Prognostic value of deep learning-derived body composition in advanced pancreatic cancer-a retrospective multicenter study

Abstract

Background: Despite the prognostic relevance of cachexia in pancreatic cancer, individual body composition has not been routinely integrated into treatment planning. In this multicenter study, we investigated the prognostic value of sarcopenia and myosteatosis automatically extracted from routine computed tomography (CT) scans of patients with advanced pancreatic ductal adenocarcinoma (PDAC).

Patients and methods: We retrospectively analyzed clinical imaging data of 601 patients from three German cancer centers. We applied a deep learning approach to assess sarcopenia by the abdominal muscle-to-bone ratio (MBR) and myosteatosis by the ratio of abdominal inter- and intramuscular fat to muscle volume. In the pooled cohort, univariable and multivariable analyses were carried out to analyze the association between body composition markers and overall survival (OS). We analyzed the relationship between body composition markers and laboratory values during the first year of therapy in a subgroup using linear regression analysis adjusted for age, sex, and American Joint Committee on Cancer (AJCC) stage.

Results: Deep learning-derived MBR [hazard ratio (HR) 0.60, 95% confidence interval (CI) 0.47-0.77, P < 0.005] and myosteatosis (HR 3.73, 95% CI 1.66-8.39, P < 0.005) were significantly associated with OS in univariable analysis. In multivariable analysis, MBR (P = 0.019) and myosteatosis (P = 0.02) were associated with OS independent of age, sex, and AJCC stage. In a subgroup, MBR and myosteatosis were associated with albumin and C-reactive protein levels after initiation of therapy. Additionally, MBR was also associated with hemoglobin and total protein levels.

Conclusions: Our work demonstrates that deep learning can be applied across cancer centers to automatically assess sarcopenia and myosteatosis from routine CT scans. We highlight the prognostic role of our proposed markers and show a strong relationship with protein levels, inflammation, and anemia. In clinical practice, automated body composition analysis holds the potential to further personalize cancer treatment.

Keywords: body composition; computed tomography; deep learning; pancreatic cancer; prognosis.

Figure 1

Body composition markers in relation to age and sex. (A) Association between MBR or myosteatosis and age. (B) Association between MBR or myosteatosis and sex. MBR, muscle-to-bone ratio.

Figure 2

Kaplan–Meier plot showing overall survival according to MBRand myosteatosisin the pooled cohort. (A) Overall survival of patients with high and low MBR. (B) Overall survival of patients with high and low myosteatosis. MBR, muscle-to-bone ratio.

Figure 3

Development of laboratory values in high (highest 25%) and low (lowest 25%) MBRand myosteatosissubgroups. (A) MBR. (B) Myosteatosis. Only laboratory markers significant in linear regression analysis are shown. CRP, C-reactive protein; MBR, muscle-to-bone ratio.