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Objectives To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software. Methods T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient. Results Image features differed significantly (p Conclusions Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models. Key Points • Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly (> 60%) caused by hardware and image acquisition differences and less so ( • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible.

PURPOSE: The sigmoid take-off (STO) was recently introduced as a preferred landmark, agreed upon by expert consensus recommendation, to discern rectal from sigmoid cancer on imaging. Aim of this study was to assess the reproducibility of the STO, explore its potential treatment impact and identify its main interpretation pitfalls.METHODS: Eleven international radiologists (with varying expertise) retrospectively assessed n = 155 patients with previously clinically staged upper rectal/rectosigmoid tumours and re-classified them using the STO as completely below (rectum), straddling the STO (rectosigmoid) or completely above (sigmoid), after which scores were dichotomized as rectum (below/straddling STO) and sigmoid (above STO), being the clinically most relevant distinction. A random subset of n = 48 was assessed likewise by 6 colorectal surgeons. .RESULTS: Interobserver agreement (IOA) for the 3-category score ranged from κ0.19-0.82 (radiologists) and κ0.32-0.72 (surgeons), with highest scores for the most experienced radiologists (κ0.69-0.76). Of the 155 cases, 44 (28%) were re-classified by ≥ 80% of radiologists as sigmoid cancers; 36 of these originally received neoadjuvant treatment which in retrospect might have been omitted if the STO had been applied. Main interpretation pitfalls were related to anatomical variations, borderline cases near the STO and angulation of axial imaging planes.CONCLUSIONS: Good agreement was reached for experienced radiologists. Despite considerable variation among less-expert readers, use of the STO could have changed treatment in ±1/4 of patients in our cohort. Identified interpretation pitfalls may serve as a basis for teaching and to further optimize MR protocols.

To investigate sources of variation in a multicenter rectal cancer MRI dataset focusing on hardware and image acquisition, segmentation methodology, and radiomics feature extraction software.T2W and DWI/ADC MRIs from 649 rectal cancer patients were retrospectively acquired in 9 centers. Fifty-two imaging features (14 first-order/6 shape/32 higher-order) were extracted from each scan using whole-volume (expert/non-expert) and single-slice segmentations using two different software packages (PyRadiomics/CapTk). Influence of hardware, acquisition, and patient-intrinsic factors (age/gender/cTN-stage) on ADC was assessed using linear regression. Feature reproducibility was assessed between segmentation methods and software packages using the intraclass correlation coefficient.Image features differed significantly (p 0.001) between centers with more substantial variations in ADC compared to T2W-MRI. In total, 64.3% of the variation in mean ADC was explained by differences in hardware and acquisition, compared to 0.4% by patient-intrinsic factors. Feature reproducibility between expert and non-expert segmentations was good to excellent (median ICC 0.89-0.90). Reproducibility for single-slice versus whole-volume segmentations was substantially poorer (median ICC 0.40-0.58). Between software packages, reproducibility was good to excellent (median ICC 0.99) for most features (first-order/shape/GLCM/GLRLM) but poor for higher-order (GLSZM/NGTDM) features (median ICC 0.00-0.41).Significant variations are present in multicenter MRI data, particularly related to differences in hardware and acquisition, which will likely negatively influence subsequent analysis if not corrected for. Segmentation variations had a minor impact when using whole volume segmentations. Between software packages, higher-order features were less reproducible and caution is warranted when implementing these in prediction models.• Features derived from T2W-MRI and in particular ADC differ significantly between centers when performing multicenter data analysis. • Variations in ADC are mainly ( 60%) caused by hardware and image acquisition differences and less so ( 1%) by patient- or tumor-intrinsic variations. • Features derived using different image segmentations (expert/non-expert) were reproducible, provided that whole-volume segmentations were used. When using different feature extraction software packages with similar settings, higher-order features were less reproducible.