The following abstracts were submitted to the Cancer Biomarkers AI and Bioinformatics Workshop, 2024.

Oral Talks

• Deep Learning AI Predicts HRD and Platinum Response from Histologic Slides, Erik Bergstrom (UC San Diego), submitted by Erik Bergstrom (UC San Diego), presented by Erik Bergstrom (UC San Diego)
• Hybrid Extended Reality Data Visualization for Medicine, S.G. Djorgovski (Caltech), submitted by S.G. Djorgovski (Caltech), presented by S.G. Djorgovski (Caltech) and Santiago Lombeyda (Caltech)
• A Vision-Transformer based pipeline for investigating the association between cancer pathology images, T-cell receptors and immuno-characteristics, Meiling Liu (Fred Hutchinson Cancer Center), submitted by Chad He (Fred Hutchinson Cancer Center)
• Multimodal and Generative AI for Pathology, Faisal Mahmood (Harvard Medical School), submitted by Faisal Mahmood (Harvard Medical School), presented by Faisal Mahmood (Harvard Medical School)
• Lessons Learned in Federated Cancer Research Pilots Spanning Four NIH-Designated Comprehensive Cancer Centers, Sahil Nalawade (Dana-Farber Cancer Institute), submitted by Sahil Nalawade (Dana-Farber Cancer Institute), presented by Michael Rosenthal (Dana-Farber Cancer Institute)
• Curating AI-ready datasets for pediatric oncology: the Pediatric Cancer Data Commons, Kaitlyn Ott (University of Chicago), submitted by Kaitlyn Ott (University of Chicago), presented by Kaitlyn Ott (University of Chicago)
• Evaluating the Robustness of Features Generated by a Foundation Model from Lung Nodule Regions in CT with Different Reconstruction Parameters, Stephen Park (UCLA), submitted by Stephen Park (UCLA), presented by Stephen Park (UCLA)
• Advancing Health at the Speed of AI, Hoifung Poon (Microsoft Health Futures), submitted by Hoifung Poon (Microsoft Health Futures), presented by Hoifung Poon (Microsoft Health Futures)
• Machine Learning and Biomedical Applications: Promise and Peril, Padhraic Smyth (UC Irvine), submitted by Padhraic Smyth (UC Irvine), presented by Padhraic Smyth (UC Irvine)
• Veridical Data Science and PCS Uncertainty Quantification, Bin Yu (UC Berkeley), submitted by Bin Yu (UC Berkeley), presented by Bin Yu (UC Berkeley)

Posters

• Computational pathology reveals the association of collagen fiber architecture in both peritumoral and stromal areas with clinically relevant outcomes in high-grade serous ovarian carcinomas, Arpit Aggarwal (Georgia Tech and Emory University), submitted by Arpit Aggarwal (Georgia Tech and Emory University), presented by Arpit Aggarwal (Georgia Tech and Emory University), №1
• Contrastive feature representation for enhanced spatial transcriptomics analysis, Muhammad Aminu (MD Anderson), submitted by Muhammad Aminu (MD Anderson), presented by Muhammad Aminu (MD Anderson), №2
• Delineating spatially significant intra- and inter-tumoral cancer biomarkers using graph attention on local subgraphs of tumor microenvironments, Das Arun (University of Pittsburgh), submitted by Das Arun (University of Pittsburgh), presented by Das Arun (University of Pittsburgh), №4
• LabCAS: An integrated data-intensive environment for machine learning, archiving, processing, AI-based analyzing, and sharing data, Dan Crichton (Jet Propulsion Laboratory), submitted by Sean Kelly (Jet Propulsion Laboratory), presented by Dan Crichton (Jet Propulsion Laboratory), №3
• Vanderbilt Biorepository and Clinical Validation Center, EDRN-Lung Group: Support for the Early Detection of Lung Cancer, Stephen Deppen (Vanderbilt), submitted by Stephen Deppen (Vanderbilt), presented by Stephen Deppen (Vanderbilt), №5
• Optimizing and Harnessing 20 Million NLST CT Lung Screening Images for Robust Foundation Model Training, Md Enamul Hoq (University of Arkansas), submitted by Md Enamul Hoq (University of Arkansas), presented by Md Enamul Hoq (University of Arkansas), №8
• Optimizing Biomarker Models for Biologically-Heterogeneous Cancers: A Nested Model Approach for Lung Cancer Diagnosis, Laurel Jackson (Abbott Laboratories), submitted by Laurel Jackson (Abbott Laboratories), presented by Susan Gawel (Abbott Laboratories), №9
• Dynamic Incidence Prediction using Categorical EHR Data with Timestamps (DIPCAT), Ben Jacob (RCSI University of Medicine and Health Sciences), submitted by Ben Jacob (RCSI University of Medicine and Health Sciences), presented by Ben Jacob (RCSI University of Medicine and Health Sciences), №11
• The need for high sensitivity biomarkers in lung cancer screening: results of a modelling study, Ben Jacob (RCSI University of Medicine and Health Sciences), submitted by Ben Jacob (RCSI University of Medicine and Health Sciences), presented by Ben Jacob (RCSI University of Medicine and Health Sciences), №10
• Curation and Evaluation of an Open Cohort for Early Detection of Pancreatic Cancer, Zhiwei Liang (Dana-Farber Cancer Institute), submitted by Zhiwei Liang (Dana-Farber Cancer Institute), presented by Zhiwei Liang (Dana-Farber Cancer Institute), №16
• Predicting Prostate Cancer Gene Alteration with Deep Learning on Histopathological Images, Lucas J Liu (Fred Hutch Cancer Center), submitted by Lucas J Liu (Fred Hutch Cancer Center), presented by Lucas J Liu (Fred Hutch Cancer Center), №12
• Predicting Immune Checkpoint Inhibitor Pneumonitis in Lung Cancer Patients Using Deep Learning and Baseline CT scans, Amgad Muneer (MD Anderson), submitted by Ambad Abdulraheem (MD Anderson), presented by Ambad Abdulraheem (MD Anderson), №14
• SCENT: Multi Scale Ensemble Transformer for Non-Invasive Prediction of PD-L1 Expression and Response to Immune Checkpoint Inhibitors in NSCLC Using CT scans, Amgad Muneer (MD Anderson), submitted by Ambad Abdulraheem (MD Anderson), presented by Ambad Abdulraheem (MD Anderson), №13
• Optimizing Sample Size for Statistical Learning with Bulk Transcriptomic Sequencing: A Learning Curve Approach, Yunhui Qi (Iowa State University), submitted by Yunhui Qi (Iowa State University), presented by Yunhui Qi (Iowa State University), № unknown
• Evaluating Study Replicability in Supervised Machine Learning with Epi-transcriptomic Data: Impact of Data Harmonization and Classifier Validation, Li-Xuan Qin (Memorial Sloan Kettering Cancer Center), submitted by Li-Xuan Qin (Memorial Sloan Kettering Cancer Center), presented by Li-Xuan Qin (Memorial Sloan Kettering Cancer Center), №15
• Dynamic Lung Cancer Risk Modeling in Non or Light-Smokers: Leveraging Radiomics and Machine Learning, Morteza Salehjahromi (MD Anderson), submitted by Morteza Salehjahromi (MD Anderson), presented by Morteza Salehjahromi (MD Anderson), № 19
• The Reliability of Generative AI from CT to PET on Lung Cancer: An Extensive Validation, Morteza Salehjahromi (MD Anderson), submitted by Morteza Salehjahromi (MD Anderson), presented by Morteza Salehjahromi (MD Anderson), №18
• Leveraging Large Language Models and Transformer Architectures for Data Extraction from Unstructured Clinical Notes: Experiences and Challenges, Soujanya Samineni (Dana-Farber Cancer Institute), submitted by Michael Rosenthal (Dana-Farber Cancer Institute), №17
• Enhancement Pattern Mapping (EPM) Signatures in pre-diagnostic MRIs Associate with Hepatocellular Carcinoma Time-to-Disease and Differentiate Cases/Controls, Shane Smith (MD Anderson), submitted by Shane Smith (MD Anderson), presented by Shane Smith (MD Anderson), №20
• Novel cell-free genomic sequencing data: Technique, Application and Analysis of Broad Range cell-free DNA sequencing, Neeti Swarup (UCLA), submitted by Neeti Swarup (UCLA), presented by Neeti Swarup (UCLA), № 21
• Estimating predictive value of T cell receptor sequences in cancerous tissue identification with deep neural networks, Raditya Utama (Cold Spring Harbor Laboratory), submitted by Raditya Utama (Cold Spring Harbor Laboratory), presented by Raditya Utama (Cold Spring Harbor Laboratory), № 22
• Integrating Imaging Biomarkers in Nomograms to Enhance Thyroid Cancer Malignancy Predictions, Alekhya Vittalam (UCLA), submitted by Alekhya Vittalam (UCLA), presented by Alekhya Vittalam (UCLA), №23
• Proprietary vs Open-Source Radiomic Platform for Lung Cancer Diagnosis, David Xiao (Vanderbilt), submitted by Stephen Deppen (Vanderbilt), presented by Stephen Deppen (Vanderbilt), №6
• Genomic Atlas of Oral Leukoplakia During the Transition to Oral Squamous Cell Carcinoma for Biomarker Identification, Tina Yang (UC San Diego), submitted by Tina Yang (UC San Diego), presented by Tina Yang (UC San Diego), №25
• Early Pancreatic Cancer Detection Using Extracellular Vesicle DNA Methylation Signatures in Blood, Sheng Zhang (Carnegie Mellon University), submitted by Hongzhang He (Carnegie Mellon University), presented by Hongzhang He (Carnegie Mellon University), №7
• Interpretable AI Identifies Protein Complexes to Predict Chemoresistance, Xiaoyu Zhao (UC San Diego), submitted by Xiaoyu Zhao (UC San Diego), presented by Xiaoyu Zhao (UC San Diego), № unknown
• Semantic-Guided Robust Imaging Biomarker for Early Detection of Lung Cancer, Luoting Zhuang (UCLA), submitted by Luoting Zhuang (UCLA), presented by Luoting Zhuang (UCLA), №27