Objectives To assess the influence of age and sex on 10 cerebrospinal fluid (CSF) flow dynamics parameters measured with an MR phase contrast (PC) sequence within the cerebral aqueduct at the level of the intercollicular sulcus. Materials and Methods 128 healthy subjects (66 female subjects with a mean age of 52.9 years and 62 male subjects with a mean age of 51.8 years) with a normal Evans index, normal medial temporal atrophy (MTA) score, and without known disorders of the CSF circulation were included in the study. A PC MR sequence on a 3T MR scanner was used. Ten different flow parameters were analyzed using postprocessing software. Ordinal and linear regression models were calculated. Results The parameters stroke volume (sex: p < 0.001, age: p = 0.003), forward flow volume (sex: p < 0.001, age: p = 0.002), backward flow volume (sex: p < 0.001, age: p = 0.018), absolute stroke volume (sex: p < 0.001, age: p = 0.005), mean flux (sex: p < 0.001, age: p = 0.001), peak velocity (sex: p = 0.009, age: p = 0.0016), and peak pressure gradient (sex: p = 0.029, age: p = 0.028) are significantly influenced by sex and age. The parameters regurgitant fraction, stroke distance, and mean velocity are not significantly influenced by sex and age. Conclusion CSF flow dynamics parameters measured in the cerebral aqueduct are partly age and sex dependent. For establishment of reliable reference values for clinical use in future studies, the impact of sex and age should be considered and incorporated.

Radiologists are among the first physicians to be directly affected by advances in computer technology. Computers are already capable of analyzing medical imaging data, and with decades worth of digital information available for training, will an artificial intelligence (AI) one day signal the end of the human radiologist? With the ever increasing work load combined with the looming doctor shortage, radiologists will be pushed far beyond their current estimated 3 s allotted time-of-analysis per image; an AI with super-human capabilities might seem like a logical replacement. We feel, however, that AI will lead to an augmentation rather than a replacement of the radiologist. The AI will be relied upon to handle the tedious, time-consuming tasks of detecting and segmenting outliers while possibly generating new, unanticipated results that can then be used as sources of medical discovery. This will affect not only radiologists but all physicians and also researchers dealing with medical imaging. Therefore, we must embrace future technology and collaborate interdisciplinary to spearhead the next revolution in medicine.

ImportanceThe association of primary tumor volume with outcomes in T3 glottic cancers treated with radiotherapy with concurrent chemotherapy remains unclear, with some evidence suggesting worse locoregional control in larger tumors.ObjectiveTo evaluate the association of primary tumor volume with oncologic outcomes in patients with T3 N0-N3 M0 glottic cancer treated with primary (chemo)radiotherapy in a large multi-institutional study.Design, Setting, and ParticipantsThis multi-institutional retrospective cohort study involved 7 Canadian cancer centers from 2002 to 2018. Tumor volume was measured by expert neuroradiologists on diagnostic imaging. Clinical and outcome data were extracted from electronic medical records. Overall survival (OS) and disease-free survival (DFS) outcomes were assessed with marginal Cox regression. Laryngectomy-free survival (LFS) was modeled as a secondary analysis. Patients diagnosed with cT3 N0-N3 M0 glottic cancers from 2002 to 2018 and treated with curative intent intensity-modulated radiotherapy (IMRT) with or without chemotherapy. Overall, 319 patients met study inclusion criteria.ExposuresTumor volume as measured on diagnostic imaging by expert neuroradiologists.Main Outcomes and MeasuresPrimary outcomes were OS and DFS; LFS was assessed as a secondary analysis, and late toxic effects as an exploratory analysis determined before start of the study.ResultsThe mean (SD) age of participants was 66 (12) years and 279 (88%) were men. Overall, 268 patients (84%) had N0 disease, and 150 (47%) received concurrent systemic therapy. The mean (SD) tumor volume was 4.04 (3.92) cm3. With a mean (SD) follow-up of 3.85 (3.04) years, there were 91 (29%) local, 35 (11%) regional, and 38 (12%) distant failures. Increasing tumor volume (per 1-cm3 increase) was associated with significantly worse adjusted OS (hazard ratio [HR], 1.07; 95% CI, 1.03-1.11) and DFS (HR, 1.04; 95% CI, 1.01-1.07). A total of 62 patients (19%) underwent laryngectomies with 54 (87%) of these within 800 days after treatment. Concurrent systemic therapy was associated with improved LFS (subdistribution HR, 0.63; 95% CI, 0.53-0.76).Conclusions and RelevanceIncreasing tumor volumes in cT3 glottic cancers was associated with worse OS and DFS, and systemic therapy was associated with improved LFS. In absence of randomized clinical trial evidence, patients with poor pretreatment laryngeal function or those ineligible for systemic therapy may be considered for primary surgical resection with postoperative radiotherapy.

BACKGROUND AND PURPOSE: B-Raf proto-oncogene, serine/threonine kinase (BRAF) status has important implications for prognosis and therapy of pediatric low-grade gliomas. Currently, BRAF status classification relies on biopsy. Our aim was to train and validate a radiomics approach to predict BRAF fusion and BRAF V600E mutation. MATERIALS AND METHODS: In this bi-institutional retrospective study, FLAIR MR imaging datasets of 115 pediatric patients with low-grade gliomas from 2 children's hospitals acquired between January 2009 and January 2016 were included and analyzed. Radiomics features were extracted from tumor segmentations, and the predictive model was tested using independent training and testing datasets, with all available tumor types. The model was selected on the basis of a grid search on the number of trees, opting for the best split for a random forest. We used the area under the receiver operating characteristic curve to evaluate model performance.

Fractional volumes may provide an optimal trade-off for texture analysis in the clinical setting. All texture parameters proved significantly different with minimal expansion of the ROI, underlining the susceptibility of texture analysis to generating misrepresentative tumor information.

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Background Digital subtraction angiography is the gold standard for detecting and characterising aneurysms. Here, we assess the feasibility of commercial-grade deep learning software for the detection of intracranial aneurysms on whole-brain anteroposterior and lateral 2D digital subtraction angiography images. Material and methods Seven hundred and six digital subtraction angiography images were included from a cohort of 240 patients (157 female, mean age 59 years, range 20–92; 83 male, mean age 55 years, range 19–83). Three hundred and thirty-five (47%) single frame anteroposterior and lateral images of a digital subtraction angiography series of 187 aneurysms (41 ruptured, 146 unruptured; average size 7±5.3 mm, range 1–5 mm; total 372 depicted aneurysms) and 371 (53%) aneurysm-negative study images were retrospectively analysed regarding the presence of intracranial aneurysms. The 2D data was split into testing and training sets in a ratio of 4:1 with 3D rotational digital subtraction angiography as gold standard. Supervised deep learning was performed using commercial-grade machine learning software (Cognex, ViDi Suite 2.0). Monte Carlo cross validation was performed. Results Intracranial aneurysms were detected with a sensitivity of 79%, a specificity of 79%, a precision of 0.75, a F1 score of 0.77, and a mean area-under-the-curve of 0.76 (range 0.68–0.86) after Monte Carlo cross-validation, run 45 times. Conclusion The commercial-grade deep learning software allows for detection of intracranial aneurysms on whole-brain, 2D anteroposterior and lateral digital subtraction angiography images, with results being comparable to more specifically engineered deep learning techniques.

PurposeThe aim of this pilot study was to assess the clinical feasibility, diagnostic yield, advantages, and disadvantages of structured reporting for routine MRI-reading in patients with primary diagnosis of intracranial tumors as compared to traditional neuroradiological free text reporting.MethodsA structured MRI reporting template was developed covering pathological, anatomical, and functional aspects in an itemized fashion. Retrospectively, 60 consecutive patients with first diagnosis of an intracranial tumor were selected from the radiology information system/PACS system. Structured reporting was performed by a senior neuroradiologist, blinded to clinical and radiological data. Reporting times were measured per patient. The diagnostic content was compared to free text reporting which was independently performed on the same MRI exams by two other neuroradiologists. The comparisons were categorized per item as: “congruent,” “partially congruent,” “incongruent,” or “not mentioned in free-style report.”ResultsTumor-related items: congruent findings were found for all items (17/17) with congruence rates ranging between 98 and 39% per item. Four items achieved congruence rates ≥90%, 5 items >80%, and 9 items ≥70%. Partially congruent findings were found for all items in up to 50% per item. Incongruent findings were present in 7/17 items in up to 5% per item. Free text reports did not mention 12 of 17 items (range 7–43% per item). Non-tumor-related items, including brain atrophy, microangiopathy, vascular pathologies, and various extracranial pathologies, which were not mentioned in free-text reports between 18 and 85% per item. Mean reporting time for structured reporting was 7:49 min (3:12–17:06 min).ConclusionFirst results showed that expert structured reporting ensured reliable detection of all relevant brain pathologies along with reproducible documentation of all predefined diagnostic items, which was not always the case for free text reporting. A mean reporting time of 8 min per patient seems clinically feasible.