ArticleCancers2022
MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Medical Image-Based Deep Learning for Detecting Microvascular Invasion in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Advancing Diagnostic Accuracy in Liver Cancer: A Systematic Review of Artificial Intelligence Applications in Hepatocellular Carcinoma and Cholangiocarcinoma Detection Using Abdominal CT Imaging.Asian Pacific journal of cancer prevention : APJCP · 2026Pooled it
- Development and validation of a nomogram based on tumor margin irregularity and alpha-fetoprotein for predicting microvascular invasion in hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Deep learning-based MRI model for predicting P53-mutated hepatocellular carcinoma.BMC medical imaging · 2025Article
- Preoperative MVI prediction in intrahepatic cholangiocarcinoma via deep learning analysis of intratumoral and peritumoral features on multi-sequence MRI.BMC medical imaging · 2025Article
- The Potential of Artificial Intelligence to Improve Selection Criteria for Liver Transplantation in HCC.Cancers · 2025Review
- Neuromorphic chips for biomedical engineering.Mechanobiology in medicine · 2025Review
- Chinese expert consensus on the diagnosis and treatment of hepatocellular carcinoma with microvascular invasion (2024 edition).Hepatobiliary surgery and nutrition · 2025Review
- Advances in multi-omics studies of microvascular invasion in hepatocellular carcinoma.European journal of medical research · 2025Review
- The Protective Role of miR-125b in Hepatocellular Carcinoma: Unraveling Tumor-Suppressive Mechanisms.Current molecular medicine · 2025Review
- 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
- Deep learning of pretreatment multiphase CT images for predicting response to lenvatinib and immune checkpoint inhibitors in unresectable hepatocellular carcinoma.Computational and structural biotechnology journal · 2024Article
- Preoperative prediction of microvascular invasion risk in hepatocellular carcinoma with MRI: peritumoral versus tumor region.Insights into imaging · 2024Article
- Dual-energy computed tomography iodine quantification combined with laboratory data for predicting microvascular invasion in hepatocellular carcinoma: a two-centre study.The British journal of radiology · 2024Article
- Early warning of hepatocellular carcinoma in cirrhotic patients by three-phase CT-based deep learning radiomics model: a retrospective, multicentre, cohort study.EClinicalMedicine · 2024Article
- A new method for predicting the microvascular invasion status of hepatocellular carcinoma through neural network analysis.BMC surgery · 2023Article
- A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images.Scientific reports · 2023Article
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- Application of spectral CT in diagnosis, classification and prognostic monitoring of gastrointestinal cancers: progress, limitations and prospects.Frontiers in molecular biosciences · 2023Review
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Authors and funding
6 authors.
Funding
Abstract
Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) directly affects a patient's prognosis. The development of preoperative noninvasive diagnostic methods is significant for guiding optimal treatment plans. In this study, we investigated 138 patients with HCC and presented a novel end-to-end deep learning strategy based on computed tomography (CT) radiomics (MVI-Mind), which integrates data preprocessing, automatic segmentation of lesions and other regions, automatic feature extraction, and MVI prediction. A lightweight transformer and a convolutional neural network (CNN) were proposed for the segmentation and prediction modules, respectively. To demonstrate the superiority of MVI-Mind, we compared the framework's performance with that of current, mainstream segmentation, and classification models. The test results showed that MVI-Mind returned the best performance in both segmentation and prediction. The mean intersection over union (mIoU) of the segmentation module was 0.9006, and the area under the receiver operating characteristic curve (AUC) of the prediction module reached 0.9223. Additionally, it only took approximately 1 min to output a prediction for each patient, end-to-end using our computing device, which indicated that MVI-Mind could noninvasively, efficiently, and accurately predict the presence of MVI in HCC patients before surgery. This result will be helpful for doctors to make rational clinical decisions.
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