{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T03:45:48Z","timestamp":1768880748766,"version":"3.49.0"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"1146","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BJR"],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:sec><jats:title>Objectives:<\/jats:title><jats:p> To determine whether radiomics data can predict local tumor progression (LTP) following radiofrequency ablation (RFA) of colorectal cancer (CRC) lung metastases on the first revaluation chest CT. <\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p> This case\u2013control single-center retrospective study included 95 distinct lung metastases treated by RFA (in 39 patients, median age: 63.1 years) with a contrast-enhanced CT-scan performed 3 months after RFA. Forty-eight radiomics features (RFs) were extracted from the 3D-segmentation of the ablation zone. Several supervised machine-learning algorithms were trained in 10-fold cross-validation on reproducible RFs to predict LTP, with\/without denoising CT-scans. An unsupervised classification based on reproducible RFs was built with k-means algorithm. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p> There were 20\/95 (26.7%) relapses within a median delay of 10 months. The best model was a stepwise logistic regression on raw CT-scans. Its cross-validated performances were: AUROC = 0.72 (0.58\u20130.86), area under the Precision-Recall curve (AUPRC) = 0.44. Cross-validated balanced-accuracy, sensitivity and specificity were 0.59, 0.25 and 0.93, respectively, using p = 0.5\u2009to dichotomize the model predicted probabilities (vs 0.71, 0.70 and 0.72, respectively using p = 0.188 according to Youden index). The unsupervised approach identified two clusters, which were not associated with LTP (p = 0.8211) but with the occurrence of per-RFA intra-alveolar hemorrhage, post-RFA cavitations and fistulizations (p = 0.0150). <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion:<\/jats:title><jats:p> Predictive models using RFs from the post-RFA ablation zone on the first revaluation CT-scan of CRC lung metastases seemed moderately informative regarding the occurrence of LTP. <\/jats:p><\/jats:sec><jats:sec><jats:title>Advances in knowledge:<\/jats:title><jats:p> Radiomics approach on interventional radiology data is feasible. However, patterns of heterogeneity detected with RFs on early re-evaluation CT-scans seem biased by different healing processes following benign RFA complications. <\/jats:p><\/jats:sec>","DOI":"10.1259\/bjr.20201371","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T09:28:19Z","timestamp":1681723699000},"source":"Crossref","is-referenced-by-count":5,"title":["Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?"],"prefix":"10.1093","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0098-6482","authenticated-orcid":false,"given":"Amandine","family":"Cromb\u00e9","sequence":"first","affiliation":[{"name":"Department of Diagnostic and Interventional Oncological Imaging, Institut Bergoni\u00e9, Regional Comprehensive Cancer of Nouvelle-Aquitaine, Bordeaux cedex, France"},{"name":"Univ. 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