Quantification of body composition in renal cell carcinoma patients: Comparing computed tomography and magnetic resonance imaging measurements

Development of a CT-assessed adiposity nomogram for predicting outcome in localized ccRCC

PURPOSE

This study aimed to develop and validate a computed tomography-based nomogram assessing visceral and subcutaneous adiposity for predicting outcomes in localized clear cell renal cell carcinoma (ccRCC).

METHODS

A cohort of 364 patients with pathologically confirmed ccRCC participated in this retrospective study, with 254 patients assigned to the training set and 110 to the validation set (a 7:3 distribution ratio). The adipose score (AS) was generated using the least absolute shrinkage and selection operator Cox regression. Subsequently, a nomogram was constructed by integrating the clinical independent predictor with the AS to predict disease-free survival (DFS) in localized ccRCC after surgery. The performance of the nomogram was compared with the University of California, Los Angeles, Integrated Staging System (UISS), and the Stage, Size, Grade, and Necrosis (SSIGN) score.

RESULTS

In both the training and validation cohorts, the nomogram exhibited superior discrimination compared to SSIGN and UISS (C-index: 0.897 vs. 0.781 vs. 0.776 in the training cohort, and 0.752 vs. 0.596 vs. 0.686 in the validation cohort; 5 year AUC: 0.907 vs. 0.805 vs. 0.820 in the training cohort, and 0.832 vs. 0.577 vs. 0.726 in the validation cohort). Decision curve analysis (DCA) revealed a superior net benefit across a wider range of threshold probabilities for predicting 5 year DFS compared to UISS and SSIGN scores.

CONCLUSIONS

The developed prognostic nomogram demonstrated high accuracy and overall superior performance compared to existing prognostic models.

Body composition analysis by radiological imaging - methods, applications, and prospects

BACKGROUND

 This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging.

METHODS

 The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups.

RESULTS AND CONCLUSION

 Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner - in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation.

KEY POINTS

  · Radiological imaging methods are increasingly used to determine body composition (BC).. · BC parameters are usually quantitative and well reproducible.. · CT image data from routine clinical examinations can be used retrospectively for BC analysis.. · Prospectively, MRI examinations can be used to determine organ-specific BC parameters.. · Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future..

CITATION FORMAT

· Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2263-1501.

Impact of preoperative body composition in patients with renal cell carcinoma submitted to surgical treatment

Objective

To evaluate the impact of preoperative body composition in patients with renal cell carcinoma (RCC) undergoing surgical treatment.

Materials and Methods

This was a retrospective study of 52 patients with RCC undergoing total or partial nephrectomy. Body composition assessment was performed using the body mass index, together with computed tomography analysis at the level of the third lumbar vertebra to measure the area of visceral adipose tissue, as well as the area and density of skeletal muscle mass.

Results

Malnutrition, obesity and inadequate skeletal muscle gauge (SMG) were associated with higher hospital length of stay (p = 0.028, p = 0.02 and p = 0.012, respectively). Although the rates of postoperative symptoms and readmissions were low, survival was better among the patients with an adequate SMG than among those with an inadequate SMG (p = 0.003).

Conclusion

Among patients with RCC undergoing surgical treatment, preoperative body composition does not seem to be associated with the rates of perioperative complications, although an inadequate SMG seems to be associated with worse overall survival.

Low Skeletal Muscle as a Risk Factor for Worse Survival in Nonmetastatic Renal Cell Carcinoma with Venous Tumor Thrombus

BACKGROUND

Renal cell carcinoma (RCC) with tumor thrombosis often requires nephrectomy and tumor thrombectomy. As an extensive and potentially morbid operation, patient preoperative functional reserve and body composition is an important consideration. Sarcopenia is a risk factor for increased postoperative complications, systemic therapy toxicity, and death solid organ tumors, including RCC. The influence of sarcopenia in RCC patients with tumor thrombus is not well defined. This study evaluates the prognostic ability of sarcopenia regarding surgical outcomes and complications in patients undergoing surgery for RCC with tumor thrombus.

METHODS

We retrospectively analyzed patients with nonmetastatic RCC and tumor thrombus undergoing radical nephrectomy and tumor thrombectomy. Skeletal muscle index (SMI; cm²/m²) was measured on preoperative CT/MRI. Sarcopenia was defined using body mass index- and sex-stratified thresholds optimally fit via a receiver-operating characteristic analysis for survival. Associations between preoperative sarcopenia and overall (OS), cancer-specific survival (CSS), and 90-day major complications were determined using multivariable analysis.

RESULTS

115 patients were analyzed, with median (IQR) age and body mass index of 69 (56-72) and 28.6 kg/m² (23.6-32.9), respectively. 96 (83.4%) of the cohort had ccRCC. Sarcopenia was associated with shorter median OS (P = .0017) and CSS (P = .0019) in Kaplan-Meier analysis. In multivariable analysis, preoperative sarcopenia was prognostic of shorter OS (HR = 3.38, 95% confidence interval [CI] 1.61-7.09) and CSS (HR = 5.15, 95% CI 1.46-18.18). Notably, 1 unit increases in SMI were associated with improved OS (HR = 0.97, 95% CI 0.94-0.999) but not CSS (HR = 0.95, 95% CI 0.90-1.01). No significant relationship between preoperative sarcopenia and 90-day major surgical complications was observed in this cohort (HR = 2.04, 95% CI 0.65-6.42).

CONCLUSION

Preoperative sarcopenia was associated with decreased OS and CSS in patients surgically managed for nonmetastatic RCC and VTT, however, was not predictive of 90-day major postoperative complications. Body composition analysis has prognostic utility for patients with nonmetastatic RCC and venous tumor thrombus undergoing surgery.

Imaging Techniques to Determine Degree of Sarcopenia and Systemic Inflammation in Advanced Renal Cell Carcinoma

PURPOSE OF REVIEW

The purpose of this review is to provide an up-to-date understanding regarding the literature on sarcopenia and inflammation as prognostic factors in the context of renal cell carcinoma (RCC).

RECENT FINDINGS

Sarcopenia is increasingly recognized as a prognostic factor in RCC. Emerging literature suggests monitoring quantity of muscle on successive imaging and examining muscle density may be additionally informative. Inflammation has prognostic ability in RCC and is also considered a key contributor to development and progression of both RCC and sarcopenia. Recent studies suggest these two prognostic factors together may provide additional prognostic ability when used in combination. Ongoing developments include quality control regarding sarcopenia research and imaging, improving understanding of muscle loss mechanisms, and enhancing clinical incorporation of sarcopenia via improving imaging analysis practicality (i.e., artificial intelligence) and feasible biomarkers. Sarcopenia and systemic inflammation are complementary prognostic factors for adverse outcomes in patients with RCC. Further study on high-quality sarcopenia assessment standardization and expedited sarcopenia assessment is desired for eventual routine clinical incorporation of these prognostic factors.

Clinic friendly estimation of muscle composition: Preoperative linear segmentation shows overall survival correlated with muscle mass in patients with nonmetastatic renal cell carcinoma

Purpose

Sarcopenia is associated with decreased survival and increased complications in patients with renal cell carcinoma. Readily identifying patients with low muscle composition that may experience worse outcomes or would benefit from preoperative intervention is of clinical interest. Traditional body composition analysis methods are resource intensive; therefore, linear segmentation with routine imaging has been proposed as a clinically practical alternative. This study assesses linear segmentation's prognostic utility in nonmetastatic renal cell carcinoma.

Materials and Methods

A single institution retrospective analysis of patients that underwent nephrectomy for nonmetastatic renal cell carcinoma from 2005-2021 was conducted. Linear segmentation of the bilateral psoas/paraspinal muscles was completed on preoperative imaging. Total muscle area and total muscle index associations with overall survival were determined by multivariable analysis.

Results

532 (388 clear cell) patients were analyzed, with median (IQR) total muscle index of 28.6cm²/m² (25.8-32.5) for women and 33.3cm²/m² (29.1-36.9) for men. Low total muscle index was associated with decreased survival (HR=1.96, 95% CI 1.32-2.90, p<0.001). Graded increases in total muscle index were associated with better survival (HR=0.95, 95% CI 0.92-0.99, p=0.006).

Conclusions

Linear segmentation, a clinically feasible technique to assess muscle composition, has prognostic utility in patients with localized renal cell carcinoma, allowing for incorporation of muscle composition analysis into clinical decision-making. Muscle mass determined by linear segmentation was associated with overall survival in patients with nonmetastatic renal cell carcinoma.

Sarcopenia and systemic inflammation are associated with decreased survival after cytoreductive nephrectomy for metastatic renal cell carcinoma

BACKGROUND

This study was aimed at assessing the associations of sarcopenia, muscle density, adiposity, and inflammation with overall survival (OS) after cytoreductive nephrectomy (CN) for metastatic renal cell carcinoma.

METHODS

In all, 158 patients undergoing CN from 2001 to 2014 had digitized preoperative imaging for tissue segmentation via Slice-O-Matic software (version 5.0) at the mid-L3 level. The skeletal muscle index was calculated with the skeletal muscle area (cm² ) normalized for height (m² ), and the skeletal muscle density (SMD) was calculated with average Hounsfield units. Adiposity was measured with the cross-sectional area (cm² ) of visceral, subcutaneous, and intramuscular adiposity compartments and was similarly normalized for height. The average fat density was obtained in Hounsfield units. OS was estimated with the Kaplan-Meier method. Associations between body composition, inflammation metrics, and relevant clinicopathology and OS were assessed with univariable and multivariate Cox analyses.

RESULTS

Seventy-six of the 158 patients (48%) were sarcopenic. Sarcopenia was associated with elevated neutrophil to lymphocyte ratios (NLRs; P = .02), increased age (P = .001), lower body mass indices (P = .009), greater modified Motzer scores (P = .019), and lower SMD (P = .006). The median OS was 15.0 and 29.4 months for sarcopenic and nonsarcopenic patients, respectively (P = .04). Elevated inflammation (NLR or C-reactive protein), in addition to sarcopenia, was independently associated with OS, with an elevated NLR ≥ 3.5 and sarcopenia associated with the poorest OS at 10.2 months. No associations were observed between measurements of muscle density or adiposity and OS.

CONCLUSIONS

Sarcopenia and measures of high systemic inflammation are additively associated with inferior OS after CN and may be of use in preoperative risk stratification.

LAY SUMMARY

Body composition and sarcopenia (a deficiency in skeletal musculature) have been shown to affect outcomes in cancer. We found that sarcopenic patients had poor survival in comparison with nonsarcopenic patients in the setting of metastatic renal cell carcinoma (mRCC). Patients with both elevated inflammation and sarcopenia had the poorest survival. Sarcopenia is an objective measure of nutrition that can assist in therapeutic counseling and decision-making for individualized treatment in mRCC.

Body composition assessment: comparison of quantitative values between magnetic resonance imaging and computed tomography

Background

The primary objective of this study was to compare measurements of skeletal muscle index (SMI), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) at the level of L3, on subjects who underwent computed tomography (CT) and magnetic resonance imaging (MRI) examinations within a three-month period. The secondary objective was to compare the automatic and semi-automatic quantifications of the same values for CT images.

Methods

Among subjects who underwent CT and MRI at our Institution between 2011 and 2020, exclusion criteria were: presence of extensive artifacts; images not including the whole waist circumference; CT acquired with low-dose technique and lack of non-contrast images. A set of three axial images (CT, MRI T1-weighted and T2-weighted) were used to extract the following measurements with semi-automatic segmentations: SMI [calculated normalizing skeletal muscle area (SMA) by the square height], SAT, VAT. For the CT images only, the same values were also calculated by using automatic segmentation. Statistical analysis was performed comparing quantitative MRI and CT measurements by Pearson correlation analysis and by Bland-Altman agreement analysis.

Results

A total of 123 patients were included. By performing linear regression analysis, CT and MRI measurements of SMI showed a high correlation (r²=0.81 for T1, r²=0.89 for T2), with a mean logarithmic difference between CT and MRI quantitative values of 0.041 for T1-weighted and 0.072 for T2-weighted images. CT and MRI measurements of SAT showed high correlation (r²=0.81 for T1; r²=0.81 for T2), with a mean logarithmic difference between CT and MRI values of 0.0174 for T1-weighted and 0.201 for T2-weighted images. CT and MRI measurements of VAT showed high correlation (r²=0.94 for T1; r²=0.93 for T2), with a mean logarithmic difference of 0.040 for T1-weighted and -0.084 for T2-weighted images. The comparison of values extracted by semi-automatic and automatic segmentations were highly correlated.

Conclusions

Quantification of body composition values at MRI from T1-weighted and T2-weighted images was highly correlated to same values at CT, therefore quantitative values of body composition among patients who underwent either one of the examinations may be compared. CT body composition values extracted by semi-automatic and automatic segmentations showed high correlation.

Body Composition Variables as Radiographic Biomarkers of Clinical Outcomes in Metastatic Renal Cell Carcinoma Patients Receiving Immune Checkpoint Inhibitors

BACKGROUND

Immune checkpoint inhibitors (ICI) have revolutionized the treatment of metastatic renal cell carcinoma (mRCC). Biomarkers for mRCC patients treated with ICI are limited, and body composition is underutilized in mRCC. We investigated the association between body composition and clinical outcomes in ICI-treated mRCC patients.

METHODS

We performed a retrospective analysis of 79 ICI-treated mRCC patients at Winship Cancer Institute from 2015-2020. Baseline CT images were collected at mid-L3 and segmented using SliceOMatic v5.0 (TomoVision). Density of skeletal muscle (SM), subcutaneous fat, inter-muscular fat, and visceral fat were measured and converted to indices by dividing by height(m)2 (SMI, SFI, IFI, and VFI, respectively). Total fat index (TFI) was defined as the sum of SFI, IFI, and VFI. Patients were characterized as high versus low for each variable at gender-specific optimal cuts using overall survival (OS) as the primary outcome. A prognostic risk score was created based on the beta coefficient from the multivariable Cox model after best subset variable selection. Body composition risk score was calculated as IFI + 2*SM mean + SFI and patients were classified as poor (0-1), intermediate (2), or favorable risk (3-4). Kaplan-Meier method and Log-rank test were used to estimate OS and PFS and compare the risk groups. Concordance statistics (C-statistics) were used to measure the discriminatory magnitude of the model.

RESULTS

Most patients were male (73%) and most received ICI as first (35%) or second-line (51%) therapy. The body composition poor-risk patients had significantly shorter OS (HR: 6.37, p<0.001), PFS (HR: 4.19, p<0.001), and lower chance of CB (OR: 0.23, p=0.044) compared to favorable risk patients in multivariable analysis. Patients with low TFI had significantly shorter OS (HR: 2.72, p=0.002), PFS (HR: 1.91, p=0.025), and lower chance of CB (OR: 0.25, p=0.008) compared to high TFI patients in multivariable analysis. The C-statistics were higher for body composition risk groups and TFI (all C-statistics ≥ 0.598) compared to IMDC and BMI.

CONCLUSIONS

Risk stratification using the body composition variables IFI, SM mean, SFI, and TFI may be prognostic and predictive of clinical outcomes in mRCC patients treated with ICI. Larger, prospective studies are warranted to validate this hypothesis-generating data.

Machine Learning in Body Composition Analysis

Body composition analysis (BCA) generates objective anthropometric data that can inform prognostication and treatment decisions across a wide variety of urologic conditions. A patient's body composition, specifically muscle and adipose tissue mass, may be characterized via segmentation of cross-sectional images (computed tomography, magnetic resonance imaging) obtained as part of routine clinical care. Unfortunately, conventional semi-automated segmentation techniques are time- and resource-intensive, precluding translation into clinical practice. Machine learning (ML) offers the potential to automate and scale rapid and accurate BCA. To date, ML for BCA has relied on algorithms called convolutional neural networks designed to detect and analyze images in ways similar to human neuronal connections. This mini review provides a clinically oriented overview of ML and its use in BCA. We address current limitations and future directions for translating ML and BCA into clinical practice. PATIENT SUMMARY: Body composition analysis is the measurement of muscle and fat in your body based on analysis of computed tomography or magnetic resonance imaging scans. We discuss the use of machine learning to automate body composition analysis. The information provided can be used to guide shared decision-making and to help in identifying the best therapy option.