BACKGROUND AND PURPOSE: Despite availability of advanced imaging, distinguishing radiation necrosis from recurrent brain tumors noninvasively is a big challenge in neuro-oncology. Our aim was to determine the feasibility of radiomic (computer-extracted texture) features in differentiating radiation necrosis from recurrent brain tumors on routine MR imaging (gadolinium T1WI, T2WI, FLAIR). MATERIALS AND METHODS: A retrospective study of brain tumor MR imaging performed 9 months (or later) post-radiochemotherapy was performed from 2 institutions. Fifty-eight patient studies were analyzed, consisting of a training (n = 43) cohort from one institution and an independent test (n = 15) cohort from another, with surgical histologic findings confirmed by an experienced neuropathologist at the respective institutions. Brain lesions on MR imaging were manually annotated by an expert neuroradiologist. A set of radiomic features was extracted for every lesion on each MR imaging sequence: gadolinium T1WI, T2WI, and FLAIR. Feature selection was used to identify the top 5 most discriminating features for every MR imaging sequence on the training cohort. These features were then evaluated on the test cohort by a support vector machine classifier. The classification performance was compared against diagnostic reads by 2 expert neuroradiologists who had access to the same MR imaging sequences (gadolinium T1WI, T2WI, and FLAIR) as the classifier. RESULTS: On the training cohort, the area under the receiver operating characteristic curve was highest for FLAIR with 0.79; 95% CI, 0.77–0.81 for primary (n = 22); and 0.79, 95% CI, 0.75–0.83 for metastatic subgroups (n = 21). Of the 15 studies in the holdout cohort, the support vector machine classifier identified 12 of 15 studies correctly, while neuroradiologist 1 diagnosed 7 of 15 and neuroradiologist 2 diagnosed 8 of 15 studies correctly, respectively. CONCLUSIONS: Our preliminary results suggest that radiomic features may provide complementary diagnostic information on routine MR imaging sequences that may improve the distinction of radiation necrosis from recurrence for both primary and metastatic brain tumors.

To evaluate nnU-Net–based segmentation models for automated delineation of medulloblastoma tumors on multi-institutional MRI scans. This retrospective study included 78 pediatric patients (52 male, 26 female), with ages ranging from 2 to 18 years, with medulloblastomas, from three different sites (28 from hospital A, 18 from hospital B, and 32 from hospital C), who had data available from three clinical MRI protocols (gadolinium-enhanced T1-weighted, T2-weighted, and fluid-attenuated inversion recovery). The scans were retrospectively collected from the year 2000 until May 2019. Reference standard annotations of the tumor habitat, including enhancing tumor, edema, and cystic core plus nonenhancing tumor subcompartments, were performed by two experienced neuroradiologists. Preprocessing included registration to age-appropriate atlases, skull stripping, bias correction, and intensity matching. The two models were trained as follows: (a) the transfer learning nnU-Net model was pretrained on an adult glioma cohort (n = 484) and fine-tuned on medulloblastoma studies using Models Genesis and (b) the direct deep learning nnU-Net model was trained directly on the medulloblastoma datasets, across fivefold cross-validation. Model robustness was evaluated on the three datasets when using different combinations of training and test sets, with data from two sites at a time used for training and data from the third site used for testing. Analysis on the three test sites yielded Dice scores of 0.81, 0.86, and 0.86 and 0.80, 0.86, and 0.85 for tumor habitat; 0.68, 0.84, and 0.77 and 0.67, 0.83, and 0.76 for enhancing tumor; 0.56, 0.71, and 0.69 and 0.56, 0.71, and 0.70 for edema; and 0.32, 0.48, and 0.43 and 0.29, 0.44, and 0.41 for cystic core plus nonenhancing tumor for the transfer learning and direct nnU-Net models, respectively. The models were largely robust to site-specific variations. nnU-Net segmentation models hold promise for accurate, robust automated delineation of medulloblastoma tumor subcompartments, potentially leading to more effective radiation therapy planning in pediatric medulloblastoma. Keywords: Pediatrics, MR Imaging, Segmentation, Transfer Learning, Medulloblastoma, nnU-Net, MRI Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Rudie and Correia de Verdier in this issue.

BACKGROUND AND PURPOSE: Predicting the appropriate needle length to use in oblique interlaminar-approach fluoroscopy-guided lumbar punctures in patients with a large body mass index is difficult. Using the wrong needle length can lead to an increased radiation dose and patient discomfort. We hypothesized that body mass index could help determine the appropriate needle length to use in patients. MATERIALS AND METHODS: We randomly selected patients who underwent oblique interlaminar-approach fluoroscopy-guided lumbar punctures and had cross-sectional imaging of the lumbar spine within 1 year of imaging (n = 50). The distance from the skin to the midlumbar spinal canal (skin-canal distance) at the level of the lumbar puncture was measured by using an oblique angle of 8.6°, which is an average of angles most often used to perform the procedure. A formula was devised using the skin-canal distance and body mass index to predict the appropriate needle length, subsequently confirmed in 45 patients. RESULTS: The body mass index and skin-canal distance were significantly higher (P < .001) in patients who underwent fluoroscopy-guided lumbar puncture with 5- or 7-inch needles (n = 22) than in patients requiring 3.5-inch needles (n = 28). Using linear regression, we determined the formula to predict the needle length as Skin-Canal Distance (inches) = 0.077 × Body Mass Index + 0.88. We found a strong correlation (P < .001) between the predicted and actual skin canal distance in 45 patients, and our formula better predicted the skin-canal distance than others. CONCLUSIONS: We designed a formula that uses body mass index to predict the appropriate needle length in oblique interlaminar-approach fluoroscopy-guided lumbar punctures and validated it by demonstrating a strong correlation between the predicted and actual skin-canal distance.

Knowledge of retropharyngeal carotid arteries in patients is important in order to avoid potential catastrophic injuries. Previous studies demonstrated that carotid arteries can change in position at or near the level of the hyoid bone on serial scans. We report the presence of wandering carotid arteries at the level of the cricoid cartilage and superiorly in the neck of a 74-year-old female patient over multiple months. The potential for carotid arteries to randomly change positions in the suprahyoid and infrahyoid neck should be known by clinicians to avoid misdiagnosing occult neck masses and to avoid potential arterial injury during neck surgery. Although the exact etiology for wandering carotid arteries remains uncertain, we propose that reduced axial tension on carotid arteries and increased body mass Index may play a causative role.

BACKGROUND AND PURPOSE: Aside from basic Accreditation Council for Graduate Medical Education guidelines, few metrics are in place to monitor fellows’ progress. The purpose of this study was to determine objective trends in neuroradiology fellowship training on-call performance during an academic year. MATERIALS AND METHODS: We retrospectively reviewed the number of cross-sectional neuroimaging studies dictated with complete reports by neuroradiology fellows during independent call. Monthly trends in total call cases, report turnaround times, relationships between volume and report turnaround times, and words addended to preliminary reports by attending neuroradiologists were evaluated with regression models. Monthly variation in frequencies of call-discrepancy macros were assessed via χ² tests. Changes in frequencies of specific macro use between fellowship semesters were assessed via serial 2-sample tests of proportions. RESULTS: From 2012 to 2017, for 29 fellows, monthly median report turnaround times significantly decreased during the academic year: July (first month) = 79 minutes (95% CI, 71–86 minutes) and June (12th month) = 55 minutes (95% CI , 52–60 minutes; P value = .023). Monthly report turnaround times were inversely correlated with total volumes for CT (r = –0.70, F = 9.639, P value = .011) but not MR imaging. Words addended to preliminary reports, a surrogate measurement of report clarity, slightly improved and discrepancy rates decreased during the last 6 months of fellowship. A nadir for report turnaround times, discrepancy errors, and words addended to reports was seen in December and January. CONCLUSIONS: Progress through fellowship correlates with a decline in report turnaround times and discrepancy rates for cross-sectional neuroimaging call studies and slight improvement in indirect quantitative measurement of report clarity. These metrics can be tracked throughout the academic year, and the midyear would be a logical time point for programs to assess objective progress of fellows and address any deficiencies.