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

Antonino Guerrisi; Michelangelo Russillo; Emiliano Loi; Balaji Ganeshan; Sara Ungania; Flora Desiderio; Vicente Bruzzaniti; Italia Falcone; Davide Renna; Virginia Ferraresi; Mauro Caterino; Francesco Maria Solivetti; Francesco Cognetti; Aldo Morrone

doi:10.3389/fonc.2021.704607

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

Front Oncol. 2021 Oct 7:11:704607. doi: 10.3389/fonc.2021.704607. eCollection 2021.

Affiliations

¹ Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy.
² Medical Oncology Unit 1, Department of Clinical and Cancer Research IRCCS Regina Elena National Cancer Institute, Rome, Italy.
³ Medical Physics and Expert Systems Laboratory, 3 Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri - IRCCS Regina Elena National Cancer Institute, Rome, Italy.
⁴ Institute of Nuclear Medicine, Imaging Department, University College Hospital, London, United Kingdom.
⁵ Medical Oncology 1, IRCCS-Regina Elena National Cancer Institute, Rome, Italy.
⁶ Scientific Director, San Gallicano Dermatological Institute IRCCS, Rome, Italy.

Abstract

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 the PD-1 inhibitor Nivolumab. Ninety-four lesions from 32 patients treated with Nivolumab were analyzed. Manual segmentation was performed using a free-hand polygon approach by drawing a region of interest (ROI) around each target lesion (up to five lesions were selected per patient according to RECIST 1.1). Filtration-histogram-based texture analysis was employed using a commercially available research software called TexRAD (Feedback Medical Ltd, London, UK; https://fbkmed.com/texrad-landing-2/) 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 (fine texture: 192 HR = 0.56, 95% CI = 0.32-0.96, p = 0.03; medium texture: HR = 0.54, 95% CI = 0.29-0.99, p = 0.04) and PFS (fine texture: HR = 0.53, 95% CI = 0.29-0.95, p = 0.03; medium texture: HR = 0.49, 209 95% CI = 0.25-0.96, p = 0.03). 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.

Keywords: biomarker; image analysis; immunotherapy; melanoma; precision medicine; radiomics; x-ray computed tomography.