Validation for measurements of skeletal muscle areas using low-dose chest computed tomography

Prognostic impact of low muscle mass on clinical outcomes in patients who undergo lung transplant

BACKGROUND

Low muscle mass (LMM) is recognized as a poor prognostic factor in various chronic lung diseases. However, its prognostic impact on recipients of lung transplants remains inconclusive.

METHODS

We retrospectively analyzed patients who underwent lung transplantation at a tertiary referral center in South Korea. Pretransplant skeletal muscle mass was quantified at the L1 vertebral level by computed tomography scans of the chest using a commercially available body composition analysis software. Patients were classified into LMM and non-LMM group using a threshold for LMM that had been previously validated in the South Korean population. We then evaluated the prognostic impact of preoperative LMM on clinical outcomes after lung transplantation.

RESULTS

A total of 107 patients were included in this analysis, of whom 44 (41.1%) were classified into the LMM group. The median follow-up duration was 958 days posttransplantation. A preoperative LMM was identified as an independent factor associated with a greater risk of overall mortality (adjusted hazard ratio, 2.15; 95% confidence interval, 1.07-4.34). In addition, patients with LMM had a greater risk of developing primary graft dysfunction (adjusted odds ratio, 3.56; 95% confidence interval, 1.25-10.18). At the 1-year follow-up, 37.5% of the patients with baseline LMM had recovered and were reclassified into the non-LMM group, and this improvement was found to mitigate the negative impact of preoperative LMM.

CONCLUSIONS

Pretransplant LMM was significantly associated with poor clinical outcomes in recipients of lung transplants. These findings highlight the importance of maintaining adequate muscle mass during the waiting period for lung transplantation.

Non-cancerous CT findings as predictors of survival outcome in advanced non-small cell lung cancer patients treated with first-generation EGFR-TKIs

PURPOSE

To identify non-cancerous factors from baseline CT chest affecting survival in advanced non-small cell lung cancer (NSCLC) treated with first-generation Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitors (EGFR-TKIs).

METHODS

Retrospective study of 172 advanced NSCLC patients treated with first-generation EGFR-TKIs as a first-line systemic treatment (January 2012 to September 2022). Baseline CT chest assessed visceral/subcutaneous fat (L1 level), sarcopenia, and myosteatosis (multiple levels), main pulmonary artery (MPA) size, MPA to aorta ratio, emphysema, and bone mineral density. Cox regression analyzed prognostic factors at 18-month outcome.

RESULTS

Median overall survival was 17.57 months (14.87-20.10) with 76 (44.19%) patients died at 18 months. Deceased had lower baseline BMI (21.10 ± 3.44) vs. survived (23.25 ± 4.45) (p < 0.001). Univariable analysis showed 5 significant prognostic factors: low total adiposity with/without cutoff [HR 2.65 (1.68-4.18), p < 0.001; 1.00 (0.99-1.00), p = 0.006;], low subcutaneous adipose tissue (SAT) with/without cutoff [HR 1.95 (1.23-3.11), p = 0.005; 0.99 (0.98-0.99), p = 0.005], low SAT index (SATI) with/without cutoff [1.74 (1.10-2.78), p = 0.019; 0.98 (0.97-0.99), p = 0.003], high VSR [1.67 (1.06-2.62), p = 0.026], and high MPA size with/without cutoff [2.23 (1.23-4.04), p = 0.005; 1.09 (1.04-1.16), p = 0.001]. MPA size, MPA size > 29 mm, and total adiposity ≤85 cm2 remained significant in multivariable analysis, adjusted by BMI [HR 1.14 (1.07-1.21), p < 0.001; 3.10 (1.81-5.28), p < 0.001; 3.91 (1.63-9.40), p = 0.002]. There was no significant difference of sarcopenic and myosteatotic parameters between the two groups.

CONCLUSION

In advanced EGFR-mutated NSCLC patients, assessing pre-treatment prognosis is warranted to predict the survival outcome and guide decision regarding EGFR-TKI therapy. Enlarged MPA size, low total adiposity, and low subcutaneous fat (lower SAT, lower SATI, and higher VSR) are indicators of poor survival. Large MPA size (>29 mm) or low total adiposity (≤85 cm2) alone predict 18-month death.

Skeletal muscle estimation: A review of techniques and their applications

Quantifying skeletal muscle size is necessary to identify those at risk for conditions that increase frailty, morbidity, and mortality, as well as decrease quality of life. Although muscle strength, muscle quality, and physical performance have been suggested as important assessments in the screening, prevention, and management of sarcopenic and cachexic individuals, skeletal muscle size is still a critical objective marker. Several techniques exist for estimating skeletal muscle size; however, each technique presents with unique characteristics regarding simplicity/complexity, cost, radiation dose, accessibility, and portability that are important factors for assessors to consider before applying these modalities in practice. This narrative review presents a discussion centred on the theory and applications of current non-invasive techniques for estimating skeletal muscle size in diverse populations. Common instruments for skeletal muscle assessment include imaging techniques such as computed tomography, magnetic resonance imaging, peripheral quantitative computed tomography, dual-energy X-ray absorptiometry, and Brightness-mode ultrasound, and non-imaging techniques like bioelectrical impedance analysis and anthropometry. Skeletal muscle size can be acquired from these methods using whole-body and/or regional assessments, as well as prediction equations. Notable concerns when conducting assessments include the absence of standardised image acquisition/processing protocols and the variation in cut-off thresholds used to define low skeletal muscle size by clinicians and researchers, which could affect the accuracy and prevalence of diagnoses. Given the importance of evaluating skeletal muscle size, it is imperative practitioners are informed of each technique and their respective strengths and weaknesses.

Prediction of hematologic toxicity in luminal type breast cancer patients receiving neoadjuvant chemotherapy using CT L1 level skeletal muscle index

This study aims to explore the correlation between the CT-L1 and L3 body composition parameters and analyze the relationship between L1 body composition and hematologic toxicity in luminal-type breast cancer patients undergoing neoadjuvant chemotherapy. Data from 140 luminal-type breast cancer patients who underwent surgical treatment after neoadjuvant chemotherapy were analyzed retrospectively. Spearman analysis was used to assess the correlation between CT-L1 and CT-L3 body composition parameters pre-neoadjuvant chemotherapy. Additionally, univariate and multivariate logistic regression analyses were performed to identify factors influencing hematologic toxicity. CT-L1 body composition parameters were positively correlated with CT-L3 body composition parameters in 34 patients. Severe hematological toxicity occurred in 46 cases among the patient cohort. A skeletal muscle index (SMI) of < 32.91 cm2/m2, initial tumor size ≥ 3.335 cm, and a glucose-to-neutrophil ratio (GLR) ≥ 2.88 were identified as independent risk factors for severe hematologic toxicity during neoadjuvant chemotherapy in luminal-type breast cancer patients. The sample size in this study is small, and the predictive capacity of GLR in hematologic toxicity requires further research for comprehensive validation. CT-L1 analysis represents a viable alternative to CT-L3 analysis for body composition assessment. Patients with a low skeletal muscle index were more prone to experiencing severe hematologic toxicity during neoadjuvant chemotherapy.

Cigarette smoking and disproportionate changes of thoracic skeletal muscles in low-dose chest computed tomography

Association between smoking intensity and the quantity and quality of thoracic skeletal muscles (TSMs) remains unexplored. Skeletal muscle index (SMI; skeletal muscle area/height2) and percentage of normal attenuation muscle area (NAMA%) were measured to represent the quantity and quality of the skeletal muscles, respectively, and quantification was performed in pectoralis muscle at aortic arch (AA-PM), TSM at carina (C-TSM), erector spinae muscle at T12 (T12-ESM), and skeletal muscle at L1 (L1-SM). Among the 258 men (median age, 62 years [IQR: 58-69]), 183 were current smokers (median smoking intensity, 40 pack-years [IQR: 30-46]). SMI and NAMA% of AA-PM significantly decreased with pack-year (β =  - 0.028 and - 0.076; P < 0.001 and P = 0.021, respectively). Smoking intensity was inversely associated with NAMA% of C-TSM (β =  - 0.063; P = 0.001), whereas smoking intensity showed a borderline association with SMI of C-TSM (β =  - 0.023; P = 0.057). Smoking intensity was associated with the change in NAMA% of L1-SM (β =  - 0.040; P = 0.027), but was not associated with SMI of L1-SM (P > 0.05). Neither NAMA% nor SMI of T12-ESM was affected by smoking intensity (P > 0.05). In conclusion, smoking intensity was associated with the change of TSMs. Its association varied according to the location of TSMs, with the most associated parts being the upper (AA-PM) and middle TSMs (C-TSM).

Diagnosis of sarcopenia on thoracic computed tomography and its association with postoperative survival after anatomic lung cancer resection

Computer tomography-derived skeletal muscle index normalized for height in conjunction with muscle density enables single modality-based sarcopenia assessment that accounts for all diagnostic criteria and cutoff recommendations as per the widely accepted European consensus. Yet, the standard approach to quantify skeletal musculature at the third lumbar vertebra is limited for certain patient groups, such as lung cancer patients who receive chest CT for tumor staging that does not encompass this lumbar level. As an alternative, this retrospective study assessed sarcopenia in lung cancer patients treated with curative intent at the tenth thoracic vertebral level using appropriate cutoffs. We showed that skeletal muscle index and radiation attenuation at level T10 correlate well with those at level L3 (Pearson's R = 0.82 and 0.66, p < 0.001). During a median follow-up period of 55.7 months, sarcopenia was independently associated with worse overall (hazard ratio (HR) = 2.11, 95%-confidence interval (95%-CI) = 1.38-3.23, p < 0.001) and cancer-specific survival (HR = 2.00, 95%-CI = 1.19-3.36, p = 0.009) of lung cancer patients following anatomic resection. This study highlights feasibility to diagnose sarcopenia solely by thoracic CT in accordance with the European consensus recommendations. The straightforward methodology offers easy translation into routine clinical care and potential to improve preoperative risk stratification of lung cancer patients scheduled for surgery.

CT analysis of thoracolumbar body composition for estimating whole-body composition

BACKGROUND

To evaluate the correlation between single- and multi-slice cross-sectional thoracolumbar and whole-body compositions.

METHODS

We retrospectively included patients who underwent whole-body PET-CT scans from January 2016 to December 2019 at multiple institutions. A priori-developed, deep learning-based commercially available 3D U-Net segmentation provided whole-body 3D reference volumes and 2D areas of muscle, visceral fat, and subcutaneous fat at the upper, middle, and lower endplate of the individual T1-L5 vertebrae. In the derivation set, we analyzed the Pearson correlation coefficients of single-slice and multi-slice averaged 2D areas (waist and T12-L1) with the reference values. We then built prediction models using the top three correlated levels and tested the models in the validation set.

RESULTS

The derivation and validation datasets included 203 (mean age 58.2 years; 101 men) and 239 patients (mean age 57.8 years; 80 men). The coefficients were distributed bimodally, with the first peak at T4 (coefficient, 0.78) and the second peak at L2-3 (coefficient 0.90). The top three correlations in the abdominal scan range were found for multi-slice waist averaging (0.92) and single-slice L3 and L2 (0.90, each), while those in the chest scan range were multi-slice T12-L1 averaging (0.89), single-slice L1 (0.89), and T12 (0.86). The model performance at the top three levels for estimating whole-body composition was similar in the derivation and validation datasets.

CONCLUSIONS

Single-slice L2-3 (abdominal CT range) and L1 (chest CT range) analysis best correlated with whole-body composition around 0.90 (coefficient). Multi-slice waist averaging provided a slightly higher correlation of 0.92.