Objectives Tumour size measurement is pivotal for staging and stratifying patients with pancreatic ductal adenocarcinoma (PDA). However, computed tomography (CT) frequently underestimates tumour size due to insufficient depiction of the tumour rim. CT-derived fractal dimension (FD) maps might help to visualise perfusion chaos, thus allowing more realistic size measurement. Methods In 46 patients with histology-proven PDA, we compared tumour size measurements in routine multiphasic CT scans, CT-derived FD maps, multi-parametric magnetic resonance imaging (mpMRI), and, where available, gross pathology of resected specimens. Gross pathology was available as reference for diameter measurement in a discovery cohort of 10 patients. The remaining 36 patients constituted a separate validation cohort with mpMRI as reference for diameter and volume. Results Median RECIST diameter of all included tumours was 40 mm (range: 18–82 mm). In the discovery cohort, we found significant (p = 0.03) underestimation of tumour diameter on CT compared with gross pathology (Δdiameter3D = −5.7 mm), while realistic diameter measurements were obtained from FD maps (Δdiameter3D = 0.6 mm) and mpMRI (Δdiameter3D = −0.9 mm), with excellent correlation between the two (R2 = 0.88). In the validation cohort, CT also systematically underestimated tumour size in comparison to mpMRI (Δdiameter3D = −10.6 mm, Δvolume = −10.2 mL), especially in larger tumours. In contrast, FD map measurements agreed excellently with mpMRI (Δdiameter3D = +1.5 mm, Δvolume = −0.6 mL). Quantitative perfusion chaos was significantly (p = 0.001) higher in the tumour rim (FDrim = 4.43) compared to the core (FDcore = 4.37) and remote pancreas (FDpancreas = 4.28). Conclusions In PDA, fractal analysis visualises perfusion chaos in the tumour rim and improves size measurement on CT in comparison to gross pathology and mpMRI, thus compensating for size underestimation from routine CT. Key Points • CT-based measurement of tumour size in pancreatic adenocarcinoma systematically underestimates both tumour diameter (Δdiameter = −10.6 mm) and volume (Δvolume = −10.2 mL), especially in larger tumours. • Fractal analysis provides maps of the fractal dimension (FD), which enable a more reliable and size-independent measurement using gross pathology or multi-parametric MRI as reference standards. • FD quantifies perfusion chaos—the underlying pathophysiological principle—and can separate the more chaotic tumour rim from the tumour core and adjacent non-tumourous pancreas tissue.

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Background Splenic artery aneurysm (SAA) is a rare but potentially fatal condition. Rupture results in 25% mortality up to 75% in pregnant women with 95% fetal mortality. Brief reports suggest an increased risk of developing SAA in patients with HHT. Methods We analyzed enhanced multidetector CT data in 186 HHT patients matched (gender and ± 5 year old) with 186 controls. We screened for SAA and recorded diameter of splenic and hepatic arteries and hepatic, pancreatic and splenic parenchymal involvements. We determined by univariate and multivariate analysis, the relationship with age, sex, genetic status, cardiovascular risk factors (CVRF) and visceral involvement. Results SAA concerned 24.7% of HHT patients and 5.4% of controls, p<0.001. Factors associated with increased risk of SAA in HHT were female gender (p = 0.04, OR = 2.12, IC 95% = 1.03-4.50), age (p = 0.0003, OR = 1.04, 95% CI = 1.02-1.06) and pancreatic parenchymal involvement (p = 0.04, OR = 2.13, 95% CI = 1.01-4.49), but not type of mutation, hepatic or splenic parenchymal involvements, splenic size or splenic artery diameter or CVRF. Conclusions We found a 4.57 higher rate of SAA in HHT patients without evidence of splenic high output related disease or increased CVRF. These results suggest the presence of a vascular intrinsic involvement. It should lead to screening all HHT patients for SAA. The vasculopathy

Purpose: The 2019 edition of the data challenge was organized by the French Society of Radiology (SFR) during the Journées Francophones de Radiologie with the aim to: (i) work on relevant problematics of public health (ii) build large multicentric and prospective databases and (iii) boost the French AI community around radiologists. In comparison to the 2018 edition a first objective was to increase the question's complexity by including 3D information and prognostic analysis. The second objective was to improve the database quality and quantity with more balance among classes and data from at least 1000 examinations per question. Material and method: Relevant clinical questions were proposed by organ societies of the SFR. Their feasibility was assessed by experts in the field of AI. A dedicated platform was set up for inclusion centers to safely upload their anonymized examinations in compliance with European regulation. The quality of the database was checked by experts weekly with annotations performed by radiologists. Multidisciplinary teams competed between September 11 th and November 13 th 2019. Results: Three questions were selected using different imaging and evaluation modalities, including: pulmonary nodule detection and classification from 3D CT, prediction of expanded disability status scale in multiple sclerosis using 3D MRIs and segmentation of muscular surface for sarcopenia estimation from 2D CT. A total of 4347 examinations were gathered of which only 6% were excluded. Three independent databases from 24 individual centers were created. A total of 143 participants was split into 20 multidisciplinary teams. Conclusion: Three data challenges with over 1200 GDPR compliant, multicentric, 2D/3D CT and MRI databases were organized for 20 multidisciplinary teams.