The Association of Metabolic Brain MRI, Amyloid PET, and Clinical Factors: A Study of Alzheimer's Disease and Normal Controls From the Open Access Series of Imaging Studies Dataset.

BACKGROUND: Although brain activities in Alzheimer's disease (AD) might be evaluated MRI and PET, the relationships between brain temperature (BT), the index of diffusivity along the perivascular space (ALPS index), and amyloid deposition in the cerebral cortex are still unclear. PURPOSE: To investigate the relationship between metabolic imaging measurements and clinical information in patients with AD and normal controls (NCs). STUDY TYPE: Retrospective analysis of a prospective dataset. POPULATION: 58 participants (78.3 ± 6.8 years; 30 female): 29 AD patients and 29 age- and sex-matched NCs from the Open Access Series of Imaging Studies dataset. FIELD STRENGTH/SEQUENCE: 3T; T1-weighted magnetization-prepared rapid gradient-echo, diffusion tensor imaging with 64 directions, and dynamic 18 F-florbetapir PET. ASSESSMENT: Imaging metrics were compared between AD and NCs. These included BT calculated by the diffusivity of the lateral ventricles, ALPS index that reflects the glymphatic system, the mean standardized uptake value ratio (SUVR) of amyloid PET in the cerebral cortex and clinical information, such as age, sex, and MMSE. STATISTICAL TESTS: Pearson's or Spearman's correlation and multiple linear regression analyses. P values <0.05 were defined as statistically significant. RESULTS: Significant positive correlations were found between BT and ALPS index (r = 0.44 for NCs), while significant negative correlations were found between age and ALPS index (rs = -0.43 for AD and - 0.47 for NCs). The SUVR of amyloid PET was not significantly associated with BT (P = 0.81 for AD and 0.21 for NCs) or ALPS index (P = 0.10 for AD and 0.52 for NCs). In the multiple regression analysis, age was significantly associated with BT, while age, sex, and presence of AD were significantly associated with the ALPS index. DATA CONCLUSION: Impairment of the glymphatic system measured using MRI was associated with lower BT and aging. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 1.

Accuracy of ChatGPT generated diagnosis from patient's medical history and imaging findings in neuroradiology cases.

PURPOSE: The noteworthy performance of Chat Generative Pre-trained Transformer (ChatGPT), an artificial intelligence text generation model based on the GPT-4 architecture, has been demonstrated in various fields; however, its potential applications in neuroradiology remain unexplored. This study aimed to evaluate the diagnostic performance of GPT-4 based ChatGPT in neuroradiology. METHODS: We collected 100 consecutive "Case of the Week" cases from the American Journal of Neuroradiology between October 2021 and September 2023. ChatGPT generated a diagnosis from patient's medical history and imaging findings for each case. Then the diagnostic accuracy rate was determined using the published ground truth. Each case was categorized by anatomical location (brain, spine, and head & neck), and brain cases were further divided into central nervous system (CNS) tumor and non-CNS tumor groups. Fisher's exact test was conducted to compare the accuracy rates among the three anatomical locations, as well as between the CNS tumor and non-CNS tumor groups. RESULTS: ChatGPT achieved a diagnostic accuracy rate of 50% (50/100 cases). There were no significant differences between the accuracy rates of the three anatomical locations (p = 0.89). The accuracy rate was significantly lower for the CNS tumor group compared to the non-CNS tumor group in the brain cases (16% [3/19] vs. 62% [36/58], p < 0.001). CONCLUSION: This study demonstrated the diagnostic performance of ChatGPT in neuroradiology. ChatGPT's diagnostic accuracy varied depending on disease etiologies, and its diagnostic accuracy was significantly lower in CNS tumors compared to non-CNS tumors.

Evaluation of cranial nerve involvement in chordomas and chondrosarcomas: a retrospective imaging study.

PURPOSE: Cranial nerve involvement (CNI) influences the treatment strategies and prognosis of head and neck tumors. However, its incidence in skull base chordomas and chondrosarcomas remains to be investigated. This study evaluated the imaging features of chordoma and chondrosarcoma, with a focus on the differences in CNI. METHODS: Forty-two patients (26 and 16 patients with chordomas and chondrosarcomas, respectively) treated at our institution between January 2007 and January 2023 were included in this retrospective study. Imaging features, such as the maximum diameter, tumor location (midline or off-midline), calcification, signal intensity on T2-weighted image, mean apparent diffusion coefficient (ADC) values, contrast enhancement, and CNI, were evaluated and compared using Fisher's exact test or the Mann-Whitney U-test. The odds ratio (OR) was calculated to evaluate the association between the histological type and imaging features. RESULTS: The incidence of CNI in chondrosarcomas was significantly higher than that in chordomas (63% vs. 8%, P < 0.001). An off-midline location was more common in chondrosarcomas than in chordomas (86% vs. 13%; P < 0.001). The mean ADC values of chondrosarcomas were significantly higher than those of chordomas (P < 0.001). Significant associations were identified between chondrosarcomas and CNI (OR = 20.00; P < 0.001), location (OR = 53.70; P < 0.001), and mean ADC values (OR = 1.01; P = 0.002). CONCLUSION: The incidence of CNI and off-midline location in chondrosarcomas was significantly higher than that in chordomas. CNI, tumor location, and the mean ADC can help distinguish between these entities.

AI-based Virtual Synthesis of Methionine PET from Contrast-enhanced MRI: Development and External Validation Study.

Background Carbon 11 (11C)-methionine is a useful PET radiotracer for the management of patients with glioma, but radiation exposure and lack of molecular imaging facilities limit its use. Purpose To generate synthetic methionine PET images from contrast-enhanced (CE) MRI through an artificial intelligence (AI)-based image-to-image translation model and to compare its performance for grading and prognosis of gliomas with that of real PET. Materials and Methods An AI-based model to generate synthetic methionine PET images from CE MRI was developed and validated from patients who underwent both methionine PET and CE MRI at a university hospital from January 2007 to December 2018 (institutional data set). Pearson correlation coefficients for the maximum and mean tumor to background ratio (TBRmax and TBRmean, respectively) of methionine uptake and the lesion volume between synthetic and real PET were calculated. Two additional open-source glioma databases of preoperative CE MRI without methionine PET were used as the external test set. Using the TBRs, the area under the receiver operating characteristic curve (AUC) for classifying high-grade and low-grade gliomas and overall survival were evaluated. Results The institutional data set included 362 patients (mean age, 49 years ± 19 [SD]; 195 female, 167 male; training, n = 294; validation, n = 34; test, n = 34). In the internal test set, Pearson correlation coefficients were 0.68 (95% CI: 0.47, 0.81), 0.76 (95% CI: 0.59, 0.86), and 0.92 (95% CI: 0.85, 0.95) for TBRmax, TBRmean, and lesion volume, respectively. The external test set included 344 patients with gliomas (mean age, 53 years ± 15; 192 male, 152 female; high grade, n = 269). The AUC for TBRmax was 0.81 (95% CI: 0.75, 0.86) and the overall survival analysis showed a significant difference between the high (2-year survival rate, 27%) and low (2-year survival rate, 71%; P < .001) TBRmax groups. Conclusion The AI-based model-generated synthetic methionine PET images strongly correlated with real PET images and showed good performance for glioma grading and prognostication. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Identification of reversed portal flow on 4DCT and of factors contributing to reversed portal flow in patients with liver cirrhosis and portosystemic shunt before interventional radiology procedures.

AIM: Patients with liver cirrhosis and portosystemic shunt occasionally develop reversed portal flow in the portal venous system. The factors contributing to reversed portal flow in these patients remain unclear. The aim of this study was to identify factors contributing to reversed portal flow in patients with portosystemic shunts based on four-dimensional computed tomography (4DCT), which visualized flow dynamics in the portal venous system. METHODS: Data from 34 consecutive patients with portosystemic shunts who had undergone 4DCT before interventional radiology procedures were retrospectively investigated in this study. Uni- and multivariate analyses were performed to identify factors contributing to reversed portal flow. RESULTS: Flow dynamics could be visualized on 4DCT in 32 of the 34 patients. Fifteen patients had forward portal flow; 17 had reversed portal flow. The main portal, splenic, and superior mesenteric veins displayed reversed portal flow in five, 12, and five vessels, respectively. Portosystemic shunt originating from splenic and superior mesenteric veins, worse albumin-bilirubin score, and small main portal vein diameter were significant factors contributing to reversed portal flow in both univariate (p = 0.049, p = 0.027, and p = 0.002) and multivariate (odds ratio [OR] 6.345, p = 0.012; OR 4.279, p = 0.039; and OR 5.516, p = 0.019) analyses. CONCLUSIONS: The reversed portal flow was visualized on 4DCT. Portosystemic shunt originating distant to the liver, worse albumin-bilirubin score, and small diameter of the main portal vein were factors contributing to reversed flow in the portal venous system.

Brain temperature remains stable during the day: a study of diffusion-weighted imaging thermometry in healthy individuals.

PURPOSE: To investigate the daily fluctuations in brain temperature in healthy individuals using magnetic resonance (MR) diffusion-weighted imaging (DWI) thermometry and to clarify the associations between the brain and body temperatures and sex. METHODS: Thirty-two age-matched healthy male and female volunteers (male = 16, 20-38 years) were recruited between July 2021 and January 2022. Brain MR examinations were performed in the morning and evening phases on the same day to calculate the brain temperatures using DWI thermometry. Body temperature was also measured in each MR examination. Group comparisons of body and brain temperatures between the two phases were performed using paired t-tests. A multiple linear regression model was used to predict the morning brain temperature using sex, evening brain temperature, and the interaction between sex and evening brain temperature as covariates. RESULTS: Body temperatures were significantly higher in the evening than in the morning in all participants, male group, and female group (p < 0.001, = 0001, and < 0.001, respectively). Meanwhile, no significant difference was observed between the morning and evening brain temperatures in each analysis (p = 0.23, 0.70, and 0.16, respectively). Multiple linear regression analysis showed significant associations of morning brain temperature with sex (p = 0.038), evening brain temperature (p < 0.001), and the interaction between sex and evening brain temperature (p = 0.036). CONCLUSION: Unlike body temperature, brain temperature showed no significant daily fluctuations; however, daily fluctuations in brain temperature may vary depending on sex.

An autopsy case of progressive multifocal leukoencephalopathy with massive iron deposition in juxtacortical lesions.

Progressive multifocal leukoencephalopathy (PML) is a severe demyelinating disease caused by JC virus infection of oligodendrocytes. Little has been reported on iron deposits in patients with PML. Herein, we report a case of PML with massive iron deposition in the juxtacortical regions attaching white matter lesions in a 71-year-old woman who developed bilateral visual disturbance and progressive aphasia after 16 months of rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone treatment for follicular lymphoma. Magnetic resonance imaging revealed white matter lesions in the left parietal and other lobes with massive iron deposition in the juxtacortical lesions. A PCR test for JC virus was positive, confirming the diagnosis of PML. Despite treatment with mefloquine and mirtazapine, the patient died six months later. At autopsy, demyelination was found dominantly in the left parietal lobe. Moreover, hemosiderin-laden macrophages and reactive astrocytes containing ferritin were abundant in the juxtacortical regions adjacent to the white matter lesions. This is a previously unreported case of PML after lymphoma, in which iron deposition was confirmed both radiologically and pathologically.

Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs.

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel's concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75-0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69-0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63-0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63-0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.

Artificial intelligence-based detection of atrial fibrillation from chest radiographs.

OBJECTIVE: The purpose of this study was to develop an artificial intelligence (AI)-based model to detect features of atrial fibrillation (AF) on chest radiographs. METHODS: This retrospective study included consecutively collected chest radiographs of patients who had echocardiography at our institution from July 2016 to May 2019. Eligible radiographs had been acquired within 30 days of the echocardiography. These radiographs were labeled as AF-positive or AF-negative based on the associated electronic medical records; then, each patient was randomly divided into training, validation, and test datasets in an 8:1:1 ratio. A deep learning-based model to classify radiographs as with or without AF was trained on the training dataset, tuned with the validation dataset, and evaluated with the test dataset. RESULTS: The training dataset included 11,105 images (5637 patients; 3145 male, mean age ± standard deviation, 68 ± 14 years), the validation dataset included 1388 images (704 patients, 397 male, 67 ± 14 years), and the test dataset included 1375 images (706 patients, 395 male, 68 ± 15 years). Applying the model to the validation and test datasets gave a respective area under the curve of 0.81 (95% confidence interval, 0.78-0.85) and 0.80 (0.76-0.84), sensitivity of 0.76 (0.70-0.81) and 0.70 (0.64-0.76), specificity of 0.75 (0.72-0.77) and 0.74 (0.72-0.77), and accuracy of 0.75 (0.72-0.77) and 0.74 (0.71-0.76). CONCLUSION: Our AI can identify AF on chest radiographs, which provides a new way for radiologists to infer AF. KEY POINTS: • A deep learning-based model was trained to detect atrial fibrillation in chest radiographs, showing that there are indicators of atrial fibrillation visible even on static images. • The validation and test datasets each gave a solid performance with area under the curve, sensitivity, and specificity of 0.81, 0.76, and 0.75, respectively, for the validation dataset, and 0.80, 0.70, and 0.74, respectively, for the test dataset. • The saliency maps highlighted anatomical areas consistent with those reported for atrial fibrillation on chest radiographs, such as the atria.

Maskless 2-Dimensional Digital Subtraction Angiography Generation Model for Abdominal Vasculature using Deep Learning.

PURPOSE: To develop a deep learning (DL) model to generate synthetic, 2-dimensional subtraction angiograms free of artifacts from native abdominal angiograms. MATERIALS AND METHODS: In this retrospective study, 2-dimensional digital subtraction angiography (2D-DSA) images and native angiograms were consecutively collected from July 2019 to March 2020. Images were divided into motion-free (training, validation, and motion-free test datasets) and motion-artifact (motion-artifact test dataset) sets. A total of 3,185, 393, 383, and 345 images from 87 patients (mean age, 71 years ± 10; 64 men and 23 women) were included in the training, validation, motion-free, and motion-artifact test datasets, respectively. Native angiograms and 2D-DSA image pairs were used to train and validate an image-to-image translation model to generate synthetic DL-based subtraction angiography (DLSA) images. DLSA images were quantitatively evaluated by the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) using the motion-free dataset and were qualitatively evaluated via visual assessments by radiologists with a numerical rating scale using the motion-artifact dataset. RESULTS: The DLSA images showed a mean PSNR (± standard deviation) of 43.05 dB ± 3.65 and mean SSIM of 0.98 ± 0.01, indicating high agreement with the original 2D-DSA images in the motion-free dataset. Qualitative visual evaluation by radiologists of the motion-artifact dataset showed that DLSA images contained fewer motion artifacts than 2D-DSA images. Additionally, DLSA images scored similar to or higher than 2D-DSA images for vascular visualization and clinical usefulness. CONCLUSIONS: The developed DL model generated synthetic, motion-free subtraction images from abdominal angiograms with similar imaging characteristics to 2D-DSA images.

Frequency and imaging features of the adjacent osseous changes of salivary gland carcinomas in the head and neck region.

PURPOSE: The association between salivary gland carcinomas and adjacent osseous changes in the head and neck region is not clear. We evaluated the frequency and imaging features of such changes and investigated the specific characteristics of salivary gland carcinomas associated with them. METHODS: A total of 118 patients with histologically proven salivary gland carcinomas were retrospectively reviewed. The imaging characteristics of osseous changes were sorted into three categories based on computed tomography images: sclerotic change, erosive change, and lytic change. The frequency of all these osseous changes and any one of them was compared between different pathologies using Fisher's exact test. Odds ratios were calculated to evaluate the association between these changes and perineural spread. RESULTS: Osseous changes were found in 21 (18%) of 118 cases. Among these, seven (6%) cases were with sclerotic, nine (8%) with erosive, and nine (8%) with lytic changes (four with mixed change). Adenoid cystic carcinoma showed a significantly higher frequency of sclerotic and erosive changes, and either osseous change, than the other salivary gland carcinomas (p < 0.001 for each). Sclerotic changes were only present in the adenoid cystic carcinomas. Perineural spread was a significant factor in showing higher osseous change frequencies (odds ratio = 3.98, p = 0.006). CONCLUSION: Among salivary gland carcinomas in the head and neck region, adenoid cystic carcinomas had a significantly higher frequency of adjacent osseous changes, especially sclerotic changes, than other salivary gland carcinomas.

Changes in pedicle pericardial fat tissue around the anastomotic site after tracheobronchoplasty.

PURPOSE: Pericardial fat is appropriate tissue to cover the bronchial anastomotic site because its harvesting is minimally invasive. We investigated the changes in pericardial fat tissue around the anastomotic site after pulmonary resection with tracheobronchoplasty. METHODS: The subjects of this study were 43 lung cancer patients who underwent pulmonary resection with tracheobronchoplasty. We measured the maximum cross-sectional area and average computed tomography (CT) values of the pedicle pericardial fat pad around the anastomotic site 1 week and then 6 months after the operation. RESULTS: The average volume of the residual pedicle pericardial fat pad 6 months postoperatively was 61%. A body mass index (BMI) < 21.2 kg/m2 (P = 0.031) and a blood albumin level < 3.4 g/dl (P = 0.005) were significant predictors of pedicle flap shrinkage. Patients with fat tissue shrinkage had significantly elevated CT values 6 months postoperatively (P = 0.029), whereas those without shrinkage maintained low CT values. CONCLUSIONS: Preoperative nutritional conditions, reflected in high BMI and blood albumin levels, correlated with a high residual pedicle pericardial fat pad. Conversely, patients with pedicle flap shrinkage had significantly increased CT values, suggesting that the fat might have taken on another form such as scar tissue.

Deep Learning-based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts.

Background Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement-caused misregistration artifacts can result in unclear DSA images that interrupt procedures. Purpose To train and to validate a deep learning (DL)-based model to produce DSA-like cerebral angiograms directly from dynamic angiograms and then quantitatively and visually evaluate these angiograms for clinical usefulness. Materials and Methods A retrospective model development and validation study was conducted on dynamic and DSA image pairs consecutively collected from January 2019 through April 2019. Angiograms showing misregistration were first separated per patient by two radiologists and sorted into the misregistration test data set. Nonmisregistration angiograms were divided into development and external test data sets at a ratio of 8:1 per patient. The development data set was divided into training and validation data sets at ratio of 3:1 per patient. The DL model was created by using the training data set, tuned with the validation data set, and then evaluated quantitatively with the external test data set and visually with the misregistration test data set. Quantitative evaluations used the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) with mixed liner models. Visual evaluation was conducted by using a numerical rating scale. Results The training, validation, nonmisregistration test, and misregistration test data sets included 10 751, 2784, 1346, and 711 paired images collected from 40 patients (mean age, 62 years ± 11 [standard deviation]; 33 women). In the quantitative evaluation, DL-generated angiograms showed a mean PSNR value of 40.2 dB ± 4.05 and a mean SSIM value of 0.97 ± 0.02, indicating high coincidence with the paired DSA images. In the visual evaluation, the median ratings of the DL-generated angiograms were similar to or better than those of the original DSA images for all 24 sequences. Conclusion The deep learning-based model provided clinically useful cerebral angiograms free from clinically significant artifacts directly from dynamic angiograms. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Lenvatinib-Induced Tumor-Related Hemorrhages in Patients with Large Hepatocellular Carcinomas.

INTRODUCTION: Lenvatinib has been approved as a systemic therapy for patients with unresectable hepatocellular carcinoma (HCC). We recently experienced lenvatinib-induced tumor-related hemorrhage in patients with HCC. The full details of tumor-related hemorrhage as a lenvatinib-related adverse event have not been elucidated. METHODS: This was a retrospective single-center study that enrolled consecutive patients treated with lenvatinib for unresectable HCC from April 2018 to February 2020. RESULTS: Sixty-eight consecutive patients were enrolled in this study. Among them, 5 cases developed intraperitoneal or intratumoral hemorrhages. The patients with hemorrhage had larger tumors (maximum tumor size, 97.5 ± 46.4 and 38.2 ± 28.8 mm, respectively; p = 0.009) than the patients without hemorrhage. The dosing period of lenvatinib (median, 3 and 93 days, respectively; p < 0.001) and the survival time from initial administration of lenvatinib (median, 77 and 495 days, respectively; p < 0.001) of the patients with hemorrhage were shorter than those of the patients without hemorrhage. Especially, in 4 cases with large HCCs (maximum tumor diameter was >90 mm), tumor hemorrhage with vascular lake-like phenomenon was evident, although most tumor blood flow was suppressed. DISCUSSION/CONCLUSION: It becomes clear that lenvatinib treatment brings about tumor-related hemorrhages despite rapid suppression of tumor blood flow. We speculate that lenvatinib quickly blocks the feeding circulation, resulting in tumor hemorrhage by necrosis. Clinicians should pay careful attention to the development of life-threatening hemorrhages when treating large HCCs with lenvatinib.

Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study.

BACKGROUND: We investigated the performance improvement of physicians with varying levels of chest radiology experience when using a commercially available artificial intelligence (AI)-based computer-assisted detection (CAD) software to detect lung cancer nodules on chest radiographs from multiple vendors. METHODS: Chest radiographs and their corresponding chest CT were retrospectively collected from one institution between July 2017 and June 2018. Two author radiologists annotated pathologically proven lung cancer nodules on the chest radiographs while referencing CT. Eighteen readers (nine general physicians and nine radiologists) from nine institutions interpreted the chest radiographs. The readers interpreted the radiographs alone and then reinterpreted them referencing the CAD output. Suspected nodules were enclosed with a bounding box. These bounding boxes were judged correct if there was significant overlap with the ground truth, specifically, if the intersection over union was 0.3 or higher. The sensitivity, specificity, accuracy, PPV, and NPV of the readers' assessments were calculated. RESULTS: In total, 312 chest radiographs were collected as a test dataset, including 59 malignant images (59 nodules of lung cancer) and 253 normal images. The model provided a modest boost to the reader's sensitivity, particularly helping general physicians. The performance of general physicians was improved from 0.47 to 0.60 for sensitivity, from 0.96 to 0.97 for specificity, from 0.87 to 0.90 for accuracy, from 0.75 to 0.82 for PPV, and from 0.89 to 0.91 for NPV while the performance of radiologists was improved from 0.51 to 0.60 for sensitivity, from 0.96 to 0.96 for specificity, from 0.87 to 0.90 for accuracy, from 0.76 to 0.80 for PPV, and from 0.89 to 0.91 for NPV. The overall increase in the ratios of sensitivity, specificity, accuracy, PPV, and NPV were 1.22 (1.14-1.30), 1.00 (1.00-1.01), 1.03 (1.02-1.04), 1.07 (1.03-1.11), and 1.02 (1.01-1.03) by using the CAD, respectively. CONCLUSION: The AI-based CAD was able to improve the ability of physicians to detect nodules of lung cancer in chest radiographs. The use of a CAD model can indicate regions physicians may have overlooked during their initial assessment.

Ectopic thyroid tissue at the skull base: a case report.

Ectopic thyroid tissue is a rare entity, resulting from developmental abnormality during the migration of the embryonic thyroid germ from the floor of the primitive foregut to its final pre-tracheal position. Although ectopic thyroid tissue may be located anywhere, its location at the skull base is extremely rare. We report a case of ectopic thyroid tissue at the skull base in a 19-year-old man with multimodality imaging findings.

Deep Learning for MR Angiography: Automated Detection of Cerebral Aneurysms.

Purpose To develop and evaluate a supportive algorithm using deep learning for detecting cerebral aneurysms at time-of-flight MR angiography to provide a second assessment of images already interpreted by radiologists. Materials and Methods MR images reported by radiologists to contain aneurysms were extracted from four institutions for the period from November 2006 through October 2017. The images were divided into three data sets: training data set, internal test data set, and external test data set. The algorithm was constructed by deep learning with the training data set, and its sensitivity to detect aneurysms in the test data sets was evaluated. To find aneurysms that had been overlooked in the initial reports, two radiologists independently performed a blinded interpretation of aneurysm candidates detected by the algorithm. When there was disagreement, the final diagnosis was made in consensus. The number of newly detected aneurysms was also evaluated. Results The training data set, which provided training and validation data, included 748 aneurysms (mean size, 3.1 mm ± 2.0 [standard deviation]) from 683 examinations; 318 of these examinations were on male patients (mean age, 63 years ± 13) and 365 were on female patients (mean age, 64 years ± 13). Test data were provided by the internal test data set (649 aneurysms [mean size, 4.1 mm ± 3.2] in 521 examinations, including 177 male patients and 344 female patients with mean age of 66 years ± 12 and 67 years ± 13, respectively) and the external test data set (80 aneurysms [mean size, 4.1 mm ± 2.1] in 67 examinations, including 19 male patients and 48 female patients with mean age of 63 years ± 12 and 68 years ± 12, respectively). The sensitivity was 91% (592 of 649) and 93% (74 of 80) for the internal and external test data sets, respectively. The algorithm improved aneurysm detection in the internal and external test data sets by 4.8% (31 of 649) and 13% (10 of 80), respectively, compared with the initial reports. Conclusion A deep learning algorithm detected cerebral aneurysms in radiologic reports with high sensitivity and improved aneurysm detection compared with the initial reports. © RSNA, 2018 See also the editorial by Flanders in this issue.