Automated Characterization of Body Composition and Frailty with Clinically Acquired CT

Hu, Patrick J.; Huo, Yuankai; Kong, Dexing; Carr, J. Jeffrey; Abramson, Richard G.; Hartley, Katherine G.; Landman, Bennett A.

doi:10.1007/978-3-319-74113-0_3

Cited by 13 publications

(21 citation statements)

References 12 publications

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“…Several approaches have been proposed for the automated segmentation of muscle, VAT, and SAT within the 3 rd lumbar vertebrae region; which have demonstrated segmentation performance ranging from DSC: 0.85 -0.99 [15,17,[19][20][21][41][42][43][44] (Table 4). AutoMATiCA displays DSC scores on-par with the most accurate automated approaches for muscle, VAT, and SAT.…”

Section: Discussionmentioning

confidence: 99%

Automated body composition analysis of clinically acquired computed tomography scans using neural networks

et al. 2020

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Background & Aims-The quantity and quality of skeletal muscle and adipose tissue is an important prognostic factor for clinical outcomes across several illnesses. Clinically acquired computed tomography (CT) scans are commonly used for quantification of body composition, but manual analysis is laborious and costly. The primary aim of this study was to develop an automated body composition analysis framework using CT scans.Methods-CT scans of the 3 rd lumbar vertebrae from critically ill, liver cirrhosis, pancreatic cancer, and clear cell renal cell carcinoma patients, as well as renal and liver donors, were manually analyzed for body composition. Ninety percent of scans were used for developing and validating a neural network for the automated segmentation of skeletal muscle and adipose tissues. Network accuracy was evaluated with the remaining 10 percent of scans using the Dice similarity coefficient (DSC), which quantifies the overlap (0 = no overlap, 1 = perfect overlap) between human and automated segmentations.Results-Of the 893 patients, 44% were female, with a mean (±SD) age and body mass index of 52.7 (±15.8) years old and 28.0 (±6.1) kg/m 2 , respectively. In the testing cohort (n=89), DSC scores indicated excellent agreement between human and network-predicted segmentations for

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Section: Discussionmentioning

confidence: 99%

Automated body composition analysis of clinically acquired computed tomography scans using neural networks

et al. 2020

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“…However, manual segmentation of skeletal muscle and adipose tissue is time‐consuming and requires trained experts, which were not necessary in this study [31, 32]. Therefore, we believe that machine learning‐based body composition analysis will increasingly make body composition data available across clinical care settings [17, 27, 33, 34].…”

Section: Discussionmentioning

confidence: 99%

Muscle Loss Is Associated with Overall Survival in Patients with Metastatic Colorectal Cancer Independent of Tumor Mutational Status and Weight Loss

et al. 2021

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Background Survival in patients with metastatic colorectal cancer (mCRC) has been associated with tumor mutational status, muscle loss, and weight loss. We sought to explore the combined effects of these variables on overall survival. Materials and Methods We performed an observational cohort study, prospectively enrolling patients receiving chemotherapy for mCRC. We retrospectively assessed changes in muscle (using computed tomography) and weight, each dichotomized as >5% or ≤5% loss, at 3, 6, and 12 months after diagnosis of mCRC. We used regression models to assess relationships between tumor mutational status, muscle loss, weight loss, and overall survival. Additionally, we evaluated associations between muscle loss, weight loss, and tumor mutational status. Results We included 226 patients (mean age 59 ± 13 years, 53% male). Tumor mutational status included 44% wild type, 42% RAS‐mutant, and 14% BRAF‐mutant. Patients with >5% muscle loss at 3 and 12 months experienced worse survival controlling for mutational status and weight (3 months hazard ratio, 2.66; p < .001; 12 months hazard ratio, 2.10; p = .031). We found an association of >5% muscle loss with BRAF‐mutational status at 6 and 12 months. Weight loss was not associated with survival nor mutational status. Conclusion Increased muscle loss at 3 and 12 months may identify patients with mCRC at risk for decreased overall survival, independent of tumor mutational status. Specifically, >5% muscle loss identifies patients within each category of tumor mutational status with decreased overall survival in our sample. Our findings suggest that quantifying muscle loss on serial computed tomography scans may refine survival estimates in patients with mCRC. Implications for Practice In this study of 226 patients with metastatic colorectal cancer, it was found that losing >5% skeletal muscle at 3 and 12 months after the diagnosis of metastatic disease was associated with worse overall survival, independent of tumor mutational status and weight loss. Interestingly, results did not show a significant association between weight loss and overall survival. These findings suggest that muscle quantification on serial computed tomography may refine survival estimates in patients with metastatic colorectal cancer beyond mutational status.

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“…The previous results were obtained in the automatic segmentation of abdominal muscle (9,14–17). The DSC was 0.92 by Weston et al (18) and 0.93 by Lee et al (10), and those results were wonderful.…”

Section: Introductionmentioning

confidence: 95%

Deep learning for automatic segmentation of paraspinal muscle on computed tomography

Yao

Cheng

et al. 2022

Acta Radiol

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Background Muscle quantification is an essential step in sarcopenia evaluation. Purpose To develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on either abdominal or lumbar (L) computed tomography (CT) scans. Material and Methods A novel deep neural network algorithm for automated segmentation of paraspinous muscle was developed, CT scans of 504 consecutive patients conducted between January 2019 and February 2020 were assembled. The muscle was manually segmented at L3 vertebra level by three radiologists as ground truth, divided into training and testing subgroups. Muscle cross-sectional area (CSA) was recorded. Dice similarity coefficients (DSCs) and CSA errors were calculated to evaluate system performance. The degree of muscle fat infiltration (MFI) recording by percentage value was the fat area within the region of interest divided by the muscle area. An analysis of the factors influencing the performance of the V-net-based segmentation system was also implemented. Results The mean DSCs for paraspinous muscles were high for both the training (0.963, 0.970, 0.941, and 0.968, respectively) and testing (0.950, 0.960, 0.929, and 0.961, respectively) datasets, while the CSA errors were low for both training (1.9%, 1.6%, 3.1%, and 1.3%, respectively) and testing (3.4%, 3.0%, 4.6%, and 1.9%, respectively) datasets. MFI and muscle area index (MI) were major factors affecting DSCs of the posterior paraspinous and paraspinous muscle groups. Conclusion The ML algorithm for the measurement of paraspinous muscles was compared favorably to manual ground truth measurements.

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Automated Characterization of Body Composition and Frailty with Clinically Acquired CT

Cited by 13 publications

References 12 publications

Automated body composition analysis of clinically acquired computed tomography scans using neural networks

Automated body composition analysis of clinically acquired computed tomography scans using neural networks

Muscle Loss Is Associated with Overall Survival in Patients with Metastatic Colorectal Cancer Independent of Tumor Mutational Status and Weight Loss

Deep learning for automatic segmentation of paraspinal muscle on computed tomography

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