Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors

Abstract

Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan–Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%, p = 0.035), 2-year (32% versus 13%, p = 0.017), and 3-year mortality (38% versus 19%, p = 0.016). No significant differences with regard to adipose tissue compartments were observed (3-year mortality: VAI, p = 0.448; SAI, p = 0.731). On multivariable analysis, low SMI (hazard ratio (HR), 2.245; 95% confidence interval (CI), 1.005–5.017; p = 0.049), neutrophil-to-lymphocyte ratio (HR, 1.170; 95% CI, 1.076–1.273; p < 0.001), and Karnofsky index (HR, 0.965; 95% CI, 0.945–0.985; p = 0.001) remained as significant predictors of 3-year mortality. Lowered skeletal muscle index as an indicator of sarcopenia was associated with worse outcome in patients with metastatic melanoma receiving immune checkpoint inhibitor therapy.

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.…”

Sarcopenia – Definition, Radiological Diagnosis, Clinical Significance

“…In some studies, muscle and fat tissue in CT and MRI datasets has already been analyzed using artificial intelligence (AI) 9 54 55 56 57 . In their study including 1143 CT datasets, Nowak et al used two neuronal networks 57 .…”

“…In einigen Studien wurden die Muskulatur und das Fettgewebe in CT- und MRT-Datensätzen bereits mittels künstlicher Intelligenz (KI) analysiert 9 54 55 56 57 . Nowak et al verwendeten in ihrer Studie mit 1143 CT-Datensätzen 2 neuronale Netze 57 .…”

“…In some studies, muscle and fat tissue in CT and MRI datasets has already been analyzed using artificial intelligence (AI) [9,[54][55][56][57].…”

Clinical Applications of Artificial Intelligence—An Updated Overview

“…An interesting AI approach for cancer care was proposed by Faron et al [49]. The authors used an automated DL-based body composition analysis pipeline to predict the outcome in patients with melanoma receiving immune checkpoint inhibitor therapy.…”