ArticleEClinicalMedicine2021
Multi-task deep learning network to predict future macrovascular invasion in hepatocellular carcinoma.
Article in EClinicalMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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
22 citing papers in PubMed.
- Article
- Bayesian machine learning enables discovery of risk factors for hepatosplenic multimorbidity related to schistosomiasis.Nature communications · 2026Article
- Predicting prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure from longitudinal ultrasound images using a multi-task deep learning approach.Annals of medicine · 2025Article
- Review
- Prognostic nomogram for hepatocellular carcinoma with major vascular invasion: a population-based study from the SEER database and a Chinese cohort.European journal of medical research · 2025Article
- Predicting Pathological Characteristics of HER2-Positive Breast Cancer from Ultrasound Images: a Deep Ensemble Approach.Journal of imaging informatics in medicine · 2025Article
- Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT.Precision radiation oncology · 2025Article
- 3D synergistic tumor-liver analysis further improves the efficacy prediction in hepatocellular carcinoma: a multi-center study.BMC cancer · 2025Article
- Tumor Compression of the Hepatic or Portal Vein Predicts the Presence of Microvascular Invasion and Satellite Nodules in Hepatocellular Carcinoma: A Retrospective Study.Journal of hepatocellular carcinoma · 2025Article
- Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients.NPJ precision oncology · 2024Article
- Hepatic-associated vascular morphological assessment to predict overt hepatic encephalopathy before TIPS: a multicenter study.Hepatology international · 2024Article
- Whole-orbit radiomics: machine learning-based multi- and fused- region radiomics signatures for intravenous glucocorticoid response prediction in thyroid eye disease.Journal of translational medicine · 2024Article
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- A Deep Learning Model Combining Multimodal Factors to Predict the Overall Survival of Transarterial Chemoembolization.Journal of hepatocellular carcinoma · 2024Article
- Research Progress in Predicting Hepatocellular Carcinoma with Portal Vein Tumour Thrombus in the Era of Artificial Intelligence.Journal of hepatocellular carcinoma · 2024Review
- Article
- Development and validation of convolutional neural network-based model to predict the risk of sentinel or non-sentinel lymph node metastasis in patients with breast cancer: a machine learning study.EClinicalMedicine · 2023Article
- Article
- Technological Advancements in Interventional Oncology.Diagnostics (Basel, Switzerland) · 2023Review
- Multimodal deep learning for liver cancer applications: a scoping review.Frontiers in artificial intelligence · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundModels predicting future macrovascular invasion in hepatocellular carcinoma are constructed to assist timely interventions.
methodsA total of 366 HCC cases were retrospectively collected from five Chinese hospitals between April 2007 and November 2016: the training dataset comprised 281 patients from four hospitals; the external validation dataset comprised 85 patients from another hospital. Multi-task deep learning network-based models were constructed to predict future macrovascular invasion. The discrimination, calibration, and decision curves were compared to identify the best model. We compared the time to macrovascular invasion and overall survival using the best model and related image heterogeneity scores (H-score). Then, we determined the need for a segmentation subnet or the replacement deep learning algorithm by logistic regression in screening clinical/radiological factors. Finally, an applet was constructed for future application.
findingsThe best model combined clinical/radiological factors and radiomic features. It achieved best discrimination (areas under the curve: 0·877 in the training dataset and 0·836 in the validation dataset), calibration, and decision curve. Its performance was not affected by the treatments and disease stages. The subgroups had statistical significance for time to macrovascular invasion (training: hazard ratio [HR] = 0·073, 95% confidence interval [CI]: 0·032-0·167,
interpretationOur multi-task deep learning network-based model successfully predicted future macrovascular invasion. In high-risk populations, besides the current first-line treatments, more therapies may be explored for macrovascular invasion.
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.