Exploring CT Texture Parameters as Predictive and Response Imaging Biomarkers of Survival in Patients With Metastatic Melanoma Treated With PD-1 Inhibitor Nivolumab: A Pilot Study Using a Delta-Radiomics Approach - Guerrisi, Antonino; Russillo, Michelangelo; Loi, Emiliano; Ganeshan, Balaji; Ungania, Sara; Desiderio, Flora; Bruzzaniti, Vicente; Falcone, Italia; Renna, Davide; Ferraresi, Virginia; Caterino, Mauro; Solivetti, Francesco Maria; et al. (Frontiers in Oncology, 2021)
In the era of artificial intelligence and precision medicine, the use of quantitative imaging methodological approaches could improve the cancer patient's therapeutic approaches. Specifically, our pilot study aims to explore whether CT texture features on both baseline and first post-treatment contrast-enhanced CT, may act as a predictor of overall survival (OS) and progression-free survival (PFS) in metastatic melanoma (MM) patients treated with PD-1 inhibitor Nivolumab. 94 lesions from 32 patients treated with Nivolumab were analyzed. Segmentation of the regions of interest (ROI) around the lesions was performed on CT. Radiomic features were extracted from each ROI lesion using texture analysis software. Percentage changes in texture features were calculated to perform delta-radiomics analysis. Texture feature kurtosis at fine and medium filter scale predicted OS and PFS. A higher kurtosis is correlated with good prognosis; kurtosis values greater than 1.11 for SSF=2 and 1.20 for SSF=3 were indicators of higher OS and PFS. In delta-radiomics analysis, the entropy percentage variation correlated with OS and PFS. Increasing entropy indicates a worse outcome. An entropy variation greater than 5% was an indicator of bad prognosis. CT delta-texture analysis quantified as entropy predicted OS and PFS. Baseline CT texture quantified as kurtosis also predicted survival baseline. Further studies with larger cohorts are mandatory to confirm these promising exploratory results.