Radiomics complements clinical, radiological, and technical features to assess local control of colorectal cancer lung metastases treated with radiofrequency ablation

Abstract

Objectives: Radiofrequency ablation (RFA) of lung metastases of colorectal origin can improve patient survival and quality of life. Our aim was to identify pre- and per-RFA features predicting local control of lung metastases following RFA.

Methods: This case-control single-center retrospective study included 119 lung metastases treated with RFA in 48 patients (median age: 60 years). Clinical, technical, and radiological data before and on early CT scan (at 48 h) were retrieved. After CT scan preprocessing, 64 radiomics features were extracted from pre-RFA and early control CT scans. Log-rank tests were used to detect categorical variables correlating with post-RFA local tumor progression-free survival (LTPFS). Radiomics prognostic scores (RPS) were developed on reproducible radiomics features using Monte-Carlo cross-validated LASSO Cox regressions.

Results: Twenty-six of 119 (21.8%) nodules demonstrated local progression (median delay: 11.2 months). In univariate analysis, four non-radiomics variables correlated with post-RFA-LTPFS: nodule size (> 15 mm, p < 0.001), chosen electrode (with difference between covered array and nodule diameter < 20 mm or non-expandable electrode, p = 0.03), per-RFA intra-alveolar hemorrhage (IAH, p = 0.002), and nodule location into the ablation zone (not seen or in contact with borders, p = 0.005). The highest prognostic performance was reached with the multivariate model including a RPS built on 4 radiomics features from pre-RFA and early revaluation CT scans (cross-validated concordance index= 0.74) in which this RPS remained an independent predictor (cross-validated HR = 3.49, 95% confidence interval = [1.76 - 6.96]).

Conclusions: Technical, radiological, and radiomics features of the lung metastases before RFA and of the ablation zone at 48 h can help discriminate nodules at risk of local progression that could benefit from complementary local procedure.

Key points: • The highest prognostic performance to predict post-RFA LTPFS was reached with a parsimonious model including a radiomics score built with 4 radiomics features. • Nodule size, difference between electrode diameter, use of non-expandable electrode, per-RFA hemorrhage, and a tumor not seen or in contact with the ablation zone borders at 48-h CT were correlated with post-RFA LTPFS.

Keywords: Colorectal neoplasms; Lung neoplasms; Machine learning; Radiology, interventional; Tomography, X-ray computed.