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.
Highlights
The approval of ipilimumab as the first immune checkpoint inhibitor for treatment of advanced melanoma by the United States Food and Drug Administration in 2011 introduced a new era in cancer treatment [1,2,3]
The majority of patients were treated with PD-1 monotherapy, while the remainder received either a combination therapy of PD-1 and cytotoxic T-lymphocyte associated protein 4 (CTLA-4) or CTLA-4 monotherapy
The feasibility and clinical potential of automated body composition assessment from staging computed tomography (CT) was evaluated in melanoma patients receiving immune checkpoint inhibitor therapy
Summary
The approval of ipilimumab as the first immune checkpoint inhibitor for treatment of advanced melanoma by the United States Food and Drug Administration in 2011 introduced a new era in cancer treatment [1,2,3]. Two other immune checkpoint inhibitors (nivolumab and pembrolizumab) were introduced shortly thereafter for treatment of advanced melanoma, targeting the programed cell death 1 (PD1) signaling pathway [4,5,6]. A survival benefit was demonstrated for overweight patients receiving immune checkpoint inhibitors for treatment of metastatic melanoma, and it was hypothesized that this observation may be related to a higher amount of skeletal muscle mass [14]. It was demonstrated that deep learning algorithms may be used to obtain body composition parameters from CT examinations in an automated fashion [16]. Such an approach may be of great clinical interest, as automatization may facilitate clinical applicability
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