BACKGROUNDAI-driven automated body composition analysis (BCA) may provide quantitative prognostic biomarkers derived from routine staging CTs. This two-centre study evaluates the prognostic value of these volumetric markers for overall survival in lung cancer patients.METHODSLung cancer cohorts from Hospital A (n = 3345, median age 65, 86% NSCLC, 40% M1, 40% female) and B (n = 1364, median age 66, 87% NSCLC, 37% M1, 38% female) underwent automated BCA of abdominal CTs ±60 days of primary diagnosis. A deep learning network segmented muscle, bone and adipose tissues (visceral = VAT, subcutaneous = SAT, intra-/intermuscular = IMAT and total = TAT) to derive three markers: Sarcopenia Index (SI = Muscle/Bone), Myosteatotic Fat Index (MFI = IMAT/TAT) and Abdominal Fat Index (AFI = VAT/SAT). Kaplan-Meier survival analysis, Cox proportional hazards modelling and machine learning-based survival prediction were performed. A survival model including clinical data (BMI, ECOG, L3-SMI, -SATI, -VATI and -IMATI) was fitted on Hospital A data and validated on Hospital B data.RESULTSIn nonmetastatic NSCLC, high SI predicted longer survival across centres for males (Hospital A: 24.6 vs. 46.0 months; Hospital B: 13.3 vs. 28.9 months; both p < 0.001) and females (Hospital A: 37.9 vs. 53.6 months, p = 0.008; Hospital B: 23.0 vs. 28.6 months, p = 0.018). High MFI indicated reduced survival in males at both hospitals (Hospital A: 43.7 vs. 28.2 months; Hospital B: 28.8 vs. 14.3 months; both p ≤ 0.001) but showed center-dependent effects in females (significant only in Hospital A, p < 0.01). In metastatic disease, SI remained prognostic for males at both centres (p < 0.05), while MFI was significant only in Hospital A (p ≤ 0.001) and AFI only in Hospital B (p = 0.042). Multivariate Cox regression confirmed that higher SI was protective (A: HR 0.53, B: 0.59, p ≤ 0.001), while MFI was associated with shorter survival (A: HR 1.31, B: 1.12, p < 0.01). The multivariate survival model trained on Hospital A's data demonstrated prognostic differentiation of groups in internal (n = 209, p ≤ 0.001) and external (Hospital B, n = 361, p = 0.044) validation, with SI feature importance (0.037) ranking below ECOG (0.082) and M-status (0.078), outperforming all other features including conventional L3-single-slice measurements.CONCLUSIONCT-based volumetric BCA provides prognostic biomarkers in lung cancer with varying significance by sex, disease stage and centre. SI was the strongest prognostic marker, outperforming conventional L3-based measurements, while fat-related markers showed varying associations. Our multivariate model suggests that BCA markers, particularly SI, may enhance risk stratification in lung cancer, pending centre-specific and sex-specific validation. Integration of these markers into clinical workflows could enable personalized care and targeted interventions for high-risk patients.
{"title":"Exploration of Fully-Automated Body Composition Analysis Using Routine CT-Staging of Lung Cancer Patients for Survival Prognosis.","authors":"Marc-David Künnemann,Christian Römer,Anne Helfen,Annalen Bleckmann,Marcel Kemper,Walter Heindel,Tobias J Brix,Michael Forsting,Johannes Haubold,Marcel Opitz,Martin Schuler,Felix Nensa,Katarzyna Borys,René Hosch","doi":"10.1002/jcsm.70021","DOIUrl":"https://doi.org/10.1002/jcsm.70021","url":null,"abstract":"BACKGROUNDAI-driven automated body composition analysis (BCA) may provide quantitative prognostic biomarkers derived from routine staging CTs. This two-centre study evaluates the prognostic value of these volumetric markers for overall survival in lung cancer patients.METHODSLung cancer cohorts from Hospital A (n = 3345, median age 65, 86% NSCLC, 40% M1, 40% female) and B (n = 1364, median age 66, 87% NSCLC, 37% M1, 38% female) underwent automated BCA of abdominal CTs ±60 days of primary diagnosis. A deep learning network segmented muscle, bone and adipose tissues (visceral = VAT, subcutaneous = SAT, intra-/intermuscular = IMAT and total = TAT) to derive three markers: Sarcopenia Index (SI = Muscle/Bone), Myosteatotic Fat Index (MFI = IMAT/TAT) and Abdominal Fat Index (AFI = VAT/SAT). Kaplan-Meier survival analysis, Cox proportional hazards modelling and machine learning-based survival prediction were performed. A survival model including clinical data (BMI, ECOG, L3-SMI, -SATI, -VATI and -IMATI) was fitted on Hospital A data and validated on Hospital B data.RESULTSIn nonmetastatic NSCLC, high SI predicted longer survival across centres for males (Hospital A: 24.6 vs. 46.0 months; Hospital B: 13.3 vs. 28.9 months; both p < 0.001) and females (Hospital A: 37.9 vs. 53.6 months, p = 0.008; Hospital B: 23.0 vs. 28.6 months, p = 0.018). High MFI indicated reduced survival in males at both hospitals (Hospital A: 43.7 vs. 28.2 months; Hospital B: 28.8 vs. 14.3 months; both p ≤ 0.001) but showed center-dependent effects in females (significant only in Hospital A, p < 0.01). In metastatic disease, SI remained prognostic for males at both centres (p < 0.05), while MFI was significant only in Hospital A (p ≤ 0.001) and AFI only in Hospital B (p = 0.042). Multivariate Cox regression confirmed that higher SI was protective (A: HR 0.53, B: 0.59, p ≤ 0.001), while MFI was associated with shorter survival (A: HR 1.31, B: 1.12, p < 0.01). The multivariate survival model trained on Hospital A's data demonstrated prognostic differentiation of groups in internal (n = 209, p ≤ 0.001) and external (Hospital B, n = 361, p = 0.044) validation, with SI feature importance (0.037) ranking below ECOG (0.082) and M-status (0.078), outperforming all other features including conventional L3-single-slice measurements.CONCLUSIONCT-based volumetric BCA provides prognostic biomarkers in lung cancer with varying significance by sex, disease stage and centre. SI was the strongest prognostic marker, outperforming conventional L3-based measurements, while fat-related markers showed varying associations. Our multivariate model suggests that BCA markers, particularly SI, may enhance risk stratification in lung cancer, pending centre-specific and sex-specific validation. Integration of these markers into clinical workflows could enable personalized care and targeted interventions for high-risk patients.","PeriodicalId":186,"journal":{"name":"Journal of Cachexia, Sarcopenia and Muscle","volume":"27 1","pages":"e70021"},"PeriodicalIF":8.9,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144787090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pamela N. Klassen, Vera C. Mazurak, Jessica Thorlakson, Stephane Servais
Investigators are increasingly measuring skeletal muscle (SM) and adipose tissue (AT) change during cancer treatment to understand impact on patient outcomes. Recent meta‐analyses have reported high heterogeneity in this literature, representing uncertainty in the resulting estimates. Using the setting of palliative‐intent chemotherapy as an exemplar, we aimed to systematically summarize the sources of variability among studies evaluating SM and AT change during cancer treatment and propose standards for future studies to enable reliable meta‐analysis. Studies that measured computed tomography‐defined SM and/or AT change in adult patients during palliative‐intent chemotherapy for solid tumours were included, with no date or geographical limiters. Of 2496 publications screened by abstract/title, 83 were reviewed in full text and 38 included for extraction, representing 34 unique cohorts across 8 tumour sites. The timing of baseline measurement was frequently defined as prior to treatment, while endpoint timing ranged from 6 weeks after treatment start to time of progression. Fewer than 50% specified the actual time interval between measurements. Measurement error was infrequently discussed (8/34). A single metric (cm2/m2, cm2 or %) was used to describe SM change in 18/34 cohorts, while multiple metrics were presented for 10/34 and no descriptive metrics for 6/34. AT change metrics and sex‐specific reporting were available for 10/34 cohorts. Associations between SM loss and overall survival were evaluated in 24 publications, with classification of SM loss ranging from any loss to >14% loss over variable time intervals. Age and sex were the most common covariates, with disease response in 50% of models. Despite a wealth of data and effort, heterogeneity in study design, reporting and statistical analysis hinders evidence synthesis regarding the severity and outcomes of SM and AT change during cancer treatment. Proposed standards for study design include selection of homogenous cohorts, clear definition of baseline/endpoint timing and attention to measurement error. Standard reporting should include baseline SM and AT by sex, actual scan interval, SM and AT change using multiple metrics and visualization of the range of change observed. Reporting by sex would advance understanding of sexual dimorphism in SM and AT change. Evaluating the impact of tissue change on outcomes requires adjustment for relevant covariates and concurrent disease response. Adoption of these standards by researchers and publishers would alter the current paradigm to enable meta‐analysis of future studies and move the field towards meaningful application of SM and AT change to clinical care.
{"title":"Call for standardization in assessment and reporting of muscle and adipose change using computed tomography analysis in oncology: A scoping review","authors":"Pamela N. Klassen, Vera C. Mazurak, Jessica Thorlakson, Stephane Servais","doi":"10.1002/jcsm.13318","DOIUrl":"10.1002/jcsm.13318","url":null,"abstract":"Investigators are increasingly measuring skeletal muscle (SM) and adipose tissue (AT) change during cancer treatment to understand impact on patient outcomes. Recent meta‐analyses have reported high heterogeneity in this literature, representing uncertainty in the resulting estimates. Using the setting of palliative‐intent chemotherapy as an exemplar, we aimed to systematically summarize the sources of variability among studies evaluating SM and AT change during cancer treatment and propose standards for future studies to enable reliable meta‐analysis. Studies that measured computed tomography‐defined SM and/or AT change in adult patients during palliative‐intent chemotherapy for solid tumours were included, with no date or geographical limiters. Of 2496 publications screened by abstract/title, 83 were reviewed in full text and 38 included for extraction, representing 34 unique cohorts across 8 tumour sites. The timing of baseline measurement was frequently defined as prior to treatment, while endpoint timing ranged from 6 weeks after treatment start to time of progression. Fewer than 50% specified the actual time interval between measurements. Measurement error was infrequently discussed (8/34). A single metric (cm2/m2, cm2 or %) was used to describe SM change in 18/34 cohorts, while multiple metrics were presented for 10/34 and no descriptive metrics for 6/34. AT change metrics and sex‐specific reporting were available for 10/34 cohorts. Associations between SM loss and overall survival were evaluated in 24 publications, with classification of SM loss ranging from any loss to >14% loss over variable time intervals. Age and sex were the most common covariates, with disease response in 50% of models. Despite a wealth of data and effort, heterogeneity in study design, reporting and statistical analysis hinders evidence synthesis regarding the severity and outcomes of SM and AT change during cancer treatment. Proposed standards for study design include selection of homogenous cohorts, clear definition of baseline/endpoint timing and attention to measurement error. Standard reporting should include baseline SM and AT by sex, actual scan interval, SM and AT change using multiple metrics and visualization of the range of change observed. Reporting by sex would advance understanding of sexual dimorphism in SM and AT change. Evaluating the impact of tissue change on outcomes requires adjustment for relevant covariates and concurrent disease response. Adoption of these standards by researchers and publishers would alter the current paradigm to enable meta‐analysis of future studies and move the field towards meaningful application of SM and AT change to clinical care.","PeriodicalId":186,"journal":{"name":"Journal of Cachexia, Sarcopenia and Muscle","volume":"14 5","pages":"1918-1931"},"PeriodicalIF":8.9,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jcsm.13318","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10524503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolas Collao, Donna D'Souza, Laura Messeiller, Evan Pilon, Jessica Lloyd, Jillian Larkin, Matthew Ngu, Alexanne Cuillerier, Alexander E. Green, Keir J. Menzies, Yan Burelle, Michael De Lisio