多区域单层CT肌肉面积对肌少症的增量诊断价值：基于深度学习分割的研究

OBJECTIVE: To compare the diagnostic performance of multiregional CT-based muscle assessment with conventional single-level (L3) evaluation.

MATERIALS AND METHODS: This retrospective study included 83 adults who underwent multiregional non-contrast CT and completed sarcopenia assessments based on the Asian Working Group for Sarcopenia (AWGS) 2019 criteria. Skeletal muscle areas at six anatomical levels (T4, L3, ASIS, femoral head, midthigh, and proximal calf) were automatically quantified using a deep learning-based segmentation software (DeepCatch). The diagnostic performance of single-slice muscle areas was evaluated using the area under the receiver operating characteristic curve (AUC). Pearson's or Spearman's correlation coefficients were analyzed to assess the relationship between CT-derived muscle metrics and functional status.

RESULTS: Multiregional muscle assessment demonstrated the highest diagnostic performance. In a clinical prediction model, models incorporating three-site and six-site muscle areas improved discrimination compared with the clinical base model (ΔAUC 0.123 and 0.136, respectively), and these improvements remained significant after BH-FDR adjustment (both q = 0.034). In contrast, the addition of the midthigh muscle area showed a modest improvement (ΔAUC 0.089; p = 0.029), which did not remain significant after FDR adjustment (q = 0.064), and L3 muscle area provided limited incremental value.

CONCLUSION: Multiregional CT-based muscle assessment provides improved diagnostic performance for sarcopenia compared with single-level evaluation. Lower-extremity muscle measurements, particularly at the midthigh, contribute to this improvement, whereas reliance on L3 alone may be insufficient.