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

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.

Multimodal predictors for precision immunotherapy

“…Data-driven approaches such as artificial intelligence, machine learning, and predictive modeling are powerful tools for precision biomarker research. Such approaches are currently widely exploited for identification of image-based biomarkers using radiomics- 115,116 or digital pathology-based approaches. 117,118 Likewise, the use of machine learning applications for investigating fundamental biological processes has been described 119 and may therefore also provide a powerful tool for deconvoluting highdimensional genomic or proteomic data aimed at biomarker discovery.…”

Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy

“…More importantly, the study followed a whole-body segmentation approach and used all visible metastases of the baseline CTs. In some studies, radiomics has been proven to generate additional information for the prediction of response and overall survival of stage-IV melanoma patients undergoing immune therapy [ 14 , 15 , 16 , 17 , 18 , 37 ]. Unfortunately, many of those studies use very homogenous patient cohorts, lacking proof of usability in a real-life scenario [ 16 ].…”

“…Radiomics aims to non-invasively extract phenotypic features from medical imaging by using automated algorithms, based either on manually programmed algorithms or deep learning, and subsequently attempts to develop imaging biomarkers from the derived features using machine- or deep-learning methods [ 12 ]. Radiomics has been used in some studies to generate added value for the prediction of OS and PFS [ 13 , 14 , 15 , 16 , 17 , 18 ]; however, no biomarker is widely accepted for routine clinical use [ 1 ].…”

Artificial Intelligence and Advanced Melanoma: Treatment Management Implications

“…Indeed, it is an extremely complex disease characterized by many genetic mutations and by an immune microenvironment that favors drug resistance and disease progression [ 6 ]. For melanoma, but not only melanoma, AI is greatly assisting the clinicians in their therapeutic choices, as it has made it possible to rapidly study, process, and analyze personal variability in response to treatments [ 54 , 55 , 56 ]. A tumor’s qualitative and quantitative analysis goes through the study of multiple genetic, molecular, and biochemical features.…”