ArticlePeerJ2020
Prognostic analysis of histopathological images using pre-trained convolutional neural networks: application to hepatocellular carcinoma.
Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of ovarian cancer using convolutional neural network: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Comparative Analysis of Deep Learning Approaches for Histopathology-Based Survival Prediction in Hepatocellular Carcinoma.Cancers · 2026Article
- Multi-platform integration of histopathological images and omics data predicts molecular features and prognosis of hepatocellular carcinoma.Frontiers in oncology · 2025Article
- The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review.BMC medical informatics and decision making · 2024Review
- Review
- The role of artificial intelligence and image processing in the diagnosis, treatment, and prognosis of liver cancer: a narrative-review.Przeglad gastroenterologiczny · 2024Review
- State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs).Journal of healthcare informatics research · 2023Review
- Classification of multi-differentiated liver cancer pathological images based on deep learning attention mechanism.BMC medical informatics and decision making · 2022Article
- Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma.Journal of hepatology · 2022Review
- Deep learning in hepatocellular carcinoma: Current status and future perspectives.World journal of hepatology · 2021Review
- imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics.Molecular oncology · 2021Article
- LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images.International journal of computer assisted radiology and surgery · 2021Article
- A generalized deep learning framework for whole-slide image segmentation and analysis.Scientific reports · 2021Article
- AI based colorectal disease detection using real-time screening colonoscopy.Precision clinical medicine · 2021Article
- Update on Hepatocellular Carcinoma: a Brief Review from Pathologist Standpoint.Journal of gastrointestinal cancer · 2020Review
- Toward automated severe pharyngitis detection with smartphone camera using deep learning networks.Computers in biology and medicine · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histopathological images contain rich phenotypic descriptions of the molecular processes underlying disease progression. Convolutional neural networks, state-of-the-art image analysis techniques in computer vision, automatically learn representative features from such images which can be useful for disease diagnosis, prognosis, and subtyping. Hepatocellular carcinoma (HCC) is the sixth most common type of primary liver malignancy. Despite the high mortality rate of HCC, little previous work has made use of CNN models to explore the use of histopathological images for prognosis and clinical survival prediction of HCC. We applied three pre-trained CNN models-VGG 16, Inception V3 and ResNet 50-to extract features from HCC histopathological images. Sample visualization and classification analyses based on these features showed a very clear separation between cancer and normal samples. In a univariate Cox regression analysis, 21.4% and 16% of image features on average were significantly associated with overall survival (OS) and disease-free survival (DFS), respectively. We also observed significant correlations between these features and integrated biological pathways derived from gene expression and copy number variation. Using an elastic net regularized Cox Proportional Hazards model of OS constructed from Inception image features, we obtained a concordance index (C-index) of 0.789 and a significant log-rank test (
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