Browsing by Author "Manthey, David"

Now showing 1 - 4 of 4
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    FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology
    (bioRxiv, 2024-08-22) Border, Samuel; Ferreira, Ricardo Melo; Lucarelli, Nicholas; Manthey, David; Kumar, Suhas; Paul, Anindya; Mimar, Sayat; Naglah, Ahmed; Cheng, Ying-Hua; Barisoni, Laura; Ray, Jessica; Strekalova, Yulia; Rosenberg, Avi Z.; Tomaszewski, John E.; Hodgin, Jeffrey B.; HuBMAP consortium; El-Achkar, Tarek M.; Jain, Sanjay; Eadon, Michael T.; Sarder, Pinaki; Medicine, School of Medicine
    Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.
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    NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer
    (Oxford University Press, 2022) Amgad, Mohamed; Atteya, Lamees A.; Hussein, Hagar; Mohammed, Kareem Hosny; Hafiz, Ehab; Elsebaie, Maha A.T.; Alhusseiny, Ahmed M.; AlMoslemany, Mohamed Atef; Elmatboly, Abdelmagid M.; Pappalardo, Philip A.; Sakr, Rokia Adel; Mobadersany, Pooya; Rachid, Ahmad; Saad, Anas M.; Alkashash, Ahmad M.; Ruhban, Inas A.; Alrefai, Anas; Elgazar, Nada M.; Abdulkarim, Ali; Farag, Abo-Alela; Etman, Amira; Elsaeed, Ahmed G.; Alagha, Yahya; Amer, Yomna A.; Raslan, Ahmed M.; Nadim, Menatalla K.; Elsebaie, Mai A.T.; Ayad, Ahmed; Hanna, Liza E.; Gadallah, Ahmed; Elkady, Mohamed; Drumheller, Bradley; Jaye, David; Manthey, David; Gutman, David A.; Elfandy, Habiba; Cooper, Lee A.D.; Pathology and Laboratory Medicine, School of Medicine
    Background: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists. Results: This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes. Conclusions: This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.
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    Rapid Adaptation to Remote Didactics and Learning in GME
    (Wiley, 2020-09-08) Hickam, Grace; Santen, Sally A.; Cico, Stephen John; Manthey, David; Wolff, Margaret; Moll, Joel; Lambert, Alexandra; Jordan, Jaime; Haas, Mary R. C.; Emergency Medicine, School of Medicine
    Weekly didactic conference in emergency medicine education has traditionally united residents and faculty for learning and fostered community within the residency program. The global pandemic Coronavirus Disease-19 (COVID-19) has fueled a rapid transition to remote learning that has disrupted the typical in-person format. To maintain ACGME educational experiences and requirements for residents in a safe manner, many residencies have moved to videoconferencing platforms such as Zoom™, Teams™, and WebEX.™ Given the importance of didactic conference as a ritual, educational experience and community-building activity, most residency programs have worked to maintain an active and robust didactic conference despite the many logistical challenges. Engaging residency program members in the transition to remote learning and utilizing opportunities for innovation can help to maintain normalcy and combat isolation resulting from the loss of weekly in-person contact. Herein, we propose practical tips for optimizing remote learning for weekly emergency medicine residency didactics.
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    Validity evidence for an instrument for cognitive load for virtual didactic sessions
    (Wiley, 2022-02-01) Hickam, Grace; Jordan, Jaime; Haas, Mary R. C.; Wagner, Jason; Manthey, David; Cico, Stephen John; Wolff, Margaret; Santen, Sally A.; Emergency Medicine, School of Medicine
    Background: COVID necessitated the shift to virtual resident instruction. The challenge of learning via virtual modalities has the potential to increase cognitive load. It is important for educators to reduce cognitive load to optimize learning, yet there are few available tools to measure cognitive load. The objective of this study is to identify and provide validity evidence following Messicks' framework for an instrument to evaluate cognitive load in virtual emergency medicine didactic sessions. Methods: This study followed Messicks' framework for validity including content, response process, internal structure, and relationship to other variables. Content validity evidence included: (1) engagement of reference librarian and literature review of existing instruments; (2) engagement of experts in cognitive load, and relevant stakeholders to review the literature and choose an instrument appropriate to measure cognitive load in EM didactic presentations. Response process validity was gathered using the format and anchors of instruments with previous validity evidence and piloting amongst the author group. A lecture was provided by one faculty to four residency programs via ZoomTM. Afterwards, residents completed the cognitive load instrument. Descriptive statistics were collected; Cronbach's alpha assessed internal consistency of the instrument; and correlation for relationship to other variables (quality of lecture). Results: The 10-item Leppink Cognitive Load instrument was selected with attention to content and response process validity evidence. Internal structure of the instrument was good (Cronbach's alpha = 0.80). Subscales performed well-intrinsic load (α = 0.96, excellent), extrinsic load (α = 0.89, good), and germane load (α = 0.97, excellent). Five of the items were correlated with overall quality of lecture (p < 0.05). Conclusions: The 10-item Cognitive Load instrument demonstrated good validity evidence to measure cognitive load and the subdomains of intrinsic, extraneous, and germane load. This instrument can be used to provide feedback to presenters to improve the cognitive load of their presentations.