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MRI has gained prominence in the diagnostic workup of prostate cancer (PCa) patients, with the Prostate Imaging Reporting and Data System (PI-RADS) being widely used for cancer detection. Beyond PI-RADS, other MRI-based scoring tools have emerged to address broader aspects within the PCa domain. However, the multitude of available MRI-based grading systems has led to inconsistencies in their application within clinical workflows. The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) assesses the likelihood of clinically significant radiological changes of PCa during active surveillance, and the Prostate Imaging for Local Recurrence Reporting (PI-RR) scoring system evaluates the risk of local recurrence after whole-gland therapies with curative intent. Underlying any system is the requirement to assess image quality using the Prostate Imaging Quality Scoring System (PI-QUAL). This article offers practicing radiologists a comprehensive overview of currently available scoring systems with clinical evidence supporting their use for managing PCa patients to enhance consistency in interpretation and facilitate effective communication with referring clinicians. KEY POINTS: Assessing image quality is essential for all prostate MRI interpretations and the PI-QUAL score represents  the standardized tool for this purpose. Current urological clinical guidelines for prostate cancer diagnosis and localization recommend adhering to the PI-RADS recommendations. The PRECISE and PI-RR scoring systems can be used for assessing radiological changes of prostate cancer during active surveillance and the likelihood of local recurrence after radical treatments respectively., (© 2024. The Author(s).)

Objectives: To evaluate the methodological quality and diagnostic accuracy of MRI-based radiomic studies predicting O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in gliomas., Methods: PubMed Medline, EMBASE, and Web of Science were searched to identify MRI-based radiomic studies on MGMT methylation in gliomas published until December 31, 2022. Three raters evaluated the study methodological quality with Radiomics Quality Score (RQS, 16 components) and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis Or Diagnosis (TRIPOD, 22 items) scales. Risk of bias and applicability concerns were assessed with QUADAS-2 tool. A meta-analysis was performed to estimate the pooled area under the curve (AUC) and to assess inter-study heterogeneity., Results: We included 26 studies, published from 2016. The median RQS total score was 8 out of 36 (22%, range 8-44%). Thirteen studies performed external validation. All studies reported AUC or accuracy, but only 4 (15%) performed calibration and decision curve analysis. No studies performed phantom analysis, cost-effectiveness analysis, and prospective validation. The overall TRIPOD adherence score was between 50% and 70% in 16 studies and below 50% in 10 studies. The pooled AUC was 0.78 (95% CI, 0.73-0.83, I 2 = 94.1%) with a high inter-study heterogeneity. Studies with external validation and including only WHO-grade IV gliomas had significantly lower AUC values (0.65; 95% CI, 0.57-0.73, p < 0.01)., Conclusions: Study RQS and adherence to TRIPOD guidelines was generally low. Radiomic prediction of MGMT methylation status showed great heterogeneity of results and lower performances in grade IV gliomas, which hinders its current implementation in clinical practice., Clinical Relevance Statement: MGMT promoter methylation status appears to be variably correlated with MRI radiomic features; radiomic models are not sufficiently robust to be integrated into clinical practice to accurately predict MGMT promoter methylation status in patients with glioma before surgery., Key Points: • Adherence to the indications of TRIPOD guidelines was generally low, as was RQS total score. • MGMT promoter methylation status prediction with MRI radiomic features provided heterogeneous diagnostic accuracy results across studies. • Studies that included grade IV glioma only and performed external validation had significantly lower diagnostic accuracy than others., (© 2024. The Author(s).)

Background: The study of central nervous system (CNS) tumors is particularly relevant in the pediatric population because of their relatively high frequency in this demographic and the significant impact on disease- and treatment-related morbidity and mortality. While both morphological and non-morphological magnetic resonance imaging techniques can give important information concerning tumor characterization, grading, and patient prognosis, increasing evidence in recent years has highlighted the need for personalized treatment and the development of quantitative imaging parameters that can predict the nature of the lesion and its possible evolution. For this purpose, radiomics and the use of artificial intelligence software, aimed at obtaining valuable data from images beyond mere visual observation, are gaining increasing importance. This brief review illustrates the current state of the art of this new imaging approach and its contributions to understanding CNS tumors in children., Data Sources: We searched the PubMed, Scopus, and Web of Science databases using the following key search terms: ("radiomics" AND/OR "artificial intelligence") AND ("pediatric AND brain tumors"). Basic and clinical research literature related to the above key research terms, i.e., studies assessing the key factors, challenges, or problems of using radiomics and artificial intelligence in pediatric brain tumors management, was collected., Results: A total of 63 articles were included. The included ones were published between 2008 and 2024. Central nervous tumors are crucial in pediatrics due to their high frequency and impact on disease and treatment. MRI serves as the cornerstone of neuroimaging, providing cellular, vascular, and functional information in addition to morphological features for brain malignancies. Radiomics can provide a quantitative approach to medical imaging analysis, aimed at increasing the information obtainable from the pixels/voxel grey-level values and their interrelationships. The "radiomic workflow" involves a series of iterative steps for reproducible and consistent extraction of imaging data. These steps include image acquisition for tumor segmentation, feature extraction, and feature selection. Finally, the selected features, via training predictive model (CNN), are used to test the final model., Conclusions: In the field of personalized medicine, the application of radiomics and artificial intelligence (AI) algorithms brings up new and significant possibilities. Neuroimaging yields enormous amounts of data that are significantly more than what can be gained from visual studies that radiologists can undertake on their own. Thus, new partnerships with other specialized experts, such as big data analysts and AI specialists, are desperately needed. We believe that radiomics and AI algorithms have the potential to move beyond their restricted use in research to clinical applications in the diagnosis, treatment, and follow-up of pediatric patients with brain tumors, despite the limitations set out., (© 2024. The Author(s).)

Purpose: To evaluate the impact of a four-month training program on radiology residents' diagnostic accuracy in assessing deep myometrial invasion (DMI) in endometrial cancer (EC) using MRI., Method: Three radiology residents with limited EC MRI experience participated in the training program, which included conventional didactic sessions, case-centric workshops, and interactive classes. Utilizing a training dataset of 120 EC MRI scans, trainees independently assessed subsets of cases over five reading sessions. Each subset consisted of 30 scans, the first and the last with the same cases, for a total of 150 reads. Diagnostic accuracy metrics, assessment time (rounded to the nearest minute), and confidence levels (using a 5-point Likert scale) were recorded. The learning curve was obtained plotting the diagnostic accuracy of the three trainees and the average over the subsets. Anatomopathological results served as the reference standard for DMI presence., Results: The three trainees exhibited heterogeneous starting point, with a learning curve and a trend to more homogeneous performance with training. The diagnostic accuracy of the average trainee raised from 64 % (56 %-76 %) to 88 % (80 %-94 %) across the five subsets (p < 0.001). Reductions in assessment time (5.92 to 4.63 min, p < 0.018) and enhanced confidence levels (3.58 to 3.97, p = 0.12) were observed. Improvements in sensitivity, specificity, positive predictive value, and negative predictive value were noted, particularly for specificity which raised from 56 % (41 %-68 %) in the first to 86 % (74 %-94 %) in the fifth subset (p = 0.16). Although not reaching statistical significance, these advancements aligned the trainees with literature performance benchmarks., Conclusions: The structured training program significantly enhanced radiology residents' diagnostic accuracy in assessing DMI for EC on MRI, emphasizing the effectiveness of active case-based training in refining oncologic imaging skills within radiology residency curricula., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Segmentation and image intensity discretization impact on radiomics workflow. The aim of this study is to investigate the influence of interobserver segmentation variability and intensity discretization methods on the reproducibility of MRI-based radiomic features in lipoma and atypical lipomatous tumor (ALT). Thirty patients with lipoma or ALT were retrospectively included. Three readers independently performed manual contour-focused segmentation on T1-weighted and T2-weighted sequences, including the whole tumor volume. Additionally, a marginal erosion was applied to segmentations to evaluate its influence on feature reproducibility. After image pre-processing, with included intensity discretization employing both fixed bin number and width approaches, 1106 radiomic features were extracted from each sequence. Intraclass correlation coefficient (ICC) 95% confidence interval lower bound ≥ 0.75 defined feature stability. In contour-focused vs. margin shrinkage segmentation, the rates of stable features extracted from T1-weighted and T2-weighted images ranged from 92.68 to 95.21% vs. 90.69 to 95.66% after fixed bin number discretization and from 95.75 to 97.65% vs. 95.39 to 96.47% after fixed bin width discretization, respectively, with no difference between the two segmentation approaches (p ≥ 0.175). Higher stable feature rates and higher feature ICC values were found when implementing discretization with fixed bin width compared to fixed bin number, regardless of the segmentation approach (p < 0.001). In conclusion, MRI radiomic features of lipoma and ALT are reproducible regardless of the segmentation approach and intensity discretization method, although a certain degree of interobserver variability highlights the need for a preliminary reliability analysis in future studies., (© 2024. The Author(s).)

Objectives: Extraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction., Materials and Methods: Multiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I 2 value. Publication bias was evaluated with a funnel plot. Subgroup analyses were performed to explore heterogeneity., Results: Thirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76., Conclusion: MRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed., Clinical Relevance Statement: Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements., Key Points: • MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level., (© 2023. The Author(s).)

Objective: To evaluate the methodological rigor of radiomics-based studies using noninvasive imaging in ovarian setting., Methods: Multiple medical literature archives (PubMed, Web of Science, and Scopus) were searched to retrieve original studies focused on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), or positron emission tomography (PET) radiomics for ovarian disorders' assessment. Two researchers in consensus evaluated each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to first author category, study aim and topic, imaging modality, and journal quartile., Results: From a total of 531 items, 63 investigations were finally included in the analysis. The studies were greatly focused (94%) on the field of oncology, with CT representing the most used imaging technique (41%). Overall, the papers achieved a median total RQS 6 (IQR, -0.5 to 11), corresponding to a percentage of 16.7% of the maximum score (IQR, 0-30.6%). The scoring was low especially due to the lack of prospective design and formal validation of the results. At subgroup analysis, the 4 studies not focused on oncological topic showed significantly lower quality scores than the others., Conclusions: The overall methodological rigor of radiomics studies in the ovarian field is still not ideal, limiting the reproducibility of results and potential translation to clinical setting. More efforts towards a standardized methodology in the workflow are needed to allow radiomics to become a viable tool for clinical decision-making., Key Points: • The 63 included studies using noninvasive imaging for ovarian applications were mostly focused on oncologic topic (94%). • The included investigations achieved a median total RQS 6 (IQR, -0.5 to 11), indicating poor methodological rigor. • The RQS was low especially due to the lack of prospective design and formal validation of the results., (© 2022. The Author(s).)

Objective: To systematically review and evaluate the methodological quality of studies using magnetic resonance imaging (MRI) and computed tomography (CT) radiomics for cardiac applications., Methods: Multiple medical literature archives (PubMed, Web of Science, and EMBASE) were systematically searched to retrieve original studies focused on cardiac MRI and CT radiomics applications. Two researchers in consensus assessed each investigation using the radiomics quality score (RQS). Subgroup analyses were performed to assess whether the total RQS varied according to study aim, journal quartile, imaging modality, and first author category., Results: From a total of 1961 items, 53 articles were finally included in the analysis. Overall, the studies reached a median total RQS of 7 (IQR, 4-12), corresponding to a percentage score of 19.4% (IQR, 11.1-33.3%). Item scores were particularly low due to lack of prospective design, cost-effectiveness analysis, and open science. Median RQS percentage score was significantly higher in papers where the first author was a medical doctor and in those published on first quartile journals., Conclusions: The overall methodological quality of radiomics studies in cardiac MRI and CT is still lacking. A higher degree of standardization of the radiomics workflow and higher publication standards for studies are required to allow its translation into clinical practice., Key Points: • RQS has been recently proposed for the overall assessment of the methodological quality of radiomics-based studies. • The 53 included studies on cardiac MRI and CT radiomics applications reached a median total RQS of 7 (IQR, 4-12), corresponding to a percentage of 19.4% (IQR, 11.1-33.3%). • A more standardized methodology in the radiomics workflow is needed, especially in terms of study design, validation, and open science, in order to translate the results to clinical applications., (© 2021. The Author(s), under exclusive licence to European Society of Radiology.)

Purpose: To investigate radiomics and machine learning (ML) as possible tools to enhance MRI-based risk stratification in patients with endometrial cancer (EC)., Method: From two institutions, 133 patients (Institution1 = 104 and Institution2 = 29) with EC and pre-operative MRI were retrospectively enrolled and divided in two a low-risk and a high-risk group according to EC stage and grade. T2-weighted (T2w) images were three-dimensionally annotated to obtain volumes of interest of the entire tumor. A PyRadiomics based and previously validated pipeline was used to extract radiomics features and perform feature selection. In particular, feature stability, variance and pairwise correlation were analyzed. Then, the least absolute shrinkage and selection operator technique and recursive feature elimination were used to obtain the final feature set. The performance of a Support Vector Machine (SVM) algorithm was assessed on the dataset from Institution 1 via 2-fold cross-validation. Then, the model was trained on the entire Institution 1 dataset and tested on the external test set from Institution 2., Results: In total, 1197 radiomics features were extracted. After the exclusion of unstable, low variance and intercorrelated features least absolute shrinkage and selection operator and recursive feature elimination identified 4 features that were used to build the predictive ML model. It obtained an accuracy of 0.71 and 0.72 in the train and test sets respectively., Conclusions: Whole-lesion T2w-derived radiomics showed encouraging results and good generalizability for the identification of low-risk EC patients., (Copyright © 2022 Elsevier B.V. All rights reserved.)

This study aims to investigate the influence of interobserver manual segmentation variability on the reproducibility of 2D and 3D unenhanced computed tomography (CT)- and magnetic resonance imaging (MRI)-based texture analysis. Thirty patients with cartilaginous bone tumors (10 enchondromas, 10 atypical cartilaginous tumors, 10 chondrosarcomas) were retrospectively included. Three radiologists independently performed manual contour-focused segmentation on unenhanced CT and T1-weighted and T2-weighted MRI by drawing both a 2D region of interest (ROI) on the slice showing the largest tumor area and a 3D ROI including the whole tumor volume. Additionally, a marginal erosion was applied to both 2D and 3D segmentations to evaluate the influence of segmentation margins. A total of 783 and 1132 features were extracted from original and filtered 2D and 3D images, respectively. Intraclass correlation coefficient ≥ 0.75 defined feature stability. In 2D vs. 3D contour-focused segmentation, the rates of stable features were 74.71% vs. 86.57% (p < 0.001), 77.14% vs. 80.04% (p = 0.142), and 95.66% vs. 94.97% (p = 0.554) for CT and T1-weighted and T2-weighted images, respectively. Margin shrinkage did not improve 2D (p = 0.343) and performed worse than 3D (p < 0.001) contour-focused segmentation in terms of feature stability. In 2D vs. 3D contour-focused segmentation, matching stable features derived from CT and MRI were 65.8% vs. 68.7% (p = 0.191), and those derived from T1-weighted and T2-weighted images were 76.0% vs. 78.2% (p = 0.285). 2D and 3D radiomic features of cartilaginous bone tumors extracted from unenhanced CT and MRI are reproducible, although some degree of interobserver segmentation variability highlights the need for reliability analysis in future studies., (© 2021. The Author(s).)

Rationale and Objectives: To evaluate an MRI radiomics-powered machine learning (ML) model's performance for the identification of deep myometrial invasion (DMI) in endometrial cancer (EC) patients and explore its clinical applicability., Materials and Methods: Preoperative MRI scans of EC patients were retrospectively selected. Three radiologists performed whole-lesion segmentation on T2-weighted images for feature extraction. Feature robustness was tested before randomly splitting the population in training and test sets (80/20% proportion). A multistep feature selection was applied to the first, excluding noninformative, low variance features and redundant, highly-intercorrelated ones. A Random Forest wrapper was used to identify the most informative among the remaining. An ensemble of J48 decision trees was tuned and finalized in the training set using 10-fold cross-validation, and then assessed on the test set. A radiologist evaluated all MRI scans without and with the aid of ML to detect the presence of DMI. McNemars's test was employed to compare the two readings., Results: Of the 54 patients included, 17 had DMI. In all, 1132 features were extracted. After feature selection, the Random Forest wrapper identified the three most informative which were used for ML training. The classifier reached an accuracy of 86% and 91% and areas under the Receiver Operating Characteristic curve of 0.92 and 0.94 in the cross-validation and final testing, respectively. The radiologist performance increased from 82% to 100% when using ML (p = 0.48)., Conclusion: We proved the feasibility of a radiomics-powered ML model for DMI detection on MR T2-w images that might help radiologists to increase their performance., (Copyright © 2020 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

OBJECTIVE. The purpose of this study was to perform a systematic review and a meta-analysis of diagnostic accuracy studies that used biparametric MRI (bpMRI) for the detection of clinically significant prostate cancer (csPCa). MATERIALS AND METHODS. Multiple medical databases were systematically searched to identify articles using bpMRI for csPCa detection. Sensitivity, specificity, PPV, and NPV were calculated for each study after enough data were extracted to create a 2 × 2 contingency table. Risk of bias was assessed using the QUADAS-2 tool. Meta-analyses based on bivariate random-effects methods were used to calculate pooled sensitivity, specificity, and summary ROC (SROC) curves. A meta-regression analysis was performed to assess heterogeneity sources. RESULTS. A total of 17 studies (3964 patients) that adopted PI-RADS or other scoring systems were included. Sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio of bpMRI in the detection of csPCa were 0.83 (95% CI, 0.76-0.88), 0.71 (95% CI, 0.63-0.79), 2.9 (95% CI, 2.3-3.7), 0.24 (95% CI, 0.17-0.33), and 12 (95% CI, 8-19), respectively, with an area under the SROC curve of 0.84 (95% CI, 0.81-0.87). The overall quality of the included studies was heterogeneous. CONCLUSION. Our results confirm the feasibility of bpMRI for the detection of csPCa and for reducing acquisition time, patient discomfort, and costs. Nevertheless, the available studies proved to be heterogeneous, indicating a need for a more robust validation of this imaging protocol and a standardization of prostate bpMRI acquisition and reporting.

Purpose: Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could help in extracting mineable quantitative tissue characteristics. We aimed to assess the accuracy of texture analysis combined with machine learning in the preoperative evaluation of pituitary macroadenoma consistency in patients undergoing endoscopic endonasal surgery., Methods: Data of 89 patients (68 soft and 21 fibrous macroadenomas) who underwent MRI and transsphenoidal surgery at our institution were retrospectively reviewed. After manual segmentation, radiomic texture features were extracted from original and filtered MR images. Feature stability analysis and a multistep feature selection were performed. After oversampling to balance the classes, 80% of the data was used for hyperparameter tuning via stratified 5-fold cross-validation, while a 20% hold-out set was employed for its final testing, using an Extra Trees ensemble meta-algorithm. The reference standard was based on surgical findings., Results: A total of 1118 texture features were extracted, of which 741 were stable. After removal of low variance (n = 4) and highly intercorrelated (n = 625) parameters, recursive feature elimination identified a subset of 14 features. After hyperparameter tuning, the Extra Trees classifier obtained an accuracy of 93%, sensitivity of 100%, and specificity of 87%. The area under the receiver operating characteristic and precision-recall curves was 0.99., Conclusion: Preoperative T2-weighted MRI texture analysis and machine learning could predict pituitary macroadenoma consistency.

Purpose: The clinical presentation of idiopathic normal pressure hydrocephalus (iNPH) may overlap with progressive supranuclear palsy (PSP). The Magnetic Resonance Parkinsonism Index (MRPI), MRPI 2.0, and the interpeduncular angle (IPA) have been investigated to differentiate PSP from healthy controls (HC) and other parkinsonisms. We aimed to assess equivalences and differences in MRPI, MRPI 2.0, and IPA in iNPH, PSP, and HC groups., Methods: We retrospectively recruited 99 subjects (30 iNPH, 32 PSP, 37 HC) from two institutions. MRI exams, acquired on either 1.5 T or 3 T scanners, included 3D T1-weighted images to measure MRPI, MRPI 2.0, and IPA. Inter- and intra-rater reliability was investigated with the intra-class correlation coefficient (ICC), and the two one-sided t tests (TOST) procedure was used to assess these markers in iNPH, PSP, and HC., Results: For all the three measures, intra-rater and inter-rater ICC were excellent (range = 0.91-0.93). In the comparison of iNPH and PSP with HC, differences for MRPI and MRPI 2.0 (p < 0.01 in all cases) and no equivalence (p = 1.00 in all cases) were found at TOST. iNPH and PSP MRPI showed no difference (p = 0.06) and no equivalence (p = 0.08). MRPI 2.0 was not equivalent (p = 0.06) and not different (p = 0.09) in the same two populations. PSP and HC IPA proved equivalent (p < 0.01) while iNPH IPA was different (p < 0.01) and not equivalent (p = 0.96 and 0.82) from both PSP and HC., Conclusion: MRPI and MRPI 2.0 significantly overlap in iNPH and PSP, with risk of misdiagnosis, and for this reason may not be helpful in the differential diagnosis.

Objectives: The aim of this study was to systematically review the literature and perform a meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically significant prostate cancer (csPCa) identification on MRI., Methods: Multiple medical databases were systematically searched for studies on ML applications in csPCa identification up to July 31, 2019. Two reviewers screened all papers independently for eligibility. The area under the receiver operating characteristic curves (AUC) was pooled to quantify predictive accuracy. A random-effects model estimated overall effect size while statistical heterogeneity was assessed with the I 2 value. A funnel plot was used to investigate publication bias. Subgroup analyses were performed based on reference standard (biopsy or radical prostatectomy) and ML type (deep and non-deep)., Results: After the final revision, 12 studies were included in the analysis. Statistical heterogeneity was high both in overall and in subgroup analyses. The overall pooled AUC for ML in csPCa identification was 0.86, with 0.81-0.91 95% confidence intervals (95%CI). The biopsy subgroup (n = 9) had a pooled AUC of 0.85 (95%CI = 0.79-0.91) while the radical prostatectomy one (n = 3) of 0.88 (95%CI = 0.76-0.99). Deep learning ML (n = 4) had a 0.78 AUC (95%CI = 0.69-0.86) while the remaining 8 had AUC = 0.90 (95%CI = 0.85-0.94)., Conclusions: ML pipelines using prostate MRI to identify csPCa showed good accuracy and should be further investigated, possibly with better standardisation in design and reporting of results., Key Points: • Overall pooled AUC was 0.86 with 0.81-0.91 95% confidence intervals. • In the reference standard subgroup analysis, algorithm accuracy was similar with pooled AUCs of 0.85 (0.79-0.91 95% confidence intervals) and 0.88 (0.76-0.99 95% confidence intervals) for studies employing biopsies and radical prostatectomy, respectively. • Deep learning pipelines performed worse (AUC = 0.78, 0.69-0.86 95% confidence intervals) than other approaches (AUC = 0.90, 0.85-0.94 95% confidence intervals).

Background: Biliary atresia (BA) is a rare obliterative cholangiopathy and Kasai portoenterostomy (KP) represents its first-line treatment; clinical and laboratory parameters together with abdominal ultrasound (US) are usually performed during the follow-up. Shear-wave elastography (SWE) is able to evaluate liver parenchyma stiffness; magnetic resonance imaging (MRI) has also been proposed to study these patients., Purpose: To correlate US, SWE, and MRI imaging findings with medical outcome in patients with BA who are native liver survivors after KP., Material and Methods: We retrospectively enrolled 24 patients. They were divided in two groups based on "ideal" (n = 15) or "non-ideal" (n = 9) medical outcome. US, SWE, and MRI exams were analyzed qualitatively and quantitatively for imaging signs suggestive of chronic liver disease (CLD)., Results: Significant differences were found in terms of liver surface ( P  = 0.007) and morphology ( P  = 0.013), portal vein diameter ( P  = 0.012) and spleen size ( P  = 0.002) by US, liver signal intensity ( P  = 0.013), portal vein diameter ( P  = 0.010), presence of portosystemic collaterals ( P  = 0.042), and spleen size ( P  = 0.001) by MRI. The evaluation of portal vein diameter (moderate, κ = 0.44), of portosystemic collaterals (good, κ = 0.78), and spleen size (very good, κ = 0.92) showed the best agreement between US and MRI. A significant ( P  = 0.01) difference in liver parenchyma stiffness by SWE was also found between the two groups (cut-off = 9.6 kPa, sensitivity = 55.6%, specificity = 100%, area under the ROC curve = 0.82)., Conclusion: US, SWE, and MRI findings correlate with the medical outcome in native liver survivor patients with BA treated with KP.

Purpose: To test whether T2 mapping of the sacro-iliac joints (SIJs) might help identifying patients with spondyloarthritis., Method: This study included 20 biologic-naive patients with axial spondyloarthritis (10 females; mean age: 38 ± 9years; range, 19-47) and 27 controls (16 males; mean age = 39 ± 13years; range = 28-71) who prospectively underwent SIJs MRI at 1.5 T, including a multislice multiecho spin-echo sequence. Standard MRIs were reviewed to assess the SIJs according to the Assessment of SpondyloArthritis International Society (ASAS) criteria and SPondyloArthritis Research Consortium of Canada (SPARCC) MRI index. T2 maps obtained from multiecho sequences were used to draw regions of interests in the cartilaginous part of the SIJs. Disease activity was assessed using BASDAI questionnaire. Bland-Altman method, ROC curve analysis, Chi square, Mann-Whitney U, Pearson's and Spearman's correlation coefficient were used for data analysis., Results: According to ASAS criteria, MRI was positive for sacroiliitis in 5/20 patients (25 %). Inter-observer reproducibility of T2 values was 87 % (coefficient of repeatability = 7.0; bias = 0.49; p < .001). Mean T2 values of patients (58.5 ± 4.4 ms, range: 52.6-68.2 ms) were significantly higher (p < .001) than those of controls (44.1 ± 6.6 ms, range: 33.6-67.2 ms). A T2 value of 52.51 ms yielded 100 % sensitivity and 91.7 % specificity to differentiate patients from controls. No statistically significant association/correlation was found between T2 values and BASDAI (r=-.026, p = .827), disease duration (r = .024, p = .871), SPARCC (r=-.004, p = .981), ASAS criteria (p = .476), HLA-B27-positivity (p = .139), age (r=-.2.53, p = .891), and gender (p = .404)., Conclusions: T2 relaxation times of the SIJs were significantly higher in patients than in healthy controls, making this tool potentially helpful to early identify patients with spondyloarthritis., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Radiomics have the potential to further increase the value of MRI in prostate cancer management. However, implementation in clinical practice is still far and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the literature to assess the quality of prostate MRI radiomic studies using the radiomics quality score (RQS)., Methods: Multiple medical literature archives (PubMed, Web of Science and EMBASE) were searched to retrieve original investigations focused on prostate MRI radiomic approaches up to the end of June 2019. Three researchers independently assessed each paper using the RQS. Data from the most experienced researcher were used for descriptive analysis. Inter-rater reproducibility was assessed using the intraclass correlation coefficient (ICC) on the total RQS score., Results: 73 studies were included in the analysis. Overall, the average RQS total score was 7.93 ± 5.13 on a maximum of 36 points, with a final average percentage of 23 ± 13%. Among the most critical items, the lack of feature robustness testing strategies and external validation datasets. The ICC resulted poor to moderate, with an average value of 0.57 and 95% Confidence Intervals between 0.44 and 0.69., Conclusions: Current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: To evaluate the diagnostic performance of machine learning for discrimination between low-grade and high-grade cartilaginous bone tumors based on radiomic parameters extracted from unenhanced magnetic resonance imaging (MRI)., Methods: We retrospectively enrolled 58 patients with histologically-proven low-grade/atypical cartilaginous tumor of the appendicular skeleton (n = 26) or higher-grade chondrosarcoma (n = 32, including 16 appendicular and 16 axial lesions). They were randomly divided into training (n = 42) and test (n = 16) groups for model tuning and testing, respectively. All tumors were manually segmented on T1-weighted and T2-weighted images by drawing bidimensional regions of interest, which were used for first order and texture feature extraction. A Random Forest wrapper was employed for feature selection. The resulting dataset was used to train a locally weighted ensemble classifier (AdaboostM1). Its performance was assessed via 10-fold cross-validation on the training data and then on the previously unseen test set. Thereafter, an experienced musculoskeletal radiologist blinded to histological and radiomic data qualitatively evaluated the cartilaginous tumors in the test group., Results: After feature selection, the dataset was reduced to 4 features extracted from T1-weighted images. AdaboostM1 correctly classified 85.7 % and 75 % of the lesions in the training and test groups, respectively. The corresponding areas under the receiver operating characteristic curve were 0.85 and 0.78. The radiologist correctly graded 81.3 % of the lesions. There was no significant difference in performance between the radiologist and machine learning classifier (P = 0.453)., Conclusions: Our machine learning approach showed good diagnostic performance for classification of low-to-high grade cartilaginous bone tumors and could prove a valuable aid in preoperative tumor characterization., Competing Interests: Declaration of Competing Interest The Authors have no conflicts of interest to disclose., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: to assess if tumor segmentation analysis performed at different post-contrast time points (TPs) on dynamic images could influence the extraction of dynamic contrast enhanced (DCE)-MRI parameters in locally advanced breast cancer (LABC), and potentially represent a source of variability., Method: forty patients with forty-two LABC lesions were prospectively enrolled and underwent breast DCE-MRI examination at 3 T. On post-processed dynamic images, enhancing tumor lesions were manually segmented at four different TPs: at the first post-contrast dynamic image in which the lesion was appreciable (TP 1) and at 1, 5 and 10 min after contrast-agent administration (TPs 2, 3 and 4, respectively) and corresponding DCE-MRI parameters were extracted. Friedman's test followed by Bonferroni-adjusted Wilcoxon signed rank test for post-hoc analysis was used to compare DCE-MRI parameters. Intra- and inter-observer reliability of DCE-MRI parameters measurements was assessed using the Intraclass Correlation Coefficient (ICC) analysis., Results: Ktrans, Kep and iAUC were significantly higher when extracted from ROIs placed at TP1 and progressively decreased from TP 2-4. The intra-observer reliability ranged from good to excellent (ICC's: 0.894 to 0.990). The inter-observer reliability varied from moderate to excellent (0.770 to 0.942). The inter-observer reliability was significantly higher for Ktrans and Kep extracted at TPs1 and 2 as compared to TPs 3 and 4., Conclusions: A significant variability of DCE-MRI quantitative parameters occurs when tumor segmentation is performed at different TPs. We suggest to performing tumor delineation at an established TP, preferably the earliest, in order to extract reliable and comparable DCE-MRI data., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: To assess the feasibility and reproducibility of T2 relaxation time measurements of the trapeziometacarpal joint (TM) and triangular fibrocartilage complex (TFCC) on healthy subjects at 1.5 T MR., Materials and Methods: Thirty-four healthy volunteers underwent an axial oblique multislice multiecho spin-echo sequence of the wrist at 1.5 T, with 10 of them having performed another MR scan on a different 1.5 T scanner. Regions of interest were independently manually drawn by two musculoskeletal radiologists to include the cartilaginous part of the TM and TFCC. Intra-observer, inter-observer and inter-scanner reproducibility of T2 relaxation time measurements was tested using the Bland-Altman method., Results: The mean T2 values obtained by the two radiologists were 29.9 ± 6.5 ms and 30.0 ± 6.1 ms in the TM and 24.5 ± 2.3 ms and 24.6 ± 2.8 ms in the TFCC, respectively. The mean values of the second series of T2 measurements obtained by the senior radiologist were 29.9 ± 6.5 ms and 30.0 ± 6.3 ms in the TM and 24.3 ± 2.9 ms in the TFCC. Inter-observer reproducibility in the TM and in the TFCC was 76% and 82%, respectively. Intra-observer reproducibility in the TM and TFCC was 71% and 76%, respectively. Inter-scanner reproducibility of T2 measurements was 36% in the TM and 85% in the TFCC, respectively., Conclusion: The assessment of T2 relaxation time measurements of the cartilage of the TM and the TFCC seems to be feasible and reproducible, although the inter-scanner reproducibility of T2 measurements of the TM is suboptimal. Further studies including patients are warranted to prove the utility of this tool.

Purpose: to assess whether MRI T stage of breast cancer lesions (BCLs) is affected by maximum diameter (MD) measured at different post-contrast time points (TPs) on different acquisition planes on dynamic contrast-enhanced (DCE) MRI sequence., Methods: 53 DCE-MRI examinations of patients with BCLs were retrospectively selected. MD of BCLs was measured on axial, coronal and sagittal planes on DCE images at five different post-contrast TPs. Friedman test followed by Bonferroni-adjusted Wilcoxon-signed rank test for post-hoc analysis was performed to evaluate differences among the five measurements. Reliability of the measurements was evaluated with the intraclass correlation coefficient analysis. Differences between pathological and MRI T stage assessed at each TP on each acquisition plane were assessed using the Wilcoxon-sign rank test; p values <0.05 were considered statistically significant., Results: on axial, coronal and sagittal planes, MD measured at TP1 was significantly different (p < 0.0001) compared to those obtained at the subsequent TPs. No significant differences were found between MD measured at TPs 3, 4 and 5. Intra and inter-observer reliability resulted as very good, with ICC ranging between 0.915-0.992 and 0.845-0.911, respectively. MRI T stage assessed at TP1 on axial and sagittal plane as well as at all TPs on coronal plane was significantly different from pathological T stage., Conclusion: MRI T stage definition of BCLs is significantly affected by the TP used for lesions' MD measurement. TPs 3, 4 and 5 are the preferred TPs for the assessment of MRI T stage of BCLs on both axial and sagittal planes., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose: To evaluate whether a machine learning (ML) analysis employing MRI-derived texture analysis (TA) features could be useful in assessing the presence of placenta accreta spectrum (PAS) in patients with placenta previa (PP). The hypothesis is that TA features may reflect histological abnormalities underlying PAS in patients with PP thus helping in differentiating positive from negative cases., Materials and Methods: Pre-operative MRI examinations of 64 patients with PP of which 20 positive (12 accreta, 7 increta and 1 percreta) and 44 negative for PAS were retrospectively selected. Multiple (n = 3) rounded regions of interest (ROIs) were manually positioned on sagittal or coronal T2-weighted images over homogeneous placental tissue close to the placental-myometrial interface for each patient to extract TA features. After balancing the dataset with the Synthetic Minority Over-sampling Technique, training and testing sets were obtained using Hold-out with a 75/25% split. Different algorithms were applied on the training set using the wrapper method, which looks for the best combination of features based on the optimization of a heuristic function in order to get the highest accuracy, and a 10-fold Cross-validation. The accuracy of the best models was also assessed on the test set. Histology was used as the standard of reference., Results: A total of 192 ROIs were positioned and a ROI-based analysis was then conducted. Among the different algorithms, k-nearest neighbors obtained the highest accuracy (98.1%), precision (98.7%), sensitivity (97.5%) and specificity (98.7%) while exploiting the lowest number of features (n = 26); conversely, the Naïve Bayes algorithm got the lowest scores showing an accuracy of 80.5%., Conclusion: ML analysis using MRI-derived TA features could be a feasible tool in the identification of placental tissue abnormalities underlying PAS in patients with PP. This approach might represent an additional tool in the clinical practice, thus expanding the application field of artificial intelligence to medical images., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Purpose: Pituitary adenomas are among the most frequent intracranial tumors. They may exhibit clinically aggressive behavior, with recurrent disease and resistance to multimodal therapy. The ki-67 labeling index represents a proliferative marker which correlates with pituitary adenoma aggressiveness. Aim of our study was to assess the accuracy of machine learning analysis of texture-derived parameters from pituitary adenomas preoperative MRI for the prediction of ki-67 proliferation index class., Methods: A total of 89 patients who underwent an endoscopic endonasal procedure for pituitary adenoma removal with available ki-67 labeling index were included. From T2w MR images, 1128 quantitative imaging features were extracted. To select the most informative features, different supervised feature selection methods were employed. Subsequently, a k-nearest neighbors (k-NN) classifier was employed to predict macroadenoma high or low proliferation index. Algorithm validation was performed with a train-test approach., Results: Of the 12 subsets derived from feature selection, the best performing one was constituted by the 4 highest correlating parameters at Pearson's test. These all showed very good (ICC ≥ 0.85) inter-observer reproducibility. The overall accuracy of the k-NN in the test group was of 91.67% (33/36) of correctly classified patients., Conclusions: Machine learning analysis of texture-derived parameters from preoperative T2 MRI has proven to be effective for the prediction of pituitary macroadenomas ki-67 proliferation index class. This might aid the surgical strategy making a more accurate preoperative lesion classification and allow for a more focused and cost-effective follow-up and long-term management.

Rationale and Objectives: Extraprostatic extension of disease (EPE) has a major role in risk stratification of prostate cancer patients. Currently, pretreatment local staging is performed with MRI, while the gold standard is represented by histopathological analysis after radical prostatectomy. Texture analysis (TA) is a quantitative postprocessing method for data extraction, while machine learning (ML) employs artificial intelligence algorithms for data classification. Purpose of this study was to assess whether ML algorithms could predict histopathological EPE using TA features extracted from unenhanced MR images., Materials and Methods: Index lesions from biparametric MRI examinations of 39 patients with prostate cancer who underwent radical prostatectomy were manually segmented on both T2-weighted images and ADC maps for TA data extraction. Combinations of different feature selection methods and ML classifiers were tested, and their performance was compared to a baseline accuracy reference., Results: The classifier showing the best performance was the Bayesian Network, using the dataset obtained by the Subset Evaluator feature selection method. It showed a percentage of correctly classified instances of 82%, an area under the curve of 0.88, a weighted true positive rate of 0.82 and a weighted true negative rate of 0.80., Conclusion: A combined ML and TA approach appears as a feasible tool to predict histopathological EPE on biparametric MR images., (Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background/aim: The differentiation between cerebral metastases (CM) and high-grade gliomas (HGG) can be difficult on magnetic resonance imaging (MRI). The aim of this study was to evaluate the usefulness of searching two MRI signs (signal alteration in the adjacent cortex, SAAC, and peripheral rim sign, PRS), in order to distinguish between these entities., Patients and Methods: A total of 61 patients were retrospectively enrolled (28 HGG, 33 CM). Fluid Attenuated Inversion Recovery (FLAIR) sequences were used to assess SAAC and contrast-enhanced T1-weighted sequences for PRS., Results: A positive SAAC sign was present in 61% of HGG, and 12% of CM. Conversely, in SAAC-negative lesions, PRS was observed in 78% of CM and in 32% of HGG. Their association had a higher frequency in HGG than in the CM group (21 vs. 3%)., Conclusion: While SAAC is specific for HGG and PRS, in the absence of SAAC, is relatively specific for CMs, their combined presence is highly suggestive of HGG., (Copyright© 2019, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

With this review, we aimed to provide a synopsis of recently proposed applications of machine learning (ML) in radiology focusing on prostate magnetic resonance imaging (MRI). After defining the difference between ML and classical rule-based algorithms and the distinction among supervised, unsupervised and reinforcement learning, we explain the characteristic of deep learning (DL), a particular new type of ML, including its structure mimicking human neural networks and its 'black box' nature. Differences in the pipeline for applying ML and DL to prostate MRI are highlighted. The following potential clinical applications in different settings are outlined, many of them based only on MRI-unenhanced sequences: gland segmentation; assessment of lesion aggressiveness to distinguish between clinically significant and indolent cancers, allowing for active surveillance; cancer detection/diagnosis and localisation (transition versus peripheral zone, use of prostate imaging reporting and data system (PI-RADS) version 2), reading reproducibility, differentiation of cancers from prostatitis benign hyperplasia; local staging and pre-treatment assessment (detection of extraprostatic disease extension, planning of radiation therapy); and prediction of biochemical recurrence. Results are promising, but clinical applicability still requires more robust validation across scanner vendors, field strengths and institutions.

Background/aim: Prostate multiparametric magnetic resonance imaging (mpMRI) is the reference imaging modality for extraprostatic extension of disease (EPE) assessment. We aimed to compare the diagnostic accuracy of different abbreviated MRI protocols to the standard prostate mpMRI in the identification of EPE of PCa., Patients and Methods: Fifty patients were retrospectively enrolled. Dual-pulse (dpMRI) and biparametric (bpMRI) abbreviated protocols were obtained from mpMRI. The performance of two experienced radiologists and two radiology residents was correlated with a reference standard and compared. Inter and intra-reader agreements were evaluated., Results: All protocols were strongly correlated to the reference standard (p≤0.001). A significant difference was found between dpMRI and mpMRI (p=0.009), no differences emerged between bpMRI and mpMRI (p=0.27). All readers showed moderate agreement (ĸ=0.47, ĸ=0.50 and ĸ=0.53 for dpMRI, bpMRI and mpMRI, respectively). Intra-reader agreement was good (all ĸ values ≥0.70)., Conclusion: Only bpMRI showed similar diagnostic performance to mpMRI, thus appearing as a feasible alternative to the standard protocol for EPE detection., (Copyright© 2019, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Purpose: Prostate multiparametric MRI (mpMRI) is the imaging modality of choice for detecting clinically significant prostate cancer (csPCa). Among various parameters, lesion maximum diameter and volume are currently considered of value to increase diagnostic accuracy. Quantitative radiomics allows for the extraction of more advanced shape features. Our aim was to assess which shape features derived from MRI index lesions correlate with csPCa presence., Materials and Methods: We retrospectively enrolled 75 consecutive subjects, who underwent mpMRI on a 3 T scanner, divided based on MRI index lesion Gleason Score in a csPCa group (GS > 3 + 4, n = 41) and a non-csPCa one (n = 34). Ten shape features were extracted both from axial T2-weighted and ADC maps images, after lesion tridimensional segmentation. Univariable and multivariable logistic analysis were used to evaluate the relationship between shape features and csPCa. Diagnostic performance was assessed measuring the area under the curve of the receiver operating characteristic (ROC) analysis. Diagnostic accuracy, sensitivity, and specificity were determined using the best cut-off on each ROC. A P value < 0.05 was considered statistically significant., Results: Univariable analysis demonstrated that almost every shape feature was statistically significant between csPCa e non-csPCa groups. However, multivariable analysis revealed that the parameter defined as surface area to volume ratio (SAVR), especially when extracted from ADC maps is the strongest independent predictor of csPCa among tested shape features., Conclusion: The radiomic shape feature SAVR, extracted from ADC maps after index lesion segmentation, appears as a promising tool for csPCa detection., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: To compare cardiac magnetic resonance (CMR) qualitative and quantitative analysis methods for the noninvasive assessment of myocardial inflammation in patients with suspected acute myocarditis (AM)., Methods: A total of 61 patients with suspected AM underwent coronary angiography and CMR. Qualitative analysis was performed applying Lake-Louise Criteria (LLC), followed by quantitative analysis based on the evaluation of edema ratio (ER) and global relative enhancement (RE). Diagnostic performance was assessed for each method by measuring the area under the curves (AUC) of the receiver operating characteristic analyses. The final diagnosis of AM was based on symptoms and signs suggestive of cardiac disease, evidence of myocardial injury as defined by electrocardiogram changes, elevated troponin I, exclusion of coronary artery disease by coronary angiography, and clinical and echocardiographic follow-up at 3 months after admission to the chest pain unit., Results: In all patients, coronary angiography did not show significant coronary artery stenosis. Troponin I levels and creatine kinase were higher in patients with AM compared to those without (both P < .001). There were no significant differences among LLC, T2-weighted short inversion time inversion recovery (STIR) sequences, early (EGE), and late (LGE) gadolinium-enhancement sequences for diagnosis of AM. The AUC for qualitative (T2-weighted STIR 0.92, EGE 0.87 and LGE 0.88) and quantitative (ER 0.89 and global RE 0.80) analyses were also similar., Conclusions: Qualitative and quantitative CMR analysis methods show similar diagnostic accuracy for the diagnosis of AM. These findings suggest that a simplified approach using a shortened CMR protocol including only T2-weighted STIR sequences might be useful to rule out AM in patients with acute coronary syndrome and normal coronary angiography.

Objective: To evaluate the reproducibility of T2 relaxation time measurements of the sacroiliac joints at 1.5 T., Materials and Methods: Healthy volunteers underwent an oblique axial multislice multiecho spin-echo sequence of the sacroiliac joints at 1.5 T. Regions of interest were manually drawn using a dedicated software by two musculoskeletal radiologists to include the cartilaginous part of the sacroiliac joints. A senior radiologist performed the measurement twice, while a resident measured once. Intra- and inter-observer reproducibility was tested using the Bland-Altman method. Association between sex and T2 relaxation times was tested using the Mann-Whitney U test. Correlation between T2 relaxation times and body mass index (BMI) was tested using the Spearman's rho., Results: Eighty sacroiliac joints of 40 subjects (mean age: 28 ± 4.8 years, range: 20-43; mean BMI: 23.3 ± 3.1, range: 18.9-30) were imaged. The mean T2 values obtained by the senior radiologist in the first series of measurements were 42 ± 4.4 ms, whereas in the second series were 40.7 ± 4.5 ms. The mean T2 values obtained by the radiology resident were 41.1 ± 4.2 ms. Intra-observer reproducibility was 88% (coefficient of repeatability = 3.8; bias = 1.28; p < .001), while inter-observer reproducibility was 86% (4.7; -.88; p < .001). There was significant association between sex and T2 relaxation times (p = .024) and significant inverse correlation between T2 relaxation times and BMI (r = -.340, p = .002)., Conclusion: The assessment of T2 relaxation time measurements of sacroiliac joints seems to be highly reproducible at 1.5 T. Further studies could investigate the potential clinical application of this tool in the sacroiliac joints.

Background: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest., Purpose/hypothesis: To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach., Study Type: Retrospective, observational study., Population/subjects/phantom/specimen/animal Model: Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL., Field Strength/sequence: Unenhanced T 1 -weighted in-phase (IP) and out-of-phase (OP) as well as T 2 -weighted (T 2 -w) MR images acquired at 3T., Assessment: Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T 2 -w images. Different selection methods were trained and tested using the J48 machine-learning classifiers., Statistical Tests: The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test., Results: A total of 138 TA-derived features were extracted; among these, four features were selected, extracted from the IP (Short&#95;Run&#95;High&#95;Gray&#95;Level&#95;Emphasis), OP (Mean&#95;Intensity and Maximum&#95;3D&#95;Diameter), and T 2 -w (Standard&#95;Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%. The expert radiologist obtained a diagnostic accuracy of 73%. McNemar's test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist., Data Conclusion: Machine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions., Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: Bi-parametric prostate MR (bp-MR) is a valuable tool for detection and characterization of prostate cancer (PCa). Recent studies suggested that PSA-density (PSA-D) in combination with multi-parametric prostate MR as well as bp-MR may achieve a higher diagnostic accuracy than either alone. We aimed to evaluate the diagnostic performance of bp-MR, PSA-D and their combination in biopsy-naïve patients., Methods and Materials: We retrospectively analyzed 334 consecutive patients who underwent prostate MR on a 3T scanner. Only patients (n = 114) who underwent TRUS-biopsy within 30 days following MR with no previous prostate biopsies were considered. Our protocol included T2-weighted and DWI sequences. A Likert score based on PI-RADS v2 was used for bp-MR evaluation. Lesions were graded histopathologically using the ISUP score. We assessed three scenarios: detection of lesions independently of ISUP score (ISUP ≥ 1), detection of both intermediate and clinically significant lesions (ISUP ≥ 2) and detection of clinically significant lesions alone (ISUP ≥ 3). Predictive value of bp-MR and PSA-D was evaluated by ROC curves and logistic regression analysis. A p value < 0.05 was considered statistically significant., Results: In all evaluated scenarios, bp-MR showed a significantly higher predictive power (AUC = 0.87-0.95) compared to the performance of PSA-D (AUC = 0.73-0.79), while their combination (AUC = 0.91-0.95) showed no statistically significant improvement compared to bp-MR alone., Conclusion: Our results confirm that bp-MR is a powerful tool in detection of clinically significant PCa. Contrary to findings in the recent literature, PSA-D does not appear to significantly improve its diagnostic performance., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Background: In Neurofibromatosis type 1, cerebral Unidentified Bright Objects are a well-known benign entity that has been extensively reported in the literature. In our case series, we wish to focus on a further possible location of such lesions, the spinal cord, which we have defined as medullary Unidentified Bright Objects. These have been, to our knowledge, scarcely described in previous works., Case Presentation: We report the cases of 7 patients with medullary Unidentified Bright Objects in Neurofibromatosis type 1 that we have followed for up to 9 years in our Regional Referral Center for Neurofibromatosis. In all of our patients, these lesions were completely asymptomatic and reported on Magnetic Resonance exams the patients underwent for other clinical indications., Conclusions: The aim of our work is to increase awareness of the possibility of medullary Unidentified Bright Objects in Neurofibromatosis type 1 patients, which can simulate neoplastic lesions, suggesting a more conservative approach in these cases.

Rationale and Objectives: This study aimed to investigate whether a simplified breast magnetic resonance imaging (MRI) protocol consisting of a localizer, one precontrast sequence, and three time-point postcontrast sequences (at 28 seconds, 84 seconds and 252 seconds after the contrast agent administration) is suitable for the characterization of breast lesions as compared to a full diagnostic protocol (FDP). This study also aimed to review the current literature concerning abbreviated breast MRI protocols and offer an alternative protocol., Materials and Methods: Breast magnetic resonance (MR) examinations with detected breast lesions of 98 patients were retrospectively evaluated. Two expert radiologists in consensus reviewed the simplified breast protocol (SBP) first and only thereafter the regular FDP, recording a diagnosis for each detected lesion for both protocols. Receiver operating characteristic curve analysis was performed to determine the diagnostic performance of the SBP compared to the standard FDP. A revision of the previously reported abbreviated breast magnetic resonance protocols was also carried out., Results: A total of 180 lesions were identified; of these, 110 (61%) were malignant and 70 (39%) were benign. Of the 110 malignant lesions, 86 (78%) were invasive ductal carcinoma, 18 (16%) were invasive lobular carcinoma, and 6 (6%) were ductal carcinoma in situ. Areas under the curve for the receiver operating characteristic curves for the SBP vs the FDP were equivalent (0.98 vs 0.99, respectively; P = 0.76). The SBP could be performed in approximately 6 minutes and 58 seconds, compared to 14 minutes and 48 seconds for the FDP., Conclusions: An SBP protocol including a late postcontrast time point is accurate for the characterization of breast lesions and was comparable to the standard FDP protocol, allowing a potential reduction of the total acquisition and interpretation times., (Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objective: We compared metaiodobenzylguanidine (MIBG) uptake and magnetic resonance (MR) signal intensity ratio in differentiating benign and malignant disease in patients with pheochromocytoma or paraganglioma., Methods: Eighteen patients (9 men, mean age 37 ± 8 years) with pheochromocytoma or paraganglioma underwent MR imaging and iodine-131 MIBG scintigraphy. MR signal intensity ratio was measured on T1 and T2-weighted images using region of interest analysis and intensity ratio of MIBG uptake was calculated for each tumor lesion on 48 h images., Results: A total of 28 tumor lesions was analyzed of which 12 were benign and 16 malignant. MIBG uptake intensity ratio was significantly higher in malignant lesions compared to benign (5.2 ± 2.4 and 2.9 ± 1.4, respectively, p < 0.01). On the contrary, no significant difference in tumor size and MR signal intensity ratio between malignant and benign tumor lesions was observed., Conclusions: In patients with pheochromocytoma or paraganglioma, iodine-131 MIBG uptake is able to differentiate between benign and malignant disease, while MR imaging is not useful for this purpose. The higher MIBG uptake observed in malignant lesions could reflect major tumor storage of catecholamines compared to benign lesions.