Assessing the Performance of Artificial Intelligence Models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) models in radiology are frequently developed and validated using datasets from a single institution and are rarely tested on independent, external datasets, raising questions about their generalizability and applicability in clinical practice. The American Society of Functional Neuroradiology (ASFNR) organized a multi-center AI competition to evaluate the proficiency of developed models in identifying various pathologies on NCCT, assessing age-based normality and estimating medical urgency. MATERIALS AND METHODS: In total, 1201 anonymized, full-head NCCT clinical scans from five institutions were pooled to form the dataset. The dataset encompassed normal studies as well as pathologies including acute ischemic stroke, intracranial hemorrhage, traumatic brain injury, and mass effect (detection of these-task 1). NCCTs were also assessed to determine if findings were consistent with expected brain changes for the patient's age (task 2: age-based normality assessment) and to identify any abnormalities requiring immediate medical attention (task 3: evaluation of findings for urgent intervention). Five neuroradiologists labeled each NCCT, with consensus interpretations serving as the ground truth. The competition was announced online, inviting academic institutions and companies. Independent central analysis assessed each model's performance. Accuracy, sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curves were generated for each AI model, along with the area under the ROC curve (AUROC). RESULTS: 1177 studies were processed by four teams. The median age of patients was 62, with an interquartile range of 33. 19 teams from various academic institutions registered for the competition. Of these, four teams submitted their final results. No commercial entities participated in the competition. For task 1, AUROCs ranged from 0.49 to 0.59. For task 2, two teams completed the task with AUROC values of 0.57 and 0.52. For task 3, teams had little to no agreement with the ground truth. CONCLUSIONS: To assess the performance of AI models in real-world clinical scenarios, we analyzed their performance in the ASFNR AI Competition. The first ASFNR Competition underscored the gap between expectation and reality; the models largely fell short in their assessments. As the integration of AI tools into clinical workflows increases, neuroradiologists must carefully recognize the capabilities, constraints, and consistency of these technologies. Before institutions adopt these algorithms, thorough validation is essential to ensure acceptable levels of performance in clinical settings.ABBREVIATIONS: AI = artificial intelligence; ASFNR = American Society of Functional Neuroradiology; AUROC = area under the receiver operating characteristic curve; DICOM = Digital Imaging and Communications in Medicine; GEE = generalized estimation equation; IQR = interquartile range; NPV = negative predictive value; PPV = positive predictive value; ROC = receiver operating characteristic; TBI = traumatic brain injury.

Radiomics-Based Prediction of Collateral Status from CT Angiography of Patients Following a Large Vessel Occlusion Stroke.

BACKGROUND: A major driver of individual variation in long-term outcomes following a large vessel occlusion (LVO) stroke is the degree of collateral arterial circulation. We aimed to develop and evaluate machine-learning models that quantify LVO collateral status using admission computed tomography angiography (CTA) radiomics. METHODS: We extracted 1116 radiomic features from the anterior circulation territories from admission CTAs of 600 patients experiencing an acute LVO stroke. We trained and validated multiple machine-learning models for the prediction of collateral status based on consensus from two neuroradiologists as ground truth. Models were first trained to predict (1) good vs. intermediate or poor, or (2) good vs. intermediate or poor collateral status. Then, model predictions were combined to determine a three-tier collateral score (good, intermediate, or poor). We used the receiver operating characteristics area under the curve (AUC) to evaluate prediction accuracy. RESULTS: We included 499 patients in training and 101 in an independent test cohort. The best-performing models achieved an averaged cross-validation AUC of 0.80 ± 0.05 for poor vs. intermediate/good collateral and 0.69 ± 0.05 for good vs. intermediate/poor, and AUC = 0.77 (0.67-0.87) and AUC = 0.78 (0.70-0.90) in the independent test cohort, respectively. The collateral scores predicted by the radiomics model were correlated with (rho = 0.45, p = 0.002) and were independent predictors of 3-month clinical outcome (p = 0.018) in the independent test cohort. CONCLUSIONS: Automated tools for the assessment of collateral status from admission CTA-such as the radiomics models described here-can generate clinically relevant and reproducible collateral scores to facilitate a timely treatment triage in patients experiencing an acute LVO stroke.

Genetic and Environmental Contributions to Subcortical Gray Matter Microstructure and Volume in the Developing Brain.

Using baseline (ages 9-10) and two-year follow-up (ages 11-12) data from monozygotic and dizygotic twins enrolled in the longitudinal Adolescent Brain Cognitive DevelopmentSM Study, we investigated the genetic and environmental contributions to microstructure and volume of nine subcortical gray matter regions. Microstructure was assessed using diffusion MRI data analyzed using restriction spectrum imaging (RSI) and diffusion tensor imaging (DTI) models. The highest heritability estimates (estimate [95% confidence interval]) for microstructure were found using the RSI model in the pallidum (baseline: 0.859 [0.818, 0.889], follow-up: 0.835 [0.787, 0.871]), putamen (baseline: 0.859 [0.819, 0.889], follow-up: 0.874 [0.838, 0.902]), and thalamus (baseline: 0.855 [0.814, 0.887], follow-up: 0.819 [0.769, 0.857]). For volumes the corresponding regions were the caudate (baseline: 0.831 [0.688, 0.992], follow-up: 0.848 [0.701, 1.011]) and putamen (baseline: 0.906 [0.875, 0.914], follow-up: 0.906 [0.885, 0.923]). The subcortical regions displayed high genetic stability (rA = 0.743-1.000) across time and exhibited unique environmental correlations (rE = 0.194-0.610). Individual differences in both gray matter microstructure and volumes can be largely explained by additive genetic effects in this sample.

Using artificial intelligence in medical school admissions screening to decrease inter- and intra-observer variability.

Objectives: Inter- and intra-observer variability is a concern for medical school admissions. Artificial intelligence (AI) may present an opportunity to apply a fair standard to all applicants systematically and yet maintain sensitivity to nuances that have been a part of traditional screening methods. Material and Methods: Data from 5 years of medical school applications were retrospectively accrued and analyzed. The applicants (m = 22 258 applicants) were split 60%-20%-20% into a training set (m = 13 354), validation set (m = 4452), and test set (m = 4452). An AI model was trained and evaluated with the ground truth being whether a given applicant was invited for an interview. In addition, a "real-world" evaluation was conducted simultaneously within an admissions cycle to observe how it would perform if utilized. Results: The algorithm had an accuracy of 95% on the training set, 88% on the validation set, and 88% on the test set. The area under the curve of the test set was 0.93. The SHapely Additive exPlanations (SHAP) values demonstrated that the model utilizes features in a concordant manner with current admissions rubrics. By using a combined human and AI evaluation process, the accuracy of the process was demonstrated to be 96% on the "real-world" evaluation with a negative predictive value of 0.97. Discussion and Conclusion: These results demonstrate the feasibility of an AI approach applied to medical school admissions screening decision-making. Model explainability and supplemental analyses help ensure that the model makes decisions as intended.

Coronavirus Disease: Subacute to Chronic Neuroimaging Findings.

Several neurologic disorders are associated with coronavirus disease 2019 (COVID-19). In this article, clinical syndromes typically occurring in the subacute to chronic phase of illness and their neuroimaging findings are described with discussion of their COVID-19 specific features and prognosis. Proposed pathogenic mechanisms of these neuroimaging findings and challenges in determining etiology are reviewed.

Dataset on acute stroke risk stratification from CT angiographic radiomics.

With advances in high-throughput image processing technologies and increasing availability of medical mega-data, the growing field of radiomics opened the door for quantitative analysis of medical images for prediction of clinically relevant information. One clinical area in which radiomics have proven useful is stroke neuroimaging, where rapid treatment triage is vital for patient outcomes and automated decision assistance tools have potential for significant clinical impact. Recent research, for example, has applied radiomics features extracted from CT angiography (CTA) images and a machine learning framework to facilitate risk-stratification in acute stroke. We here provide methodological guidelines and radiomics data supporting the referenced article "CT angiographic radiomics signature for risk-stratification in anterior large vessel occlusion stroke." The data were extracted from the stroke center registry at Yale New Haven Hospital between 1/1/2014 and 10/31/2020; and Geisinger Medical Center between 1/1/2016 and 12/31/2019. It includes detailed radiomics features of the anterior circulation territories on admission CTA scans in stroke patients with large vessel occlusion stroke who underwent thrombectomy. We also provide the methodological details of the analysis framework utilized for training, optimization, validation and external testing of the machine learning and feature selection algorithms. With the goal of advancing the feasibility and quality of radiomics-based analyses to improve patient care within and beyond the field of stroke, the provided data and methodological support can serve as a baseline for future studies applying radiomics algorithms to machine-learning frameworks, and allow for analysis and utilization of radiomics features extracted in this study.

CT angiographic radiomics signature for risk stratification in anterior large vessel occlusion stroke.

BACKGROUND AND PURPOSE: As "time is brain" in acute stroke triage, the need for automated prognostication tools continues to increase, particularly in rapidly expanding tele-stroke settings. We aimed to create an automated prognostication tool for anterior circulation large vessel occlusion (LVO) stroke based on admission CTA radiomics. METHODS: We automatically extracted 1116 radiomics features from the anterior circulation territory on admission CTAs of 829 acute LVO stroke patients who underwent mechanical thrombectomy in two academic centers. We trained, optimized, validated, and compared different machine-learning models to predict favorable outcome (modified Rankin Scale ≤ 2) at discharge and 3-month follow-up using four different input sets: "Radiomics", "Radiomics + Treatment" (radiomics, post-thrombectomy reperfusion grade, and intravenous thrombolysis), "Clinical + Treatment" (baseline clinical variables and treatment), and "Combined" (radiomics, treatment, and baseline clinical variables). RESULTS: For discharge outcome prediction, models were optimized/trained on n = 494 and tested on an independent cohort of n = 100 patients from Yale. Receiver operating characteristic analysis of the independent cohort showed no significant difference between best-performing Combined input models (area under the curve, AUC = 0.77) versus Radiomics + Treatment (AUC = 0.78, p = 0.78), Radiomics (AUC = 0.78, p = 0.55), or Clinical + Treatment (AUC = 0.77, p = 0.87) models. For 3-month outcome prediction, models were optimized/trained on n = 373 and tested on an independent cohort from Yale (n = 72), and an external cohort from Geisinger Medical Center (n = 232). In the independent cohort, there was no significant difference between Combined input models (AUC = 0.76) versus Radiomics + Treatment (AUC = 0.72, p = 0.39), Radiomics (AUC = 0.72, p = 0.39), or Clinical + Treatment (AUC = 76, p = 0.90) models; however, in the external cohort, the Combined model (AUC = 0.74) outperformed Radiomics + Treatment (AUC = 0.66, p < 0.001) and Radiomics (AUC = 0.68, p = 0.005) models for 3-month prediction. CONCLUSION: Machine-learning signatures of admission CTA radiomics can provide prognostic information in acute LVO stroke candidates for mechanical thrombectomy. Such objective and time-sensitive risk stratification can guide treatment decisions and facilitate tele-stroke assessment of patients. Particularly in the absence of reliable clinical information at the time of admission, models solely using radiomics features can provide a useful prognostication tool.

The coronal plane maximum diameter of deep intracerebral hemorrhage predicts functional outcome more accurately than hematoma volume.

BACKGROUND: Among prognostic imaging variables, the hematoma volume on admission computed tomography (CT) has long been considered the strongest predictor of outcome and mortality in intracerebral hemorrhage. AIMS: To examine whether different features of hematoma shape are associated with functional outcome in deep intracerebral hemorrhage. METHODS: We analyzed 790 patients from the ATACH-2 trial, and 14 shape features were quantified. We calculated Spearman's Rho to assess the correlation between shape features and three-month modified Rankin scale (mRS) score, and the area under the receiver operating characteristic curve (AUC) to quantify the association between shape features and poor outcome defined as mRS>2 as well as mRS > 3. RESULTS: Among 14 shape features, the maximum intracerebral hemorrhage diameter in the coronal plane was the strongest predictor of functional outcome, with a maximum coronal diameter >∼3.5 cm indicating higher three-month mRS scores. The maximum coronal diameter versus hematoma volume yielded a Rho of 0.40 versus 0.35 (p = 0.006), an AUC[mRS>2] of 0.71 versus 0.68 (p = 0.004), and an AUC[mRS>3] of 0.71 versus 0.69 (p = 0.029). In multiple regression analysis adjusted for known outcome predictors, the maximum coronal diameter was independently associated with three-month mRS (p < 0.001). CONCLUSIONS: A coronal-plane maximum diameter measurement offers greater prognostic value in deep intracerebral hemorrhage than hematoma volume. This simple shape metric may expedite assessment of admission head CTs, offer a potential biomarker for hematoma size eligibility criteria in clinical trials, and may substitute volume in prognostic intracerebral hemorrhage scoring systems.

Report from the RSNA COVID-19 Task Force: COVID-19 Impact on Academic Radiology Research-A Survey of Vice Chairs of Research.

OBJECTIVE: Survey vice chairs of research from academic radiology departments on the impact of coronavirus disease 2019 (COVID-19) on research activities. METHODS: The survey asked respondents to quantify changes in research performed during the shutdown and ramp-up, relative to pre-COVID-19 levels. Respondents estimated research activity changes by overall research type (wet, instrumentation, or core facilities: prospective non-COVID-19 clinical research and computational laboratories) and then by the research activity type (data analysis, grant or manuscript writing, clinician involvement, summer student participation, and international research fellow appointments).The χ2 test was used for comparison between shutdown and ramp-up, with Yates correction when necessary. RESULTS: Of 105 vice chairs contacted, 46 (43.8%) responded. For 95.5%, wet, instrumentation, or core facilities research decreased to ≤50% during shutdown and for 83.3% during ramp-up (P < .0001). In addition, 89.2% and 46.5% indicated reduction to ≤25% of non-COVID-19 clinical research during shutdown and ramp-up, respectively (P < .0001). Only computational research increased to 120% during shutdown (39.5%) or ramp-up (50%) (P = .8984). For data analysis from closed laboratories, 75% and 86% showed decreased activity during shutdown and ramp-up, respectively (P = .28). Increased grant writing during shutdown and ramp-up was reported by 45.5% and 23.3% (P = .093). For 52.3% and 23.3%, manuscript writing and submission increased during shutdown and ramp-up, respectively (P < .02). Clinician research involvement trended toward relative decreases during shutdown (84.1% versus 60.5%, P = .05). There was similar drop in summer student participation (shutdown: 86.4%, ramp-up: 83.7%, P = .95) and international researcher appointment (shutdown: 85.7%, ramp-up: 86.1%; P = .96). CONCLUSION: Many radiology research activities diminished during the COVID-19 shutdown and to a lesser extent during the ramp-up. Activities that could be done remotely, such as computational analysis and grant and manuscript writing and submission, increased.

Repeated superselective intraarterial bevacizumab after blood brain barrier disruption for newly diagnosed glioblastoma: a phase I/II clinical trial.

PURPOSE: Pre-clinical evidence suggests bevacizumab (BV) depletes the GBM peri-vascular cancer-stem cell niche. This phase I/II study assesses the safety and efficacy of repeated doses of superselective intra-arterial cerebral infusion (SIACI) of BV after blood-brain barrier disruption (BBBD). METHODS: Date of surgery was day 0. Evaluated patients received repeated SIACI bevacizumab (15 mg/kg) with BBBD at days 30 ± 7, 120 ± 7, and 210 ± 7 along with 6 weeks of standard chemoradiation. Response assessment in neuro-oncology criteria and the Kaplan-Meier product-limit method was used to evaluate progression free and overall survival (PFS and OS, respectively). RESULTS: Twenty-three patients with a median age of 60.5 years (SD = 12.6; 24.7-78.3) were included. Isocitrate dehydrogenase mutation was found in 1/23 (4%) patients. MGMT status was available for 11/23 patients (7 unmethylated; 3 methylated; 1 inconclusive). Median tumor volume was 24.0 cm3 (SD = 31.1, 1.7-48.3 cm3). Median PFS was 11.5 months (95% CI 7.7-25.9) with 6, 12, 24 and 60 month PFS estimated to be 91.3% (95% CI 69.5-97.8), 47.4% (26.3-65.9), 32.5% (14.4-52.2) and 5.4% (0.4-21.8), respectively. Median OS was 23.1 months (95% CI 12.2-36.9) with 12, 24, and 36 month OS as 77.3% (95% CI 53.6-89.9), 45.0% (22.3-65.3) and 32.1% (12.5-53.8), respectively. CONCLUSIONS: Repeated dosing of IA BV after BBBD offers an encouraging outcome in terms of PFS and OS. Phase III trials are warranted to determine whether repeated IA BV combined with Stupp protocol is superior to Stupp protocol alone for newly diagnosed GBM.

Admission computed tomography radiomic signatures outperform hematoma volume in predicting baseline clinical severity and functional outcome in the ATACH-2 trial intracerebral hemorrhage population.

BACKGROUND AND PURPOSE: Radiomics provides a framework for automated extraction of high-dimensional feature sets from medical images. We aimed to determine radiomics signature correlates of admission clinical severity and medium-term outcome from intracerebral hemorrhage (ICH) lesions on baseline head computed tomography (CT). METHODS: We used the ATACH-2 (Antihypertensive Treatment of Acute Cerebral Hemorrhage II) trial dataset. Patients included in this analysis (n = 895) were randomly allocated to discovery (n = 448) and independent validation (n = 447) cohorts. We extracted 1130 radiomics features from hematoma lesions on baseline noncontrast head CT scans and generated radiomics signatures associated with admission Glasgow Coma Scale (GCS), admission National Institutes of Health Stroke Scale (NIHSS), and 3-month modified Rankin Scale (mRS) scores. Spearman's correlation between radiomics signatures and corresponding target variables was compared with hematoma volume. RESULTS: In the discovery cohort, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.47 vs. 0.44, p = 0.008), admission NIHSS (0.69 vs. 0.57, p < 0.001), and 3-month mRS scores (0.44 vs. 0.32, p < 0.001). Similarly, in independent validation, radiomics signatures, compared to ICH volume, had a significantly stronger association with admission GCS (0.43 vs. 0.41, p = 0.02), NIHSS (0.64 vs. 0.56, p < 0.001), and 3-month mRS scores (0.43 vs. 0.33, p < 0.001). In multiple regression analysis adjusted for known predictors of ICH outcome, the radiomics signature was an independent predictor of 3-month mRS in both cohorts. CONCLUSIONS: Limited by the enrollment criteria of the ATACH-2 trial, we showed that radiomics features quantifying hematoma texture, density, and shape on baseline CT can provide imaging correlates for clinical presentation and 3-month outcome. These findings couldtrigger a paradigm shift where imaging biomarkers may improve current modelsfor prognostication, risk-stratification, and treatment triage of ICH patients.

Special report of the RSNA COVID-19 task force: systematic review of outcomes associated with COVID-19 neuroimaging findings in hospitalized patients.

OBJECTIVE: We reviewed the literature to describe outcomes associated with abnormal neuroimaging findings among adult COVID-19 patients. METHODS: We performed a systematic literature review using PubMed and Embase databases. We included all studies reporting abnormal neuroimaging findings among hospitalized patients with confirmed COVID-19 and outcomes. Data elements including patient demographics, neuroimaging findings, acuity of neurological symptoms and/or imaging findings relative to COVID-19 onset (acute, subacute, chronic), and patient outcomes were recorded and summarized. RESULTS: After review of 775 unique articles, a total of 39 studies comprising 884 COVID-19 patients ≥ 18 years of age with abnormal neuroimaging findings and reported outcomes were included in our analysis. Ischemic stroke was the most common neuroimaging finding reported (49.3%, 436/884) among patients with mortality outcomes data. Patients with intracranial hemorrhage (ICH) had the highest all-cause mortality (49.7%, 71/143), followed by patients with imaging features consistent with leukoencephalopathy (38.5%, 5/13), and ischemic stroke (30%, 131/436). There was no mortality reported among COVID-19 patients with acute disseminated encephalomyelitis without necrosis (0%, 0/8) and leptomeningeal enhancement alone (0%, 0/12). Stroke was a common acute or subacute neuroimaging finding, while leukoencephalopathy was a common chronic finding. CONCLUSION: Among hospitalized COVID-19 patients with abnormal neuroimaging findings, those with ICH had the highest all-cause mortality; however, high mortality rates were also seen among COVID-19 patients with ischemic stroke in the acute/subacute period and leukoencephalopathy in the chronic period. ADVANCES IN KNOWLEDGE: Specific abnormal neuroimaging findings may portend differential mortality outcomes, providing a potential prognostic marker for hospitalized COVID-19 patients.

Special Report of the RSNA COVID-19 Task Force: Crisis Leadership of Major Health System Radiology Departments during COVID-19.

Severe acute respiratory syndrome coronavirus 2 has spread across the world since December 2019, infecting 100 million and killing millions. The impact on health care institutions during the coronavirus disease 2019 pandemic has been considerable, with exhaustion of institutional and personal protective equipment resources during local outbreaks and crushing financial consequences for many institutions. Establishing adaptive principles of leadership is necessary during crises, fostering quick decision-making and workflow modifications, while a rapid review of data must determine necessary course corrections. This report describes concepts of crisis leadership teams that can help maximize their effectiveness during the current and future pandemics.

The Spectrum of Neuroimaging Findings on CT and MRI in Adults With COVID-19.

Neurologic involvement is well-recognized in COVID-19. This article reviews the neuroimaging manifestations of COVID-19 on CT and MRI, presenting cases from the New York City metropolitan region encountered by the authors during the first surge of the pandemic. The most common neuroimaging manifestations are acute infarcts with large clot burden and intracranial hemorrhage, including microhemorrhages. However, a wide range of additional imaging patterns occur, including leukoencephalopathy, global hypoxic injury, acute disseminated encephalomyelitis, cytotoxic lesions of the corpus callosum, olfactory bulb involvement, cranial nerve enhancement, and Guillain-Barré syndrome. The described CNS abnormalities largely represent secondary involvement from immune activation that leads to a prothrombotic state and cytokine storm; evidence for direct neuroinvasion is scant. Comorbidities such as hypertension, complications of prolonged illness and hospitalization, and associated supportive treatments also contribute to the CNS involvement in COVID-19. Routine long-term neurologic follow-up may be warranted, given emerging evidence of long-term microstructural and functional changes on brain imaging after COVID-19 recovery.

Updates on Deep Learning and Glioma: Use of Convolutional Neural Networks to Image Glioma Heterogeneity.

Deep learning represents end-to-end machine learning in which feature selection from images and classification happen concurrently. This articles provides updates on how deep learning is being applied to the study of glioma and its genetic heterogeneity. Deep learning algorithms can detect patterns in routine and advanced MR imaging that elude the eyes of neuroradiologists and make predictions about glioma genetics, which impact diagnosis, treatment response, patient management, and long-term survival. The success of these deep learning initiatives may enhance the performance of neuroradiologists and add greater value to patient care by expediting treatment.

Cerebrovascular Complications of COVID-19.

BACKGROUND AND PURPOSE: Coronavirus disease 2019 (COVID-19) evolved quickly into a global pandemic with myriad systemic complications, including stroke. We report the largest case series to date of cerebrovascular complications of COVID-19 and compare with stroke patients without infection. METHODS: Retrospective case series of COVID-19 patients with imaging-confirmed stroke, treated at 11 hospitals in New York, between March 14 and April 26, 2020. Demographic, clinical, laboratory, imaging, and outcome data were collected, and cases were compared with date-matched controls without COVID-19 from 1 year prior. RESULTS: Eighty-six COVID-19-positive stroke cases were identified (mean age, 67.4 years; 44.2% women). Ischemic stroke (83.7%) and nonfocal neurological presentations (67.4%) predominated, commonly involving multivascular distributions (45.8%) with associated hemorrhage (20.8%). Compared with controls (n=499), COVID-19 was associated with in-hospital stroke onset (47.7% versus 5.0%; P<0.001), mortality (29.1% versus 9.0%; P<0.001), and Black/multiracial race (58.1% versus 36.9%; P=0.001). COVID-19 was the strongest independent risk factor for in-hospital stroke (odds ratio, 20.9 [95% CI, 10.4-42.2]; P<0.001), whereas COVID-19, older age, and intracranial hemorrhage independently predicted mortality. CONCLUSIONS: COVID-19 is an independent risk factor for stroke in hospitalized patients and mortality, and stroke presentations are frequently atypical.

Vascularized Temporoparietal Fascial Flap: A Novel Surgical Technique to Bypass the Blood-Brain Barrier in Glioblastoma.

BACKGROUND: The major difficulty in treating glioblastoma stems from the intrinsic privileged nature of the brain. This complicates therapy, as many traditionally potent chemotherapeutics cannot access their target sites in the brain. Several techniques have been investigated to overcome this barrier and facilitate drug delivery. However, these techniques have inherent shortcomings related to the delivery system, the drug itself, or its bioactivity. Periosteal flaps and temporoparietal fascial flaps (TPFFs) are widely used options because they have predictable vasculature and a wide rotational arc. These flaps are not restricted by the blood-brain barrier, as they derive their vascular supply from branches of the external carotid artery, which can be readily identified with Doppler ultrasound. We hypothesized that transposition of a vascularized TPFF to the walls of a resected tumor surgical cavity may bring autologous tissue not restricted by the blood-brain barrier in close vicinity of the resected tumor bed microenvironment. This offers a nonselective, long-lasting gateway to target the residual tumor cells nesting in the brain adjacent to the tumor. CASE DESCRIPTION: A 47-year-old, right-handed woman with newly diagnosed multifocal glioblastoma underwent excision of the tumor and TPFF placement. This illustrative case report represents the first case of the use of this novel surgical technique with radiologic follow-up. CONCLUSIONS: The blood-brain barrier is identified as a major barrier for effective drug delivery in glioblastoma. This study demonstrates the feasibility of the TPFF technique to bypass this barrier and help facilitate the goal of improving drug delivery.