ArticleCancers2023
Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Diagnostic Accuracy of USG/CT-Guided FNAC With Rapid On-Site Evaluation in Nodular Liver Lesions: Cytology-Histopathology Correlation.Diagnostic cytopathology · 2026Article
- Comparative Analysis of Deep Learning Approaches for Histopathology-Based Survival Prediction in Hepatocellular Carcinoma.Cancers · 2026Article
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer.Frontiers in oncology · 2026Review
- Liver ischemia-reperfusion promotes tumor metastasis via neutrophil-endothelial interactions: targeting ICAM1 to prevent metastatic recurrence.Journal of translational medicine · 2025Article
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
- Multi-algorithm radiomics machine learning models integrating ultrasound imaging and inflammation-immune features for hepatic metastases identification.BMC medical imaging · 2025Article
- Review
- Advances in Endoscopic Diagnosis and Management of Cholangiocarcinoma.Journal of clinical medicine · 2025Review
- Radiomics Beyond Radiology: Literature Review on Prediction of Future Liver Remnant Volume and Function Before Hepatic Surgery.Journal of clinical medicine · 2025Review
- Artificial intelligence-driven pathomics in hepatocellular carcinoma: current developments, challenges and perspectives.Discover oncology · 2025Review
- Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification.World journal of clinical oncology · 2025Review
- Histogram analysis of continuous-time random walk and restrictive spectrum imaging for identifying hepatocellular carcinoma and intrahepatic cholangiocarcinoma.Frontiers in oncology · 2025Article
- AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.Journal of clinical medicine · 2024Review
- Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies.ACS pharmacology & translational science · 2024Review
- Research trends and hotspots evolution of artificial intelligence for cholangiocarcinoma over the past 10 years: a bibliometric analysis.Frontiers in oncology · 2024Review
- Applications of artificial intelligence in digital pathology for gastric cancer.Frontiers in oncology · 2024Review
- Artificial intelligence: clinical applications and future advancement in gastrointestinal cancers.Frontiers in artificial intelligence · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Diagnosing primary liver cancers, particularly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), is a challenging and labor-intensive process, even for experts, and secondary liver cancers further complicate the diagnosis. Artificial intelligence (AI) offers promising solutions to these diagnostic challenges by facilitating the histopathological classification of tumors using digital whole slide images (WSIs). This study aimed to develop a deep learning model for distinguishing HCC, CC, and metastatic colorectal cancer (mCRC) using histopathological images and to discuss its clinical implications. The WSIs from HCC, CC, and mCRC were used to train the classifiers. For normal/tumor classification, the areas under the curve (AUCs) were 0.989, 0.988, and 0.991 for HCC, CC, and mCRC, respectively. Using proper tumor tissues, the HCC/other cancer type classifier was trained to effectively distinguish HCC from CC and mCRC, with a concatenated AUC of 0.998. Subsequently, the CC/mCRC classifier differentiated CC from mCRC with a concatenated AUC of 0.995. However, testing on an external dataset revealed that the HCC/other cancer type classifier underperformed with an AUC of 0.745. After combining the original training datasets with external datasets and retraining, the classification drastically improved, all achieving AUCs of 1.000. Although these results are promising and offer crucial insights into liver cancer, further research is required for model refinement and validation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.