Exploration of Fully-Automated Body Composition Analysis Using Routine CT-Staging of Lung Cancer Patients for Survival Prognosis

Abstract

Background: AI-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.

Methods: Lung 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.

Results: In 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.

Conclusion: CT-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.

Keywords: body composition analysis; deep learning; lung cancer; myosteatosis; sarcopenia.

FIGURE 1

Overview of the body composition analysis (BCA) inference and marker extraction. Routine CT abdominal or whole‐body scans were introduced as input to the network. Muscle, bone and multiple adipose tissues were extracted from a uniform body region (abdominal cavity) in millilitres. In a final step, the extracted volumes were combined into specific markers: Sarcopenia Index (SI), Myosteatotic Fat Index (MFI) and Abdominal Fat Index (AFI).

FIGURE 2

Kaplan–Meier survival curves for NSCLC patients by Sarcopenia Index, Myosteatotic Fat Index and Abdominal Fat Index at Hospitals A and B, stratified by sex and M‐status. The curves demonstrate survival probabilities over time. The significance of differences between groups with markers below and above the median is indicated by the log‐rank test p‐values. NSCLC: non‐small cell lung cancer.

FIGURE 3

Kaplan–Meier survival curves and feature importance for survival models. (A) Test set evaluation for the survival model trained on the Hospital A data with a total of 209 patients, showing significant survival differences (p ≤ 0.001) between groups divided by the predicted risk score median. (B) External validation of the survival model at Hospital B with 361 patients, demonstrating consistent survival differences (p = 0.044). (C) Feature importance for the survival model trained on Hospital A data.