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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 compare four previously published methods for rectal tumor response evaluation after chemoradiotherapy on MRI.Twenty-two radiologists (5 rectal MRI experts, 17 general/abdominal radiologists) retrospectively reviewed the post-chemoradiotherapy MRIs of 90 patients, scanned at 10 centers (with non-standardized protocols). They applied four response methods; two based on T2W-MRI only (MRI tumor regression grade (mrTRG); split-scar sign), and two based on T2W-MRI+DWI (modified-mrTRG; DWI-patterns). Image quality was graded using a 0-6-point score (including slice thickness and in-plane resolution; sequence angulation; DWI b-values, signal-to-noise, and artefacts); scores4 were classified below average. Mixed model linear regression was used to calculate average sensitivity/specificity/accuracy to predict a complete response (versus residual tumor) and assess the impact of reader experience and image quality. Group interobserver agreement (IOA) was calculated using Krippendorff's alpha. Readers were asked to indicate their preferred scoring method(s).Average sensitivity/specificity/accuracy was 57%/64%/62% (mrTRG), 36%/79%/66% (split-scar), 40%/79%/67% (modified-mrTRG), and 37%/82%/68% (DWI-patterns); mrTRG showed higher sensitivity but lower specificity and accuracy (p0.001) compared to the other methods. IOA was lower for the split scar method (0.18 vs. 0.39-0.43). Higher reader experience had a significant positive effect on diagnostic performance and IOA (except for the split scar sign); below-average imaging quality had a significant negative effect on diagnostic performance. DWI pattern was selected as the preferred method by 73% of readers.Methods incorporating DWI showed the most favorable results when combining diagnostic performance, IOA, and reader preference. Reader experience and image quality clearly impacted diagnostic performance emphasizing the need for state-of-the-art imaging and dedicated radiologist training.• In a multireader study comparing 4 MRI methods for rectal tumor response evaluation, those incorporating DWI showed the best results when combining diagnostic performance, IOA, and reader preference. • The most preferred method (by 73% of readers) was the "DWI patterns" approach with an accuracy of 68%, high specificity of 82%, and group IOA of 0.43. • Reader experience level and MRI quality had an evident effect on diagnostic performance and IOA.

Background The sigmoid take-off (STO) is a recently established landmark to discern rectal from sigmoid cancer on imaging. STO-assessment can be challenging on magnetic resonance imaging (MRI) due to varying axial planes. Purpose To establish the benefit of using computed tomography (CT; with consistent axial planes), in addition to MRI, to anatomically classify rectal versus sigmoid cancer using the STO. Material and Methods A senior and junior radiologist retrospectively classified 40 patients with rectal/rectosigmoid cancers using the STO, first on MRI-only (sagittal and oblique-axial views) and then using a combination of MRI and axial CT. Tumors were classified as rectal/rectosigmoid/sigmoid (according to published STO definitions) and then dichotomized into rectal versus sigmoid. Diagnostic confidence was documented using a 5-point scale. Results Adding CT resulted in a change in anatomical tumor classification in 4/40 cases (10%) for the junior reader and in 6/40 cases (15%) for the senior reader. Diagnostic confidence increased significantly after adding CT for the junior reader (mean score 3.85 vs. 4.27; P Conclusion Availability of a consistent axial imaging plane – in the case of this study provided by CT – in addition to a standard MRI protocol with sagittal and oblique-axial imaging views can be helpful to more confidently localize tumors using the STO as a landmark, especially for more junior readers.

Purpose To analyze how the MRI reporting of rectal cancer has evolved (following guideline updates) in The Netherlands. Methods Retrospective analysis of 712 patients (2011–2018) from 8 teaching hospitals in The Netherlands with available original radiological staging reports that were re-evaluated by a dedicated MR expert using updated guideline criteria. Original reports were classified as “free-text,” “semi-structured,” or “template” and completeness of reporting was documented. Patients were categorized as low versus high risk, first based on the original reports (high risk = cT3-4, cN+, and/or cMRF+) and then based on the expert re-evaluations (high risk = cT3cd-4, cN+, MRF+, and/or EMVI+). Evolutions over time were studied by splitting the inclusion period in 3 equal time periods. Results A significant increase in template reporting was observed (from 1.6 to 17.6–29.6%; p Conclusion Updated concepts of risk stratification have increasingly been adopted, accompanied by an increase in template reporting and improved completeness of reporting. Use of updated guideline criteria resulted in considerable downstaging (of mainly high-risk cT-stage and nodal stage). Graphic abstract

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.