322 citations · 619 across the 10 of their papers we have counts for
10 papers
Efficient and generalizable prediction of molecular alterations in multiple cancer cohorts using H&E whole slide images
Kshitij Ingale, Sun Hae Hong, Qiyuan Hu +7
Molecular testing of tumor samples for targetable biomarkers is restricted by a lack of standardization, turnaround-time, cost, and tissue availability across cancer types. Additio…
Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping
Will E. Thompson, David M. Vidmar, Jessica K. De Freitas +9
Identifying disease phenotypes from electronic health records (EHRs) is critical for numerous secondary uses. Manually encoding physician knowledge into rules is particularly chall…
Development and Validation of a Deep Learning-Based Microsatellite Instability Predictor from Prostate Cancer Whole-Slide Images
Qiyuan Hu, Abbas A. Rizvi, Geoffery Schau +13
Microsatellite instability-high (MSI-H) is a tumor agnostic biomarker for immune checkpoint inhibitor therapy. However, MSI status is not routinely tested in prostate cancer, in pa…
Prediction of MET Overexpression in Non-Small Cell Lung Adenocarcinomas from Hematoxylin and Eosin Images
Kshitij Ingale, Sun Hae Hong, Josh S. K. Bell +8
MET protein overexpression is a targetable event in non-small cell lung cancer (NSCLC) and is the subject of active drug development. Challenges in identifying patients for these t…
AI-augmented histopathologic review using image analysis to optimize DNA yield and tumor purity from FFPE slides
Bolesław L. Osinski, Aïcha BenTaieb, Irvin Ho +8
To achieve minimum DNA input and tumor purity requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Misestima…
Imaging-based histological features are predictive of MET alterations in Non-Small Cell Lung Cancer
Rohan P. Joshi, Bolesław L. Osinski, Niha Beig +3
MET is a proto-oncogene whose somatic activation in non-small cell lung cancer leads to increased cell growth and tumor progression. The two major classes of MET alterations are ge…
Deep Orthogonal Fusion: Multimodal Prognostic Biomarker Discovery Integrating Radiology, Pathology, Genomic, and Clinical Data
Nathaniel Braman, Jacob W. H. Gordon, Emery T. Goossens +3
Clinical decision-making in oncology involves multimodal data such as radiology scans, molecular profiling, histopathology slides, and clinical factors. Despite the importance of t…
Predicting Prostate Cancer-Specific Mortality with A.I.-based Gleason Grading
Ellery Wulczyn, Kunal Nagpal, Matthew Symonds +20
Gleason grading of prostate cancer is an important prognostic factor but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial i…
Interpretable Survival Prediction for Colorectal Cancer using Deep Learning
Ellery Wulczyn, David F. Steiner, Melissa Moran +20
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) f…
Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data
Sushravya Raghunath, Alvaro E. Ulloa Cerna, Linyuan Jing +12
The electrocardiogram (ECG) is a widely-used medical test, typically consisting of 12 voltage versus time traces collected from surface recordings over the heart. Here we hypothesi…