Radiomic analysis for early differentiation of lung cancer recurrence from fibrosis in patients treated with lung stereotactic ablative radiotherapy.

Abstract

The development of radiation-induced fibrosis after SABR can obscure follow-up images and delay detection of a local recurrence in early-stage lung cancer patients. The objective of this study was to develop a radiomics model for computer-assisted detection of local recurrence and fibrosis for an earlier timepoint (<1 year) after the SABR treatment.&#xD;&#xD;This retrospective clinical study included CT images (n=107) of 66 patients treated with SABR. A z-score normalization technique was used for radiomic feature standardization across scanner protocols. The training set for the radiomics model consisted of CT images (66 patients; 22 recurrences and 44 fibrosis) obtained at 24 months (median) follow-up. The test set included CT-images of 41 patients acquired at 5-12 months follow-up. Combinations of four widely used machine learning techniques (support vector machines (SVMs), gradient boosting, random forests (RF), and logistic regression (LR)) and feature selection methods (Relief feature scoring, maximum relevance minimum redundancy (MRMR), mutual information maximization (MIM), forward feature selection, and LASSO) were investigated. Pyradiomics was used to extract 106 radiomic features from the CT-images for feature selection and classification. &#xD;&#xD;An RF + LASSO model scored the highest in terms of AUC (0.87) and obtained a sensitivity of 75% and a specificity of 88% in identifying a local recurrence in the test set. In the training set, 86% accuracy was achieved using five-fold cross-validation. Delong's test indicated that AUC achieved by the RF+LASSO is significantly better than 11 other machine learning models presented here. The top three radiomic features: Interquartile Range (first order), Cluster Prominence (GLCM), and Autocorrelation (GLCM), were revealed as differentiating a recurrence from fibrosis with this model.&#xD;&#xD;The radiomics model selected, out of multiple machine learning and feature selection algorithms, was able to differentiate a recurrence from fibrosis in earlier follow-up CT-images with a high specificity rate and satisfactory sensitivity performance. &#xD.