ArticleBMC cancer2018
Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images.
Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 6 of them syntheses that pooled it.
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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.
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Who cites it
69 citing papers in PubMed, 6 syntheses or guidelines pooled it, 139 citations in OpenAlex.
- Machine learning for malignant versus benign focal liver lesions on US and CEUS: a meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Quality of radiomics for predicting microvascular invasion in hepatocellular carcinoma: a systematic review.European radiology · 2023Pooled it
- Diagnostic value of radiomics in predicting Ki-67 and cytokeratin 19 expression in hepatocellular carcinoma: a systematic review and meta-analysis.Frontiers in oncology · 2023Pooled it
- Radiomics for the Preoperative Evaluation of Microvascular Invasion in Hepatocellular Carcinoma: A Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis.Frontiers in oncology · 2022Pooled it
- Preoperative Prediction Power of Imaging Methods for Microvascular Invasion in Hepatocellular Carcinoma: A Systemic Review and Meta-Analysis.Frontiers in oncology · 2020Pooled it
- Multimodal Imaging under Artificial Intelligence Algorithm for the Diagnosis of Liver Cancer and Its Relationship with Expressions of EZH2 and p57.Computational intelligence and neuroscience · 2022Trial
- Predicting intrahepatic recurrence of colorectal cancer liver metastases after curative hepatectomy using a machine learning model with data integration of ultrasound radiomics and clinicopathological parameters.Insights into imaging · 2026Article
- Deep learning prediction model based on multi-modal transvaginal ultrasound scan images for endometrial cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Preoperative differentiation of benign and malignant gallbladder lesions using multimodal ultrasound radiomics: a retrospective study.Frontiers in medicine · 2026Article
- Preoperative prediction of early recurrence and microvascular invasion in hepatocellular carcinoma using conventional ultrasound, elastography, and clinical nutritional indicators: a radiomics-based study.American journal of translational research · 2026Article
- Predictive modeling of obstructive sleep apnea using pharyngeal magnetic resonance imaging radiomics and clinical data.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Deep learning HRNet FCN for blood vessel identification in laparoscopic pancreatic surgery.NPJ digital medicine · 2025Article
- Radiomics predicting immunohistochemical markers in primary hepatic carcinoma: Current status and challenges.Heliyon · 2024Review
- A combined model based on radiomics features of Sonazoid contrast-enhanced ultrasound in the Kupffer phase for the diagnosis of well-differentiated hepatocellular carcinoma and atypical focal liver lesions: a prospective, multicenter study.Abdominal radiology (New York) · 2024Article
- Preoperative prediction of microvascular invasion: new insights into personalized therapy for early-stage hepatocellular carcinoma.Quantitative imaging in medicine and surgery · 2024Review
- Targeted sequencing of DNA/RNA combined with radiomics predicts lymph node metastasis of papillary thyroid carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Predicting microvascular invasion in small (≤ 5 cm) hepatocellular carcinomas using radiomics-based peritumoral analysis.Insights into imaging · 2024Article
- Radiomics-Based Classification of Tumor and Healthy Liver on Computed Tomography Images.Cancers · 2024Article
- Advancing Hepatocellular Carcinoma Management Through Peritumoral Radiomics: Enhancing Diagnosis, Treatment, and Prognosis.Journal of hepatocellular carcinoma · 2024Review
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThis study aims to establish a radiomics analysis system for the diagnosis and clinical behaviour prediction of hepatocellular carcinoma (HCC) based on multi-parametric ultrasound imaging.
methodsA total of 177 patients with focal liver lesions (FLLs) were included in the study. Every patient underwent multi-modal ultrasound examination, including B-mode ultrasound (BMUS), shear wave elastography (SWE), and shear wave viscosity (SWV) imaging. The radiomics analysis system was built on sparse representation theory (SRT) and support vector machine (SVM) for asymmetric data. Through the sparse regulation from the SRT, the proposed radiomics system can effectively avoid over-fitting issues that occur in regular radiomics analysis. The purpose of the proposed system includes differential diagnosis between benign and malignant FLLs, pathologic diagnosis of HCC, and clinical prognostic prediction. Three biomarkers, including programmed cell death protein 1 (PD-1), antigen Ki-67 (Ki-67) and microvascular invasion (MVI), were included and analysed. We calculated the accuracy (ACC), sensitivity (SENS), specificity (SPEC) and area under the receiver operating characteristic curve (AUC) to evaluate the performance of the radiomics models.
resultsA total of 2560 features were extracted from the multi-modal ultrasound images for each patient. Five radiomics models were built, and leave-one-out cross-validation (LOOCV) was used to evaluate the models. In LOOCV, the AUC was 0.94 for benign and malignant classification (95% confidence interval [CI]: 0.88 to 0.98), 0.97 for malignant subtyping (95% CI: 0.93 to 0.99), 0.97 for PD-1 prediction (95% CI: 0.89 to 0.98), 0.94 for Ki-67 prediction (95% CI: 0.87 to 0.97), and 0.98 for MVI prediction (95% CI: 0.93 to 0.99). The performance of each model improved when the viscosity modality was included.
conclusionsRadiomics analysis based on multi-modal ultrasound images could aid in comprehensive liver tumor evaluations, including diagnosis, differential diagnosis, and clinical prognosis.
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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.