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Aims: During the COVID-19 public health emergency, we previously identified decreased rates of radiotherapy (RT) peer review (PR) discussion and plan changes in virtual versus in-person PR conferences. To expand on these findings, we continued to prospectively collect data on all PR conferences from 2021 to 2023 and performed a follow-up analysis before and after the transition back to in-person PR., Materials and Methods: A prospectively maintained database of weekly PR cases was queried for consecutive cases reviewed before and after the transition from virtual to in-person conferences. Rates of PR discussion and change recommendations were summarized and compared between the virtual and in-person groups. A survey was developed and administered to assess participants' perceived levels of engagement, opinions on optimal PR format, and preferences for future meetings before and 3 months after the transition back to in-person PR., Results: In total, 2,103 RT plans were reviewed: 1,590 virtually and 513 after the transition back to in-person. There was no difference in faculty attendance between groups. The proportion of cases with PR discussion increased from virtual (9.8%) to in-person (25.5%) format (p < 0.001). In the virtual group, 8.1% of cases had 1 topic and 1.7% had 2+ topics discussed. This increased to 15.8% and 9.7% during in-person PR, respectively (p < 0.001). The rate of change recommendation also increased from 1.5% (virtual) to 3.3% (in-person, p = 0.016). Among cases with at least 1 topic discussed, there was no difference in changes. Survey-reported distraction significantly decreased from virtual to in-person PR (p < 0.001)., Conclusion: Upon returning to in-person PR conferences, peer discussion and plan change recommendations significantly increased and returned to pre-pandemic levels, and participants' perceived levels of distraction were reduced. In an increasingly virtual world, additional efforts to develop best practices that maximize PR discussion and minimize distraction outside virtual conferences are warranted., (Copyright © 2024. Published by Elsevier Ltd.)

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: Alzheimer's disease (AD) and Lewy body disease (LBD) are the two most common neurodegenerative dementias and can occur in combination (AD+LBD). Due to overlapping biomarkers and symptoms, clinical differentiation of these subtypes could be difficult. However, it is unclear how the magnitude of diagnostic uncertainty varies across dementia spectra and demographic variables. We aimed to compare clinical diagnosis and post-mortem autopsy-confirmed pathological results to assess the clinical subtype diagnosis quality across these factors., Methods: We studied data of 1,920 participants recorded by the National Alzheimer's Coordinating Center from 2005 to 2019. Selection criteria included autopsy-based neuropathological assessments for AD and LBD, and the initial visit with Clinical Dementia Rating (CDR) stage of normal, mild cognitive impairment, or mild dementia. Longitudinally, we analyzed the first visit at each subsequent CDR stage. This analysis included positive predictive values, specificity, sensitivity and false negative rates of clinical diagnosis, as well as disparities by sex, race, age, and education. If autopsy-confirmed AD and/or LBD was missed in the clinic, the alternative clinical diagnosis was analyzed., Findings: In our findings, clinical diagnosis of AD+LBD had poor sensitivities. Over 61% of participants with autopsy-confirmed AD+LBD were diagnosed clinically as AD. Clinical diagnosis of AD had a low sensitivity at the early dementia stage and low specificities at all stages. Among participants diagnosed as AD in the clinic, over 32% had concurrent LBD neuropathology at autopsy. Among participants diagnosed as LBD, 32% to 54% revealed concurrent autopsy-confirmed AD pathology. When three subtypes were missed by clinicians, "No cognitive impairment" and "primary progressive aphasia or behavioral variant frontotemporal dementia" were the leading primary etiologic clinical diagnoses. With increasing dementia stages, the clinical diagnosis accuracy of black participants became significantly worse than other races, and diagnosis quality significantly improved for males but not females., Discussion: These findings demonstrate that clinical diagnosis of AD, LBD, and AD+LBD are inaccurate and suffer from significant disparities on race and sex. They provide important implications for clinical management, anticipatory guidance, trial enrollment and applicability of potential therapies for AD, and promote research into better biomarker-based assessment of LBD pathology., Competing Interests: HW and NR report grants from Leon Lowenstein Foundation. NR and AM report grants from National Institute of Health/National Institute on Aging (NIH/NIA) (grants number P30AG066512). AM reports grants from NIH (grants numbers RF1AG072507, R21AG070880, and P30AG008051), BrightFocus Foundation (grants number A2019602S), and Alzheimer’s Association (grants number AACF-17-524288). AM declares the payment from Rockefeller University (Nov. 2019) for education events. AM is a member of the Alzheimer’s Association International Research Grant Program Council and reports the payment in the form of waiving the ISTAART membership fee: $150/year; AM is on the steering committee of the Alzheimer’s Disease Cooperative Study with no payments. AM is on the Editorial Advisory Board of the Journal of Neuro-ophthalmology and Editorial Board of Alzheimer’s & Dementia: Translational Research and Clinical Interventions., (Copyright © 2023 Wei, Masurkar and Razavian.)

Purpose: Head and neck (HN) radiotherapy (RT) is complex, involving multiple target and organ at risk (OAR) structures delineated by the radiation oncologist. Site-agnostic peer review after RT plan completion is often inadequate for thorough review of these structures. In-depth review of RT contours is critical to maintain high-quality RT and optimal patient outcomes., Materials and Methods: In August 2020, the HN RT Quality Assurance Conference, a weekly teleconference that included at least one radiation oncology HN specialist, was activated at our institution. Targets and OARs were reviewed in detail prior to RT plan creation. A parallel implementation study recorded patient factors and outcomes of these reviews. A major change was any modification to the high-dose planning target volume (PTV) or the prescription dose/fractionation; a minor change was modification to the intermediate-dose PTV, low-dose PTV, or any OAR. We analysed the results of consecutive RT contour review in the first 20 months since its initiation., Results: A total of 208 patients treated by 8 providers were reviewed: 86·5% from the primary tertiary care hospital and 13·5% from regional practices. A major change was recommended in 14·4% and implemented in 25 of 30 cases (83·3%). A minor change was recommended in 17·3% and implemented in 32 of 36 cases (88·9%). A survey of participants found that all ( n = 11) strongly agreed or agreed that the conference was useful., Conclusion: Dedicated review of RT targets/OARs with a HN subspecialist is associated with substantial rates of suggested and implemented modifications to the contours., Competing Interests: Competing Interests. The author(s) declare none

Purpose: Increasingly complex MRI studies and variable series naming conventions reveal limitations of rule-based image routing, especially in health systems with multiple scanners and sites. Accurate methods to identify series based on image content would aid post-processing and PACS viewing. Recent deep/machine learning efforts classify 5-8 basic brain MR sequences. We present an ensemble model combining a convolutional neural network and a random forest classifier to differentiate 25 brain sequences and image orientation., Methods: Series were grouped by descriptions into 25 sequences and 4 orientations. Dataset A, obtained from our institution, was divided into training (16,828 studies; 48,512 series; 112,028 images), validation (4746 studies; 16,612 series; 26,222 images) and test sets (6348 studies; 58,705 series; 3,314,018 images). Dataset B, obtained from a separate hospital, was used for out-of-domain external validation (1252 studies; 2150 series; 234,944 images). We developed an ensemble model combining a 2D convolutional neural network with a custom multi-task learning architecture and random forest classifier trained on DICOM metadata to classify sequence and orientation by series., Results: The neural network, random forest, and ensemble achieved 95%, 97%, and 98% overall sequence accuracy on dataset A, and 98%, 99%, and 99% accuracy on dataset B, respectively. All models achieved > 99% orientation accuracy on both datasets., Conclusion: The ensemble model for series identification accommodates the complexity of brain MRI studies in state-of-the-art clinical practice. Expanding on previous work demonstrating proof-of-concept, our approach is more comprehensive with greater sequence diversity and orientation classification., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Early diagnosis of Alzheimer's disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer's disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer's dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer's disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease., (© 2022. The Author(s).)

Image-based analysis as a method for mutation detection can be advantageous in settings when tumor tissue is limited or unavailable for direct testing. In this study, we utilize two distinct and complementary machine-learning methods of analyzing whole-slide images for predicting mutated BRAF. In the first method, whole-slide images of melanomas from 256 patients were used to train a deep convolutional neural network to develop a fully automated model that first selects for tumor-rich areas (area under the curve = 0.96) and then predicts for mutated BRAF (area under the curve = 0.71). Saliency mapping was performed and revealed that pixels corresponding to nuclei were the most relevant to network learning. In the second method, whole-slide images were analyzed using a pathomics pipeline that first annotates nuclei and then quantifies nuclear features, showing that mutated BRAF nuclei were significantly larger and rounder than BRAF‒wild-type nuclei. Finally, we developed a model that combines clinical information, deep learning, and pathomics that improves the predictive performance for mutated BRAF to an area under the curve of 0.89. Not only does this provide additional insights on how BRAF mutations affect tumor structural characteristics, but machine learning‒based analysis of whole-slide images also has the potential to be integrated into higher-order models for understanding tumor biology., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Biopsies of inflammatory tissue contain a complex network of interacting cells, orchestrating the immune or autoimmune response. While standard histological examination can identify relationships, it is clear that a great amount of data on each slide is not quantitated or categorized in standard microscopic examinations. To deal with the huge amount of data present in biopsy tissue in an unbiased and comprehensive way, we have developed a deep learning algorithm to identify immune cells in biopsies of inflammatory lesions. We focused on T follicular helper (Tfh) cell subsets and B cells in dermatomyositis biopsy images. We achieved strong performance on detection and classification of cells, including the rare Tfh cell subsets present in the tissue. This algorithm could be used to perform distance mapping between cell types in tissue, and could be easily adapted to other disease states., (Copyright © 2022. Published by Elsevier B.V.)

Background: We previously developed and validated a predictive model to help clinicians identify hospitalized adults with coronavirus disease 2019 (COVID-19) who may be ready for discharge given their low risk of adverse events. Whether this algorithm can prompt more timely discharge for stable patients in practice is unknown., Objectives: The aim of the study is to estimate the effect of displaying risk scores on length of stay (LOS)., Methods: We integrated model output into the electronic health record (EHR) at four hospitals in one health system by displaying a green/orange/red score indicating low/moderate/high-risk in a patient list column and a larger COVID-19 summary report visible for each patient. Display of the score was pseudo-randomized 1:1 into intervention and control arms using a patient identifier passed to the model execution code. Intervention effect was assessed by comparing LOS between intervention and control groups. Adverse safety outcomes of death, hospice, and re-presentation were tested separately and as a composite indicator. We tracked adoption and sustained use through daily counts of score displays., Results: Enrolling 1,010 patients from May 15, 2020 to December 7, 2020, the trial found no detectable difference in LOS. The intervention had no impact on safety indicators of death, hospice or re-presentation after discharge. The scores were displayed consistently throughout the study period but the study lacks a causally linked process measure of provider actions based on the score. Secondary analysis revealed complex dynamics in LOS temporally, by primary symptom, and hospital location., Conclusion: An AI-based COVID-19 risk score displayed passively to clinicians during routine care of hospitalized adults with COVID-19 was safe but had no detectable impact on LOS. Health technology challenges such as insufficient adoption, nonuniform use, and provider trust compounded with temporal factors of the COVID-19 pandemic may have contributed to the null result., Trial Registration: ClinicalTrials.gov identifier: NCT04570488., Competing Interests: None declared., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

The determination of endometrial carcinoma histological subtypes, molecular subtypes, and mutation status is critical for the diagnostic process, and directly affects patients' prognosis and treatment. Sequencing, albeit slower and more expensive, can provide additional information on molecular subtypes and mutations that can be used to better select treatments. Here, we implement a customized multi-resolution deep convolutional neural network, Panoptes, that predicts not only the histological subtypes but also the molecular subtypes and 18 common gene mutations based on digitized H&E-stained pathological images. The model achieves high accuracy and generalizes well on independent datasets. Our results suggest that Panoptes, with further refinement, has the potential for clinical application to help pathologists determine molecular subtypes and mutations of endometrial carcinoma without sequencing., Competing Interests: The authors declare no competing interests., (© 2021 The Author(s).)

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

Importance: To date, the association of psychiatric diagnoses with mortality in patients infected with coronavirus disease 2019 (COVID-19) has not been evaluated., Objective: To assess whether a diagnosis of a schizophrenia spectrum disorder, mood disorder, or anxiety disorder is associated with mortality in patients with COVID-19., Design, Setting, and Participants: This retrospective cohort study assessed 7348 consecutive adult patients for 45 days following laboratory-confirmed COVID-19 between March 3 and May 31, 2020, in a large academic medical system in New York. The final date of follow-up was July 15, 2020. Patients without available medical records before testing were excluded., Exposures: Patients were categorized based on the following International Statistical Classification of Diseases, Tenth Revision, Clinical Modification diagnoses before their testing date: (1) schizophrenia spectrum disorders, (2) mood disorders, and (3) anxiety disorders. Patients with these diagnoses were compared with a reference group without psychiatric disorders., Main Outcomes and Measures: Mortality, defined as death or discharge to hospice within 45 days following a positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test result., Results: Of the 26 540 patients tested, 7348 tested positive for SARS-CoV-2 (mean [SD] age, 54 [18.6] years; 3891 [53.0%] women). Of eligible patients with positive test results, 75 patients (1.0%) had a history of a schizophrenia spectrum illness, 564 (7.7%) had a history of a mood disorder, and 360 (4.9%) had a history of an anxiety disorder. After adjusting for demographic and medical risk factors, a premorbid diagnosis of a schizophrenia spectrum disorder was significantly associated with mortality (odds ratio [OR], 2.67; 95% CI, 1.48-4.80). Diagnoses of mood disorders (OR, 1.14; 95% CI, 0.87-1.49) and anxiety disorders (OR, 0.96; 95% CI, 0.65-1.41) were not associated with mortality after adjustment. In comparison with other risk factors, a diagnosis of schizophrenia ranked behind only age in strength of an association with mortality., Conclusions and Relevance: In this cohort study of adults with SARS-CoV-2-positive test results in a large New York medical system, adults with a schizophrenia spectrum disorder diagnosis were associated with an increased risk for mortality, but those with mood and anxiety disorders were not associated with a risk of mortality. These results suggest that schizophrenia spectrum disorders may be a risk factor for mortality in patients with COVID-19.

Purpose: To identify and define the incidence, risk factors, clinical characteristics, and treatment approaches to pelvic insufficiency fractures (PIFs) that develop as a consequence of pelvic radiation therapy for gynecologic malignancies., Materials and Methods: A systematic literature review (PubMed and Embase indexed from January 1, 1980, to May 1, 2020) of studies describing PIFs that result from radiation therapy for gynecologic malignancies. A random-effects model weighted by the inverse variance was used to calculate the pooled crude incidence, actuarial incidence, and proportion of symptomatic PIFs, and to evaluate the relationship between PIF incidence and various risk factors., Results: Thirty-eight studies describing PIFs following radiation therapy for gynecologic malignancies were reviewed. A meta-analysis of 6488 patients (37 studies) identified the crude incidence of PIF as 9.4% (95% confidence interval [CI] 6.8%-12.4%), and a meta-analysis of 2131 patients (9 studies) identified the 5-year actuarial incidence of PIF as 15.3% (95% CI 7.5%-25.0%). Factors that significantly correlated with increased risk of PIF development included evidence of osteoporosis (P < .001), postmenopausal state (P < .001), and history of diabetes mellitus (P = .005). Median time to PIF development ranged from 8 to 39 months after radiation therapy with the sacrum being the most frequent location for fracture development (60%). From 18 studies, 58.5% (95% CI 50.6%-66.2%) of PIFs were symptomatic, with pain as the most common presenting symptom of PIFs. Conservative management was used more than bone-directed therapies for treatment of PIFs (85% and 6% of patients, respectively)., Conclusions: PIFs cause significant morbidity in gynecologic cancer patients after radiation therapy. In this systematic review, we discuss the incidence and risk factors associated with PIF development as it relates to the different detection methods, radiation techniques, doses, and gynecologic cancers treated. Additional studies are needed to further define prevention and treatment approaches for insufficiency fractures., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The COVID-19 pandemic has challenged front-line clinical decision-making, leading to numerous published prognostic tools. However, few models have been prospectively validated and none report implementation in practice. Here, we use 3345 retrospective and 474 prospective hospitalizations to develop and validate a parsimonious model to identify patients with favorable outcomes within 96 h of a prediction, based on real-time lab values, vital signs, and oxygen support variables. In retrospective and prospective validation, the model achieves high average precision (88.6% 95% CI: [88.4-88.7] and 90.8% [90.8-90.8]) and discrimination (95.1% [95.1-95.2] and 86.8% [86.8-86.9]) respectively. We implemented and integrated the model into the EHR, achieving a positive predictive value of 93.3% with 41% sensitivity. Preliminary results suggest clinicians are adopting these scores into their clinical workflows., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2020.)

Artificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice., Competing Interests: Florian Knoll reports grants from the National Institutes of Health during the conduct of the study. In addition, he is one of the inventors of US patent 20,170,309,019: A1: System, method and computer-accessible medium for learning an optimized variational network. Narges Razavian and Krzysztof J. Geras have declared no conflicts of interest for this article., (Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.)