Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography.

BACKGROUND: Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO. METHODS: We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1-M3, and M4-M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models' performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature. RESULTS: Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850-0.925), with a sensitivity of 80.1% (95% CI, 72.0-88.1) and a specificity of 88.6% (95% CI, 84.7-92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity. CONCLUSIONS: Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.

Diagnostic assessment of magnetic resonance imaging for patients with intralabyrinthine schwannoma: A systematic review.

OBJECTIVES: Recent advancements in high-resolution imaging have improved the diagnostic assessment of magnetic resonance imaging (MRI) for intralabyrinthine schwannoma (ILS). This systematic review aimed to evaluate the diagnostic performance of MRI for patients with ILS. METHODS: Ovid-MEDLINE and EMBASE databases were searched for related studies on the diagnostic performance of MRI for patients with ILS published up to February 10, 2020. The primary endpoint was the diagnostic performance of MRI for ILS. The quality of the enrolled studies was assessed using tailored questionnaires and the Quality Assessment of Diagnostic Accuracy Studies-2 criteria. RESULTS: Overall, 6 retrospective studies that included 122 patients with ILS from a parent population of 364 were included. The sample size, parent population and its composition, reference standard, detailed parameters of MRI, and even the diagnostic methods varied between the studies. The studies had moderate quality. The sensitivity of combination of T2WI and CE-T1WI was over 90%. Relative sensitivity of T2WI comparative to CE-T1WI ranged from 62% to 100%, and the specificity were 100%. CONCLUSIONS: MRI has acceptable diagnostic performance for ILS. There is a need for well-organized research to reduce the factors causing heterogeneity.

Diagnostic performance of neuromelanin-sensitive magnetic resonance imaging for patients with Parkinson's disease and factor analysis for its heterogeneity: a systematic review and meta-analysis.

OBJECTIVE: To determine the diagnostic performance of neuromelanin-sensitive magnetic resonance imaging discriminating between patients with Parkinson's disease and normal healthy controls and to identify factors causing heterogeneity influencing the diagnostic performance. METHODS: A systematic literature search in the Ovid-MEDLINE and EMBASE databases was performed for studies reporting the relevant topic before February 17, 2020. The pooled sensitivity and specificity values with their 95% confidence intervals were calculated using bivariate random-effects modeling. Subgroup and meta-regression analyses were also performed to determine factors influencing heterogeneity. RESULTS: Twelve articles including 403 patients with Parkinson's disease and 298 control participants were included in this systematic review and meta-analysis. Neuromelanin-sensitive magnetic resonance imaging showed a pooled sensitivity of 89% (95% confidence interval, 86-92%) and a pooled specificity of 83% (95% confidence interval, 76-88%). In the subgroup and meta-regression analysis, a disease duration longer than 5 and 10 years, comparisons using measured volumes instead of signal intensities, a slice thickness in terms of magnetic resonance imaging parameters of more than 2 mm, and semi-/automated segmentation methods instead of manual segmentation improved the diagnostic performance. CONCLUSION: Neuromelanin-sensitive magnetic resonance imaging had a favorable diagnostic performance in discriminating patients with Parkinson's disease from healthy controls. To improve diagnostic accuracy, further investigations directly comparing these heterogeneity-affecting factors and optimizing these parameters are necessary. KEY POINTS: • Neuromelanin-sensitive MRI favorably discriminates patients with Parkinson's disease from healthy controls. • Disease duration, parameters used for comparison, magnetic resonance imaging slice thickness, and segmentation methods affected heterogeneity across the studies.

Diagnostic performance of MRI of post-laminar optic nerve invasion detection in retinoblastoma: A systematic review and meta-analysis.

PURPOSE: Preoperative MRI detection of post-laminar optic nerve invasion (PLONI) offers guidance in assessing the probability of total tumor resection, an estimation of the extent of surgery, and screening of candidates for eye-preserving therapies or neoadjuvant chemotherapies in the patients with retinoblastoma (RB). The purpose of this systematic review and meta-analysis was to evaluate the diagnostic performance of MRI for detecting PLONI in patients with RB and to demonstrate the factors that may influence the diagnostic performance. METHODS: Ovid-MEDLINE and EMBASE databases were searched up to January 11, 2020, for studies identifying the diagnostic performance of MRI for detecting PLONI in patients with RB. The pooled sensitivity and specificity of all studies were calculated followed by meta-regression analysis. RESULTS: Twelve (1240 patients, 1255 enucleated globes) studies were included. The pooled sensitivity was 61%, and the pooled specificity was 88%. Higgins I2 statistic demonstrated moderate heterogeneity in the sensitivity (I2 = 72.23%) and specificity (I2 = 78.11%). Spearman correlation coefficient indicated the presence of a threshold effect. In the meta-regression, higher magnetic field strength (3 T than 1.5 T), performing fat suppression, and thinner slice thickness (< 3 mm) were factors causing heterogeneity and enhancing diagnostic power across the included studies. CONCLUSIONS: MR imaging was demonstrated to have acceptable diagnostic performance in detecting PLONI in patients with RB. The variation in the magnetic field strength and protocols was the main factor behind the heterogeneity across the included studies. Therefore, there is room for developing and optimizing the MR protocols for patients with RB.

Metal Artifact Reduction for Orthopedic Implants: Brain CT Angiography in Patients with Intracranial Metallic Implants.

BACKGROUND: The purpose of this study was to qualitatively and quantitatively evaluate the effects of a metal artifact reduction for orthopedic implants (O-MAR) for brain computed tomographic angiography (CTA) in patients with aneurysm clips and coils. METHODS: The study included 36 consecutive patients with 47 intracranial metallic implants (42 aneurysm clips, 5 coils) who underwent brain CTA. The computed tomographic images with and without the O-MAR were independently reviewed both quantitatively and qualitatively by two reviewers. For quantitative analysis, image noises near the metallic implants of non-O-MAR and O-MAR images were compared. For qualitative analysis, image quality improvement and the presence of new streak artifacts were assessed. RESULTS: Image noise was significantly reduced near metallic implants (P < 0.01). Improvement of implant-induced streak artifacts was observed in eight objects (17.0%). However, streak artifacts were aggravated in 11 objects (23.4%), and adjacent vessel depiction was worsened in eight objects (17.0%). In addition, new O-MAR-related streak artifacts were observed in 32 objects (68.1%). New streak artifacts were more prevalent in cases with overlapping metallic implants on the same axial plane than in those without (P = 0.018). Qualitative assessment revealed that the overall image quality was not significantly improved in O-MAR images. CONCLUSION: In conclusion, the use of the O-MAR in patients with metallic implants significantly reduces image noise. However, the degree of the streak artifacts and surrounding vessel depiction were not significantly improved on O-MAR images.

Paradoxical perfusion metrics of high-grade gliomas with an oligodendroglioma component: quantitative analysis of dynamic susceptibility contrast perfusion MR imaging.

INTRODUCTION: The aim of this study is to investigate perfusion characteristics of glioblastoma with an oligodendroglioma component (GBMO) compared with conventional glioblastoma (GBM) using dynamic susceptibility contrast (DSC) perfusion magnetic resonance (MR) imaging and microvessel density (MVD). METHODS: The study was approved by the institutional review board. Newly diagnosed high-grade glioma patients were enrolled (n = 72; 20 GBMs, 14 GBMOs, 19 anaplastic astrocytomas (AAs), 13 anaplastic oligodendrogliomas (AOs), and six anaplastic oligoastrocytomas (AOAs)). All participants underwent preoperative MR imaging including DSC perfusion MR imaging. Normalized cerebral blood volume (nCBV) values were analyzed using a histogram approach. Histogram parameters were subsequently compared across each tumor subtype and grade. MVD was quantified by immunohistochemistry staining and correlated with perfusion parameters. Progression-free survival (PFS) was assessed according to the tumor subtype. RESULTS: GBMO displayed significantly reduced nCBV values compared with GBM, whereas grade III tumors with oligodendroglial components (AO and AOA) exhibited significantly increased nCBV values compared with AA (p < 0.001). MVD analyses revealed the same pattern as nCBV results. In addition, a positive correlation between MVD and nCBV values was noted (r = 0.633, p < 0.001). Patients with oligodendroglial tumors exhibited significantly increased PFS compared with patients with pure astrocytomas in each grade. CONCLUSION: In contrast to grade III tumors, the presence of oligodendroglial components in grade IV tumors resulted in paradoxically reduced perfusion metrics and MVD. In addition, patients with GBMO exhibited a better clinical outcome compared with patients with GBM.

Evaluation of the degree of arteriovenous shunting in intracranial arteriovenous malformations using pseudo-continuous arterial spin labeling magnetic resonance imaging.

INTRODUCTION: Intracranial arteriovenous malformations (AVMs) display venous signals on arterial spin labeling (ASL) magnetic resonance (MR) imaging due to the presence of arteriovenous shunting. Our aim was to quantitatively correlate AVM signal intensity on ASL with the degree of arteriovenous shunting estimated on digital subtraction angiography (DSA) in AVMs. METHODS: MR imaging including pseudo-continuous ASL at 3 T and DSA were obtained on the same day in 40 patients with intracranial AVMs. Two reviewers assessed the nidus and venous signal intensities on ASL images to determine the presence of arteriovenous shunting. Interobserver agreement on ASL between the reviewers was determined. ASL signal intensity of the AVM lesion was correlated with AVM size and the time difference between normal and AVM venous transit times measured from the DSA images. RESULTS: Interobserver agreement between two reviewers for nidus and venous signal intensities was excellent (κ = 0.80 and 1.0, respectively). Interobserver agreement regarding the presence of arteriovenous shunting was perfect (κ = 1.0). AVM signal intensity showed a positive relationship with the time difference between normal and AVM venous transit times (r = 0.638, P < 0.001). AVM signal intensity also demonstrated a positive relationship with AVM size (r = 0.561, P < 0.001). CONCLUSION: AVM signal intensity on ASL in patients with AVM correlates well with the degree of early vein opacification on DSA, which corresponds to the degree of arteriovenous shunting.

Integrating Clinical Data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership Common Data Model Extension.

BACKGROUND: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. OBJECTIVE: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. METHODS: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging Common Data Model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. RESULTS: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. CONCLUSIONS: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challenges in data standardization and management, our approach contributes toward enhancing diagnostics and treatment strategies. Future research should expand the application of I-CDM to diverse disease populations and explore its wide-ranging utility for medical conditions.

Automated detection of steno-occlusive lesion on time-of-flight magnetic resonance angiography: an observer performance study.

BACKGROUND AND PURPOSE: Intracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an artificial intelligence model for detecting steno-occlusive lesions in the intracranial arteries. MATERIALS AND METHODS: Overall, 138 TOF-MRA images were collected from two institutions, which served as internal (n = 62) and external (n = 76) test sets, respectively. Each study was reviewed by five radiologists (two neuroradiologists and three radiology residents) to compare the usage and non-usage of our proposed artificial intelligence model for TOF-MRA interpretation. They identified the steno-occlusive lesions and recorded their reading time. Observer performance was assessed using the area under the Jackknife free-response receiver operating characteristic curve and reading time for comparison. RESULTS: The average area under the Jackknife free-response receiver operating characteristic curve for the five radiologists demonstrated an improvement from 0.70 without artificial intelligence to 0.76 with artificial intelligence (P = .027). Notably, this improvement was most pronounced among the three radiology residents, whose performance metrics increased from 0.68 to 0.76 (P = .002). Despite an increased reading time upon using artificial intelligence, there was no significant change among the readings by radiology residents. Moreover, the use of artificial intelligence resulted in improved inter-observer agreement among the reviewers (the intraclass correlation coefficient increased from 0.734 to 0.752). CONCLUSIONS: Our proposed artificial intelligence model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less-experienced readers may benefit the most from this model.ABBREVIATIONS: AI = Artificial intelligence; AUC = Area under the receiver operating characteristic curve; AUFROC = Area under the Jackknife free-response receiver operating characteristic curve; DL = Deep learning; ICC = Intraclass correlation coefficient; IRB = Institutional Review Boards; JAFROC = Jackknife free-response receiver operating characteristic.

Comparison of two automated CT perfusion software packages in patients with ischemic stroke presenting within 24 h of onset.

Background: We compared the ischemic core and hypoperfused tissue volumes estimated by RAPID and JLK-CTP, a newly developed automated computed tomography perfusion (CTP) analysis package. We also assessed agreement between ischemic core volumes by two software packages against early follow-up infarct volumes on diffusion-weighted images (DWI). Methods: This retrospective study analyzed 327 patients admitted to a single stroke center in Korea from January 2021 to May 2023, who underwent CTP scans within 24 h of onset. The concordance correlation coefficient (ρ) and Bland-Altman plots were utilized to compare the volumes of ischemic core and hypoperfused tissue volumes between the software packages. Agreement with early (within 3 h from CTP) follow-up infarct volumes on diffusion-weighted imaging (n = 217) was also evaluated. Results: The mean age was 70.7 ± 13.0 and 137 (41.9%) were female. Ischemic core volumes by JLK-CTP and RAPID at the threshold of relative cerebral blood flow (rCBF) < 30% showed excellent agreement (ρ = 0.958 [95% CI, 0.949 to 0.966]). Excellent agreement was also observed for time to a maximum of the residue function (T max) > 6 s between JLK-CTP and RAPID (ρ = 0.835 [95% CI, 0.806 to 0.863]). Although early follow-up infarct volume showed substantial agreement in both packages (JLK-CTP, ρ = 0.751 and RAPID, ρ = 0.632), ischemic core volumes at the threshold of rCBF <30% tended to overestimate ischemic core volumes. Conclusion: JLK-CTP and RAPID demonstrated remarkable concordance in estimating the volumes of the ischemic core and hypoperfused area based on CTP within 24 h from onset.

Prediction of treatment response after stereotactic radiosurgery of brain metastasis using deep learning and radiomics on longitudinal MRI data.

We developed artificial intelligence models to predict the brain metastasis (BM) treatment response after stereotactic radiosurgery (SRS) using longitudinal magnetic resonance imaging (MRI) data and evaluated prediction accuracy changes according to the number of sequential MRI scans. We included four sequential MRI scans for 194 patients with BM and 369 target lesions for the Developmental dataset. The data were randomly split (8:2 ratio) for training and testing. For external validation, 172 MRI scans from 43 patients with BM and 62 target lesions were additionally enrolled. The maximum axial diameter (Dmax), radiomics, and deep learning (DL) models were generated for comparison. We evaluated the simple convolutional neural network (CNN) model and a gated recurrent unit (Conv-GRU)-based CNN model in the DL arm. The Conv-GRU model performed superior to the simple CNN models. For both datasets, the area under the curve (AUC) was significantly higher for the two-dimensional (2D) Conv-GRU model than for the 3D Conv-GRU, Dmax, and radiomics models. The accuracy of the 2D Conv-GRU model increased with the number of follow-up studies. In conclusion, using longitudinal MRI data, the 2D Conv-GRU model outperformed all other models in predicting the treatment response after SRS of BM.

Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images.

BACKGROUND AND PURPOSE: Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. METHODS: Model development was done in 2,988 patients with ischemic stroke from three centers by using U-net for infarct segmentation and EfficientNetV2 for subtype classification. Experienced neurologists (n=5) determined subtypes for external test datasets, while establishing a consensus for clinical trial datasets. Automatically segmented infarcts were fed into the model (DWI-only algorithm). Subsequently, another model was trained, with AF included as a categorical variable (DWI+AF algorithm). These models were tested: (1) internally against the opinion of the labeling experts, (2) against fresh external DWI data, and (3) against clinical trial dataset. RESULTS: In the training-and-validation datasets, the mean (±standard deviation) age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only and the DWI+AF algorithms respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3%-60.7% and 73.7%-74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen's kappa were respectively 58.1% and 0.34 for the DWI-only vs. 72.9% and 0.57 for the DWI+AF algorithms. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. CONCLUSION: Our model trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes comparable to a consensus of stroke experts.

Correlation of apparent diffusion coefficient values measured by diffusion MRI and MGMT promoter methylation semiquantitatively analyzed with MS-MLPA in patients with glioblastoma multiforme.

PURPOSE: To retrospectively determine whether the apparent diffusion coefficient (ADC) values correlate with O(6)-methylguanine DNA methyltransferase (MGMT) promoter methylation semiquantitatively analyzed by methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) in patients with glioblastoma. MATERIALS AND METHODS: The study was approved by the Institutional Review Board and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Newly diagnosed patients with glioblastoma (n = 26) were analyzed with an ADC histogram approach based on enhancing solid portion. The methylation status of MGMT promoter was assessed by methylation-specific polymerase chain reaction (MSP) and by MS-MLPA. MS-MLPA is a semiquantitative method that determines the methylation ratio. The Ki-67 labeling index was also analyzed. The mean and 5th percentile ADC values were correlated with MGMT promoter methylation status and Ki-67 labeling index using a linear regression model. Progression-free survival (PFS) was also correlated with the ADC values using Kaplan-Meier survival analysis. RESULTS: The mean methylation ratio was 0.21 ± 0.20. By MSP, there were 5 methylated and 21 unmethylated tumors. The mean ADC revealed a positive relationship with MGMT promoter methylation ratio (P = 0.015) and was also significantly different according to MSP-determined methylation status (P = 0.011). Median PFS was significantly related with methylation ratio (P = 0.017) and MSP-derived methylation status (P = 0.025). A positive relationship was demonstrated between PFS and the mean ADC value (P = 0.001). The 5th percentile ADC values showed a significant negative relationship with Ki-67 labeling index (P = 0.036). CONCLUSION: We found that ADC values were significantly correlated with PFS as well as with MGMT promoter methylation status. We believe that ADC values may merit further investigation as a noninvasive biomarker for predicting treatment response.

Application of symmetry evaluation to deep learning algorithm in detection of mastoiditis on mastoid radiographs.

As many human organs exist in pairs or have symmetric appearance and loss of symmetry may indicate pathology, symmetry evaluation on medical images is very important and has been routinely performed in diagnosis of diseases and pretreatment evaluation. Therefore, applying symmetry evaluation function to deep learning algorithms in interpreting medical images is essential, especially for the organs that have significant inter-individual variation but bilateral symmetry in a person, such as mastoid air cells. In this study, we developed a deep learning algorithm to detect bilateral mastoid abnormalities simultaneously on mastoid anterior-posterior (AP) views with symmetry evaluation. The developed algorithm showed better diagnostic performance in diagnosing mastoiditis on mastoid AP views than the algorithm trained by single-side mastoid radiographs without symmetry evaluation and similar to superior diagnostic performance to head and neck radiologists. The results of this study show the possibility of evaluating symmetry in medical images with deep learning algorithms.

Intracranial steno-occlusive lesion detection on time-of-flight MR angiography using multi-task learning.

Steno-occlusive lesions in intracranial arteries refer to segments of narrowed or occluded blood vessels that increase the risk of ischemic strokes. Steno-occlusive lesion detection is crucial in clinical settings; however, automatic detection methods have hardly been studied. Therefore, we propose a novel automatic method to detect steno-occlusive lesions in sequential transverse slices on time-of-flight magnetic resonance angiography. Our method simultaneously detects lesions while segmenting blood vessels based on end-to-end multi-task learning, reflecting that the lesions are closely related to the connectivity of blood vessels. We design classification and localization modules that can be attached to arbitrary segmentation network. As blood vessels are segmented, both modules simultaneously predict the presence and location of lesions for each transverse slice. By combining outputs from the two modules, we devise a simple operation that boosts the performance of lesion localization. Experimental results show that lesion prediction and localization performance is improved by incorporating blood vessel extraction. Our ablation study demonstrates that the proposed operation enhances lesion localization accuracy. We also verify the effectiveness of multi-task learning by comparing our approach with those that individually detect lesions with extracted blood vessels.

Risk Prediction of Emergency Department Visits in Patients With Lung Cancer Using Machine Learning: Retrospective Observational Study.

BACKGROUND: Patients with lung cancer are among the most frequent visitors to emergency departments due to cancer-related problems, and the prognosis for those who seek emergency care is dismal. Given that patients with lung cancer frequently visit health care facilities for treatment or follow-up, the ability to predict emergency department visits based on clinical information gleaned from their routine visits would enhance hospital resource utilization and patient outcomes. OBJECTIVE: This study proposed a machine learning-based prediction model to identify risk factors for emergency department visits by patients with lung cancer. METHODS: This was a retrospective observational study of patients with lung cancer diagnosed at Seoul National University Bundang Hospital, a tertiary general hospital in South Korea, between January 2010 and December 2017. The primary outcome was an emergency department visit within 30 days of an outpatient visit. This study developed a machine learning-based prediction model using a common data model. In addition, the importance of features that influenced the decision-making of the model output was analyzed to identify significant clinical factors. RESULTS: The model with the best performance demonstrated an area under the receiver operating characteristic curve of 0.73 in its ability to predict the attendance of patients with lung cancer in emergency departments. The frequency of recent visits to the emergency department and several laboratory test results that are typically collected during cancer treatment follow-up visits were revealed as influencing factors for the model output. CONCLUSIONS: This study developed a machine learning-based risk prediction model using a common data model and identified influencing factors for emergency department visits by patients with lung cancer. The predictive model contributes to the efficiency of resource utilization and health care service quality by facilitating the identification and early intervention of high-risk patients. This study demonstrated the possibility of collaborative research among different institutions using the common data model for precision medicine in lung cancer.

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.

Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA). A total of 154 3D TOF-MRA datasets with intracranial aneurysms were acquired, and the gold standards were manually drawn by neuroradiologists. We also obtained 113 subjects from a public dataset for external validation. These angiograms were pre-processed by using skull-stripping, signal intensity normalization, and N4 bias correction. The 3D patches along the vessel skeleton from MRA were extracted. Values of the ratio between the aneurysmal and the normal patches ranged from 1:1 to 1:5. The semantic segmentation on intracranial aneurysms was trained using a 3D U-Net with an auxiliary classifier to overcome the imbalance in patches. The proposed method achieved an accuracy of 0.910 in internal validation and external validation accuracy of 0.883 with a 2:1 ratio of normal to aneurysmal patches. This multi-task learning method showed that the aneurysm segmentation performance was sufficient to be helpful in an actual clinical setting.

A Nationwide Web-Based Survey of Neuroradiologists' Perceptions of Artificial Intelligence Software for Neuro-Applications in Korea.

OBJECTIVE: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. MATERIALS AND METHODS: In April 2022, a 30-item online survey was conducted by neuroradiologists from the Korean Society of Neuroradiology (KSNR) to assess current user experiences, perceptions, attitudes, and future expectations regarding AI for neuro-applications. Respondents with experience in AI software were further investigated in terms of the number and type of software used, period of use, clinical usefulness, and future scope. Results were compared between respondents with and without experience with AI software through multivariable logistic regression and mediation analyses. RESULTS: The survey was completed by 73 respondents, accounting for 21.9% (73/334) of the KSNR members; 72.6% (53/73) were familiar with AI and 58.9% (43/73) had used AI software, with approximately 86% (37/43) using 1-3 AI software programs and 51.2% (22/43) having up to one year of experience with AI software. Among AI software types, brain volumetry software was the most common (62.8% [27/43]). Although 52.1% (38/73) assumed that AI is currently useful in practice, 86.3% (63/73) expected it to be useful for clinical practice within 10 years. The main expected benefits were reducing the time spent on repetitive tasks (91.8% [67/73]) and improving reading accuracy and reducing errors (72.6% [53/73]). Those who experienced AI software were more familiar with AI (adjusted odds ratio, 7.1 [95% confidence interval, 1.81-27.81]; P = 0.005). More than half of the respondents with AI software experience (55.8% [24/43]) agreed that AI should be included in training curriculums, while almost all (95.3% [41/43]) believed that radiologists should coordinate to improve its performance. CONCLUSION: A majority of respondents experienced AI software and showed a proactive attitude toward adopting AI in clinical practice, suggesting that AI should be incorporated into training and active participation in AI development should be encouraged.

Long-term outcomes and quantitative radiologic analysis of extracranial-intracranial bypass for hemodynamically compromised chronic large artery occlusive disease.

This study aimed to demonstrate the effectiveness of nonemergent extracranial-to-intracranial bypass (EIB) in symptomatic chronic large artery atherosclerotic stenosis or occlusive disease (LAA) through quantitative analysis of computed tomography perfusion (CTP) parameters using RAPID software. We retrospectively analyzed 86 patients who underwent nonemergent EIB due to symptomatic chronic LAA. CTP data obtained preoperatively, immediately postoperatively (PostOp0), and 6 months postoperatively (PostOp6M) after EIB were quantitatively analyzed through RAPID software, and their association with intraoperative bypass flow (BF) was assessed. The clinical outcomes, including neurologic state, incidence of recurrent infarction and complications, were also analyzed. The time-to-maximum (Tmax) > 8 s, > 6 s and > 4 s volumes decreased significantly at PostOp0 and up through PostOp6M (preoperative, 5, 51, and 223 ml (median), respectively; PostOp0, 0, 20.25, and 143 ml, respectively; PostOp6M, 0, 7.5, and 148.5 ml, respectively; p < 0.001, p < 0.001, and p < 0.001, respectively). The postoperative improvement in the Tmax > 6 s and > 4 s volumes was significantly correlated with the BF at PostOp0 and PostOp6M (PostOp0, r = 0.367 (p = 0.001) and r = 0.275 (p = 0.015), respectively; PostOp6M r = 0.511 (p < 0.001) and r = 0.391 (p = 0.001), respectively). The incidence of recurrent cerebral infarction was 4.7%, and there were no major complications that produced permanent neurological impairment. Nonemergent EIB under strict operation indications can be a feasible treatment for symptomatic, hemodynamically compromised LAA patients.

Prediction of hemorrhagic cerebral hyperperfusion syndrome after direct bypass surgery in adult nonhemorrhagic moyamoya disease: combining quantitative parameters on RAPID perfusion CT with clinically related factors.

OBJECTIVE: The aim of this study was to identify predictive factors for hemorrhagic cerebral hyperperfusion syndrome (hCHS) after direct bypass surgery in adult nonhemorrhagic moyamoya disease (non-hMMD) using quantitative parameters on rapid processing of perfusion and diffusion (RAPID) perfusion CT software. METHODS: A total of 277 hemispheres in 223 patients with non-hMMD who underwent combined bypass were retrospectively reviewed. Preoperative volumes of time to maximum (Tmax) > 4 seconds and > 6 seconds were obtained from RAPID analysis of perfusion CT. These quantitative parameters, along with other clinical and angiographic factors, were statistically analyzed to determine the significant predictors for hCHS after bypass surgery. RESULTS: Intra- or postoperative hCHS occurred in 13 hemispheres (4.7%). In 7 hemispheres, subarachnoid hemorrhage occurred intraoperatively, and in 6 hemispheres, intracerebral hemorrhage was detected postoperatively. All hCHS occurred within the 4 days after bypass. Advanced age (OR 1.096, 95% CI 1.039-1.163, p = 0.001) and a large volume of Tmax > 6 seconds (OR 1.011, 95% CI 1.004-1.018, p = 0.002) were statistically significant factors in predicting the risk of hCHS after surgery. The cutoff values of patient age and volume of Tmax > 6 seconds were 43.5 years old (area under the curve [AUC] 0.761) and 80.5 ml (AUC 0.762), respectively. CONCLUSIONS: In adult patients with non-hMMD older than 43.5 years or with a large volume of Tmax > 6 seconds over 80.5 ml, more prudence is required in the decision to undergo bypass surgery and in postoperative management.