Choosing the right artificial intelligence solutions for your radiology department: key factors to consider.

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.

Quantitative evaluation of Saliency-Based Explainable artificial intelligence (XAI) methods in Deep Learning-Based mammogram analysis.

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.

Awareness and level of digital literacy among students receiving health-based education.

BACKGROUND: Being digitally literate allows health-based science students to access reliable, up-to-date information efficiently and expands the capacity for continuous learning. Digital literacy facilitates effective communication and collaboration among other healthcare providers. It helps to navigate the ethical implications of using digital technologies and aids the use of digital tools in managing healthcare processes. Our aim in this study is to determine the digital literacy level and awareness of our students receiving health-based education in our university and to pave the way for supporting the current curriculum with courses on digital literacy when necessary. METHOD: Students from Acibadem University who were registered undergraduate education for at least four years of health-based education, School of Medicine, Nutrition and Dietetics, Nursing, Physiotherapy and Rehabilitation, Psychology, Biomedical Engineering, Molecular Biology, and Genetics were included. The questionnaire consisted of 24 queries evaluating digital literacy in 7 fields: software and multimedia, hardware and technical problem solving, network and communication/collaboration, ethics, security, artificial intelligence (A.I.), and interest/knowledge. Two student groups representing all departments were invited for interviews according to the Delphi method. RESULTS: The survey was completed by 476 students. Female students had less computer knowledge and previous coding education. Spearman correlation test showed that there were weak positive correlations between the years and the "software and multimedia," "ethics," "interest and knowledge" domains, and the average score. The students from Nursing scored lowest in the query after those from the Nutrition and Dietetics department. The highest scores were obtained by Biomedical Engineering students, followed by the School of Medicine. Participants scored the highest in "network" and "A.I." and lowest in "interest-knowledge" domains. CONCLUSION: It is necessary to define the level of computer skills who start health-based education and shape the curriculum by determining which domains are weak. Creating an educational environment that fosters females' digital knowledge is recommended. Elective courses across faculties may be offered to enable students to progress and discuss various digital literacy topics. The extent to which students benefit from the digital literacy-supported curriculum may be evaluated. Thus, health-based university students are encouraged to acquire the computer skills required by today's clinical settings. REGISTRATION: This study was approved by Acibadem University and Acibadem Healthcare Institutions Medical Research Ethics Committee (ATADEK) (11 November 2022, ATADEK registration: 2022-17-138) All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from the participants.

Robot-Assisted Minimally Invasive Multivessel Coronary Bypass Guided by Computerized Tomography.

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.

Inter-reader agreement of the prostate imaging quality (PI-QUAL) score for basic readers in prostate MRI: A multi-center study.

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.

Anatomically guided self-adapting deep neural network for clinically significant prostate cancer detection on bi-parametric MRI: a multi-center study.

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.

Deep learning for assessing image quality in bi-parametric prostate MRI: A feasibility study.

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.

Automated LVO detection and collateral scoring on CTA using a 3D self-configuring object detection network: a multi-center study.

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.

Subclinical atherosclerosis in patients with hidradenitis suppurativa treated with TNF inhibitors.

BACKGROUND: Hidradenitis suppurativa (HS) is an independent risk factor for the development of subclinical atherosclerosis. Tumour necrosis factor (TNF) inhibitors are effective for the treatment of recalcitrant moderate-to-severe HS. However, the effect of treatment with TNF inhibitors on subclinical atherosclerosis in HS patients has not been previously investigated. OBJECTIVES: In this study, we aimed to assess changes in biochemical parameters (fasting blood glucose and lipid levels) and carotid intima-media thickness (CIMT) values in Hurley stage II and III HS patients undergoing treatment with TNF inhibitors. METHODS: This was a single center prospective study including 30 patients with Hurley stage II and III HS and 30 healthy controls (HCs). Baseline values of biochemical parameters and CIMT were compared to the values recorded after at least 6 months of TNF inhibitor therapy. RESULTS: CIMT values of the HS patients significantly exceeded those of HCs (for right p = 0.011 and for left p = 0.017). After at least 6 months of TNF inhibitor therapy, there was a statistically significant decrease in fasting blood glucose (p = 0.001), whereas total cholesterol levels significantly increased (p = 0.001). CIMT values also significantly increased (for right p = 0.02 and for left p = 0.01). STUDY LIMITATIONS AND CONCLUSIONS: Small sample size is limitation of the current study. Our study shows that patients with Hurley stage II and III HS undergoing TNF inhibitor therapy are under risk for progression of subclinical atherosclerosis.

Does deep learning software improve the consistency and performance of radiologists with various levels of experience in assessing bi-parametric prostate MRI?

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.

The Role of Whole-Body Magnetic Resonance Imaging in Assessing Extrapulmonary Metastases in Osteosarcoma Staging and Restaging: A Pilot Study.

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.

Outcome Predictors of Lesional Posterior Cortex Epilepsy Surgery.

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.

A Comparative Study of Multiparametric MRI Sequences in Measuring Prostate Cancer Index Lesion Volume.

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-mountvol). A radiologist drew the borders of the index lesion on each mpMRI sequence-T2Wvol, DWIvol, ADCvol, and DCEvol. Additionally, we calculated the maximum index lesion volume for each patient (maxMRIvol). 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 T2Wvol, DWIvol, ADCvol, DCEvol, and maxMRIvol were 0.68 cm3, 0.97 cm3, 0.98 cm3, 0.82 cm3, and 1.13 cm3. There were good positive correlations between whole-mountvol and mpMRI sequences. However, all mpMRI-derived volumes underestimated the median whole-mountvol volume of 1.97 cm3 (P ≤ 0.001), with T2Wvol having the largest volumetric underestimation while DWIvol and ADCvol having the smallest. The mean relative index lesion volume underestimations of maxMRIvol were 39.16% ± 32.58% and 7.65% ± 51.91% with and without an ERC (P = 0.002). Conclusion: T2Wvol, DWIvol, ADCvol, DCEvol, and maxMRIvol substantially underestimate PCa index lesion volume compared with whole-mount pathology, with T2Wvol having the largest volume underestimation. Additionally, using an ERC exacerbates the volume underestimation.

The analysis of 18 F-FDG PET/MRI, electroencephalography, and semiology in patients with gray matter heterotopia: A pilot study.

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.

A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT.

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.

Performance of apparent diffusion coefficient values and ratios for the prediction of prostate cancer aggressiveness across different MRI acquisition settings.

PURPOSE: In this study, we assessed the performance of apparent diffusion coefficient (ADC) and diffusion-weighted imaging (DWI) metrics and their ratios across different magnetic resonance imaging (MRI) acquisition settings, with or without an endorectal coil (ERC), for the evaluation of prostate cancer (PCa) aggressiveness using whole-mount specimens as a reference. METHODS: We retrospectively reviewed the data of prostate carcinoma patients with a Gleason score (GS) of 3+4 or higher who underwent prostate MRI using a 3T unit at our institution. They were divided into two groups based on the use of ERC for MRI acquisition, and patients who underwent prostate MRI with an ERC constituted the ERC (n = 55) data set, while the remaining patients accounted for the non-ERC data set (n = 41). DWI was performed with b-values of 50, 500, 1000, and 1,400 s/mm2, and ADC maps were automatically calculated. Additionally, computed DWI (cDWI) was performed with a b-value of 2000 s/mm2. Six ADC and two cDWI parameters were evaluated. In the ERC data set, receiver operating characteristic (ROC) curves were plotted for each metric to determine the best cutoff threshold values for differentiating GS 3+4 PCa from that with a higher GS. The performance of these cutoff values was assessed in non-ERC dataset. The diagnostic accuracies and area under the curves (AUCs) of the metrics were compared using Fisher's exact test and De Long's method, respectively. RESULTS: Among all metrics, the ADCmean-ratio yielded the highest AUC, 0.84, for differing GS 3+4 PCa from that with a higher GS. The best threshold cutoff values of ADCmean-ratio (£0.51) for discriminating GS 3+4 PCa from that with a higher GS classified 48 patients out of 55 with an accuracy of 87.27%. However, there was no significant difference between each metric in terms of accuracy and AUC (p = 0.163 and 0.214). Similarly, in the non-ERC data set, the ADCmean-ratio provided the highest diagnostic accuracy (82.92%) by classifying 34 patients out of 41. However, Fisher's exact test yielded no significant difference between DWI and ADC metrics in terms of diagnostic accuracy in non-ERC data (p = 0.561). CONCLUSION: The mean ADC ratio of the tumor to the normal prostate showed the highest accuracy and AUC in differentiating GS 3+4 PCa and PCa with a higher GS across different MRI acquisition settings; however, the performance of different ADC and DWI metrics did not differ significantly.

Third-Generation Dual-Source Computed Tomography for Coronary Angiography With Individually Tailored Scan Protocols Can Achieve a Low Radiation Dose With Good Image Quality in Unselected Patients.

OBJECTIVE: To explore the image quality and radiation exposure associated with coronary angiography obtained with a third-generation dual-source computed tomography, using body mass index (BMI)- and heart rate (HR)-adapted protocols in real-world patients. METHODS: Three scan protocols were implemented with regard to HR: prospective turbo high-pitch spiral, sequential, and retrospective spiral modes. We adapted the reference kilovoltage value according to BMI. Image quality was evaluated using a 4-point scale, and effective dose estimates were calculated using the dose-length product. RESULTS: Among the 896 patients, 417 (46.54%), 433 (48.32%), and 45 (5.02%) were imaged using prospective turbo high-pitch spiral, sequential, and retrospective spiral modes, respectively. The median BMI was 27.3 (25-30.4) kg/m2, and the effective dose was 0.65 mSv (interquartile range, 0.33-1.56 mSv). Only 32 of 896 examinations (3.5%) had poor image quality. CONCLUSIONS: Computed tomography angiography with BMI- and HR-tailored protocols offers good image quality with low radiation dose in unselected patients.

Association between left ventricular strain and cardiac iron load in beta-thalassaemia major: a cardiac magnetic resonance study.

OBJECTIVE: To evaluate the utility of cardiac magnetic resonance feature tracking-derived left ventricular strain in assessing cardiac dysfunction and investigate the correlation between left ventricular strain and myocardial T2* in patients with beta-thalassaemia major. METHODS: Forty-two patients with beta-thalassaemia major, having a mean age of 22.49 ± 8.48 years, and age-matched healthy controls were enrolled in the study. The observer drew regions of interest on the interventricular septum, and T2* decay curves were calculated accordingly. The short-axis cine images were used to derive left ventricular circumferential and radial strains, and the long-axis four-chamber and two-chamber images were used to assess left ventricular longitudinal strain. RESULTS: The mean global left ventricular strains were lower in beta-thalassaemia major patients than the controls (p < 0.05). Left ventricular strains of beta-thalassaemia major patients with cardiac T2* values of > 20 ms were also significantly reduced compared with the controls (p < 0.05); there was no difference between the mean left ventricular ejection fractions of the two groups (p = 0.84). Cardiac T2* showed a weak correlation with left ventricular ejection fraction (r = 0.33, p = 0.03), while the left ventricular circumferential strain showed a good positive correlation with cardiac T2* (r = 0.6, p < 0.0001). CONCLUSION: Compared with healthy controls, patients with beta-thalassaemia major, including those with myocardial T2* values of >20 ms, showed reduced global left ventricular strains. Left ventricular circumferential strain was positively correlated with myocardial T2*. Left ventricular strain analysis using cardiac magnetic resonance feature tracking may have utility in beta-thalassaemia major assessment.Key FindingsPatients with beta-thalassaemia major, including those with myocardial T2* values of >20 ms, had reduced global left ventricular strains.Cardiac T2* showed a weak correlation with left ventricular ejection fraction, while the left ventricular circumferential strain showed a good positive correlation with cardiac T2*.ImportanceLeft ventricular strain using cardiac magnetic resonance feature tracking might be used as an adjunct in assessing cardiac functions in beta-thalassaemia major.

The Association between the Extent of Late Gadolinium Enhancement and Diastolic Dysfunction in Hypertrophic Cardiomyopathy.

Background Diastolic dysfunction in hypertrophic cardiomyopathy (HCM) patients is a frequent, yet poorly understood phenomenon. Purpose The purpose of this study is to assess the relationship between the myocardial fibrosis and diastolic dysfunction in patients with HCM. Materials and Methods We retrospectively investigated the impact of the myocardial fibrosis, as assessed by the extent of late gadolinium enhancement (LGE-%) on cardiac magnetic resonance imaging (CMRI), on diastolic dysfunction in 110 patients with HCM. The diastolic dysfunction was evaluated by the left atrial (LA) volume index measured on CMRI and lateral septal E/E' ratio calculated on echocardiography. Results : There was a moderate correlation between the LGE-% and LA volume ( r = 0.59, p < 0.0001). The logistic regression model of LGE-%, mitral regurgitation, and total left ventricular mass that investigated the independent predictors of LA volume identified LGE-% as the only independent parameter associated with the LA volume index ( ß = 0.30, p = 0.003). No correlation was observed between the LGE-% and E/E'( r = 0.24, p = 0.009). Conclusions Myocardial fibrosis in HCM patients is associated with a chronic diastolic burden as represented by increased LA volume. However, the fibrosis does not influence the E/E' ratio, which is a well-known parameter of ventricular relaxation, restoring forces, and filling pressure.

Is neutrophile to lymphocyte ratio a predictor of Gleason score upgrading according to risk classifications in patients with prostate cancer.

OBJECTIVES: We aimed to determine the parameters that predict Gleason Score (GS) upgrading in patients undergoing robot-assisted laparoscopic radical prostatectomy (RARP) and especially the ability of neutrophile to lymphocyte ratio (NLR) in predicting the upgrading. METHODS: Patients who underwent RARP for prostate cancer in our clinic between January 2013 and January 2018 were retrospectively analyzed. Patients' demographic data, preoperative and postoperative parameters were all recorded in the database. NLR was calculated by dividing the absolute neutrophil count (NC) by the absolute lymphocyte count (LC). Patients were classified as low, moderate and high risk according to the National Comprehensive Cancer Network (NCNN) Guidelines. Any increase in GS between biopsy results and radical prostatectomy specimens were consideredas an GS upgrading. RESULTS: After applying the inclusion and exclusion criteria, a total of 571 patients, 205 patients without GS upgrading (Group 1) and 366 patients with GS upgrading (Group 2), were included. The mean preoperative PSA values and prostate volumes were 10.8 ± 8 ng/dL and 45 ± 18.8 ml, respectively. Group 2 had a significantly high NC and NLR, significantly low platelet count (PC) and LC, (p=0.0001, 0.0001, 0.001 and0.002, respectively). Group 2 was found to have significantly higher positive surgical margin (PSM), extraprostatic extension (EPE) and seminal vesical invasion (SVI) (p<0.001). There was no significant correlation between the parameters of NLR and PSM, EPE, SVI, and lymph node invasion (LNI). Binomial logistic regression showed patients with increased NLR had 1.68 times higher odds to exhibit an upgrade in GS in the post-surgical histopathological analysis. CONCLUSIONS: NLR calculated preoperatively is an easy diagnostic method that can predict GS upgrading in patients scheduled for radical prostatectomy for prostate cancer.

OBJETIVOS: Determinamos los parámetros que predicen el grado de sobregradación de Gleason en pacientes que recibieron prostatectomía radical robótica asistida por laparoscopia (PRRL) y especialmente la habilidad de la tasa de neutrófilos/linfocitos (NLR) a la hora de predecir la sobregradación.MÉTODOS: Los pacientes que recibieron PRRL por cáncer de próstata en nuestra clínica entre enero 2013 y enero de 2018 se analizaron retrospectivamente. Los datos demográficos, parámetros preoperatorios y postoperatorios fueron reportados en la base de datos. NLR se calculo dividiendo el numero absoluto de neutrófilos (NC) por el numero absoluto de linfocitos (LC). Los pacientes se clasificaron como bajo, moderado y alto riesgo en la relación a las guías de National Comprehensive Cancer Network (NCNN). Cualquier aumento en el grado de Gleason entre los resultados de la biopsia y la prostatectomía radical fueron considerados como una sobregradación de grado deGleason. RESULTADOS: Después de aplicar los criterios de inclusión y exclusión, un total de 571 pacientes, 205 sin sobregradación de Gleason (Grupo 1) y 366 pacientes con sobregradación de Gleason (Grupo 2). La media de PSA preoperatorio y volúmenes prostáticos fueron de 10,8 ± 8 ng/dL y 45 ± 18,8 ml, respectivamente. El grupo 2 presentó un NC y NLR más alto, significativamente, bajos niveles de plaquetas y LC (p=0,0001, 0,0001, 0,001 y 0,002, respectivamente). El grupo 2 demostró tener niveles significativamente más altos de márgenes quirúrgicos (PSM), extensión extraprostatica (EPE) e invasión de vesículas seminales (SVI) (p<0,001). No se econtró una correlación significativa entre los parámetros de NLR y PSM, EPE, SVI, invasión ganglios linfáticos. El modelo de regresión binomial logística demostró que los pacientes con un incremento de NLR tuvieron 1,68 más veces de tener una sobregradación de Gleason en el análisis histopatológico postquirúrgico. CONCLUSIONES: El cálculo de NLR preoperatorio es un método fácil de diagnóstico que puede predecir la sobregradación de Gleason en pacientes que van a recibir una prostatectomía radical por cáncer de próstata.