Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL.

BACKGROUND AND PURPOSE: There are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). MATERIALS AND METHODS: Sixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). RESULTS: Whole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94-0.97)) than FreeSurfer and CAT12 (0.92 (0.88-0.96)) and FSL (0.87 (0.79-0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20-3.13% vs. mean CV 1.05, range 0.21-3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49-5.91% vs. mean CV 3.84, range 2.62-5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. CONCLUSION: Based on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.

Multinodular and Vacuolating Neuronal Tumor of the Cerebrum (MVNT): A case series and review of the literature.

BACKGROUND AND PURPOSE: Multinodular and Vacuolating Neuronal Tumor of the cerebrum (MVNT) is a benign -seizure associated- lesion affecting mostly adults. This new entity has been included in the 2016 World Health Organization classification of tumors of the central nervous system. Its pathologic hallmark consist of a subcortical cluster of nodular lesions located on the subcortical white matter. We aim to report a series of cases of presumed MVNT observed in our institution and review the literature. MATERIALS AND METHODS: In this retrospective study, a search was performed on our hospital information system. Sixteen cases were included. Demographic, clinical and radiological features were detailed in a table. All patients had an MRI acquired either on a 1.5 or a 3 Tesla scanner. Sequences performed included T1, T2, GRE/SWI, T2 FLAIR and DWI. Gadolinium enhanced T1-WI wer available in 11 patients and follow-up MRI were available in 7 patients. RESULTS: Patient ages ranged from 16 to 77 years (mean 42 years). Seizure and non-focal headache were by far the most common neurological complaints for which MRI was requested. All lesions consisted of clusters of multiple, discrete, round or ovoid, intra-axial, FLAIR and T2-WI hyperintense nodules. Follow-up MRI scans showed no changes between studies. CONCLUSIONS: MVNT is a benign, stable lesion that exhibits a typical radiological pattern that most of the times sufficed to arrive to a diagnosis, without the need of pathological confirmation. We confirm that our demographic, clinical and radiological findings are in accordance with those published in international literature.

Academic Future of Interventional Radiology Subspecialty: Are We Giving Enough Space to Radiology Trainees?

Background: The aim of this study was to investigate willingness and barriers to academic activities of radiology trainees interested in interventional radiology subspecialty. Materials and methods: Radiology trainees and fellows were called to participate a 35-question survey via online platforms and radiological societies. The research survey investigated on involvement in academic activities, willingness of a future academic career, and challenges for pursuing an academic career. Research participants interested in interventional radiology were selected for analysis. Analyses were performed by using either Fisher's exact or chi-square tests. Results: Of 892 respondents to the survey, 155 (17.4%) (112/155, 72.3% men and 43/155, 27.7% women) declared interest in interventional radiology. Active involvement in research and teaching was reported by 53.5% (83/155) and 30.3% (47/155) of the participants, respectively. The majority is willing to work in an academic setting in the future (66.8%, 103/155) and to perform a research fellowship abroad (83.9%, 130/155). Insufficient time was the greatest perceived barrier for both research and teaching activities (49.0% [76/155] and 48.4% [75/155], respectively), followed by lack of mentorship (49.0% [75/155] and 35.5% [55/155], respectively) and lack of support from faculty (40.3% [62/155] and 37.4% [58/155], respectively). Conclusion: Our international study shows that most trainees interested in interventional radiology subspecialty actively participate in research activities and plan to work in an academic setting. However, insufficient time for academia, mentorship, and support from seniors are considered challenges in pursuing an academic career.

COVID-19 pneumonia accurately detected on chest radiographs with artificial intelligence.

PURPOSE: To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support. MATERIALS AND METHODS: An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system. RESULTS: Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p < 0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (p < 0.001), although specificity decreased from 79% to 75% (p = 0.007). CONCLUSIONS: Our results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis.

Gender discrepancy in research activities during radiology residency.

OBJECTIVE: To investigate the presence of gender disparity in academic involvement during radiology residency and to identify and characterize any gender differences in perceived barriers for conducting research. METHODS: An international call for participation in an online survey was promoted via social media and through multiple international and national radiological societies. A 35-question survey invited radiology trainees worldwide to answer questions regarding exposure and barriers to academic radiology during their training. Gender differences in response proportions were analyzed using either Fisher's exact or chi-squared tests. RESULTS: Eight hundred fifty-eight participants (438 men, 420 women) from Europe (432), Asia (241), North and South America (144), Africa (37), and Oceania (4) completed the survey. Fewer women radiology residents were involved in research during residency (44.3%, 186/420 vs 59.4%, 260/438; p ≤ 0.0001) and had fewer published original articles (27.9%, 117/420 vs. 40.2%, 176/438; p = 0.001). Women were more likely to declare gender as a barrier to research (24.3%, 102/420 vs. 6.8%, 30/438; p < 0.0001) and lacked mentorship/support from faculty (65%, 273/420 vs. 55.7%, 244/438; p = 0.0055). Men were more likely to declare a lack of time (60.3%, 264/438 vs. 50.7%, 213/420; p = 0.0049) and lack of personal interest (21%, 92/438 vs. 13.6%, 57/420, p = 0.0041) in conducting research. CONCLUSION: Fewer women were involved in academic activities during radiology residency, resulting in fewer original published studies compared to their men counterparts. This is indicative of an inherent gender imbalance. Lack of mentorship reported by women radiologists was a main barrier to research.