Low subcutaneous adipose tissue and myosteatosis are prognostic factors after allogeneic hematopoietic stem cell transplantation.

OBJECTIVE: Allogeneic hematopoietic stem cell transplantation (allo-HSCT) represents the only curative treatment option for several hematological neoplasms. This study aimed to assess the parameters of body composition as predictors of post-transplant overall survival (OS) and adverse events in patients with leukemia, myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPN). METHODS: This was a retrospective study of 122 adult patients who underwent their first allo-HSCT. The CT-based semi-automated measurement of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), visceral-to-subcutaneous fat ratio (VSR), sarcopenia in terms of skeletal muscle index (SMI), and myosteatosis based on the skeletal muscle radiation attenuation (SM-RA) was performed. Cox regression analysis was used to assess the association of body composition parameters with OS. RESULTS: In the univariate analysis, low SAT and myosteatosis were associated with lower OS (hazard ratio [HR] 2.02, 95% confidence interval [CI] 1.16-3.51, p = 0.01) and (HR 2.50, 95% CI 1.48-4.25, p =< 0.001), respectively. This association remained significant after adjusting for relevant covariates, with HR 2.32, 95% CI 1.23-4.38, p = 0.01 and HR 2.86, 95% CI 1.51-5.43, p =< 0.001, respectively. On the contrary, VAT, VSR, sarcopenia, and sarcopenic obesity were not statistically significant in OS. Severe post-transplant adverse events were more common in the low SAT group (odds ratio [OR] 3.12, 95% CI 1.32-7.40, p = 0.01) and OR 3.17, 95% CI 1.31-7.70, p =< 0.01 in the age- and sex-adjusted analysis. CONCLUSION: Low SAT and myosteatosis may contribute to an increased risk of post-transplant mortality, while low SAT appears to increase the risk of severe post-transplant adverse events.

Low skeletal muscle mass predicts melanoma-specific survival in melanoma patients treated with adjuvant immune checkpoint blockade.

PURPOSE: Adjuvant immunotherapy with immune checkpoint blockade(ICB) has greatly reduced the risk of recurrence and metastatic spread in early and advanced melanoma. However, not all patients benefit from adjuvant treatment: many patients show disease recurrence despite therapy, while those without recurrence harbor the risk for potentially irreversible adverse events. Biomarkers to select patients benefitting most from adjuvant therapy are currently lacking. As body composition assessment using CT images has shown promising results as a prognostic biomarker in stage IV melanoma, we aim to study the applicability of body composition parameters also in adjuvant melanoma treatment. METHODS: We analyze body composition features via CT scans in a retrospective cohort of 109 patients with resected stage IIB-IV melanoma receiving an adjuvant first-line treatment with ICB in our department. In this analysis, we focus on the impact of body composition, especially the presence of low skeletal muscle mass (LSMM), on patients' survival and occurrence of adverse events (AEs). RESULTS: In uni- and multivariate analyses, we identify an association between CT-measured LSMM and melanoma-specific survival in patients treated with adjuvant ICB. Furthermore, LSMM is associated with a lower risk for therapy-related AEs, especially hypothyroidism, fatigue, and xerostomia. Conventional serological biomarkers e.g. S100 and LDH and measures of adipose tissue compartments did not show a correlation with survival or the occurrence of AEs. CONCLUSIONS: LSMM constitutes a novel biomarker for melanoma-specific survival in patients treated with adjuvant ICB.

Myosteatosis predicts short-term mortality in patients with COVID-19: A multicenter analysis.

OBJECTIVES: Body composition on computed tomography can predict prognosis in patients with COVID-19. The reported data are based on small retrospective studies. The aim of the present study was to analyze the prognostic relevance of skeletal muscle parameter derived from chest computed tomography for prediction of 30-d mortality in patients with COVID-19 in a multicenter setting. METHODS: The clinical databases of three centers were screened for patients with COVID-19 between 2020 and 2022. Overall, 447 patients (142 female; 31.7%) were included into the study. The mean age at the time of computed tomography acquisition was 63.8 ± 14.7 y and median age was 65 y. Skeletal muscle area and skeletal muscle density were defined on level T12 of the chest. RESULTS: Overall, 118 patients (26.3%) died within the 30-d observation period. Of the patient sample, 255 patients (57.0%) were admitted to an intensive care unit and 122 patients needed mechanical ventilation (27.3%). The mean skeletal muscle area of all patients was 96.1 ± 27.2 cm² (range = 23.2-200.7 cm²). For skeletal muscle density, the mean was 24.3 ± 11.1 Hounsfield units (range = -5.6 to 55.8 Hounsfield units). In survivors, the mean skeletal muscle density was higher compared with the lethal cases (mean 25.8 ± 11.2 versus 20.1 ± 9.6; P < 0.0001). Presence of myosteatosis was independently associated with 30-d mortality: odds ratio = 2.72 (95% CI, 1.71-4.32); P = 0.0001. CONCLUSIONS: Myosteatosis is strongly associated with 30-d mortality in patients COVID-19. Patients with COVID-19 with myosteatosis should be considered a risk group.

Parameters of body composition do not predict survival in patients with multiple myeloma undergoing autologous stem cell transplantation.

Studies regarding the influence of body composition parameters as predictors on overall survival (OS) in patients with multiple myeloma (MM) are scarce. OS and progression-free survival (PFS) were retrospectively assessed in 129 patients with MM undergoing autologous stem cell transplantation (ASCT) after a follow-up of 2 years. A computed tomography (CT) based semi-automated assessment of body composition was performed. No statistically significant differences were noted in 2-year OS, PFS, or post-transplant adverse events in the body composition groups of subcutaneous adipose tissue (SAT) (low vs. high-SAT), visceral adipose tissue (VAT) (low vs. high-VAT), visceral-to-subcutaneous fat ratio (VSR) (low vs. high VSR), and sarcopenia in terms of skeletal muscle index (SMI) (non-sarcopenic vs. sarcopenic). In conclusion, adipose and muscle tissue do not limit OS or affect the PFS in patients with MM undergoing ASCT.

CT-defined sarcopenia predicts treatment response in primary central nervous system lymphomas.

OBJECTIVE: Body composition assessment derived from cross-sectional imaging has shown promising results as a prognostic biomarker in several tumor entities. Our aim was to analyze the role of low skeletal muscle mass (LSMM) and fat areas for prognosis of dose-limiting toxicity (DLT) and treatment response in patients with primary central nervous system lymphoma (PCNSL). METHODS: Overall, 61 patients (29 female patients, 47.5%) with a mean age of 63.8 ± 12.2 years, range 23-81 years, were identified in the data base between 2012 and 2020 with sufficient clinical and imaging data. Body composition assessment, comprising LSMM and visceral and subcutaneous fat areas, was performed on one axial slice on L3-height derived from staging computed tomography (CT) images. DLT was assessed during chemotherapy in clinical routine. Objective response rate (ORR) was measured on following magnetic resonance images of the head accordingly to the Cheson criteria. RESULTS: Twenty-eight patients had DLT (45.9%). Regression analysis revealed that LSMM was associated with objective response, OR = 5.19 (95% CI 1.35-19.94, p = 0.02) (univariable regression), and OR = 4.23 (95% CI 1.03- 17.38, p = 0.046) (multivariable regression). None of the body composition parameters could predict DLT. Patients with normal visceral to subcutaneous ratio (VSR) could be treated with more chemotherapy cycles compared to patients with high VSR (mean, 4.25 vs 2.94, p = 0.03). Patients with ORR had higher muscle density values compared to patients with stable and/or progressive disease (34.46 ± vs 28.18 ± HU, p = 0.02). CONCLUSIONS: LSMM is strongly associated with objective response in patients with PCNSL. Body composition parameters cannot predict DLT. CLINICAL RELEVANCE STATEMENT: Low skeletal muscle mass on computed tomography (CT) is an independent prognostic factor of poor treatment response in central nervous system lymphoma. Analysis of the skeletal musculature on staging CT should be implemented into the clinical routine in this tumor entity. KEY POINTS: • Low skeletal muscle mass is strongly associated with the objective response rate. • No body composition parameters could predict dose-limiting toxicity.

Epicardial adipose tissue as a prognostic marker in acute pulmonary embolism.

BACKGROUND: Epicardial adipose tissue (EAT) has been established as a quantitative imaging biomarker associated with disease severity in coronary heart disease. Our aim was to use this prognostic marker derived from computed tomography pulmonary angiography (CTPA) for the prediction of mortality and prognosis in patients with acute pulmonary embolism. METHODS: The clinical database was retrospectively screened for patients with acute pulmonary embolism between 2015 and 2021. Overall, 513 patients (216 female, 42.1%) were included in the analysis. The study end-point was 30-day mortality. Epicardial adipose tissue was measured on the diagnostic CTPA in a semiquantitative manner. The volume and density of EAT were measured for every patient. RESULTS: Overall, 60 patients (10.4%) died within the 30-day observation period. The mean EAT volume was 128.3 ± 65.0 cm3 in survivors and 154.6 ± 84.5 cm3 in nonsurvivors (p = 0.02). The density of EAT was -79.4 ± 8.3 HU in survivors and -76.0 ± 8.4 HU in nonsurvivors (p = 0.86), and EAT density was associated with 30-day mortality (odds ratio [OR] = 1.07; 95% confidence interval [CI]: 1.03; 1.1, p < 0.001) but did not remain statistically significant in multivariable analysis. No association was identified between EAT volume and 30-day mortality (OR = 1.0; 95% CI: 1.0; 1.0, p = 0.48). CONCLUSION: There might be an association between EAT density and mortality in patients with acute pulmonary embolism. Further studies are needed to elucidate the prognostic relevance of EAT parameters in patients with acute pulmonary embolism.

Prognostic role of radiomics-based body composition analysis for the 1-year survival for hepatocellular carcinoma patients.

BACKGROUND: Parameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC). METHODS: Radiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups. RESULTS: We used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376-0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930-0.9134). CONCLUSIONS: Parameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.

Subcutaneous Fat Abundance and Density Are Associated with an Enhanced Response to Immunotherapy in Metastatic Melanoma: A Retrospective Cohort Study.

RATIONALE AND OBJECTIVES: Despite the impressive efficacy of immune checkpoint inhibitors (ICIs) in the treatment of metastatic melanoma, not all patients respond to therapy. In addition, ICI harbors the risk for serious adverse events (AEs), highlighting the need for novel biomarkers predicting treatment response and occurrence of AEs. Recently, the identification of enhanced response to ICI in obese patients has indicated that body composition might influence treatment efficacy. The aim of the current study is to assess radiologic measurements of body composition as biomarkers for treatment response and AEs to ICI in melanoma. MATERIALS AND METHODS: In the current work, we analyze adipose tissue abundance and density, as well as muscle mass via computed tomography scans in a retrospective cohort of 100 patients with non-resectable stage III/IV melanoma receiving first-line treatment with ICI in our department. From these, we investigate the impact of the subcutaneous adipose tissue gauge index (SATGI) and other parameters of body composition on treatment efficacy and occurrence of AEs. RESULTS: Low SATGI was associated with prolonged progression-free survival (PFS) in univariate and multivariate analyses (hazard ratio 2.56 [95% CI 1.18-5.55], P = .02), as well as an enhanced objective response rate (50.0% vs 27.1%; P = .02). Further analysis with a random forest survival model highlighted a nonlinear relationship between SATGI and PFS with a clear separation into high- and low-risk cohorts separated by the median. Finally, a significant enrichment of cases with vitiligo, but no other AEs, was observed in the SATGI-low cohort (11.5% vs 0%; P = .03). CONCLUSION: We identify SATGI as a biomarker predicting treatment response to ICI without increased risk for severe AEs in melanoma.

Impact of body composition in advanced hepatocellular carcinoma: A subanalysis of the SORAMIC trial.

BACKGROUND: Body composition parameters have been reported to be prognostic factors in patients with oncologic diseases. However, the available data on patients with HCC are conflicting. The aim of this study was to assess the impact of body composition on survival in patients with HCC treated with sorafenib or selective internal radioembolization (SIRT) and sorafenib. METHODS: This is an exploratory subanalysis of the prospective, randomized controlled SORAMIC trial. Within the palliative arm of the study, patients were selected if a baseline abdominal CT was available. A broad set of skeletal muscle and adipose tissue parameters were measured at the L3 level. Low skeletal muscle mass (LSMM) and density parameters were defined using published cutoffs. The parameters were correlated with overall survival. RESULTS: Of 424 patients in the palliative study arm, 369 patients were included in the analysis. There were 192 patients in the combined sorafenib/SIRT and 177 patients in the sorafenib group. Median overall survival was 9.9 months for the entire cohort and 10.8 and 9.2 months for the SIRT/sorafenib and sorafenib groups, respectively. There was no relevant association of either body composition parameter with overall survival in either the overall cohort or in the SIRT/sorafenib or sorafenib subgroups. CONCLUSIONS: This subanalysis of the prospective SORAMIC trial does not suggest a relevant influence of body composition parameters of survival in patients with advanced HCC. Body composition parameters therefore do not serve in patient allocation in this palliative treatment cohort.

Visceral to subcutaneous fat ratio predicts short-term mortality in patients with Covid 19. A multicenter study.

OBJECTIVE: To evaluate the association of body composition parameters with outcomes in Covid-19. METHODS: 173 patients hospitalized for Covid-19 infection in 6 European centers were included in this retrospective study. Measurements were performed at L3-level and comprised skeletal muscle index (SMI), muscle density (MD), and adipose tissue measurements [visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral-to-subcutaneous-adipose-tissue-area-ratio (VSR)]. The association with mortality, the need for intubation (MV), and the need for admission to ICU within 30 days were evaluated. RESULTS: Higher SAT density was associated with a greater risk of MV (OR = 1.071, 95%CI=(1.034;1.110), p < 0.001). Higher VAT density was associated with admission to ICU (OR = 1.068, 95%CI=(1.029;1.109), p < 0.001). Higher MD was a protective factor for MV and ICU admission (OR = 0.914, 95%CI=(0.870;0.960), p < 0.001; OR = 0.882, 95%CI=(0.832;0.934), p = 0.028). Higher VSR was associated with mortality (OR = 2.147, 95%CI=(1.022;4.512), p = 0.044). Male sex showed the strongest influence on the risk of ICU admission and MV. SMI was not associated with either parameter. CONCLUSION: In patients hospitalized for Covid-19 infection, higher VSR seems to be a strong prognostic factor of short-term mortality. Weak associations with clinical course were found for MD and adipose tissue measurements. Male sex was the strongest prognostic factor of adverse clinical course. ADVANCES IN KNOWLEDGE: VSR is a prognostic biomarker for 30-day mortality in patients hospitalized for Covid-19 disease.

Sarcopenia is an Independent Prognostic Factor in Patients With Pancreatic Cancer - a Meta-analysis.

RATIONALE AND OBJECTIVES: Sarcopenia is defined as skeletal muscle loss and can be assessed by cross-sectional imaging. Our aim was to establish the effect of sarcopenia on relevant outcomes in patients with pancreatic ductal adenocarcinoma (PDAC) in curative and palliative settings based on a large patient sample. MATERIALS AND METHODS: MEDLINE library, EMBASE and SCOPUS databases were screened for the associations between sarcopenia and mortality in patients with PDAC up to March 2022. The primary endpoint of the systematic review was the hazard ratio of Sarcopenia on survival. 22 studies were included into the present analysis. RESULTS: The included 22 studies comprised 3958 patients. The prevalence of sarcopenia was 38.7%. Sarcopenia was associated with a higher prevalence in the palliative setting (OR 53.23, CI 39.00-67.45, p<0.001) compared to the curative setting (OR 36.73, CI 27.81-45.65, p<0.001). Sarcopenia was associated with worse OS in the univariable (HR 1.79, CI 1.41-2.28, p<0.001) and multivariable analysis (HR 1.62, CI 1.27-2.07, p<0.001) in the curative setting. For the palliative setting the pooled hazards ratio showed that sarcopenia was associated with overall survival (HR 1.56, CI 1.21-2.02, p<0.001) as well as in multivariable analysis (HR 1.77, CI 1.39-2.26, p<0.001). Sarcopenia was not associated with a higher rate of post-operative complications in univariable analysis (OR 1.10, CI 0.70-1.72, p = 0.69). CONCLUSION: Sarcopenia occurs in 38.7% of patients with pancreatic cancer, significantly more in the palliative setting. Sarcopenia is associated with overall survival in both settings. The assessment of sarcopenia is therefore relevant for personalized oncology. Sarcopenia is not associated with postoperative complications.

Prognostic Impact of Quality and Distribution of Adipose Tissue in Patients With Primary Central Nervous System Lymphoma.

BACKGROUND/AIM: Body composition assessment has shown promising results as a prognostic biomarker as depicted by cross-sectional imaging of several tumor entities including lymphomas. The present study sought to elucidate the prognostic relevance of subcutaneous and visceral fat tissue (SAT and VAT) in patients with primary central nervous system lymphoma (PCNSL). PATIENTS AND METHODS: Overall, 74 patients (36 female patients, 46.7%) with a mean age of 64.2±12.8 years (range=23-81 years) were identified in the database with sufficient clinical and imaging data and included into this retrospective study. Fat area assessment was performed on one axial slide on L3-height derived from staging computed tomography (CT) images. Subcutaneous, visceral, and intramuscular adipose tissues (SAT, VAT, IMAT) were estimated. Also, density of SAT, VAT, and IMAT were estimated. Finally, the ratio VAT/SAT (VSR) was calculated. Overall and progression-free survival (OS and PFS) were used as study end points. RESULTS: In the observation period, overall, 47 patients (63.5%) died. Mean OS was 33.8±45.4 months and mean PFS was 26.6±42.7 months. The mean VAT value was 162±99.5 cm2, the mean SAT was 202.4±103.3 cm2, the mean VSR was 0.92±0.69. The hazard ratios (HRs) for overall survival were 0.87 for high VAT, 1.52 for SAT, and 0.73 for VSR in univariable analysis. For PFS it was 0.24 for VAT, 1.11 for SAT, and 1.07 for VSR. No values achieved statistical significance. Similar results were shown in Kaplan-Meier analysis for OS and PFS, respectively. CONCLUSION: Parameters of adipose tissue are not associated with OS and PFS in patients with PCNSL.

Sarcopenia does not predict outcome in patients with CNS lymphoma undergoing systemic therapy.

Low skeletal muscle mass as a proxy parameter for sarcopenia acts as a non-invasive imaging marker that is associated with poor prognosis in numerous types of cancer. The present study aimed to assess the influence of body composition parameters on overall survival (OS) and progression free survival (PFS) in patients diagnosed with primary central nervous system lymphoma (PCNSL). A total of 98 patients with PCNSL treated at University Hospital Magdeburg (Magdeburg, Germany) from 2013-2019 were retrospectively studied. Patients with a pre-treatment staging computed tomography (CT) scan that included the third lumbar vertebra were reviewed for analysis. Skeletal muscle area (SMA), skeletal muscle index (SMI), mean muscle density and skeletal muscle gauge (SMG) were measured on the CT scan prior to treatment. Parameters were associated with OS and PFS. Overall, 72 patients were included in the present study. Results of the present study demonstrated that the median OS was 10 months (range, 1-181 months), and 37 patients (51.4%) presented with sarcopenia. Moreover, the median OS was 7 months in the sarcopenic group and 32 months in the non-sarcopenic group. Results of the present study further illustrated that SMI, SMA, density and SMG did not exert a significant effect on OS. Notably, the median PFS was 2.5 months in the low SMI group and 10 months in the normal SMI group. Body composition parameters did not exert a significant effect on PFS. Overall, the results of the present study demonstrated that sarcopenia was not a risk factor for decreased OS or PFS in patients with PCNSL undergoing systemic treatment.

Deep-learning-based Segmentation of Skeletal Muscle Mass in Routine Abdominal CT Scans.

BACKGROUND: For prediction of many types of clinical outcome, the skeletal muscle mass can be used as an independent biomarker. Manual segmentation of the skeletal muscles is time-consuming, therefore we present a deeplearning-based approach for the identification of muscle mass at the L3 level in clinical routine computed tomographic (CT) data. PATIENTS AND METHODS: We conducted a retrospective study of 130 patient datasets. Individual CT slice analysis at the L3 level was fed into a U-Net architecture. As a result, we obtained segmentations of the musculus rectus abdominis, abdominal wall muscles, musculus psoas major, musculus quadratus lumborum and musculus erector spinae in the CT-slice at the L3 level. RESULTS: The Dice score was 0.95±0.02, 0.86±0.12, 0.93±0.05, 0.92±0.05, 0.86±0.08 for the erector spine, rectus, abdominal wall, psoas and quadratus lumborum muscles, respectively. For the overall skeletal muscle mass, the test data achieved a Dice score of 0.95±0.03. CONCLUSION: Our network achieved Dice scores larger than 0.86 for each of the five different muscle types and 0.95 for the overall skeletal muscle mass. The subdivision of muscle types can serve as a basis for obtaining future biomarkers. Our network is publicly available so that it might be beneficial for others to improve the clinical workflow within examination of routine CT scans.