2024

1. Intraoperative margin assessment for basal cell carcinoma with deep learning and histologic tumor mapping to surgical site

   Levy, Joshua J., Davis, Matthew J., Chacko, Rachael S., Davis, Michael J., Fu, Lucy J., Goel, Tarushii, Pamal, Akash, Nafi, Irfan, Angirekula, Abhinav, and Suvarna, Anish

   NPJ Precision Oncology 2024
2. Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis

   Azher, Zarif L., Fatemi, Michael, Lu, Yunrui, Srinivasan, Gokul, Diallo, Alos B., Christensen, Brock C., Salas, Lucas A., Kolling IV, Fred W., Perreard, Laurent, and Palisoul, Scott M.

   In Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 2024
3. A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment

   Davis, Matthew J., Srinivasan, Gokul, Chacko, Rachael, Chen, Sophie, Suvarna, Anish, Vaickus, Louis J., Torres, Veronica C., Hodge, Sassan, Chen, Eunice Y., Preum, Sarah, Samkoe, Kimberley S., Christensen, Brock C., LeBoeuf, Matthew R., and Levy, Joshua J.

   Experimental Dermatology Jan 2024

   Abs

   Abstract Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumour removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. The aim of this study was to develop and evaluate the accuracy of an AI algorithm for real‐time histologic margin analysis of cSCC. To do this, a retrospective cohort study was conducted using frozen cSCC section slides. These slides were scanned and annotated, delineating benign tissue structures, inflammation and tumour to develop an AI algorithm for real‐time margin analysis. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC. This algorithm demonstrated proof of concept for identifying cSCC with high accuracy, highlighting the potential for integration of AI into the surgical workflow. Incorporation of AI algorithms may improve efficiency and completeness of real‐time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumours/neoplasms. Further algorithmic improvement incorporating surrounding tissue context is necessary to remain sensitive to the unique epidermal landscape of well‐differentiated tumours, and to map tumours to their original anatomical position/orientation.

4. Potential to enhance large scale molecular assessments of skin photoaging through virtual inference of spatial transcriptomics from routine staining

   Srinivasan, Gokul, Davis, Matthew J., LeBoeuf, Matthew R., Fatemi, Michael, Azher, Zarif L., Lu, Yunrui, Diallo, Alos B., Montivero, Marietta K. Saldias, Kolling IV, Fred W., and Perrard, Laurent

   In Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 2024
5. An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology

   Fatemi, Michael Y., Lu, Yunrui, Diallo, Alos B., Srinivasan, Gokul, Azher, Zarif L., Christensen, Brock C., Salas, Lucas A., Tsongalis, Gregory J., Palisoul, Scott M., and Perreard, Laurent

   Briefings in Bioinformatics Jan 2024
6. Tu1703 MULTI CENTER VALIDATION OF VIDEO-BASED DEEP LEARNING TO INTERPRET ANORECTAL MANOMETRY

   Azher, Zarif, Ginnebaugh, Brian D., Levinthal, David J., Valentin, Nelson, Levy, Joshua J., and Shah, Eric D.

   Gastroenterology Jan 2024
7. Abstract B003: Preliminary machine learning integration of DNA methylation-based tumor immune microenvironment deconvolution with histopathological slides for bladder cancer prognostication

   Azher, Zarif L., Zhang, Ze, Christensen, Brock C., Salas, Lucas A., Karagas, Margaret R., Vaickus, Louis J., Fuyura, Hideki, Yao, Keluo, and Levy, Joshua J.

   Clinical Cancer Research Jan 2024
8. Abstract IA020: AI-powered tool for rapid & reliable bladder cancer screening and surveillance: multicenter validation efforts

   Levy, Joshua, Yao, Keluo, Marotti, Jonathan, Kerr, Darcy, Gutmann, Edward, Harvey, Sam, Sayegh, Yoseph, VandenBussche, Chris, Quick, Michael, and Costa, Peter

   Clinical Cancer Research Jan 2024
9. Multimodal analysis of metals, spatial transcriptomics, and histological structures in colorectal cancer

   Srivastava, Aruesha, Shaik, Neha, Lu, Yunrui, Chan, Matthew, Diallo, Alos, Han, Serin, Steiner, Ramsey, Punshon, Tracy, Jackson, Brian, and Vahdat, Linda

   Cancer Research Jan 2024
10. CHARTING THE EVOLUTION AND TRANSFORMATIVE IMPACT OF THE PACIFIC SYMPOSIUM ON BIOCOMPUTING THROUGH A 30-YEAR RETROSPECTIVE ANALYSIS OF COLLABORATIVE NETWORKS AND THEMES USING MODERN COMPUTATIONAL TOOLS

    Zhang, Leah, Garg, Sameeksha, Zhang, Edward, McOsker, Sean, Bobak, Carly, Giffin, Kristine, Christensen, Brock, and Levy, Joshua

    In Biocomputing 2025 Dec 2024
11. Integration of Elemental Imaging and Spatial Transcriptomic Profiling for Proof-of-Concept Metals-Based Pathway Analysis of Colon Tumor Microenvironment

    Srivastava, Aruesha, Shaik, Neha, Lu, Yunrui, Chan, Matthew, Diallo, Alos B., Han, Serin, Punshon, Tracy, Jackson, Brian P., Vahdat, Linda T., and Liu, Xiaoying

    medRxiv Dec 2024
12. Integrative Co-Registration of Elemental Imag-ing and Histopathology for Enhanced Spatial Multimodal Analysis of Tissue Sections through TRACE

    Lu, Yunrui, Steiner, Ramsey, Han, Serin, Srivastava, Aruesha, Shaik, Neha, Chan, Matthew, Diallo, Alos, Punshon, Tracy, Jackson, Brian, and Kolling IV, Fred

    bioRxiv Dec 2024

2023

1. Assessment of emerging pretraining strategies in interpretable multimodal deep learning for cancer prognostication

   Azher, Zarif L., Suvarna, Anish, Chen, Ji-Qing, Zhang, Ze, Christensen, Brock C., Salas, Lucas A., Vaickus, Louis J., and Levy, Joshua J.

   BioData Mining Jul 2023
2. The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows

   Fatemi, Michael Y., Lu, Yunrui, Diallo, Alos B., Srinivasan, Gokul, Azher, Zarif L., Christensen, Brock C., Salas, Lucas A., Tsongalis, Gregory J., Palisoul, Scott M., and Perreard, Laurent

   medRxiv Jul 2023
3. Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity

   Fatemi, Michael Y., Lu, Yunrui, Sharma, Cyril, Feng, Eric, Azher, Zarif L., Diallo, Alos B., Srinivasan, Gokul, Rosner, Grace M., Pointer, Kelli B., and Christensen, Brock C.

   medRxiv Jul 2023
4. Dendrite: A Structured, Accessible, and Queryable Pathology Search Database for Streamlined Experiment Planning

   Lu, Yunrui, Hamilton, Robert, Greenberg, Jack, Srinivasan, Gokul, Shah, Parth, Preum, Sarah, Pettus, Jason, Vaickus, Louis, and Levy, Joshua

   medRxiv Jul 2023

2022

1. Development of biologically interpretable multimodal deep learning model for cancer prognosis prediction

   Azher, Zarif L., Vaickus, Louis J., Salas, Lucas A., Christensen, Brock C., and Levy, Joshua J.

   In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing Apr 2022
2. Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

   Fatemi, Michael, Feng, Eric, Sharma, Cyril, Azher, Zarif, Goel, Tarushii, Ramwala, Ojas, Palisoul, Scott, Barney, Rachael, Perreard, Laurent, and Kolling, Fred

   bioRxiv Apr 2022
3. ArcticAI: a deep learning platform for rapid and accurate histological assessment of intraoperative tumor margins

   Levy, Joshua, Davis, Matthew, Chacko, Rachael, Davis, Michael, Fu, Lucy, Goel, Tarushii, Pamal, Akash, Nafi, Irfan, Angirekula, Abhinav, and Christensen, Brock

   medRxiv Apr 2022
4. Artificial Intelligence, Bioinformatics, and pathology: Emerging trends part I—An introduction to machine learning technologies

   Levy, Joshua, Lu, Yunrui, Montivero, Marietta, Ramwala, Ojas, McFadden, Jason, Miles, Carly, Diamond, Adam Gilbert, Reddy, Ramya, Reddy, Ram, and Hudson, Taylor

   Advances in Molecular Pathology Apr 2022
5. Graph neural networks ameliorate potential impacts of imprecise large-scale autonomous immunofluorescence labeling of immune cells on whole slide images

   Reddy, Ramya, Reddy, Ram, Sharma, Cyril, Jackson, Christopher, Palisoul, Scott, Barney, Rachael, Kolling, Fred, Salas, Lucas, Christensen, Brock, and Brooks, Gabriel

   In Geometric Deep Learning in Medical Image Analysis Apr 2022
6. Artificial Intelligence, Bioinformatics, and Pathology: Emerging Trends Part II—-Current Applications in Anatomic and Molecular Pathology

   Levy, Joshua, Lu, Yunrui, Montivero, Marietta, Ramwala, Ojas, McFadden, Jason, Miles, Carly, Diamond, Adam Gilbert, Reddy, Ramya, Reddy, Ram, and Hudson, Taylor

   Advances in Molecular Pathology Apr 2022