Search

Your search keyword '"Jacob Schlossman"' showing total 2 results
Start Over Author "Jacob Schlossman" Publication Year Range Last 3 years
2 results on '"Jacob Schlossman"'

1. Head-to-head comparison of commercial artificial intelligence solutions for detection of large vessel occlusion at a comprehensive stroke center

Author

Jacob Schlossman, Daniel Ro, Shirin Salehi, Daniel Chow, Wengui Yu, Peter D. Chang, and Jennifer E. Soun

Subjects
artificial intelligence, large vessel occlusion, machine learning, deep learning, stroke, Neurology. Diseases of the nervous system, RC346-429
Abstract

PurposeDespite the availability of commercial artificial intelligence (AI) tools for large vessel occlusion (LVO) detection, there is paucity of data comparing traditional machine learning and deep learning solutions in a real-world setting. The purpose of this study is to compare and validate the performance of two AI-based tools (RAPID LVO and CINA LVO) for LVO detection.Materials and methodsThis was a retrospective, single center study performed at a comprehensive stroke center from December 2020 to June 2021. CT angiography (n = 263) for suspected stroke were evaluated for LVO. RAPID LVO is a traditional machine learning model which primarily relies on vessel density threshold assessment, while CINA LVO is an end-to-end deep learning tool implemented with multiple neural networks for detection and localization tasks. Reasons for errors were also recorded.ResultsThere were 29 positive and 224 negative LVO cases by ground truth assessment. RAPID LVO demonstrated an accuracy of 0.86, sensitivity of 0.90, specificity of 0.86, positive predictive value of 0.45, and negative predictive value of 0.98, while CINA demonstrated an accuracy of 0.96, sensitivity of 0.76, specificity of 0.98, positive predictive value of 0.85, and negative predictive value of 0.97.ConclusionBoth tools successfully detected most anterior circulation occlusions. RAPID LVO had higher sensitivity while CINA LVO had higher accuracy and specificity. Interestingly, both tools were able to detect some, but not all M2 MCA occlusions. This is the first study to compare traditional and deep learning LVO tools in the clinical setting.

Published
2022
Full Text
View/download PDF

2. Diffusion MRI is an early biomarker of overall survival benefit in IDH wild-type recurrent glioblastoma treated with immune checkpoint inhibitors

Author

Akifumi Hagiwara, Talia C Oughourlian, Nicholas S Cho, Jacob Schlossman, Chencai Wang, Jingwen Yao, Catalina Raymond, Richard Everson, Kunal Patel, Sergey Mareninov, Fausto J Rodriguez, Noriko Salamon, Whitney B Pope, Phioanh L Nghiemphu, Linda M Liau, Robert M Prins, Timothy F Cloughesy, and Benjamin M Ellingson

Subjects
Cancer Research, Oncology and Carcinogenesis, Neuroimaging, Rare Diseases, Clinical Research, Humans, Oncology & Carcinogenesis, Immune Checkpoint Inhibitors, Retrospective Studies, Cancer, IDH wild type, ICI, Brain Neoplasms, Neurosciences, Evaluation of treatments and therapeutic interventions, Brain Disorders, Brain Cancer, Diffusion Magnetic Resonance Imaging, Good Health and Well Being, Oncology, ADC, 6.1 Pharmaceuticals, Biomedical Imaging, Neurology (clinical), Glioblastoma, Biomarkers, MRI, recurrent glioblastoma
Abstract

Background Diffusion MRI estimates of the apparent diffusion coefficient (ADC) have been shown to be useful in predicting treatment response in patients with glioblastoma (GBM), with ADC elevations indicating tumor cell death. We aimed to investigate whether the ADC values measured before and after treatment with immune checkpoint inhibitors (ICIs) and the changes in these ADC values could predict overall survival (OS) in patients with recurrent IDH wild-type GBM. Methods Forty-four patients who met the following inclusion criteria were included in this retrospective study: (i) diagnosed with recurrent IDH wild-type GBM and treated with either pembrolizumab or nivolumab and (ii) availability of diffusion data on pre- and post-ICI MRI. Tumor volume and the median relative ADC (rADC) with respect to the normal-appearing white matter within the enhancing tumor were calculated. Results Median OS among all patients was 8.1 months (range, 1.0–22.5 months). Log-rank test revealed that higher post-treatment rADC was associated with a significantly longer OS (median, 10.3 months for rADC ≥ 1.63 versus 6.1 months for rADC < 1.63; P = .02), whereas tumor volume, pretreatment rADC, and changes in rADC after treatment were not significantly associated with OS. Cox regression analysis revealed that post-treatment rADC significantly influenced OS (P = .02, univariate analysis), even after controlling for age and sex (P =.01, multivariate analysis), and additionally controlling for surgery after ICI treatment (P = .045, multivariate analysis). Conclusions Elevated post-treatment rADC may be an early imaging biomarker for OS benefits in GBM patients receiving ICI treatment.

Published
2022

Catalog

Books, media, physical & digital resources
See catalog results