ArticleFrontiers in oncology2022
CT-Based Radiomics Nomogram Improves Risk Stratification and Prediction of Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 4 of them syntheses that pooled it.
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14 citing papers in PubMed, 4 syntheses or guidelines pooled it, 16 citations in OpenAlex.
- Artificial intelligence for the prediction of posthepatectomy recurrence in hepatocellular carcinoma: a systematic review and meta-analysis.Annals of medicine · 2025Pooled it
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review.Abdominal radiology (New York) · 2024Pooled it
- Radiomics for preoperative prediction of early recurrence in hepatocellular carcinoma: a meta-analysis.Frontiers in oncology · 2023Pooled it
- AI-Driven Predictive Models of Early Recurrence of HCC After Surgical Resection: A Systematic Review.Cancers · 2026Review
- Development and External Validation of a Multimodal Machine Learning Model Integrating Preoperative Contrast-Enhanced CT Radiomics and Clinicopathological Features for Predicting Early Extrahepatic Recurrence After Hepatectomy for Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Prognostic prediction of primary liver cancer following transcatheter arterial chemoembolization (TACE) combined with targeted immunotherapy based on CT morphological characteristics.American journal of cancer research · 2025Article
- From detection to elimination: iron-based nanomaterials driving tumor imaging and advanced therapies.Frontiers in oncology · 2025Review
- Preoperative prediction for early recurrence of hepatocellular carcinoma using machine learning-based radiomics.Frontiers in oncology · 2024Article
- Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors.Biomedicines · 2023Review
- Gut microbiome as a biomarker for predicting early recurrence of HBV-related hepatocellular carcinoma.Cancer science · 2023Article
- Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis.Journal of clinical medicine · 2023Review
- Development of a machine learning-based model for predicting risk of early postoperative recurrence of hepatocellular carcinoma.World journal of gastroenterology · 2023Article
- Prediction of early recurrence of HCC after hepatectomy by contrast-enhanced ultrasound-based deep learning radiomics.Frontiers in oncology · 2022Article
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6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Objectives: To develop and validate an intuitive computed tomography (CT)-based radiomics nomogram for the prediction and risk stratification of early recurrence (ER) in hepatocellular carcinoma (HCC) patients after partial hepatectomy. Methods: A total of 132 HCC patients treated with partial hepatectomy were retrospectively enrolled and assigned to training and test sets. Least absolute shrinkage and selection operator and gradient boosting decision tree were used to extract quantitative radiomics features from preoperative contrast-enhanced CT images of the HCC patients. The radiomics features with predictive value for ER were used, either alone or in combination with other predictive features, to construct predictive models. The best performing model was then selected to develop an intuitive, simple-to-use nomogram, and its performance in the prediction and risk stratification of ER was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results: The radiomics model based on the radiomics score (Rad-score) achieved AUCs of 0.870 and 0.890 in the training and test sets, respectively. Among the six predictive models, the combined model based on the Rad-score, Edmondson grade, and tumor size had the highest AUCs of 0.907 in the training set and 0.948 in the test set and was used to develop an intuitive nomogram. Notably, the calibration curve and DCA for the nomogram showed good calibration and clinical application. Moreover, the risk of ER was significantly different between the high- and low-risk groups stratified by the nomogram ( Conclusions: The CT-based radiomics nomogram developed in this study exhibits outstanding performance for ER prediction and risk stratification. As such, this intuitive nomogram holds promise as a more effective and user-friendly tool in predicting ER for HCC patients after partial hepatectomy.
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