Comparison of CT derived body composition at the thoracic T4 and T12 with lumbar L3 vertebral levels and their utility in patients with rectal cancer

Influence of skeletal muscle and intermuscular fat on postoperative complications and long-term survival in rectal cancer patients

BACKGROUND

The body composition of patients with rectal cancer potentially affects postoperative outcomes. This study explored the correlations between skeletal muscle and adipose tissue quantified by computed tomography (CT) with postoperative complications and long-term prognosis in patients with rectal cancer after surgical resection.

METHODS

This retrospective cohort study included patients with rectal cancer who underwent surgical resection at the Wuhan Union Hospital between 2014 and 2018. CT images within 3 months prior to the surgery were used to quantify the indices of skeletal muscle and adipose tissue at the levels of the third lumbar vertebra (L3) and umbilicus. Optimal cut-off values for each index were defined separately for males and females. Associations between body composition and postoperative complications, overall survival (OS), and disease-free survival (DFS) were evaluated using logistic and Cox proportional hazards models.

RESULTS

We included 415 patients (240 males and 175 females; mean age: 57.8 ± 10.5 years). At the L3 level, a high skeletal muscle density (SMD; hazard ratio [HR]: 0.357, 95% confidence interval [CI]: 0.191-0.665, P = 0.001; HR: 0.571, 95% CI: 0.329-0.993, P = 0.047) and a high skeletal muscle index (SMI; HR: 0.435, 95% CI 0.254-0.747, P = 0.003; HR: 0.568, 95% CI: 0.359-0.897, P = 0.015) were independent prognostic factors for better OS and DFS. At the umbilical level, a large intermuscular fat area (IMFA; HR: 1.904, 95% CI: 1.068-3.395, P = 0.029; HR: 2.064, 95% CI: 1.299-3.280, P = 0.002) was an independent predictive factor for worse OS and DFS, and a high SMI (HR: 0.261, 95% CI: 0.132-0.517, P < 0.001; HR: 0.595, 95% CI: 0.387-0.913, P = 0.018) was an independent prognostic factor for better OS and DFS. The models combining body composition and clinical indicators had good predictive abilities for OS. The receiver operating characteristic areas under the curve were 0.848 and 0.860 at the L3 and umbilical levels, respectively (both P < 0.05).

CONCLUSIONS

No correlations existed between CT-quantified body composition parameters and postoperative complications. However, a high SMD and high SMI were significantly associated with longer OS and DFS at the L3 level, whereas a large IMFA and low SMI were associated with worse OS and DFS at the umbilical level. Combining CT-quantified body composition and clinical indicators could help physicians predict the prognosis of patients with rectal cancer after surgery.

The association of body composition and outcomes following autologous hematopoietic stem cell transplantation in patients with non-Hodgkin lymphoma

Recently there has been a growing interest in evaluating body composition as a marker for prognosis in cancer patients. The association of body composition parameters and outcomes has not been deeply investigated in patients with autologous hematopoietic stem cell transplantation (HSCT) recipients with non-Hodgkin lymphoma (NHL). We conducted a retrospective cohort study of 264 NHL patients who received autologous HSCT. PreHSCT abdominal CT scans at the levels of L3 were assessed for body composition measures. We evaluated sarcopenia, myosteatosis, high visceral adipose tissue (VAT) and high visceral adipose tissue density (VATD). Using multivariable Cox proportional regression, we analyzed the association of clinical and transplant-related characteristics with overall survival (OS), relapse-free survival (RFS), and non-relapse mortality (NRM). In a multivariate regression model, patients with higher VATD had worse OS (HR 1.78; 95% confidence intervals CI 1.08-2.95, p = 0.02) and worse NRM (HR 2.31 95% CI 1.08-4.95, p = 0.02) than with lower VATD. Patients with lower levels of VAT also had worse RFS (HR 1.49 95% CI 1.03-2.15, p = 0.03). Sarcopenia and myosteatosis were not associated with outcomes. High pre-transplant VATD was associated with lower OS and higher NRM, and low pre-transplant VAT was associated with worse RFS in patients with NHL undergoing autologous HSCT.

Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?

PURPOSE

Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting DLT in colon cancer patients.

METHODS

203 patients (60.87 ± 12.42 years; 97 males, 47.8%) receiving adjuvant chemotherapy (Oxaliplatin and/or 5-Fluorouracil) were retrospectively evaluated. An artificial intelligence segmentation model was used to extract 2D and 3D body composition measurements from each patients' single mid-L3 CT slice as well as multiple-L3 CT scans to produce a 3D BC report. DLT was defined as any incidence of dose reduction or discontinuation due to chemotherapy toxicities. A receiver operating characteristic (ROC) analysis was performed on BSA and individual body composition measurements to demonstrate their predictive performance.

RESULTS

A total of 120 (59.1%) patients experienced DLT. Age and BSA did not vary significantly between DLT and non-DLT group. Females were significantly more likely to experience DLT (p = 4.9 × 10-3). In all patients, the predictive effectiveness of 2D body composition measurements (females: AUC = 0.50-0.54; males: AUC = 0.50-0.61) was equivalent to that of BSA (females: AUC = 0.49; males: AUC = 0.58). The L3 3D skeletal muscle volume was the most predictive indicator of DLT (AUC of 0.66 in females and 0.64 in males).

CONCLUSION

Compared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict DLT in CRC patients receiving chemotherapy and this was sex dependent.

Identification of Differences in Body Composition Measures Using 3D-Derived Artificial Intelligence from Multiple CT Scans across the L3 Vertebra Compared to a Single Mid-Point L3 CT Scan

Purpose

Body composition analysis in colorectal cancer (CRC) typically utilises a single 2D-abdominal axial CT slice taken at the mid-L3 level. The use of artificial intelligence (AI) allows for analysis of the entire L3 vertebra (non-mid-L3 and mid-L3). The goal of this study was to determine if the use of an AI approach offered any additional information on capturing body composition measures.

Methods

A total of 2203 axial CT slices of the entire L3 level (4-46 slices were available per patient) were retrospectively collected from 203 CRC patients treated at Western Health, Melbourne (97 males; 47.8%). A pretrained artificial intelligence (AI) model was used to segment muscle, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) on these slices. The difference in body composition measures between mid-L3 and non-mid-L3 scans was compared for each patient, and for males and females separately.

Results

Body composition measures derived from non-mid-L3 scans exhibited a median range of 0.85% to 6.28% (average percent difference) when compared to the use of a single mid-L3 scan. Significant variation in the VAT surface area (p = 0.02) was observed in females compared to males, whereas male patients exhibited a greater variation in SAT surface area (p < 0.001) and radiodensity (p = 0.007).

Conclusion

Significant differences in various body composition measures were observed when comparing non-mid-L3 slices to only the mid-L3 slice. Researchers should be aware that considering only the use of a single midpoint L3 CT scan slice will impact the estimate of body composition measurements.

Radiomics of spinal muscles: toward a radiological biomarker for allograft rejection in lung transplant

PURPOSE

To assess the role of muscle composition and radiomics in predicting allograft rejection in lung transplant.

MATERIAL AND METHODS

The last available HRCT before surgery of lung transplant candidates referring to our tertiary center from January 2010 to February 2020 was retrospectively examined. Only scans with B30 kernel reconstructions and 1 mm slice thickness were included. One radiologist segmented the spinal muscles of each patient at the level of the 11th dorsal vertebra by an open-source software. The same software was used to extract Hu values and 72 radiomic features of first and second order. Factor analysis was applied to select highly correlating features and then their prognostic value for allograft rejection was investigated by logistic regression analysis (level of significance p < 0.05). In case of significant results, the diagnostic value of the model was computed by ROC curves.

RESULTS

Overall 200 patients had a HRCT prior to the transplant but only 97 matched the inclusion criteria (29 women; mean age 50.4 ± 13 years old). Twenty-one patients showed allograft rejection. The following features were selected by the factor analysis: cluster prominence, Imc2, gray level non-uniformity normalized, median, kurtosis, gray level non-uniformity, and inverse variance. The radiomic-based model including also Hu demonstrated that only the feature Imc2 acts as a predictor of allograft rejection (p = 0.021). The model showed 76.6% accuracy and the Imc2 value of 0.19 demonstrated 81% sensitivity and 64.5% specificity in predicting lung transplant rejection.

CONCLUSION

The radiomic feature Imc2 demonstrated to be a predictor of allograft rejection in lung transplant.