INTRODUCTION Molecular classification has transformed the management of brain tumors by enabling more accurate prognostication and personalized treatment. Access to timely molecular diagnostic testing for brain tumor patients is limited, complicating surgical and adjuvant treatment and obstructing clinical trial enrollment. OBJECTIVE We aim to develop a rapid (< 90 seconds), AI-based diagnostic screening system that can provide molecular classification of diffuse gliomas and report its use in a prospective, multicenter, international clinical trial of diffuse glioma patients (n = 153). METHODS By combining stimulated Raman histology (SRH), a rapid, label-free, non-consumptive, optical imaging method, and deep learning-based image classification, we are able to predict the molecular genetic features used by the World Health Organization (WHO) to define the adult-type diffuse glioma taxonomy, including IDH-1/2, 1p19q-codeletion, and ATRX loss. We developed a multimodal deep neural network training strategy that uses both SRH images and large-scale, public diffuse glioma genomic data in order to achieve optimal molecular classification performance. RESULTS One institution was used for model training (University of Michigan) and four institutions (NYU, UCSF, Medical University of Vienna, and University Hospital Cologne) were included for prospective patient enrollment and model testing. Using our system, called DeepGlioma, we achieved an average molecular genetic classification accuracy of 93.2% and identified the correct diffuse glioma molecular subgroup with 91.5% accuracy. DeepGlioma outperformed conventional IDH1-R132H immunohistochemistry (94.2% versus 91.4% accuracy, respectively) as a first-line molecular diagnostic screening method for diffuse gliomas, detecting canonical and non-canonical IDH mutations with high accuracy. CONCLUSION Our results demonstrate how artificial intelligence and optical histology can be used to provide a rapid and scalable alternative to wet lab methods for the molecular diagnosis of brain tumor patients during surgery.
BACKGROUND:Accurate specimen analysis of skull base tumors is essential for providing personalized surgical treatment strategies. Intraoperative specimen interpretation can be challenging because of the wide range of skull base pathologies and lack of intraoperative pathology resources.OBJECTIVE:To develop an independent and parallel intraoperative workflow that can provide rapid and accurate skull base tumor specimen analysis using label-free optical imaging and artificial intelligence.METHODS:We used a fiber laser–based, label-free, nonconsumptive, high-resolution microscopy method (<60 seconds per 1 × 1 mm2), called stimulated Raman histology (SRH), to image a consecutive, multicenter cohort of patients with skull base tumor. SRH images were then used to train a convolutional neural network model using 3 representation learning strategies: cross-entropy, self-supervised contrastive learning, and supervised contrastive learning. Our trained convolutional neural network models were tested on a held-out, multicenter SRH data set.RESULTS:SRH was able to image the diagnostic features of both benign and malignant skull base tumors. Of the 3 representation learning strategies, supervised contrastive learning most effectively learned the distinctive and diagnostic SRH image features for each of the skull base tumor types. In our multicenter testing set, cross-entropy achieved an overall diagnostic accuracy of 91.5%, self-supervised contrastive learning 83.9%, and supervised contrastive learning 96.6%. Our trained model was able to segment tumor-normal margins and detect regions of microscopic tumor infiltration in meningioma SRH images.CONCLUSION:SRH with trained artificial intelligence models can provide rapid and accurate intraoperative analysis of skull base tumor specimens to inform surgical decision-making.
Background Electronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable. Objective The aim of this study was to evaluate whether natural language processing (NLP)–powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status. Methods In this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. Results NAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P<.001; ratio median 2.2, min-max 0.4-20) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen κ=0.67). In the cases that exhibited disagreement with manual chart reviews, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semiautomated NLP features. Conclusions NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHRs.
The most common inherited cause of vascular dementia and stroke, cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), is caused by mutations in NOTCH3. Post-translationally altered NOTCH3 accumulates in the vascular media of CADASIL arteries in areas of the vessels that exhibit profound cellular degeneration. The identification of molecules that concentrate in the same location as pathological NOTCH3 may shed light on processes that drive cytopathology in CADASIL. We performed a two phase immunohistochemical screen of markers identified in the Human Protein Atlas to identify new proteins that accumulate in the vascular media in a pattern similar to pathological NOTCH3. In phase one, none of 16 smooth muscle cell (SMC) localized antigens exhibited NOTCH3-like patterns of expression; however, several exhibited disease-dependent patterns of expression, with antibodies directed against FAM124A, GZMM, MTFR1, and ST6GAL demonstrating higher expression in controls than CADASIL. In contrast, in phase two of the study that included 56 non-SMC markers, two proteins, CD63 and CTSH, localized to the same regions as pathological NOTCH3, which was verified by VesSeg, a customized algorithm that assigns relative location of antigens within the layers of the vessel. Proximity ligation assays support complex formation between NOTCH3 fragments and CD63 in degenerating CADASIL media. Interestingly, in normal mouse brain, the two novel CADASIL markers, CD63 and CTSH, are expressed in non-SMC vascular cells. The identification of new proteins that concentrate in CADASIL vascular media demonstrates the utility of querying publicly available protein databases in specific neurological diseases and uncovers unexpected, non-SMC origins of pathological antigens in small vessel disease.
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