The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Radiomic signatures of meningiomas using the Ki-67 proliferation index as a prognostic marker of clinical outcomes.

OBJECTIVE: The clinical behavior of meningiomas is not entirely captured by its designated WHO grade, therefore other factors must be elucidated that portend increased tumor aggressiveness and associated risk of recurrence. In this study, the authors identify multiparametric MRI radiomic signatures of meningiomas using Ki-67 as a prognostic marker of clinical outcomes independent of WHO grade. METHODS: A retrospective analysis was conducted of all resected meningiomas between 2012 and 2018. Preoperative MR images were used for high-throughput radiomic feature extraction and subsequently used to develop a machine learning algorithm to stratify meningiomas based on Ki-67 indices < 5% and ≥ 5%, independent of WHO grade. Progression-free survival (PFS) was assessed based on machine learning prediction of Ki-67 strata and compared with outcomes based on histopathological Ki-67. RESULTS: Three hundred forty-three meningiomas were included: 291 with WHO grade I, 43 with grade II, and 9 with grade III. The overall rate of recurrence was 19.8% (15.1% in grade I, 44.2% in grade II, and 77.8% in grade III) over a median follow-up of 28.5 months. Grade II and III tumors had higher Ki-67 indices than grade I tumors, albeit tumor and peritumoral edema volumes had considerable variation independent of meningioma WHO grade. Forty-six high-performing radiomic features (1 morphological, 7 intensity-based, and 38 textural) were identified and used to build a support vector machine model to stratify tumors based on a Ki-67 cutoff of 5%, with resultant areas under the curve of 0.83 (95% CI 0.78-0.89) and 0.84 (95% CI 0.75-0.94) achieved for the discovery (n = 257) and validation (n = 86) data sets, respectively. Comparison of histopathological Ki-67 versus machine learning-predicted Ki-67 showed excellent performance (overall accuracy > 80%), with classification of grade I meningiomas exhibiting the greatest accuracy. Prediction of Ki-67 by machine learning classifier revealed shorter PFS for meningiomas with Ki-67 indices ≥ 5% compared with tumors with Ki-67 < 5% (p < 0.0001, log-rank test), which corroborates divergent patient outcomes observed using histopathological Ki-67. CONCLUSIONS: The Ki-67 proliferation index may serve as a surrogate marker of increased meningioma aggressiveness independent of WHO grade. Machine learning using radiomic feature analysis may be used for the preoperative prediction of meningioma Ki-67, which provides enhanced analytical insights to help improve diagnostic classification and guide patient-specific treatment strategies.

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium.

PURPOSE: While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas. METHODS: Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS). RESULTS: One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS. CONCLUSION: Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.

Deep Learning-Based Digitally Reconstructed Tomography of the Chest in the Evaluation of Solitary Pulmonary Nodules: A Feasibility Study.

RATIONALE AND OBJECTIVES: Computed tomography (CT) is preferred for evaluating solitary pulmonary nodules (SPNs) but access or availability may be lacking, in addition, overlapping anatomy can hinder detection of SPNs on chest radiographs. We developed and evaluated the clinical feasibility of a deep learning algorithm to generate digitally reconstructed tomography (DRT) images of the chest from digitally reconstructed frontal and lateral radiographs (DRRs) and use them to detect SPNs. METHODS: This single-institution retrospective study included 637 patients with noncontrast helical CT of the chest (mean age 68 years, median age 69 years, standard deviation 11.7 years; 355 women) between 11/2012 and 12/2020, with SPNs measuring 10-30 mm. A deep learning model was trained on 562 patients, validated on 60 patients, and tested on the remaining 15 patients. Diagnostic performance (SPN detection) from planar radiography (DRRs and CT scanograms, PR) alone or with DRT was evaluated by two radiologists in an independent blinded fashion. The quality of the DRT SPN image in terms of nodule size and location, morphology, and opacity was also evaluated, and compared to the ground-truth CT images RESULTS: Diagnostic performance was higher from DRT plus PR than from PR alone (area under the receiver operating characteristic curve 0.95-0.98 versus 0.80-0.85; p < 0.05). DRT plus PR enabled diagnosis of SPNs in 11 more patients than PR alone. Interobserver agreement was 0.82 for DRT plus PR and 0.89 for PR alone; and interobserver agreement for size and location, morphology, and opacity of the DRT SPN was 0.94, 0.68, and 0.38, respectively. CONCLUSION: For SPN detection, DRT plus PR showed better diagnostic performance than PR alone. Deep learning can be used to generate DRT images and improve detection of SPNs.

An algorithm for sellar reconstruction following endoscopic transsphenoidal surgery for pituitary adenoma: A review of 582 cases.

BACKGROUND: Several sellar reconstruction algorithms stratify patients based on risk of postoperative cerebrospinal fluid (CSF) leak. Many proposed algorithms employ techniques that are overly complex and confer morbidity. We review our experience with sellar reconstruction following transsphenoidal pituitary surgery and propose a highly effective, yet simple and low morbidity, algorithm. METHODS: A retrospective review of 582 patients who underwent transsphenoidal surgery for pituitary adenoma by a single neurosurgeon between 2005 and 2020 was performed. Patients without an intraoperative CSF leak and without a patulous diaphragm were repaired with an oxidized cellulose onlay (group 1). Patients with a low-flow intraoperative CSF leak or a patulous diaphragm were repaired with a synthetic dural substitute inlay (group 2). Patients with a persistent leak around the inlay repair or a high-flow leak were reconstructed with a synthetic dural substitute inlay and a nasoseptal flap onlay (group 3). RESULTS: There was an overall leak rate of 1.5% (9/582) to 1.0% (2/197) in group 1, 1.7% (6/347) in group 2, and 2.6% (1/38) in group 3. Group 3 had the highest rate of postoperative morbidity, including sinusitis (23.7% vs. 8.6% and 15.0% in groups 1 and 2, p = 0.018) and crusting (42.1% vs. 4.6% and 6.3% in groups 1 and 2, p < 0.001). All techniques healed equally well radiographically. CONCLUSION: The proposed algorithm for sellar reconstruction is highly effective and minimizes complexity and morbidity, primarily utilizing single-layer reconstructions without the addition of packing material or lumbar drainage.

Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas.

BACKGROUND: Although World Health Organization (WHO) grade I meningiomas are considered "benign" tumors, an elevated Ki-67 is one crucial factor that has been shown to influence tumor behavior and clinical outcomes. The ability to preoperatively discern Ki-67 would confer the ability to guide surgical strategy. OBJECTIVE: In this study, we develop a machine learning (ML) algorithm using radiomic feature analysis to predict Ki-67 in WHO grade I meningiomas. METHODS: A retrospective analysis was performed for a cohort of 306 patients who underwent surgical resection of WHO grade I meningiomas. Preoperative magnetic resonance imaging was used to perform radiomic feature extraction followed by ML modeling using least absolute shrinkage and selection operator wrapped with support vector machine through nested cross-validation on a discovery cohort (n = 230), to stratify tumors based on Ki-67 <5% and ≥5%. The final model was independently tested on a replication cohort (n = 76). RESULTS: An area under the receiver operating curve (AUC) of 0.84 (95% CI: 0.78-0.90) with a sensitivity of 84.1% and specificity of 73.3% was achieved in the discovery cohort. When this model was applied to the replication cohort, a similar high performance was achieved, with an AUC of 0.83 (95% CI: 0.73-0.94), sensitivity and specificity of 82.6% and 85.5%, respectively. The model demonstrated similar efficacy when applied to skull base and nonskull base tumors. CONCLUSION: Our proposed radiomic feature analysis can be used to stratify WHO grade I meningiomas based on Ki-67 with excellent accuracy and can be applied to skull base and nonskull base tumors with similar performance achieved.

Phase Ib Clinical Trial of IGV-001 for Patients with Newly Diagnosed Glioblastoma.

PURPOSE: Despite standard of care (SOC) established by Stupp, glioblastoma remains a uniformly poor prognosis. We evaluated IGV-001, which combines autologous glioblastoma tumor cells and an antisense oligonucleotide against IGF type 1 receptor (IMV-001), in newly diagnosed glioblastoma. PATIENTS AND METHODS: This open-label protocol was approved by the Institutional Review Board at Thomas Jefferson University. Tumor cells collected during resection were treated ex vivo with IMV-001, encapsulated in biodiffusion chambers with additional IMV-001, irradiated, then implanted in abdominal acceptor sites. Patients were randomized to four exposure levels, and SOC was initiated 4-6 weeks later. On the basis of clinical improvements, randomization was halted after patient 23, and subsequent patients received only the highest exposure. Safety and tumor progression were primary and secondary objectives, respectively. Time-to-event outcomes were compared with the SOC arms of published studies. RESULTS: Thirty-three patients were enrolled, and median follow-up was 3.1 years. Six patients had adverse events (grade ≤3) possibly related to IGV-001. Median progression-free survival (PFS) was 9.8 months in the intent-to-treat population (vs. SOC, 6.5 months; P = 0.0003). In IGV-001-treated patients who met Stupp-eligible criteria, PFS was 11.6 months overall (n = 22; P = 0.001) and 17.1 months at the highest exposure (n = 10; P = 0.0025). The greatest overall survival was observed in Stupp-eligible patients receiving the highest exposure (median, 38.2 months; P = 0.044). Stupp-eligible patients with methylated O6-methylguanine-DNA methyltransferase promoter (n = 10) demonstrated median PFS of 38.4 months (P = 0.0008). Evidence of immune activation was noted. CONCLUSIONS: IGV-001 was well tolerated, PFS compared favorably with SOC, and evidence suggested an immune-mediated mechanism (ClinicalTrials.gov: NCT02507583).

Artificial Intelligence in Spine Care.

Artificial intelligence is an exciting and growing field in medicine to assist in the proper diagnosis of patients. Although the use of artificial intelligence in orthopedics is currently limited, its utility in other fields has been extremely valuable and could be useful in orthopedics, especially spine care. Automated systems have the ability to analyze complex patterns and images, which will allow for enhanced analysis of imaging. Although the potential impact of artificial intelligence integration into spine care is promising, there are several limitations that must be overcome. Our goal is to review current advances that machine learning has been used for in orthopedics, and discuss potential application to spine care in the clinical setting in which there is a need for the development of automated systems.

Discordant Responses Between Primary Head and Neck Tumors and Nodal Metastases Treated With Neoadjuvant Nivolumab: Correlation of Radiographic and Pathologic Treatment Effect.

PD-1 blockade represents a promising treatment in patients with head and neck squamous cell carcinoma (HNSCC). We analyzed results of a neoadjuvant randomized window-of-opportunity trial of nivolumab plus/minus tadalafil to investigate whether immunotherapy-mediated treatment effects vary by site of involvement (primary tumor, lymph nodes) and determine how radiographic tumor shrinkage correlates with pathologic treatment effect. PATIENTS AND METHODS: Forty-four patients enrolled in trial NCT03238365 were treated with nivolumab 240 mg intravenously on days 1 and 15 with or without oral tadalafil, as determined by random assignment, followed by surgery on day 31. Radiographic volumetric response (RVR) was defined as percent change in tumor volume from pretreatment to posttreatment CT scan. Responders were defined as those with a 10% reduction in the volume of the primary tumor or lymph nodes (LN). Pathologic treatment effect (PTE) was defined as the area showing fibrosis or lymphohistiocytic inflammation divided by total tumor area. RESULTS: Sixteen of 32 patients (50%) with pathologic evidence of LN involvement exhibited discordant PTE between primary sites and LN. In four patients with widely discordant adjacent LN, increased PTE was associated with increased infiltration of tumor CD8+ T cells and CD163+ macrophages, whereas stromal regulatory T cells were associated with low nodal PTE. RVR correlated with PTE at both primary tumor (slope = 0.55, p < 0.001) and in LN (slope = 0.62, p < 0.05). 89% (16/18) of radiographic non-responders with T1-T3 primary sites had no (n = 7) or minimal PTE (n = 9), whereas 15/17 (88%) of radiographic responders had moderate (n = 12) or complete (n = 3) PTE. CONCLUSION: Nivolumab often induces discordant treatment effects between primary tumor sites and metastatic lymph nodes within subjects. This treatment discordance was also demonstrated in adjacent lymph nodes, which may correlate with local immune cell makeup. Finally, although these data were generated by a relatively small population size, our data support the use of early radiographic response to assess immunotherapy treatment effect in HNSCC.

Radiomics and "radi- omics" in cancer immunotherapy: a guide for clinicians.

In recent years the concept of precision medicine has become a popular topic particularly in medical oncology. Besides the identification of new molecular prognostic and predictive biomarkers and the development of new targeted and immunotherapeutic drugs, imaging has started to play a central role in this new era. Terms such as "radiomics", "radiogenomics" or "radi…-omics" are becoming increasingly common in the literature and soon they will represent an integral part of clinical practice. The use of artificial intelligence, imaging and "-omics" data can be used to develop models able to predict, for example, the features of the tumor immune microenvironment through imaging, and to monitor the therapeutic response beyond the standard radiological criteria. The aims of this narrative review are to provide a simplified guide for clinicians to these concepts, and to summarize the existing evidence on radiomics and "radi…-omics" in cancer immunotherapy.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training.

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ''modality-agnostic training'' technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma.

BACKGROUND: Imaging of glioblastoma patients after maximal safe resection and chemoradiation commonly demonstrates new enhancements that raise concerns about tumor progression. However, in 30% to 50% of patients, these enhancements primarily represent the effects of treatment, or pseudo-progression (PsP). We hypothesize that quantitative machine learning analysis of clinically acquired multiparametric magnetic resonance imaging (mpMRI) can identify subvisual imaging characteristics to provide robust, noninvasive imaging signatures that can distinguish true progression (TP) from PsP. METHODS: We evaluated independent discovery (n = 40) and replication (n = 23) cohorts of glioblastoma patients who underwent second resection due to progressive radiographic changes suspicious for recurrence. Deep learning and conventional feature extraction methods were used to extract quantitative characteristics from the mpMRI scans. Multivariate analysis of these features revealed radiophenotypic signatures distinguishing among TP, PsP, and mixed response that compared with similar categories blindly defined by board-certified neuropathologists. Additionally, interinstitutional validation was performed on 20 new patients. RESULTS: Patients who demonstrate TP on neuropathology are significantly different (P < .0001) from those with PsP, showing imaging features reflecting higher angiogenesis, higher cellularity, and lower water concentration. The accuracy of the proposed signature in leave-one-out cross-validation was 87% for predicting PsP (area under the curve [AUC], 0.92) and 84% for predicting TP (AUC, 0.83), whereas in the discovery/replication cohort, the accuracy was 87% for predicting PsP (AUC, 0.84) and 78% for TP (AUC, 0.80). The accuracy in the interinstitutional cohort was 75% (AUC, 0.80). CONCLUSION: Quantitative mpMRI analysis via machine learning reveals distinctive noninvasive signatures of TP versus PsP after treatment of glioblastoma. Integration of the proposed method into clinical studies can be performed using the freely available Cancer Imaging Phenomics Toolkit.

Analysis of the utility of early MRI/MRA in 400 patients with spontaneous intracerebral hemorrhage.

OBJECTIVE: MRI and MRA studies are routinely obtained to identify the etiology of intracerebral hemorrhage (ICH). The diagnostic yield of MRI/MRA in the setting of an acute ICH, however, remains unclear. The authors' goal was to determine the utility of early MRI/MRA in detecting underlying structural lesions in ICH and to identify patients in whom additional imaging during hospitalization could safely be foregone. METHODS: The authors reviewed data obtained in 400 patients with spontaneous ICH diagnosed on noncontrast head CT scans who underwent MRI/MRA between 2015 and 2017 at their institution. MRI/MRA studies were reviewed to identify underlying lesions, such as arteriovenous malformations, aneurysms, cavernous malformations, arteriovenous fistulas, tumors, sinus thrombosis, moyamoya disease, and abscesses. RESULTS: The median patient age was 65 ± 15.8 years. Hypertension was the most common (72%) comorbidity. Structural abnormalities were detected on MRI/MRA in 12.5% of patients. Structural lesions were seen in 5.7% of patients with basal ganglia/thalamic ICH, 14.1% of those with lobar ICH, 20.4% of those with cerebellar ICH, and 27.8% of those with brainstem ICH. Notably, the diagnostic yield of MRI/MRA was 0% in patients > 65 years with a basal ganglia/thalamic hemorrhage and 0% in those > 85 years with any ICH location, whereas it was 37% in patients < 50 years and 23% in those < 65 years. Multivariate analysis showed that decreasing age, absence of hypertension, and non-basal ganglia/thalamic location were predictors of finding an underlying lesion. CONCLUSIONS: The yield of MRI/MRA in ICH is highly variable, depending on patient age and hemorrhage location. The findings of this study do not support obtaining early MRI/MRA studies in patients ≥ 65 years with basal ganglia/thalamic ICH or in any ICH patients ≥ 85 years. In all other situations, early MRI/MRA remains valuable in ruling out underlying lesions.

Assessment of Critical Feeding Tube Malpositions on Radiographs Using Deep Learning.

Assess the efficacy of deep convolutional neural networks (DCNNs) in detection of critical enteric feeding tube malpositions on radiographs. 5475 de-identified HIPAA compliant frontal view chest and abdominal radiographs were obtained, consisting of 174 x-rays of bronchial insertions and 5301 non-critical radiographs, including normal course, normal chest, and normal abdominal x-rays. The ground-truth classification for enteric feeding tube placement was performed by two board-certified radiologists. Untrained and pretrained deep convolutional neural network models for Inception V3, ResNet50, and DenseNet 121 were each employed. The radiographs were fed into each deep convolutional neural network, which included untrained and pretrained models. The Tensorflow framework was used for Inception V3, ResNet50, and DenseNet. Images were split into training (4745), validation (630), and test (100). Both real-time and preprocessing image augmentation strategies were performed. Receiver operating characteristic (ROC) and area under the curve (AUC) on the test data were used to assess the models. Statistical differences among the AUCs were obtained. p < 0.05 was considered statistically significant. The pretrained Inception V3, which had an AUC of 0.87 (95 CI; 0.80-0.94), performed statistically significantly better (p < .001) than the untrained Inception V3, with an AUC of 0.60 (95 CI; 0.52-0.68). The pretrained Inception V3 also had the highest AUC overall, as compared with ResNet50 and DenseNet121, with AUC values ranging from 0.82 to 0.85. Each pretrained network outperformed its untrained counterpart. (p < 0.05). Deep learning demonstrates promise in differentiating critical vs. non-critical placement with an AUC of 0.87. Pretrained networks outperformed untrained ones in all cases. DCNNs may allow for more rapid identification and communication of critical feeding tube malpositions.

Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment.

OBJECTIVE: Machine learning has potential to play a key role across a variety of medical imaging applications. This review seeks to elucidate the ways in which machine learning can aid and enhance diagnosis, treatment, and follow-up in neurooncology. CONCLUSION: Given the rapid pace of development in machine learning over the past several years, a basic proficiency of the key tenets and use cases in the field is critical to assessing potential opportunities and challenges of this exciting new technology.

Actionable vascular and other incidental findings on CTA in patients undergoing acute stroke intervention.

Providing a fast and accurate diagnosis of acute large-vessel occlusion on computed tomography angiograms (CTAs) is essential for timely intervention and good stroke outcomes. However, the detection and appropriate management of incidental findings are also important parts of any clinical radiology practice and can greatly affect patient care. The intricate anatomy covered by CTAs of the head and neck coupled with the time pressures of acute stroke diagnosis creates an environment in which unrelated, important findings can potentially be missed. The purpose of our study was to document clinically actionable incidental findings on CTA in 225 patients undergoing acute stroke intervention. After institutional review board approval, a retrospective six-year review of CTAs of the head and neck in patients undergoing acute stroke intervention was performed for actionable incidental vascular and nonvascular findings. A total of 225 patients undergoing acute stroke intervention with documented intracranial large-vessel occlusion on CTAs were consecutively enrolled in the study. Incidental vascular findings were identified in 17 of 225 patients (7.5%, 95% confidence interval 5% to 12%). Previously unreported aneurysms ranging from 2 mm to 10 mm in size represented 18 of 19 vascular findings in these patients. Incidental nonvascular findings were identified in 32 patients (14%, 95% confidence interval 5% to 12%). These included malpositioned support lines and tubes; pneumothorax; interstitial lung disease; newly diagnosed metastatic disease; nasopharyngeal, parotid, and pituitary masses; and cervical spine compression fractures. CTAs of the head and neck in patients undergoing acute stroke intervention contain a relatively high frequency of vascular and nonvascular incidental findings requiring further follow-up, and therefore should be evaluated carefully and systematically.