Quantification of adipose tissues by Dual-Energy X-Ray Absorptiometry and Computed Tomography in colorectal cancer patients

Abstract

Background & aims: Excess adipose tissue may affect colorectal cancer (CRC) patients' disease progression and treatment. In contrast to the commonly used anthropometric measurements, Dual-Energy X-Ray Absorptiometry (DXA) and Computed Tomography (CT) can differentiate adipose tissues. However, these modalities are rarely used in the clinic despite providing high-quality estimates. This study aimed to compare DXA's measurement of abdominal visceral adipose tissue (VAT) and fat mass (FM) against a corresponding volume by CT in a CRC population. Secondly, we aimed to identify the best single lumbar CT slice for abdominal VAT. Lastly, we investigated the associations between anthropometric measurements and VAT estimated by DXA and CT. Methods: Non-metastatic CRC patients between 50-80 years from the ongoing randomized controlled trial CRC-NORDIET were included in this cross-sectional study. Corresponding abdominal volumes were acquired by Lunar iDXA and from clinically acquired CT examinations. Also, single CT slices at L2-, L3-and L4-level were obtained. Agreement between the methods was investigated using univariate linear regression and BlandeAltman plots. Results: Sixty-six CRC patients were included. Abdominal volumetric VAT and FM measured by DXA explained up to 91% and 96% of the variance in VAT and FM by CT, respectively. BlandeAltman plots demonstrated an overestimation of VAT by DXA compared to CT (mean difference of 76 cm 3 ) concurrent with an underestimation of FM (mean difference of À319 cm 3 ). A higher overestimation of VAT (p ¼ 0.015) and underestimation of FM (p ¼ 0.036) were observed in obese relative to normal weight subjects. VAT in a single slice at L3-level showed the highest explained variance against CT volume (R 2 ¼ 0.97), but a combination of three slices (L2, L3, L4) explained a significantly higher variance than L3 alone (R 2 ¼ 0.98, p < 0.006). The anthropometric measurements explained between 31-65% of the variance of volumetric VAT measured by DXA and CT. Conclusions: DXA and the combined use of three CT slices (L2-L4) are valid to predict abdominal volumetric VAT and FM in CRC patients when using volumetric CT as a reference method. Due to the poor

Body composition assessment by artificial intelligence from routine computed tomography scans in colorectal cancer: Introducing BodySegAI

“…A single slice at L3 is frequently used in research settings because of its high association with whole‐body measurements 36,37 and abdominal volume 38 . We have previously shown that although a single slice at L3 is highly associated with abdominal VAT volume, the use of multiple slices (L2, L3, and L4) increased the explained variance against VAT volume 38 . However, BodySegAI was trained and tested on multiple slices at different anatomical levels (L2 to S1).…”

“…Due to the comprehensive task of semi‐manual segmentation for establishing the human ground truth and lack of standardized regions of interest, BodySegAI was only tested on single CT slices from predefined anatomical regions (L2 to S1), not entire abdominal volumes. However, the model can readily be used for volumetric data, which is proposed to further increase the accuracy compared with the usage of single slices only 38 . Although we consider BodySegAI as an automatic software, the axial CT slices still have to be manually extracted from the CT examination and uploaded into BodySegAI in order for the body composition analysis to be conducted.…”

“…However, the model can readily be used for volumetric data, which is proposed to further increase the accuracy compared with the usage of single slices only. 38 Although we consider BodySegAI as an automatic software, the axial CT slices still have to be manually extracted from the CT examination and uploaded into BodySegAI in order for the body composition analysis to be conducted.…”

Determination of lower radiation dose limit for automatic measurement of adipose tissue

“…9 Beginning around 2020, numerous studies have leveraged deep learning to perform automatic fat quantification. [10][11][12][13][14][15][16][17][18][19][20] Regarding the second limitation, patient radiation exposure, the guiding principle of medical imaging has been "as low as reasonably achievable" (ALARA). Yamada et al reported the lower limit of radiation dose of CT images for manual fat quantification.…”

“…Many of the reports on automated fat quantification methods provide insufficient information on radiation dose or use doses used for routine abdominal scans. [11][12][13][14][16][17][18][19][20] Since the early 2010′s, Pickhardt et al have published a series of reports on the application of fat quantification to abdominal CT for the purpose of colorectal cancer prevention and screening, using the CT colonoscopy (CTC) technique. 10,12,22,23 Performing opportunistic fat quantification screening on CTC images is one method of obtaining fat quantification information without additional radiation exposure.…”