Radiofrequency ablation of lung metastases of colorectal cancer: could early radiomics analysis of the ablation zone help detect local tumor progression?

Abstract

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. 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. 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). 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. 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.