Objectives: 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.
Methods: This case-control 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.
Results: 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 (0.58-0.86), 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 to 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).
Conclusion: 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.
Advances in knowledge: 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.