ArticleMolecular imaging and biology2021
Dynamic Contrast-Enhanced Ultrasound Radiomics for Hepatocellular Carcinoma Recurrence Prediction After Thermal Ablation.
Article in Molecular imaging and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- Artificial intelligence in predicting recurrence after first-line treatment of liver cancer: a systematic review and meta-analysis.BMC medical imaging · 2024Pooled it
- Ultrasound-guided microwave ablation for breast tumors: current status and future perspectives.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- Advancements in artificial intelligence applications for liver ultrasound imaging.BJR artificial intelligence · 2026Review
- Review
- Review
- Radiomic signatures from postprocedural MRI thalamotomy lesion can predict long-term clinical outcome in patients with tremor after MRgFUS: a pilot study.Frontiers in radiology · 2025Article
- Risk Factors for Recurrence in Patients with Hepatocellular Carcinoma After Curative Resection or Ablation.Journal of hepatocellular carcinoma · 2025Review
- Development of a weighted Alpha-Fetoprotein tumor burden score-integrated nomogram for predicting overall survival in locally ablated hepatocellular carcinoma patients.Frontiers in oncology · 2025Article
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion.Cancers · 2024Review
- A bibliometric analysis of contrast-enhanced ultrasound over the past twenty years.Quantitative imaging in medicine and surgery · 2024Article
- Prognostic model for predicting recurrence in hepatocellular carcinoma patients with high systemic immune-inflammation index based on machine learning in a multicenter study.Frontiers in immunology · 2024Article
- Ultrasound-based radiomics analysis in the assessment of renal fibrosis in patients with chronic kidney disease.Abdominal radiology (New York) · 2023Article
- Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review.Journal of medical systems · 2023Review
- CT-based radiomics models may predict the early efficacy of microwave ablation in malignant lung tumors.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
- Characterization of spatially mapped volumetric molecular ultrasound signals for predicting response to anti-vascular therapy.Scientific reports · 2023Article
- Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses.Bioengineering (Basel, Switzerland) · 2023Article
- Non-invasive preoperative prediction of Edmondson-Steiner grade of hepatocellular carcinoma based on contrast-enhanced ultrasound using ensemble learning.Frontiers in oncology · 2023Article
- Ultrasound-based artificial intelligence in gastroenterology and hepatology.World journal of gastroenterology · 2022Review
- An update on radiomics techniques in primary liver cancers.Infectious agents and cancer · 2022Article
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a radiomics model based on dynamic contrast-enhanced ultrasound (CEUS) to predict early and late recurrence in patients with a single HCC lesion ≤ 5 cm in diameter after thermal ablation. PROCEDURES: We enrolled patients who underwent thermal ablation for HCC in our hospital from April 2004 to April 2017. Radiomics based on two branch convolution recurrent network was utilized to analyze preoperative dynamic CEUS image of HCC lesions to establish CEUS model, in comparison to the conventional ultrasound (US), clinical, and combined models. Clinical follow-up of HCC recurrence after ablation were taken as reference standard to evaluate the predicted performance of CEUS model and other models.
resultsWe finally analyzed 318 patients (training cohort: test cohort = 255:63). The combined model showed better performance for early recurrence than CUES (in training cohort, AUC, 0.89 vs. 0.84, P < 0.001; in test cohort, AUC, 0.84 vs. 0.83, P = 0.272), US (P < 0.001), or clinical model (P < 0.001). For late recurrence prediction, the combined model showed the best performance than the CEUS (C-index, in training cohort, 0.77 vs. 0.76, P = 0.009; in test cohort, 0.77 vs. 0.68, P < 0.001), US (P < 0.001), or clinical model (P < 0.001).
conclusionsThe CEUS model based on dynamic CEUS radiomics performed well in predicting early HCC recurrence after ablation. The combined model combining CEUS, US radiomics, and clinical factors could stratify the high risk of late recurrence.
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