ArticleFrontiers in oncology2022
Use of radiomics containing an effective peritumoral area to predict early recurrence of solitary hepatocellular carcinoma ≤5 cm in diameter.
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 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics for preoperative prediction of early recurrence in hepatocellular carcinoma: a meta-analysis.Frontiers in oncology · 2023Pooled it
- Limited incremental value and cross-cohort transferability of CT radiomics for survival prediction after TACE in hepatocellular carcinoma.Abdominal radiology (New York) · 2026Article
- Noninvasive prediction of microvascular invasion in hepatocellular carcinoma using a fusion model of 5-mm optimal peritumoral radiomics and imaging features based on multiphasic gadoxetic acid-enhanced MRI.Frontiers in oncology · 2026Article
- Noninvasive prediction of Glypican-3 expression in hepatocellular carcinoma using Habitat-based and peritumoral CT radiomics: a nomogram approach.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Preoperative model for predicting early recurrence in hepatocellular carcinoma patients using radiomics and deep learning: A multicenter study.World journal of gastrointestinal oncology · 2025Article
- Preoperative prediction of microvascular invasion and relapse-free survival in hepatocellular Carcinoma ≥3 cm using CT radiomics: Development and external validation.BMC medical imaging · 2025Article
- Article
- Integrating multimodal imaging and peritumoral features for enhanced prostate cancer diagnosis: A machine learning approach.PloS one · 2025Article
- Role of Intravoxel Incoherent Motion and Dynamic-Contrast-Enhanced Perfusion in Response Prediction of Patients with Colorectal Cancer after Neoadjuvant Chemotherapy.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Omics-imaging signature-based nomogram to predict the progression-free survival of patients with hepatocellular carcinoma after transcatheter arterial chemoembolization.World journal of clinical cases · 2024Article
- Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis.World journal of gastroenterology · 2024Article
- Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis.Journal of hepatocellular carcinoma · 2024Review
- High-risk breast lesions: a combined intratumoral and peritumoral radiomics nomogram model to predict pathologic upgrade and reduce unnecessary surgical excision.Frontiers in oncology · 2024Article
- Preoperative prediction for early recurrence of hepatocellular carcinoma using machine learning-based radiomics.Frontiers in oncology · 2024Article
- Article
- Effect of multiple peritumoral regions of interest ranges based on computed tomography radiomics for the prediction of early recurrence of hepatocellular carcinoma after resection.Quantitative imaging in medicine and surgery · 2023Article
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6 authors.
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Abstract
Background: Hepatocellular carcinoma (HCC) is the sixth leading type of cancer worldwide. We aimed to develop a preoperative predictive model of the risk of early tumor recurrence after HCC treatment based on radiomic features of the peritumoral region and evaluate the performance of this model against postoperative pathology. Method: Our model was developed using a retrospective analysis of imaging and clinicopathological data of 175 patients with an isolated HCC ≤5 cm in diameter; 117 patients were used for model training and 58 for model validation. The peritumoral area was delineated layer-by-layer for the arterial and portal vein phase on preoperative dynamic enhanced computed tomography images. The volume area of interest was expanded by 5 and 10 mm and the radiomic features of these areas extracted. Lasso was used to select the most stable features. Results: The radiomic features of the 5-mm area were sufficient for prediction of early tumor recurrence, with an area under the curve (AUC) value of 0.706 for the validation set and 0.837 for the training set using combined images. The AUC of the model using clinicopathological information alone was 0.753 compared with 0.786 for the preoperative radiomics model (P >0.05). Conclusions: Radiomic features of a 5-mm peritumoral region may provide a non-invasive biomarker for the preoperative prediction of the risk of early tumor recurrence for patients with a solitary HCC ≤5 cm in diameter. A fusion model that combines the radiomic features of the peritumoral region and postoperative pathology could contribute to individualized treatment of HCC.
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