUC 0.886). The random-forest prognostic model using 46 features was able to significantly stratify patients in each set into longer- or shorter-term survival groups according to their assigned risk scores. Moreover, the prognostic model we constructed showed comparable predicting accuracy as TNM staging systems in predicting patients' survival at different time points after surgery.Conclusions Our findings suggest that machine-learning models derived from image features can assist clinicians in HCC diagnosis and its prognosis prediction after hepatectomy.
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AI-driven whole slide pathology analysis has been applied in diagnosis, prognosis, and efficacy prediction.
“Artificial intelligence (AI)–driven whole slide pathology analysis has been applied in diagnosis, prognosis, and efficacy prediction, and it has become a research hotspot (14, 24, 25).”
From A multimodal and fully automated system for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer (2025) · cites this paper as [25]
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- Authors
- Liao, H., Xiong, T., Peng, J., Xu, L., Liao, M., Zhang, Z., Wu, Z., Yuan, K., & Zeng, Y.
- Affiliations
- Sichuan University (CN)
- Northwestern Polytechnical University (CN)
- Corresponding author
- Yuan, Kefei
- Zeng, Yong
- Venue
- Annals of Surgical Oncology
- Published
- 2020-01-08
- Volume
- 27
- Issue
- 7
- Pages
- 2359-2369
- ISSN
- 1068-9265 (ISSN-L)
- 1534-4681
- OpenAlex
- W3000129002
- Cited by
- 65
- References
- 79
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 15 · 2023: 10 · 2022: 9 · 2021: 6 · 2020: 8
- FWCI
- 3.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Surgical Oncology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- hepatocellular carcinoma, histopathology image analysis, automated image classification, outcome prediction, survival prediction, quantitative imaging features, H&E-stained whole slide images, random forest, hepatectomy, TNM staging comparison, liver cancer diagnosis
- MeSH terms
- Machine Learning, Carcinoma, Hepatocellular, Humans, Liver Neoplasms, Prognosis
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