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Heart transplantation is a pivotal treatment of end-stage heart failure, and recent advancements have extended median posttransplant life expectancy. However, despite the progress in surgical techniques and medical treatment, heart transplant patients still face complications such as rejection, infections, and drug toxicity. CT is a reliable tool for detecting most of these complications, whereas MR imaging is particularly adept at identifying pericardial pathologies and signs of rejection. Awareness of these nuances by radiologists, cardiologists, and surgeons is desired to optimize care, reduce morbidities, and enhance survival., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Rationale and Objectives: We sought to determine the perceived impact of artificial intelligence (AI) and other emerging technologies (ET) on various specialties by medical students in both 2017 and 2021 and how this might affect their residency selections., Materials and Methods: We conducted a brief, anonymous survey of all medical students at a single institution in 2017 and 2021. Survey questions evaluated (1) incentives motivating residency selection and career path, (2) degree of interest in each specialty, (3) perceived effect that ET will have on job prospects for each specialty, and (4) those specialties that students would not consider because of concerns regarding ET., Results: A total of 72% (384/532) and 54% (321/598) of medical students participated in the survey in 2017 and 2021, respectively, and results were largely stable. Students perceived ET would reduce job prospects for pathology, diagnostic radiology, and anesthesiology, and enhance prospects for all other specialties (p < 0.01) except dermatology. For both surveys, 23% of students would NOT consider diagnostic radiology because ET would make it obsolete, higher than all other specialties (p < 0.01). Regarding the one student class that was surveyed twice, 50% felt ET would reduce job prospects for radiology in 2017, increasing to 71% in 2021 (p < 0.01), and similar percentages-20% in 2017 and 23% in 2021-said they explicitly would not consider radiology because of concerns levied by ET., Conclusions: Current perceptions of ET likely affect residency selection for a large proportion of medical students and may impact the future of various specialties, particularly diagnostic radiology., (Copyright © 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background Large vessel occlusion (LVO) stroke is one of the most time-sensitive diagnoses in medicine and requires emergent endovascular therapy to reduce morbidity and mortality. Leveraging recent advances in deep learning may facilitate rapid detection and reduce time to treatment. Purpose To develop a convolutional neural network to detect LVOs at multiphase CT angiography. Materials and Methods This multicenter retrospective study evaluated 540 adults with CT angiography examinations for suspected acute ischemic stroke from February 2017 to June 2018. Examinations positive for LVO ( n = 270) were confirmed by catheter angiography and LVO-negative examinations ( n = 270) were confirmed through review of clinical and radiology reports. Preprocessing of the CT angiography examinations included vasculature segmentation and the creation of maximum intensity projection images to emphasize the contrast agent-enhanced vasculature. Seven experiments were performed by using combinations of the three phases (arterial, phase 1; peak venous, phase 2; and late venous, phase 3) of the CT angiography. Model performance was evaluated on the held-out test set. Metrics included area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results The test set included 62 patients (mean age, 69.5 years; 48% women). Single-phase CT angiography achieved an AUC of 0.74 (95% confidence interval [CI]: 0.63, 0.85) with sensitivity of 77% (24 of 31; 95% CI: 59%, 89%) and specificity of 71% (22 of 31; 95% CI: 53%, 84%). Phases 1, 2, and 3 together achieved an AUC of 0.89 (95% CI: 0.81, 0.96), sensitivity of 100% (31 of 31; 95% CI: 99%, 100%), and specificity of 77% (24 of 31; 95% CI: 59%, 89%), a statistically significant improvement relative to single-phase CT angiography ( P = .01). Likewise, phases 1 and 3 and phases 2 and 3 also demonstrated improved fit relative to single phase ( P = .03). Conclusion This deep learning model was able to detect the presence of large vessel occlusion and its diagnostic performance was enhanced by using delayed phases at multiphase CT angiography examinations. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Ospel and Goyal in this issue.

Objectives: CT angiography (CTA) is essential in acute stroke to detect emergent large vessel occlusions (ELVO) and must be interpreted by radiologists with and without subspecialized training. Additionally, grayscale inversion has been suggested to improve diagnostic accuracy in other radiology applications. This study examines diagnostic performance in ELVO detection between neuroradiologists, non-neuroradiologists, and radiology residents using standard and grayscale inversion viewing methods., Methods: A random, counterbalanced experimental design was used, where 18 radiologists with varying experiences interpreted the same patient images with and without grayscale inversion. Confirmed positive and negative ELVO cases were randomly ordered using a balanced design. Sensitivity, specificity, positive and negative predictive values as well as confidence, subjective assessment of image quality, time to ELVO detection, and overall interpretation time were examined between grayscale inversion (on/off) by experience level using generalized mixed modeling assuming a binary, negative binomial, and binomial distributions, respectively., Results: All groups of radiologists had high sensitivity and specificity for ELVO detection (all > .94). Neuroradiologists were faster than non-neuroradiologists and residents in interpretation time, with a mean of 47 s to detect ELVO, as compared with 59 and 74 s, respectively. Residents were subjectively less confident than attending physicians. With respect to grayscale inversion, no differences were observed between groups with grayscale inversion vs. standard viewing for diagnostic performance (p = 0.30), detection time (p = .45), overall interpretation time (p = .97), and confidence (p = .20)., Conclusions: Diagnostic performance in ELVO detection with CTA was high across all levels of radiologist training level. Grayscale inversion offered no significant detection advantage., Key Points: • Stroke is an acute vascular syndrome that requires acute vascular imaging. • Proximal large vessel occlusions can be identified quickly and accurately by radiologists across all training levels. • Grayscale inversion demonstrated minimal detectable benefit in the detection of proximal large vessel occlusions.

This study evaluates the performance of convolutional neural networks (CNNs) in risk stratifying the malignant potential of thyroid nodules alongside traditional methods such as American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS). The data set consisted of 651 pathology-proven thyroid nodules (500 benign, 151 malignant) from 571 patients collected at a single tertiary academic medical center. Each thyroid nodule consisted of two orthogonal views (sagittal and transverse) for a total of 1,302 grayscale images. A CNN classifier was developed to identify malignancy versus benign thyroid nodules, and a nested double cross validation scheme was applied to allow for both model parameter selection and for model accuracy evaluation. All thyroid nodules were classified according to ACR TIRADS criteria and were compared with their respective CNN-generated malignancy scores. The best performing model was the MobileNet CNN ensemble with an area under the curve of 0.86 (95% confidence interval, 0.83-0.90). Thyroid nodules within the highest and lowest CNN risk strata had malignancy rates of 81.4% and 5.9%, respectively. The rate of malignancy for ACR TIRADS ranged from 0% for TR1 nodules to 60% for TR5 nodules. Convolutional neural network malignancy scores correlated well with TIRADS levels, as malignancy scores ranged from 0.194 for TR1 nodules and 0.519 for TR5 nodules. Convolutional neural networks can be trained to generate accurate malignancy risk scores for thyroid nodules. These predictive models can aid in risk stratifying thyroid nodules alongside traditional professional guidelines such as TIRADS and can function as an adjunct tool for the radiologist when identifying those patients requiring further histopathologic workup.

The authors describe the case of a large WHO Grade III anaplastic oligoastrocytoma extending through the anterior skull base and into the right nasal cavity and sinuses. Glial neoplasms are typically confined to the intracranial compartment within the brain parenchyma and rarely extend into the nasal cavity without prior surgical or radiation therapy. This 42-year-old woman presented with progressive headaches and sinus congestion. MR imaging findings revealed a large intracranial lesion with intranasal extension. Endoscopic nasal biopsy revealed pathology consistent with an infiltrating glioma. The patient subsequently underwent a combined transcranial/endonasal endoscopic approach for resection of this lesion. Pathological diagnosis revealed a WHO Grade III oligoastrocytoma. This report reviews the mechanisms of extradural glioma extension. To the authors' knowledge, it is the second report of a high-grade glioma exhibiting nasal extension without prior surgical or radiation treatment.

Progressive alexia is an acquired reading deficit caused by degeneration of brain regions that are essential for written word processing. Functional imaging studies have shown that early processing of the visual word form depends on a hierarchical posterior-to-anterior processing stream in occipito-temporal cortex, whereby successive areas code increasingly larger and more complex perceptual attributes of the letter string. A region located in the left lateral occipito-temporal sulcus and adjacent fusiform gyrus shows maximal selectivity for words and has been dubbed the 'visual word form area'. We studied two patients with progressive alexia in order to determine whether their reading deficits were associated with structural and/or functional abnormalities in this visual word form system. Voxel-based morphometry showed left-lateralized occipito-temporal atrophy in both patients, very mild in one, but moderate to severe in the other. The two patients, along with 10 control subjects, were scanned with functional magnetic resonance imaging as they viewed rapidly presented words, false font strings, or a fixation crosshair. This paradigm was optimized to reliably map brain regions involved in orthographic processing in individual subjects. All 10 control subjects showed a posterior-to-anterior gradient of selectivity for words, and all 10 showed a functionally defined visual word form area in the left hemisphere that was activated for words relative to false font strings. In contrast, neither of the two patients with progressive alexia showed any evidence for a selectivity gradient or for word-specific activation of the visual word form area. The patient with mild atrophy showed normal responses to both words and false font strings in the posterior part of the visual word form system, but a failure to develop selectivity for words in the more anterior part of the system. In contrast, the patient with moderate to severe atrophy showed minimal activation of any part of the visual word form system for either words or false font strings. Our results suggest that progressive alexia is associated with a dysfunctional visual word form system, with or without substantial cortical atrophy. Furthermore, these findings demonstrate that functional MRI has the potential to reveal the neural bases of cognitive deficits in neurodegenerative patients at very early stages, in some cases before the development of extensive atrophy.