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Post-processing software enjoys a wide sphere of use thanks to advances in technology, and the fact that central venous insufficiency may be associated with tinnitus has been revealed using MRI. Our aim in this study was to use a relatively easier and shorter MRI method (4D-MRAngiography) to reveal whether or not there is an association between tinnitus and jugular venous reflux by adding the valsalva maneuver to technique. Thirty patients with unilateral tinnitus and undergoing contrast enhanced MRI with a special protocol were included in the study. Thick slab dynamic maximum intensity' projection (MIP) images were obtained following short imaging with TWIST-MRA lasting a total of 90-110 sec. Reflux degree was graded (as grade 0, 1, 2) on MIP images obtained during valsalva maneuver. In all cases no dural arteriovenous fistula was defined. Jugular venous reflux was not identified (grade 0) in 20 cases. Grade 1 reflux was determined on the right in five of the remaining cases and on the left in two. Reflux past the base of the skull and reaching the cortical veins and cavernous sinuses was determined during TWIST-MRA in three cases exhibiting a significant increase in tinnitus severity with valsava in their clinical histories. This study reveals a relation between jugular venous reflux and tinnitus using an objective, non-operator dependent modality. In our opinion, determination of this etiology will have a positive impact on the entire diagnosis-treatment- follow-up algorithm in cases of refractory tinnitus resistant to other treatments and induced with valsava. [ABSTRACT FROM AUTHOR]

Background: Emerging evidence suggests multiparametric prostate MRI's role in evaluating prostate cancer (PCa) aggressiveness., Purpose: To evaluate the utility of quantitative sequences in prostate MRI for assessing peripheral zone PCa aggressiveness, focusing on added value of relaxation maps to ADC., Material and Methods: In total, 64 men undergoing preoperative bi-parametric prostate MRI with synthetic sequences were retrospectively enrolled. Radiologists delineated lesions on ADC maps and synthetic MRI, matched with digitized whole-mount specimens. PCa was graded as low, intermediate, or high based on Gleason grade. Statistical analyses assessed differences in T1, T2, PD, and ADC values across grades. For significant variables, AUC curves were generated. A logistic regression model evaluated the added diagnostic value of relaxation maps to ADC., Results: Of the 95 PCa foci, 11 were low-grade, 45 intermediate-grade, and 39 high-grade. T1 ( P  = 0.4) and PD ( P  = 0.3) showed no significant differences, while T2 and ADC differed significantly ( P  < 0.001). Median T2 values were 98 ms (low-grade), 81 ms (intermediate), and 73 ms (high-grade), and median ADC values were 1006 mm²/s, 836 mm²/s, and 721 mm²/s, respectively. ADC yielded AUCs of 0.888 (95% CI=0.794-0.963; P  < 0.0001) for low- versus intermediate-to-high-grade PCa, and 0.825 (95% CI=0.724-0.901; P  < 0.0001) for low-to-intermediate versus high-grade. T2 AUCs were 0.770 (95% CI=0.605-0.908; P  = 0.021) and 0.755 (95% CI=0.648-0.864; P  = 0.0051), respectively. A logistic regression model combining T2 and ADC showed no significant improvement over ADC alone in differentiating low- versus intermediate-to-high-grade and low-to-intermediate versus high-grade PCa ( P  > 0.05)., Conclusion: T1 and PD maps from synthetic MRI showed limited utility in grading PCa. Although T2 values were effective, ADC values were superior in assessing PCa aggressiveness. The addition of T2 to ADC did not significantly improve diagnostic performance., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care., Competing Interests: Conflict of interest disclosure: Deniz Alis is the CEO and co-founder of Hevi AI Health Tech. The authors declared no conflicts of interest.

Background: Explainable Artificial Intelligence (XAI) is prominent in the diagnostics of opaque deep learning (DL) models, especially in medical imaging. Saliency methods are commonly used, yet there's a lack of quantitative evidence regarding their performance., Objectives: To quantitatively evaluate the performance of widely utilized saliency XAI methods in the task of breast cancer detection on mammograms., Methods: Three radiologists drew ground-truth boxes on a balanced mammogram dataset of women (n = 1496 cancer-positive and negative scans) from three centers. A modified, pre-trained DL model was employed for breast cancer detection, using MLO and CC images. Saliency XAI methods, including Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++, and Eigen-CAM, were evaluated. We utilized the Pointing Game to assess these methods, determining if the maximum value of a saliency map aligned with the bounding boxes, representing the ratio of correctly identified lesions among all cancer patients, with a value ranging from 0 to 1., Results: The development sample included 2,244 women (75%), with the remaining 748 women (25%) in the testing set for unbiased XAI evaluation. The model's recall, precision, accuracy, and F1-Score in identifying cancer in the testing set were 69%, 88%, 80%, and 0.77, respectively. The Pointing Game Scores for Grad-CAM, Grad-CAM++, and Eigen-CAM were 0.41, 0.30, and 0.35 in women with cancer and marginally increased to 0.41, 0.31, and 0.36 when considering only true-positive samples., Conclusions: While saliency-based methods provide some degree of explainability, they frequently fall short in delineating how DL models arrive at decisions in a considerable number of instances., 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 Elsevier B.V. All rights reserved.)

Objective: Robot-assisted minimally invasive coronary bypass surgery is one of the least invasive approaches that offers multivessel revascularization and accelerated recovery. We investigated the benefits of computed tomography angiography (CTA) guidance in robotic coronary bypass (RCAB) by analyzing perioperative outcomes., Methods: Between April 2022 and April 2023, 60 consecutive patients who underwent RCAB under preoperative CTA guidance were included. The intercostal space of the minithoractomy incision was determined based on the distance from the thoracotomy site to the midsection of the left anterior descending artery (LAD) on preoperative CTA. Peripheral vascular findings on preoperative CTA guided the decision for the cannulation site. Perioperative parameters and early outcomes were evaluated., Results: The mean age of the patients was 62.3 ± 10.5 years, and 51 patients were male (85.0%). The mean number of revascularized vessels was 2.9 ± 1.1. Left thoracotomy guided by CTA measurements was performed in the fourth intercostal space in 37 patients (61.7%) and in the third intercostal space in the remaining patients. Axillary cannulation was performed in 28 (46.7%) patients because of prohibitive findings in the iliac vessels and aorta. All target coronary arteries with an indication for bypass were revascularized with CTA-guided RCAB. The left internal mammary artery (LIMA) was anastomosed to the LAD in all patients, and the LIMA was anastomosed sequentially to the diagonal artery in 17 patients (28.3%). No operative mortality or cerebrovascular event was observed. One patient underwent reoperation due to bleeding., Conclusions: Robot-assisted minimally invasive multiple-vessel coronary bypass under preoperative CTA guidance is safe and can be performed with excellent results., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: The Prostate Imaging Quality (PI-QUAL) score is the first step toward image quality assessment in multi-parametric prostate MRI (mpMRI). Previous studies have demonstrated moderate to excellent inter-rater agreement among expert readers; however, there is a need for studies to assess the inter-reader agreement of PI-QUAL scoring in basic prostate readers., Objectives: To assess the inter-reader agreement of the PI-QUAL score amongst basic prostate readers on multi-center prostate mpMRI., Methods: Five basic prostate readers from different centers assessed the PI-QUAL scores independently using T2-weighted images, diffusion-weighted imaging (DWI) including apparent diffusion coefficient (ADC) maps, and dynamic-contrast-enhanced (DCE) images on mpMRI data obtained from five different centers following Prostate Imaging-Reporting and Data System Version 2.1. The inter-reader agreements amongst radiologists for PI-QUAL were evaluated using weighted Cohen's kappa. Further, the absolute agreements in assessing the diagnostic adequacy of each mpMRI sequence were calculated., Results: A total of 355 men with a median age of 71 years (IQR, 60-78) were enrolled in the study. The pair-wise kappa scores ranged from 0.656 to 0.786 for the PI-QUAL scores, indicating good inter-reader agreements between the readers. The pair-wise absolute agreements ranged from 0.75 to 0.88 for T2W imaging, from 0.74 to 0.83 for the ADC maps, and from 0.77 to 0.86 for DCE images., Conclusions: Basic prostate radiologists from different institutions provided good inter-reader agreements on multi-center data for the PI-QUAL scores., 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 © 2023 Elsevier B.V. All rights reserved.)

Background: Although systems such as Prostate Imaging Quality (PI-QUAL) have been proposed for quality assessment, visual evaluations by human readers remain somewhat inconsistent, particularly among less-experienced readers., Objectives: To assess the feasibility of deep learning (DL) for the automated assessment of image quality in bi-parametric MRI scans and compare its performance to that of less-experienced readers., Methods: We used bi-parametric prostate MRI scans from the PI-CAI dataset in this study. A 3-point Likert scale, consisting of poor, moderate, and excellent, was utilized for assessing image quality. Three expert readers established the ground-truth labels for the development (500) and testing sets (100). We trained a 3D DL model on the development set using probabilistic prostate masks and an ordinal loss function. Four less-experienced readers scored the testing set for performance comparison., Results: The kappa scores between the DL model and the expert consensus for T2W images and ADC maps were 0.42 and 0.61, representing moderate and good levels of agreement. The kappa scores between the less-experienced readers and the expert consensus for T2W images and ADC maps ranged from 0.39 to 0.56 (fair to moderate) and from 0.39 to 0.62 (fair to good)., Conclusions: Deep learning (DL) can offer performance comparable to that of less-experienced readers when assessing image quality in bi-parametric prostate MRI, making it a viable option for an automated quality assessment tool. We suggest that DL models trained on more representative datasets, annotated by a larger group of experts, could yield reliable image quality assessment and potentially substitute or assist visual evaluations by human readers., 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 © 2023 Elsevier B.V. All rights reserved.)

Objective: The aim of the study is to investigate the role of whole-body magnetic resonance imaging (MRI) in assessing extrapulmonary metastases in primary osteosarcoma staging., Methods: We retrospectively reviewed medical data to identify primary osteosarcoma patients with available preoperative whole-body MRI obtained in the staging or restaging. Histopathology was the reference test for assessing the diagnostic performance, if available. Otherwise, oncology board decisions were used as the reference. In addition, the benefits of whole-body MRI to F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET-CT) and bone scintigraphy were investigated., Results: In all, 36 patients with osteosarcoma (24 staging, 12 restaging) with a mean age of 16.36 ± 5.63 years (range, 9-29 years) were included in the study. The median follow-up duration was 26.61 months (interquartile range, 33.3 months). Of 36 patients, 8 had skeletal, 1 had a lymph node, and 1 had a subcutaneous metastasis. Whole-body MRI correctly identified all patients with metastatic disease but incorrectly classified a bone infarct in one patient as a skeletal metastasis, equating a scan-level sensitivity, specificity, accuracy, negative predictive value, and positive predictive value of 100%, 96.3%, 97.3%, 100%, and 90.91%. Whole-body MRI contributed to bone scintigraphy by identifying a skeletal metastasis in one patient and positron emission tomography-computed tomography by ruling out a skeletal metastasis in another., Conclusions: Whole-body MRI could accurately identify extrapulmonary metastases in primary osteosarcoma patients for staging or restaging. In addition, it might contribute to the standard whole-body imaging methods., Competing Interests: The authors declare no conflict of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: To evaluate the effectiveness of a self-adapting deep network, trained on large-scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in external multi-center data from men of diverse demographics; to investigate the advantages of transfer learning., Methods: We used two samples: (i) Publicly available multi-center and multi-vendor Prostate Imaging: Cancer AI (PI-CAI) training data, consisting of 1500 bi-parametric MRI scans, along with its unseen validation and testing samples; (ii) In-house multi-center testing and transfer learning data, comprising 1036 and 200 bi-parametric MRI scans. We trained a self-adapting 3D nnU-Net model using probabilistic prostate masks on the PI-CAI data and evaluated its performance on the hidden validation and testing samples and the in-house data with and without transfer learning. We used the area under the receiver operating characteristic (AUROC) curve to evaluate patient-level performance in detecting csPCa., Results: The PI-CAI training data had 425 scans with csPCa, while the in-house testing and fine-tuning data had 288 and 50 scans with csPCa, respectively. The nnU-Net model achieved an AUROC of 0.888 and 0.889 on the hidden validation and testing data. The model performed with an AUROC of 0.886 on the in-house testing data, with a slight decrease in performance to 0.870 using transfer learning., Conclusions: The state-of-the-art deep learning method using prostate masks trained on large-scale bi-parametric MRI data provides high performance in detecting csPCa in internal and external testing data with different characteristics, demonstrating the robustness and generalizability of deep learning within and across datasets., Clinical Relevance Statement: A self-adapting deep network, utilizing prostate masks and trained on large-scale bi-parametric MRI data, is effective in accurately detecting clinically significant prostate cancer across diverse datasets, highlighting the potential of deep learning methods for improving prostate cancer detection in clinical practice., (© 2023. The Author(s).)

The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object detection network called nnDetection was used to detect LVO and assess collateralization on CTA scans using a multi-task 3D object detection approach. The model was trained on single-phase CTA scans of 2425 patients at five centers, and its performance was evaluated on an external test set of 345 patients from another center. Ground-truth labels for the presence of LVO and collateral scores were provided by three radiologists. The nnDetection model achieved a diagnostic accuracy of 98.26% (95% CI 96.25-99.36%) in identifying LVO, correctly classifying 339 out of 345 CTA scans in the external test set. The DL-based collateral scores had a kappa of 0.80, indicating good agreement with the consensus of the radiologists. These results demonstrate that the self-configuring 3D nnDetection model can accurately detect LVO on single-phase CTA scans and provide semi-quantitative collateral scores, offering a comprehensive approach for automated stroke diagnostics in patients with LVO., (© 2023. The Author(s).)

Background: Lesional posterior cortex epilepsy (PCE) is often drug resistant and may benefit from surgical intervention. In this study, we aimed to identify potential predictive factors associated with seizure recurrence after epilepsy surgery in lesional PCE., Methods: We retrospectively reviewed patients with PCE who underwent surgery between 1998 and 2021. They were divided into 2 groups according to seizure outcome; the seizure-free group (group 1) and the non-seizure-free group (group 2). The relationship among clinical factors, electroencephalography (EEG) or cranial magnetic resonance imaging findings, disease, and seizure outcome was investigated., Results: A total of 60 patients, with a mean age of 27.26 ± 12.35 years (range, 9-61 years), were included in the study. There were 31 patients (51.66%) in group 1 (Engel class I) and 29 patients (48.33%) in group 2 (13 [21.66%], 10 [16.66%], and 6 [10%] patients in Engel class II, III, and IV, respectively), with a mean follow-up of 8.95 ± 6.96 years (range, 1-24 years). No difference was observed regarding age, gender, age at seizure onset, operation type, treatment gap, and presence of bilateral lesions between the groups (P > 0.05). However, bilateral findings on interictal EEG and gliosis as the underlying disease were predictors of seizure recurrence (P < 0.05)., Conclusions: More than half of the patients (including 2 with bilateral magnetic resonance imaging lesions) were seizure free at long-term follow-up. However, patients with bilateral findings on interictal EEG and gliosis were more likely to have recurrent seizures after surgery. Because lesional PCE is almost always drug resistant and has a potential for favorable outcomes, epilepsy surgery should be considered early., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective: To investigate whether commercially available deep learning (DL) software improves the Prostate Imaging-Reporting and Data System (PI-RADS) scoring consistency on bi-parametric MRI among radiologists with various levels of experience; to assess whether the DL software improves the performance of the radiologists in identifying clinically significant prostate cancer (csPCa)., Methods: We retrospectively enrolled consecutive men who underwent bi-parametric prostate MRI at a 3 T scanner due to suspicion of PCa. Four radiologists with 2, 3, 5, and > 20 years of experience evaluated the bi-parametric prostate MRI scans with and without the DL software. Whole-mount pathology or MRI/ultrasound fusion-guided biopsy was the reference. The area under the receiver operating curve (AUROC) was calculated for each radiologist with and without the DL software and compared using De Long's test. In addition, the inter-rater agreement was investigated using kappa statistics., Results: In all, 153 men with a mean age of 63.59 ± 7.56 years (range 53-80) were enrolled in the study. In the study sample, 45 men (29.80%) had clinically significant PCa. During the reading with the DL software, the radiologists changed their initial scores in 1/153 (0.65%), 2/153 (1.3%), 0/153 (0%), and 3/153 (1.9%) of the patients, yielding no significant increase in the AUROC (p > 0.05). Fleiss' kappa scores among the radiologists were 0.39 and 0.40 with and without the DL software (p = 0.56)., Conclusions: The commercially available DL software does not increase the consistency of the bi-parametric PI-RADS scoring or csPCa detection performance of radiologists with varying levels of experience., (© 2023. The Author(s).)

Objectives: To compare the effectiveness of individual multiparametric prostate MRI (mpMRI) sequences-T2W, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC), and dynamic contrast-enhanced (DCE)-in assessing prostate cancer (PCa) index lesion volume using whole-mount pathology as the ground-truth; to assess the impact of an endorectal coil (ERC) on the measurements., Materials and Methods: We retrospectively enrolled 72 PCa patients who underwent 3T mpMRI with (n = 39) or without (n = 33) an ERC. A pathologist drew the index lesion borders on whole-mount pathology using planimetry (whole-mount vol ). A radiologist drew the borders of the index lesion on each mpMRI sequence-T2W vol , DWI vol , ADC vol , and DCE vol . Additionally, we calculated the maximum index lesion volume for each patient (maxMRI vol ). The correlation and differences between mpMRI and whole-mount pathology in measuring the index lesion volume and the impact of an ERC were investigated., Results: The median T2W vol , DWI vol , ADC vol , DCE vol , and maxMRI vol were 0.68 cm 3 , 0.97 cm 3 , 0.98 cm 3 , 0.82 cm 3 , and 1.13 cm 3 . There were good positive correlations between whole-mount vol and mpMRI sequences. However, all mpMRI-derived volumes underestimated the median whole-mount vol volume of 1.97 cm 3 (P ≤ 0.001), with T2W vol having the largest volumetric underestimation while DWI vol and ADC vol having the smallest. The mean relative index lesion volume underestimations of maxMRI vol were 39.16% ± 32.58% and 7.65% ± 51.91% with and without an ERC (P = 0.002)., Conclusion: T2W vol , DWI vol , ADC vol , DCE vol , and maxMRI vol substantially underestimate PCa index lesion volume compared with whole-mount pathology, with T2W vol having the largest volume underestimation. Additionally, using an ERC exacerbates the volume underestimation., Competing Interests: The authors have no competing interests to declare., (Copyright: © 2022 The Author(s).)

Objectives: To describe 18 F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging ( 18 F-FDG PET/MRI) along with semiology and electroencephalography (EEG) in patients with gray matter heterotopia (GMH); to evaluate the concordance between 18 F-FDG PET/MRI and clinical epileptogenic zone (EZ)., Materials & Methods: GMH (subcortical heterotopia [SCH] and periventricular nodular heterotopia [PNH]) patients with epilepsy who underwent 18 F-FDG PET/MRI were retrospectively enrolled. Two radiologists evaluated brain MRI, while two nuclear medicine specialists assessed the 18 F-FDG PET. The SUVmax values of visually hypometabolic cortical areas were compared to the contralateral cortex using a SUVmax threshold value of 10%; the SUVmax values of GMH lesions were compared with that of the right precentral gyrus. The cortex or GMH with hypometabolism on 18 F-FDG PET/MRI was considered representative of the EZ. The clinical EZ was identified using EEG and semiology., Results: Thirty patients (19 PNH; 11 SCH) with a mean age of 28.46 ± 9.52 years were enrolled. The heterotopic nodules were ametabolic in 3 patients (10%), hypometabolic in 16 (33.33%), isometabolic in 13 (26.66%), and hypermetabolic in 4 (10%). 18 F-FDG PET/MRI demonstrated hypometabolism in the cortex and GMH in 22/30 (73.33%) and 16/30 (53.33%). We could identify a clinical EZ in 18 patients, and 15 out of 18 (83.33%) had concordant 18 F-FDG PET/MRI findings., Conclusion: Heterotopic nodules in GMH patients show different metabolic patterns on 18 F-FDG PET/MRI, with nearly three-quarters of the patients having cortical hypometabolism. 18 F-FDG PET/ MRI findings are mostly concordant with the clinical EZ., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test its performance in a prospective independent sample consisting of consecutive real-world patients. All consecutive patients who underwent emergency non-contrast-enhanced head CT in five different centers were retrospectively gathered. Five neuroradiologists created the ground-truth labels. The development dataset was divided into the training and validation set. After the development phase, we integrated the deep learning model into an independent center's PACS environment for over six months for assessing the performance in a real clinical setting. Three radiologists created the ground-truth labels of the testing set with a majority voting. A total of 55,179 head CT scans of 48,070 patients, 28,253 men (58.77%), with a mean age of 53.84 ± 17.64 years (range 18-89) were enrolled in the study. The validation sample comprised 5211 head CT scans, with 991 being annotated as ICH-positive. The model's binary accuracy, sensitivity, and specificity on the validation set were 99.41%, 99.70%, and 98.91, respectively. During the prospective implementation, the model yielded an accuracy of 96.02% on 452 head CT scans with an average prediction time of 45 ± 8 s. The joint CNN-RNN model with an attention mechanism yielded excellent diagnostic accuracy in assessing ICH and its subtypes on a large-scale sample. The model was seamlessly integrated into the radiology workflow. Though slightly decreased performance, it provided decisions on the sample of consecutive real-world patients within a minute., (© 2022. The Author(s).)