ArticleCancers2022
Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT).
Article in Cancers, 2022. 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.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Emerging Perspectives of Bone Metastasis in Hepatocellular Carcinoma.Frontiers in oncology · 2022Pooled it
- Non-Invasive Assessment of Microvascular Invasion Risk in Hepatocellular Carcinoma Using Liquid Biopsy: Translational Insights and Clinical Implications.Diagnostics (Basel, Switzerland) · 2026Review
- Enhanced prediction of microvascular invasion in hepatocellular carcinoma: a comparative study of intraoperative ultrasound, CT imaging and radiomic.La Radiologia medica · 2026Article
- Liver Cancer Bone Metastasis: Molecular Mechanisms and Therapeutic Insights.Current topics in medicinal chemistry · 2026Review
- Article
- Preoperative prediction of microvascular invasion risk in hepatocellular carcinoma with MRI: peritumoral versus tumor region.Insights into imaging · 2024Article
- The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging.Sensors (Basel, Switzerland) · 2024Article
- Radiomics of Preoperative Multi-Sequence Magnetic Resonance Imaging Can Improve the Predictive Performance of Microvascular Invasion in Hepatocellular Carcinoma.World journal of oncology · 2024Article
- Article
- Deep Learning Algorithm for Differentiating Patients with a Healthy Liver from Patients with Liver Lesions Based on MR Images.Cancers · 2023Article
- Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer.Journal of personalized medicine · 2023Article
- Radiomic Features from Post-OperativeJournal of clinical medicine · 2023Article
- Article
- Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma.Frontiers in oncology · 2023Article
- Article
- [Research progress in radiomics based on CT and MRI images for prediction of microvascular invasion in hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2022Article
- Predictive machine learning model for microvascular invasion identification in hepatocellular carcinoma based on the LI-RADS system.Frontiers in oncology · 2022Article
- Use of radiomics containing an effective peritumoral area to predict early recurrence of solitary hepatocellular carcinoma ≤5 cm in diameter.Frontiers in oncology · 2022Article
- Preoperative and Prognostic Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Review Based on Artificial Intelligence.Technology in cancer research & treatmentReview
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicrovascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist's tumour segmentation.
methodsThe study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics.
resultsThe original 89 HCC nodules (32 MVI+ and 57 MVI-) became 169 (62 MVI+ and 107 MVI-) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI-), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70-0.93),
conclusionsThe classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status.
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