A CT-based radiomics nomogram for predicting local tumor progression of colorectal cancer lung metastases treated with radiofrequency ablation

Background and Purpose: The early prediction of local tumor progression-free survival (LTPFS) after radiofrequency ablation (RFA) for colorectal cancer (CRC) lung metastases has significant clinical importance. The application of radiomics in the prediction of tumor prognosis has been explored. This study aimed to construct a radiomics-based nomogram for predicting LTPFS after RFA in CRC patients with lung metastases. Methods: This study retrospectively analyzed 172 CRC patients with 401 lung metastases admitted to Department of Interventional Radiology, Fudan University Shanghai Cancer Center from August 2016 to January 2019. This study was reviewed by the medical ethics committee of Fudan University Shanghai Cancer Center (ethics number: 2402291-24). After augmentation of pre-ablation and immediate post-ablation computed tomography (CT) images, the target metastases and ablation regions were segmented manually to extract the radiomic features. Maximum relevance and minimum redundancy algorithm (MRMRA) and least absolute shrinkage and selection operator (LASSO) regression models were applied for feature selection. The clinical model, the radiomics model, and the fusion model were constructed based on the selected radiomic features and clinical variables screened by the multivariate analysis. The Harrell concordance index (C-index) and area under receiver operating characteristic (ROC) curves (AUC) were calculated to evaluate the prediction performance. Finally, the corresponding nomogram of the best model was drawn. Results: Among all the lung metastases, 102 (25.4%) had final recurrence, and 299 (74.6%) had complete response (CR). The median follow-up time was 21 months (95% CI: 19.466-22.534), and the LTPFS rates at 1, 2, and 3 years after RFA were 76.5% (95% CI: 72.0-80.4), 72.1% (95% CI: 66.6-76.9) and 69.9% (95% CI: 64.0-75.1). In both the training and test dataset, the fusion model based on the final 12 radiomic features through the LASSO regression and 4 clinical variables screened by multivariate analysis achieved the highest AUC values for LTPFS, with C-index values of 0.890 (95% CI: 0.854-0.927) and 0.843 (95% CI: 0.768-0.916), respectively. Conclusion: The fusion model based on radiomic features and clinical variables is feasible for predicting LTPFS after RFA of CRC patients with lung metastases, whose performance is superior to the single radiomic and clinical model. At the same time, the nomogram of the fusion model can intuitively predict the prognosis of CRC patients with lung metastases after RFA, thus assisting clinicians in developing individualized follow-up review plans for patients and adjusting treatment strategies flexibly.