Laura Jacqueline Jensen

In cardiac MRI, the field of view (FOV) is adapted to the individual patient’s size, influencing spatial resolution and myocardial radiomics. This study aimed to investigate the effects of parametric feature maps on radiomics derived from cine images acquired with different FOV sizes on individuals without myocardial pathologies. In the clinical setting, cardiac MRI scans from clinical care were screened retrospectively for patients without pathological findings, neither in the MRI nor the medical history or follow-up, resulting in 61 included patients. In the experimental setting, 12 healthy volunteers were prospectively examined on a 1.5 Tesla MRI scanner with cine images acquired with three different FOVs (256 × 329 mm, 279 × 359 mm, 302 × 390 mm). One midventricular end-diastolic short-axis slice of the non-enhanced cine images was extracted for healthy volunteers and patients. The left ventricular myocardium was encompassed with regions of interest (ROIs). Ninety-three features were extracted using PyRadiomics. Images were converted to parametric radiomic feature maps using pretested software. ROIs were copied to the maps to retrieve the feature quantity. The variability of features across the different FOVs from the original images and feature maps was assessed with coefficients of variation (COVs) and rated stable at up to 10%. When derived from the original images, out of the 93 extracted features, only 24 (patients) and 29 (volunteers) revealed COVs < 10%. When extracted from the parametric maps, the number of stable features increased by 63% and 66%, with 39 (patients) and 48 (volunteers) features showing COVs < 10%, respectively. Software-computed parametric feature maps improve the reproducibility of radiomics across different FOVs in cardiac cine images of individuals without myocardial pathologies. Prospective investigations with different FOVs of a patient collective with myocardial pathologies could enhance the generalizability of the findings.

Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this study was to evaluate if feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) MRI can help detect csPCa in PI-RADS category 3 lesions. In-house biparametric 3T prostate MRI examinations acquired between January 2019 and June 2023 because of elevated prostate-specific antigen (PSA) levels were retrospectively screened. Inclusion criteria were a PI-RADS 3 lesion and available results of an ultrasound-guided targeted and systematic biopsy. Exclusion criteria were a simultaneous PI-RADS category 4 or 5 lesion and hip replacement. Target lesions with the International Society of Urological Pathology (ISUP) grade group 1 were rated clinically insignificant PCa (ciPCa) and ≥2 csPCa. This resulted in 52 patients being included in the final analysis, of whom 11 (21.1%), 8 (15.4%), and 33 (63.5%) patients had csPCa, ciPCa, and no PCa, respectively, with the latter two groups being combined as non-csPCa. Eight of the csPCas were located in the peripheral zone (PZ) and three in the transition zone (TZ). In the non-csPCa group, 29 were located in the PZ and 12 in the TZ. Target lesions were marked with volumes of interest (VOIs) on axial T2w images. Axial T2w images were then converted to 93 feature maps. VOIs were copied into the maps, and feature quantity was retrieved directly. Features were tested for significant differences with the Mann–Whitney U-test. Univariate models for single feature performance and bivariate models implementing PSA density (PSAD) were calculated. Ten map-derived features differed significantly between the csPCa and non-csPCa groups (AUCs: 0.70–0.84). The diagnostic performance for TZ lesions (AUC: 0.83–1.00) was superior to PZ lesions (AUC: 0.74–0.85). In the bivariate models, performance in the PZ improved with AUCs >0.90 throughout. Parametric feature maps alone and as bivariate models with PSAD can (?) noninvasively identify csPCa in PI-RADS 3 lesions and could serve as a quantitative tool reducing ambiguity in PI-RADS 3 lesions.

Background: Different volume of interest (VOI) sizes influence radiomic features. This study examined if translating images into feature maps before feature sampling could compensate for these effects in liver magnetic resonance imaging (MRI). Methods: T1- and T2-weighted sequences from three different scanners (two 3-T scanners, one 1.5-T scanner) of 66 patients with normal abdominal MRI were included retrospectively. Three differently sized VOIs (10, 20, and 30 mm in diameter) were drawn in the liver parenchyma (right lobe), excluding adjacent structures. Ninety-three features were extracted conventionally using PyRadiomics. All images were also converted to 93 parametric feature maps using a pretested software. Agreement between the three VOI sizes was assessed with overall concordance correlation coefficients (OCCCs), while OCCCs > 0.85 were rated reproducible. OCCCs were calculated twice: for the VOI sizes of 10, 20, and 30 mm and for those of 20 and 30 mm. Results: When extracted from original images, only 4 out of the 93 features were reproducible across all VOI sizes in T1- and T2-weighted images. When the smallest VOI was excluded, 5 features (T1-weighted) and 7 features (T2-weighted) were reproducible. Extraction from parametric maps increased the number of reproducible features to 9 (T1- and T2-weighted) across all VOIs. Excluding the 10-mm VOI, reproducibility improved to 16 (T1-weighted) and 55 features (T2-weighted). The stability of all other features also increased in feature maps. Conclusions: Translating images into parametric maps before feature extraction improves reproducibility across different VOI sizes in normal liver MRI. Relevance statement: The size of the segmented VOI influences the feature quantity of radiomics, while software-based conversion of images into parametric feature maps before feature sampling improves reproducibility across different VOI sizes in MRI of normal liver tissue. Key points: • Parametric feature maps can compensate for different VOI sizes. • The effect seems dependent on the VOI sizes and the MRI sequence. • Feature maps can visualize features throughout the entire image stack. Graphical

Background: Although there is growing evidence that functional involvement and structural changes of mesenteric adipose tissue (MAT) influence the course of Crohn’s disease (CD), its viscoelastic properties remain elusive. Therefore, we aimed to investigate the viscoelastic properties of MAT in CD using magnetic resonance elastography (MRE), providing reference values for CD diagnosis. Methods: In this prospective proof-of-concept study, 31 subjects (CD: n=11; healthy controls: n=20) were consecutively enrolled in a specialized care center for inflammatory bowel diseases (tertiary/quaternary care). Inclusion criteria for the CD patients were a clinically and endoscopically established diagnosis of CD based on the clinical record, absence of other concurrent bowel diseases, scheduled surgery for the following day, and age of at least 18 years. Diagnoses were confirmed by histological analysis of the resected bowel the day after MRE. Subjects were investigated using MRE at 1.5-T with frequencies of 40–70 Hz. To retrieve shear wave speed (SWS), volumes of interest (VOIs) in MAT were drawn adjacent to CD lesions (MAT_CD) and on the opposite side without adjacent bowel lesions in patients (MAT_{CD_Opp}) and controls (MAT_CTRL). The presented study is not registered in the clinical trial platform. Results: A statistically significant decrease in mean SWS of 7% was found for MAT_{CD_Opp}vs. MAT_CTRL (0.76±0.05 vs. 0.82±0.04 m/s, P=0.012), whereas there was a nonsignificant trend with an 8% increase for MAT_CDvs. MAT_{CD_Opp} (0.82±0.07 vs. 0.76±0.05 m/s, P=0.098) and no difference for MAT_CD vs. MAT_CTRL. Preliminary area under the receiver operating characteristic curve (AUC) analysis showed diagnostic accuracy in detecting CD to be excellent for SWS of MAT_{CD_Opp} [AUC =0.82; 95% confidence interval (CI): 0.64–0.96] but poor for SWS of MAT_CD (AUC =0.52; 95% CI: 0.34–0.73). Conclusions: This study demonstrates the feasibility of MRE of MAT and presents preliminary reference values for CD patients and healthy controls. Our results motivate further studies for the biophysical characterization of MAT in inflammatory bowel disease.

18F-FDG-PET/CT is standard to assess response in Hodgkin lymphoma by quantifying metabolic activity with the Deauville score. PET/CT, however, is time-consuming, cost-extensive, linked to high radiation and has a low availability. As an alternative, we investigated radiomics from non-contrast-enhanced computed tomography (NECT) scans. 75 PET/CT examinations of 43 patients on two different scanners were included. Target lesions were classified as Deauville score 4 positive (DS4+) or negative (DS4−) based on their SUVpeak and then segmented in NECT images. From these segmentations, 107 features were extracted with PyRadiomics. All further statistical analyses were then performed scanner-wise: differences between DS4+ and DS4− manifestations were assessed with the Mann–Whitney-U-test and single feature performances with the ROC-analysis. To further verify the reliability of the results, the number of features was reduced using different techniques. The feature median showed a high sensitivity for DS4+ manifestations on both scanners (scanner A: 0.91, scanner B: 0.85). It furthermore was the only feature that remained in both datasets after applying different feature reduction techniques. The feature median from NECT concordantly has a high sensitivity for DS4+ Hodgkin manifestations on two different scanners and thus could provide a surrogate for increased metabolic activity in PET/CT.

Background: In radiomics studies, differences in the volume of interest (VOI) are often inevitable and may confound the extracted features. We aimed to correct this confounding effect of VOI variability by applying parametric maps with a fixed voxel size. Methods: Ten scans of a cup filled with sodium chloride solution were scanned using a multislice computed tomography (CT) unit. Sphere-shaped VOIs with different diameters (4, 8, or 16 mm) were drawn centrally into the phantom. A total of 93 features were extracted conventionally from the original images using PyRadiomics. Using a self-designed and pretested software tool, parametric maps for the same 93 features with a fixed voxel size of 4 mm³ were created. To retrieve the feature values from the maps, VOIs were copied from the original images to preserve the position. Differences in feature quantities between the VOI sizes were tested with the Mann-Whitney U-test and agreement with overall concordance correlation coefficients (OCCC). Results: Fifty-five conventionally extracted features were significantly different between the VOI sizes, and none of the features showed excellent agreement in terms of OCCCs. When read from the parametric maps, only 8 features showed significant differences, and 3 features showed an excellent OCCC (≥ 0.85). The OCCCs for 89 features substantially increased using the parametric maps. Conclusions: This phantom study shows that converting CT images into parametric maps resolves the confounding effect of VOI variability and increases feature reproducibility across VOI sizes.

Symptomatische Zysten des unteren männlichen Urogenitaltrakts sind selten und können sich klinisch mit Harnwegsinfektionen, Schmerzen, Hämatospermie und Infertilität manifestieren [Shebel et al. Radiographics 2013; 33: 1125–1143]. Sowohl der transrektale Ultraschall als auch die multiparametrische MRT liefern heutzutage sehr präzise Bilder der Prostata und deren direkter Umgebung [Franiel T et al. Rofo 2015; 187(09): 751–759]. Insbesondere der zunehmende Einsatz der MRT zur Karzinomdetektion setzt in der radiologischen Befundung ausreichende Kenntnisse über urogenitale Zysten als relevante Nebenbefunde voraus. Selbst im Prostate Imaging – Reporting and Data System (PI-RADS), einem strukturierten Befundungsschema der Prostata-MRT, werden Zysten unter benignen Befunden im Prostata-MRT aufgelistet (https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/PI-RADS). Article published online: 05 September 2022 © 2022. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany

We aimed to evaluate the stability of radiomic features in the liver of healthy individuals across different three-dimensional regions of interest (3D ROI) sizes in T1-weighted (T1w) and T2-weighted (T2w) images from different MR scanners. We retrospectively included 66 examinations of patients without known diseases or pathological imaging findings acquired on three MRI scanners (3 Tesla I: 25 patients, 3 Tesla II: 19 patients, 1.5 Tesla: 22 patients). 3D ROIs of different diameters (10, 20, 30 mm) were drawn on T1w GRE and T2w TSE images into the liver parenchyma (segment V–VIII). We extracted 93 radiomic features from the different ROIs and tested features for significant differences with the Mann–Whitney-U (MWU)-test. The MWU-test revealed significant differences for most second- and higher-order features, indicating a systematic difference dependent on the ROI size. The features mean, median, root mean squared (RMS), 10th percentile, and 90th percentile were not significantly different. We also assessed feature robustness to ROI size variation with overall concordance correlation coefficients (OCCCs). OCCCs across the different ROI-sizes for mean, median, and RMS were excellent (>0.90) in both sequences on all three scanners. These features, therefore, seem robust to ROI-size variation and suitable for radiomic studies of liver MRI.

Aim was to develop a user-friendly method for creating parametric maps that would provide a comprehensible visualization and allow immediate quantification of radiomics features. For this, a self-explanatory graphical user interface was designed, and for the proof of concept, maps were created for CT and MR images and features were compared to those from conventional extractions. Especially first-order features were concordant between maps and conventional extractions, some even across all examples. Potential clinical applications were tested on CT and MR images for the differentiation of pulmonary lesions. In these sample applications, maps of Skewness enhanced the differentiation of non-malignant lesions and non-small lung carcinoma manifestations on CT images and maps of Variance enhanced the differentiation of pulmonary lymphoma manifestations and fungal infiltrates on MR images. This new and simple method for creating parametric maps makes radiomics features visually perceivable, allows direct feature quantification by placing a region of interest, can improve the assessment of radiological images and, furthermore, can increase the use of radiomics in clinical routine.