Computed tomography (CT)-based skeletal muscle vertebral-related index to assess low muscle mass in patients with non-small cell lung cancer.

Background: Patients with lung cancer accompanied by sarcopenia may have a poor prognosis. Normally, low muscle mass associated with sarcopenia is assessed using the skeletal muscle index (SMI). It remains unclear whether the standardized skeletal muscle area (SMA) using 2-dimensional (2D) vertebral metrics (called the skeletal muscle vertebral related index, SMVI) could substitute for SMI when it is missing. The aim of this study was to investigate the feasibility of SMVI as an alternative to SMI, and their associations with overall survival (OS) in patients with non-small cell lung cancer (NSCLC). Methods: In this single-center study, a retrospective analysis was conducted on 433 NSCLC patients who underwent computed tomography (CT) scans. At the third lumbar vertebra (L3) level, measurements were taken for SMA, vertebral body area, transverse vertebral diameter (TVD), longitudinal vertebral diameter (LVD), and vertebral height (VH). The 4 SMVIs were skeletal muscle vertebral ratio (SMVR) (SMA/vertebral body area), skeletal muscle transverse vertebral diameter index (SMTVDI) (SMA/TVD2), skeletal muscle longitudinal vertebral diameter index (SMLVDI) (SMA/LVD2), and skeletal muscle vertebral height index (SMVHI) (SMA/VH2). The patients were categorized into low and high muscle mass groups based on SMI, and the differences in SMVIs between the 2 groups were compared to assess their correlation with SMI. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were utilized to assess the discriminatory ability. Kaplan-Meier curves were employed to compare the survival disparity between the 2 groups. Results: We included 191 male and 242 female patients in this study. Compared to the high muscle mass group, patients in the low muscle mass group exhibited significantly lower SMVR, SMTVDI, SMLVDI, and SMVHI (all P<0.05). All 4 SMVIs showed a positive correlation with SMI, with Spearman correlation coefficients of 0.83, 0.76, 0.75, and 0.67, respectively (all P<0.001). The AUC for diagnosing low muscle mass was higher than 0.8 for all 4 SMVI parameters. The Kaplan-Meier curve revealed that the low-risk group had a better survival probability than the high-risk group in the SMVR, SMTVDI, and SMLVDI. Conclusions: The SMVI functions as an alternative metric for evaluating skeletal muscle mass in the assessment of NSCLC based on SMI.

Low thigh muscle strength in relation to myosteatosis in patients with type 2 diabetes mellitus.

This study aimed to investigate the association of thigh muscle fat infiltration by quantitative MRI with muscle strength in patients with type 2 diabetes mellitus (T2DM). Seventy T2DM patients and sixty control subjects (71 males; age: 52 ± 8 years) underwent 3.0T MRI and isokinetic muscle strength measurements to obtain the skeletal muscle index (SMI), intermuscular adipose tissue (IMAT) proton density fat fraction (PDFF), intramuscular fat (IMF) PDFF, peak torque (PT) and total work (TW) of knee extensors and flexors. The differences of measurements between T2DM patients and asymptomatic volunteers were compared. Multivariate regression analysis was used to determine significant predictors of thigh extension and flexion strength. The SMI, IMAT and IMF PDFF of thigh muscles in T2DM patients were higher than that in the control group (p < 0.001), while PT and TW were lower than those in the control subjects (p < 0.05). Both IMF and IMAT PDFF were negatively correlated with PT, TW in participants with T2DM (extensors: r = - 0.72, - 0.70, p < 0.001; r = - 0.62, - 0.56, p < 0.05. flexors: r = - 0.37, - 0.43, p < 0.05; r = - 0.39, - 0.46, p < 0.05). Moderate and strong correlations between HOMA-IR and muscle strength measurements, muscle PDFFs were observed in extensors and flexors. IMF PDFF and age were the statistically significant predictor of PT and TW of extensors of thigh in multivariate regression analysis. Therefore, the thigh muscle PDFF increased was associated with muscle strength decreased in T2DM patients beyond SMI. Age are also important factors influencing thigh muscle PDFF and strength in T2DM patients.

An MRI-based Scoring System for Preoperative Prediction of Axillary Response to Neoadjuvant Chemotherapy in Node-Positive Breast Cancer: A Multicenter Retrospective Study.

RATIONALE AND OBJECTIVES: To develop and validate a simplified scoring system by integrating MRI and clinicopathologic features for preoperative prediction of axillary pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in clinically node-positive breast cancer. MATERIALS AND METHODS: A total of 389 patients from three hospitals were retrospectively analyzed. To identify independent predictors for axillary pCR, univariable and multivariable logistic regression analyses were performed on pre- and post-NAC MRI and clinicopathologic features. Then, a simplified scoring system was constructed based on regression coefficients of predictors in the multivariable model, and its predictive performance was assessed with the receiver operating characteristic curve and calibration curve. The added value of the scoring system for reducing false-negative rate (FNR) of the sentinel lymph node biopsy (SLNB) was also evaluated. RESULTS: The simplified scoring system including seven predictors: progesterone receptor-negative (Three points), HER2-positive (Two points), post-NAC clinical T0-1 stage (Two points), pre-NAC higher ADC value of breast tumor (One point), absence of perinodal infiltration at pre-NAC (One point) and post-NAC MRI (Two points), and absence of enhancement in the tumor bed at post-NAC MRI (Two points), showed good calibration and discrimination, with AUCs of 0.835, 0.828 and 0.798 in the training, internal and external validation cohorts, respectively. The axillary pCR rates were increased with the total points of the scoring system, and patients with a score of ≥11 points had a pCR rate of 86%-100%. In test cohorts for simulating clinical application, the diagnostic accuracy for axillary pCR was 80%-90% among four different radiologists. Compared to standalone SLNB, combining the scoring system with SLNB reduced the FNR from 14.5% to 4.8%. CONCLUSION: The clinicopathologic-image scoring system with good predictive performance for axillary pCR in clinically node-positive breast cancer, may guide axillary management after NAC and improve patient selection for de-escalating axillary surgery to reduce morbidity.