Details

Autor(en) / Beteiligte

• Keyl, J
• Bucher, A
• Jungmann, F
• Hosch, R
• Ziller, A
• Armbruster, R
• Malkomes, P
• Reissig, T M
• Koitka, S
• Tzianopoulos, I
• Keyl, P
• Kostbade, K
• Albers, D
• Markus, P
• Treckmann, J
• Nassenstein, K
• Haubold, J
• Makowski, M
• Forsting, M
• Baba, H A
• Kasper, S
• Siveke, J T
• Nensa, F
• Schuler, M
• Kaissis, G
• Kleesiek, J
• Braren, R

Titel

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

Ist Teil von

• ESMO open, 2024-01, Vol.9 (1), p.102219-102219, Article 102219

Ort / Verlag

England

Erscheinungsjahr

2024

Quelle

EZB-FREE-00999 freely available EZB journals

Beschreibungen/Notizen

• 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). 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. 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. 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.