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

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.

Unloader bracing in osteoarthritis of the knee - Is there a direct effect on the damaged cartilage?

BACKGROUND: Unloading knee braces represent a conservative treatment option for non-pharmalogical management of unicompartmental osteoarthritis of the knee. Though there is consensus on the clinical effectiveness of unloading, the effect mechanism of bracing remains part of a debate. Our study was designed to assess the effect of unloader bracing on damaged cartilage via MRI cartilage mappings. METHODS: Fourteen patients (7 female, 7 male, mean age 43.1 ± 9.4 years) with unicompartmental cartilage wear in knees with varus or valgus malalignment were enrolled. Clinical scores, radiographs and MR-graphic properties (T2/T2* mapping, T1 Delayed Gadolinium Enhanced MRI of the cartilage (dGEMRIC) mapping, high-resolution PDw sequences) of knee cartilage were recorded before and three months after brace use. RESULTS: Bracing the knees for a mean of 14.4 ± 2.0 weeks (range 11 to 18 weeks) resulted in significant pain reduction (VAS changed from 5.9 ± 2.0 to 2.0 ± 1.3, p < 0.001) and improvement in knee function (KOOS increased from 42.1 ± 22.7 to 64.8 ± 18.7, p < 0.001). In the affected cartilage regions T2 relaxation times significantly decreased from 56.1 ± 11.4 ms to 46.5 ± 11.2 ms (p < 0.05). No changes in T1-dGEMRIC and T2* relaxation times, thickness or the extent of the damaged cartilage area could be detected. CONCLUSIONS: Our results suggest, that unloader bracing improves the biochemical properties of the damaged cartilage by increasing collagen and proteoglycan concentration as well as decreasing the cartilage edema.