ArticleEClinicalMedicine2020
Prediction of prognostic risk factors in hepatocellular carcinoma with transarterial chemoembolization using multi-modal multi-task deep learning.
Article in EClinicalMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses 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
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Artificial Intelligence Models in Predicting Responsiveness of Hepatocellular Carcinoma to Transarterial Chemoembolization (TACE): A Systematic Review and Meta-Analysis.Journal of the American College of Radiology : JACR · 2026Pooled it
- Quality of radiomics for predicting microvascular invasion in hepatocellular carcinoma: a systematic review.European radiology · 2023Pooled it
- Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma.Scientific reports · 2024Trial
- Artificial intelligence in hepatocellular carcinoma: imaging-based subtyping and prediction of treatment response and prognosis.Abdominal radiology (New York) · 2026Review
- A prognostic model utilizing disulfidptosis-related long noncoding RNAs to differentiate pathological grades in hepatocellular carcinoma: functional analysis of AL031985.3.World journal of surgical oncology · 2026Article
- Clinical MRI-radiomics model based on support vector machine to predict the efficacy of first transarterial chemoembolization for unresectable intrahepatic cholangiocarcinoma.La Radiologia medica · 2026Article
- Multimodal deep learning for prediction of postoperative recurrence in clear cell renal cell carcinoma: a clinical-radiologic-pathologic approach.Frontiers in oncology · 2026Article
- Ethical challenges and governance of artificial intelligence in hepatocellular carcinoma management : Wan DL et al. Ethical considerations in AI for hepatocellular carcinoma.Medical oncology (Northwood, London, England) · 2025Review
- Personalized prognosis in unresectable hepatocellular carcinoma: Development and validation of a model for transcatheter arterial chemoembolization plus lenvatinib.World journal of gastrointestinal oncology · 2025Article
- Development of a predictive model for distant metastasis in HCC patients post-TACE using clinical data, radiomics, and deep learning.Journal of cancer research and clinical oncology · 2025Article
- Review
- MRI-Based Topology Deep Learning Model for Noninvasive Prediction of Microvascular Invasion and Assisting Prognostic Stratification in HCC.Liver international : official journal of the International Association for the Study of the Liver · 2025Article
- Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT.Precision radiation oncology · 2025Article
- A CT-Based Deep Learning Radiomics Scoring System for Predicting the Prognosis to Repeat TACE in Patients with Hepatocellular Carcinoma: A Multicenter Cohort Study.Journal of hepatocellular carcinoma · 2025Article
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- The power of deep learning in simplifying feature selection for hepatocellular carcinoma: a review.BMC medical informatics and decision making · 2024Review
- Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma.Abdominal radiology (New York) · 2024Article
- Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data.Signal transduction and targeted therapy · 2024Article
- [Research progress of radiomics in hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2024Review
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDue to heterogeneity of hepatocellular carcinoma (HCC), outcome assessment of HCC with transarterial chemoembolization (TACE) is challenging.
methodsWe built histologic-related scores to determine microvascular invasion (MVI) and Edmondson-Steiner grade by training CT radiomics features using machine learning classifiers in a cohort of 494 HCCs with hepatic resection. Meanwhile, we developed a deep learning (DL)-score for disease-specific survival by training CT imaging using DL networks in a cohort of 243 HCCs with TACE. Then, three newly built imaging hallmarks with clinicoradiologic factors were analyzed with a Cox-Proportional Hazard (Cox-PH) model.
findingsIn HCCs with hepatic resection, two imaging hallmarks resulted in areas under the curve (AUCs) of 0.79 (95% confidence interval [CI]: 0.71-0.85) and 0.72 (95% CI: 0.64-0.79) for predicting MVI and Edmondson-Steiner grade, respectively, using test data. In HCCs with TACE, higher DL-score (hazard ratio [HR]: 3.01; 95% CI: 2.02-4.50), American Joint Committee on Cancer (AJCC) stage III+IV (HR: 1.71; 95% CI: 1.12-2.61), Response Evaluation Criteria in Solid Tumors (RECIST) with stable disease + progressive disease (HR: 2.72; 95% CI: 1.84-4.01), and TACE-course > 3 (HR: 0.65; 95% CI: 0.45-0.76) were independent prognostic factors. Using these factors via a Cox-PH model resulted in a concordance index of 0.73 (95% CI: 0.71-0.76) for predicting overall survival and AUCs of 0.85 (95% CI: 0.81-0.89), 0.90 (95% CI: 0.86-0.94), and 0.89 (95% CI: 0.84-0.92), respectively, for predicting 3-year, 5-year, and 10-year survival.
interpretationOur study offers a DL-based, noninvasive imaging hallmark to predict outcome of HCCs with TACE.
fundingThis work was supported by the key research and development program of Jiangsu Province (Grant number: BE2017756).
Indexed as
Identifiers
What OpenQuestion holds
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.