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

Background & aims: High quality and precise methods are needed when monitoring changes in body composition among colorectal cancer (CRC) patients and healthy subjects. The aim of this study was to estimate precision of the Dual-energy X-ray absorptiometry (Lunar iDXA, GE Healthcare software enCORE version 16) in measuring body composition in CRC patients and healthy subjects. Methods: Precision error of iDXA in measuring body composition was investigated in the current study. Thirty CRC patients and 30 healthy subjects, including both men and women underwent two consecutive whole-body DXA scan with repositioning. Precision estimates of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) in the abdominal region, and total fat mass (FM), fat-free mass (FFM), lean mass (LM), bone mineral density (BMD) and bone mineral content (BMC) were calculated. Results: Precision error expressed as coefficient of variation (% CV) of VAT and SAT were estimated to be 3.56% and 3.28% among CRC patients, and 5.30% and 3.46% among healthy subjects. Estimated precision errors for body masses in the total region ranged between 0.49-1.01% and 0.40e0.88% in CRC patients and healthy subjects, respectively. Least significant change (LSC) in VAT mass, SAT mass, FM and LM were 140.9 g, 121.4 g, 637.0 g and 701.0 g, respectively, among CRC patients. Among healthy subjects the LSC in VAT, SAT, FM and LM were 80.93 g, 98.90 g, 484.0 g and 618.0 g, respectively. Only minor and nonsignificant differences between the two consecutive measurements for each body compartment were observed within both populations, and we found no systematic bias in the distribution of the differences. Conclusion:The Lunar iDXA demonstrated high precision in body composition measurements among both CRC patients and healthy subjects. Hence, iDXA is a useful tool in clinical following-up and interventions targeted towards changes in body composition.

Background Body composition is of clinical importance in colorectal cancer patients, but is rarely assessed because of time-consuming manual segmentation. We developed and tested BodySegAI, a deep learning-based software for automated body composition quantification from routinely acquired computed tomography (CT) scans. Methods A two-dimensional U-Net convolutional network was trained on 2989 abdominal CT slices from L2 to S1 to segment skeletal muscle (SM), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intermuscular and intramuscular adipose tissue (IMAT). Human ground truth was established by combining segmentations from three human readers. BodySegAI was tested using 154 slices against the human ground truth and compared with a software named AutoMATiCA. Results Median Dice scores for BodySegAI against human ground truth were 0.969, 0.814, 0.986, and 0.990 for SM, IMAT, VAT, and SAT, respectively. The mean differences per slice for SM were À0.09 cm 3 , IMAT: À0.17 cm 3 , VAT: À0.12 cm 3 , and SAT: 0.67 cm 3 . Median absolute errors for SM, IMAT, VAT, and SAT were 1.35, 10.54, 0.91, and 1.07%, respectively. When analysing different anatomical levels separately, L3 and S1 demonstrated the overall highest and lowest Dice scores, respectively. On average, BodySegAI segmented 148 times faster than human readers (4.9 vs. 726.5 seconds, P < 0.001). Also, BodySegAI presented higher Dice scores for SM, IMAT, SAT, and VAT than AutoMATiCA (slices = 154). Conclusions BodySegAI rapidly generates excellent segmentation of SM, VAT, and SAT and good segmentation of IMAT in L2 to S1 among colorectal cancer patients and may replace semi-manual segmentation.

Bør beregning av cellegiftdose ved tarmkreft baseres på kroppssammensetning?OVERSIKTSARTIKKEL Tarmscreeningseksjonen Kreftregisteret Hun har bidra med idé, li eratursøk og utforming, revisjon og godkjenning av manus. Ane Sørlie Kvaerner er klinisk ernaeringsfysiolog og postdok.

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