Purpose To investigate whether sarcopenia and myosteatosis correlate with the degree of hypertrophy (DH) and kinetic growth rate (KiGR) of the future liver remnant (FLR) in patients with colorectal liver metastases undergoing portal vein embolization (PVE) in preparation for right hepatectomy. Materials and Methods Forty-two patients were included. Total liver volume and FLR volume were measured before and 2-4 weeks after PVE. KiGR of the FLR was calculated. Sarcopenia was assessed using the total psoas muscle volume (PMV), the psoas muscle cross-sectional area (PMCS) and the total skeletal muscle index (L3SMI) at the level of 3rd lumbar vertebra. Degree of myosteatosis was assessed by mean muscle attenuation at L3 (L3MA). Correlations between muscle indices and DH and KiGR were assessed using simple linear regression analyses. Results Mean DH was 8.9 ± 5.7%, and mean KiGR was 3.6 ± 2.3. Mean PMV was 55.56 ± 14.19 cm 3 /m 3 , mean PMCS was 8.76 ± 2.3 cm 2 /m 2 , mean L3SMI was 45.6 ± 9.89 cm 2 /m 2 , and mean L3MA was 27.9 ± 18.6 HU. There was a strong positive correlation between PMV and DH (R = 0.503, p = 0.001) and PMV and KiGR (R = 0.545, p \ 0.001). Furthermore, there was a moderate correlation between PMCS and KiGR (R = 0.389, p = 0.014). L3SMI and L3MA were neither associated with DH (p = 0.390 and p = 0.768, respectively) nor with KiGR (p = 0.188 and p = 0.929, respectively). Conclusion We identified a positive correlation between PMV and PMCS, as markers for sarcopenia, and the KiGR of the FLR after PVE. PMV and PMCS might therefore aid to identify patients who are poor candidates for FLR augmentation using PVE alone. KeywordsPortal vein embolization Á Sarcopenia Á Liver hypertrophy Á FLR Á PVE Abbreviations FLR Future liver remnant PVE Portal vein embolization TLV Total liver volume KiGR Kinetic growth rate DH Degree of hypertrophy PMV Psoas muscle volume PMCS Psoas muscle cross-sectional area at the largest diameter L3SMI Skeletal muscle index at the level of the 3rd lumbar vertebra L3MA Mean muscle attenuation at the level of the 3rd lumbar vertebra
Purpose The psoas major muscle (PMM) volume serves as an opportunistic imaging marker in cross-sectional imaging datasets for various clinical applications. Since manual segmentation is time consuming, two different automated segmentation methods, a generative adversarial network architecture (GAN) and a multi-atlas segmentation (MAS), as well as a combined approach of both, were investigated in terms of accuracy of automated volumetrics in given CT datasets. Materials and methods The bilateral PMM was manually segmented by a radiologist in 34 abdominal CT scans, resulting in 68 single 3D muscle segmentations as training data. Three different methods were tested for their ability to generate automated image segmentations: a GAN- and MAS-based approach and a combined approach of both methods (COM). Bilateral PMM volume (PMMV) was calculated in cm3 by each algorithm for every CT. Results were compared to the corresponding ground truth using the Dice similarity coefficient (DSC), Spearman’s correlation coefficient and Wilcoxon signed-rank test. Results Mean PMMV was 239 ± 7.0 cm3 and 308 ± 9.6 cm3, 306 ± 9.5 cm3 and 243 ± 7.3 cm3 for the CNN, MAS and COM, respectively. Compared to the ground truth the CNN and MAS overestimated the PMMV significantly (+ 28.9% and + 28.0%, p < 0.001), while results of the COM were quite accurate (+ 0.7%, p = 0.33). Spearman’s correlation coefficients were 0.38, 0.62 and 0.73, and the DSCs were 0.75 [95%CI: 0.56–0.88], 0.73 [95%CI: 0.54–0.85] and 0.82 [95%CI: 0.65–0.90] for the CNN, MAS and COM, respectively. Conclusion The combined approach was able to efficiently exploit the advantages of both methods (GAN and MAS), resulting in a significantly higher accuracy in PMMV predictions compared to the isolated implementations of both methods. Even with the relatively small set of training data, the segmentation accuracy of this hybrid approach was relatively close to that of the radiologist.
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