ArticleCardiovascular and interventional radiology2023
Prediction of Local Tumor Progression After Microwave Ablation in Colorectal Carcinoma Liver Metastases Patients by MRI Radiomics and Clinical Characteristics-Based Combined Model: Preliminary Results.
Article in Cardiovascular and interventional radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 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
- The influence of AI on surgical and ablative treatments for colorectal liver metastases: a review of the current literature.Insights into imaging · 2025Review
- Review
- Intra- and Peritumoral CT-Based Radiomics for Assessing Pathologic T-Staging in Clear Cell Renal Cell Carcinoma: A Multicenter Study.Annals of surgical oncology · 2025Article
- DCE-MRI quantitative analysis and MRI-based radiomics for predicting the early efficacy of microwave ablation in lung cancers.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Applications of artificial intelligence in interventional oncology: An up-to-date review of the literature.Japanese journal of radiology · 2025Review
- Magnetic Resonance-Guided Cancer Therapy Radiomics and Machine Learning Models for Response Prediction.Tomography (Ann Arbor, Mich.) · 2024Review
- Prognostication of colorectal cancer liver metastasis by CE-based radiomics and machine learning.Translational oncology · 2024Article
- Review
- Machine Learning and Radiomics Analysis for Tumor Budding Prediction in Colorectal Liver Metastases Magnetic Resonance Imaging Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- Research progress on machine algorithm prediction of liver cancer prognosis after intervention therapy.American journal of cancer research · 2024Review
- Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors.Biomedicines · 2023Review
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6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate the predictability of local tumor progression (LTP) after microwave ablation (MWA) in colorectal carcinoma liver metastases (CRLM) patients by magnetic resonance imaging (MRI) radiomics and clinical characteristics-based combined model. MATERIALS AND
methodsForty-two consecutive CRLM patients (67 tumors) with post-MWA complete response at 1st month MRI were included in this retrospective study. One hundred and eleven radiomics features were extracted for each tumor and for each phase by manual segmentation from pre-treatment MRI T2 fat-suppressed (Phase 2) and early arterial phase T1 fat-suppressed sequences (Phase 1). A clinical model was constructed using clinical data, two combined models were created with feature reduction and machine learning by combining clinical data and Phase 2 and Phase 1 radiomics features. The predicting performance for LTP development was investigated.
resultsLTP developed in 7 patients (16.6%) and 11 tumors (16.4%). In the clinical model, the presence of extrahepatic metastases before MWA was associated with a high probability of LTP (p < 0.001). The pre-treatment levels of carbohydrate antigen 19-9 and carcinoembryonic antigen were higher in the LTP group (p = 0.010, p = 0.020, respectively). Patients with LTP had statistically significantly higher radiomics scores in both phases (p < 0.001 for Phase 2 and p = 0.001 for Phase 1). The classification performance of the combined model 2, created by using clinical data and Phase 2-based radiomics features, achieved the highest discriminative performance in predicting LTP (p = 0,014; the area under curve (AUC) value 0.981 (95% CI 0.948-0.990). The combined model 1, created using clinical data and Phase 1-based radiomics features (AUC value 0,927 (95% CI 0.860-0.993, p < 0.001)) and the clinical model alone [AUC value of 0.887 (95% CI 0.807-0.967, p < 0.001)] had similar performance.
conclusionCombined models based on clinical data and radiomics features obtained from T2 fat-suppressed and early arterial-phase T1 fat-suppressed MRI are valuable markers in predicting LTP after MWA in CRLM patients. Large-scale studies with internal and external validations are needed to come to a firm conclusion on the predictability of radiomics models in CRLM patients.
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