Screen Them All: High-Throughput Pan-Cancer Genetic and Phenotypic Biomarker Screening from H&E Whole Slide Images

• URL: http://arxiv.org/abs/2408.09554v2
• Date: Tue, 20 Aug 2024 12:47:35 GMT
• Title: Screen Them All: High-Throughput Pan-Cancer Genetic and Phenotypic Biomarker Screening from H&E Whole Slide Images
• Authors: Yi Kan Wang, Ludmila Tydlitatova, Jeremy D. Kunz, Gerard Oakley, Ran A. Godrich, Matthew C. H. Lee, Chad Vanderbilt, Razik Yousfi, Thomas Fuchs, David S. Klimstra, Siqi Liu,
• Abstract summary: Using AI on routine H&E slides offers a fast and economical approach to screen for multiple molecular biomarkers. We present a high- throughput AI-based system leveraging Virchow2, a foundation model pre-trained on 3 million slides. Unlike traditional methods that train individual models for each biomarker or cancer type, our system employs a unified model to simultaneously predict a wide range of clinically relevant molecular biomarkers.
• Score: 3.119559770601732
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Many molecular alterations serve as clinically prognostic or therapy-predictive biomarkers, typically detected using single or multi-gene molecular assays. However, these assays are expensive, tissue destructive and often take weeks to complete. Using AI on routine H&E WSIs offers a fast and economical approach to screen for multiple molecular biomarkers. We present a high-throughput AI-based system leveraging Virchow2, a foundation model pre-trained on 3 million slides, to interrogate genomic features previously determined by an next-generation sequencing (NGS) assay, using 47,960 scanned hematoxylin and eosin (H&E) whole slide images (WSIs) from 38,984 cancer patients. Unlike traditional methods that train individual models for each biomarker or cancer type, our system employs a unified model to simultaneously predict a wide range of clinically relevant molecular biomarkers across cancer types. By training the network to replicate the MSK-IMPACT targeted biomarker panel of 505 genes, it identified 80 high performing biomarkers with a mean AU-ROC of 0.89 in 15 most common cancer types. In addition, 40 biomarkers demonstrated strong associations with specific cancer histologic subtypes. Furthermore, 58 biomarkers were associated with targets frequently assayed clinically for therapy selection and response prediction. The model can also predict the activity of five canonical signaling pathways, identify defects in DNA repair mechanisms, and predict genomic instability measured by tumor mutation burden, microsatellite instability (MSI), and chromosomal instability (CIN). The proposed model can offer potential to guide therapy selection, improve treatment efficacy, accelerate patient screening for clinical trials and provoke the interrogation of new therapeutic targets.

Related papers

• Biomarker based Cancer Classification using an Ensemble with Pre-trained Models [2.2436844508175224]
  We propose a novel ensemble model combining pre-trained Hyperfast model, XGBoost, and LightGBM for multi-class classification tasks. We leverage a meta-trained Hyperfast model for classifying cancer, accomplishing the highest AUC of 0.9929. We also propose a novel ensemble model combining pre-trained Hyperfast model, XGBoost, and LightGBM for multi-class classification tasks, achieving an incremental increase in accuracy (0.9464)
  arXiv  Detail & Related papers  (2024-06-14T14:43:59Z)
• MMIL: A novel algorithm for disease associated cell type discovery [58.044870442206914]
  Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. We introduce Mixture Modeling for Multiple Learning Instance (MMIL), an expectation method that enables the training and calibration of cell-level classifiers.
  arXiv  Detail & Related papers  (2024-06-12T15:22:56Z)
• Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
  Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer. The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts. This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:40:56Z)
• Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning [0.7082642128219231]
  Histologic samples stained with hematoxylin and eosin are commonly used in colorectal cancer management. Recent research highlights the potential of convolutional neural networks (CNNs) in facilitating the extraction of clinically relevant biomarkers from readily available images. CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost.
  arXiv  Detail & Related papers  (2023-10-25T19:46:27Z)
• Deep Learning Predicts Biomarker Status and Discovers Related Histomorphology Characteristics for Low-Grade Glioma [21.281553456323998]
  Biomarker detection is an indispensable part in the diagnosis and treatment of low-grade glioma (LGG) We propose an interpretable deep learning pipeline to predict the status of five biomarkers in LGG using only hematoxylin and eosin-stained whole slide images and slide-level biomarker status labels. Our pipeline not only provides a novel approach for biomarker prediction, enhancing the applicability of molecular treatments for LGG patients but also facilitates the discovery of new mechanisms in molecular functionality and LGG progression.
  arXiv  Detail & Related papers  (2023-10-11T13:05:33Z)
• A marker-less human motion analysis system for motion-based biomarker discovery in knee disorders [60.99112047564336]
  The NHS has been having increased difficulty seeing all low-risk patients, this includes but not limited to suspected osteoarthritis (OA) patients. We propose a novel method of automated biomarker identification for diagnosis of knee disorders and the monitoring of treatment progression.
  arXiv  Detail & Related papers  (2023-04-26T16:47:42Z)
• Regression-based Deep-Learning predicts molecular biomarkers from pathology slides [40.24757332810004]
  We developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the interpretability of the results over classification. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
  arXiv  Detail & Related papers  (2023-04-11T11:43:51Z)
• Artificial-intelligence-based molecular classification of diffuse gliomas using rapid, label-free optical imaging [59.79875531898648]
  DeepGlioma is an artificial-intelligence-based diagnostic screening system. DeepGlioma can predict the molecular alterations used by the World Health Organization to define the adult-type diffuse glioma taxonomy.
  arXiv  Detail & Related papers  (2023-03-23T18:50:18Z)
• Deep Learning-Based Prediction of Molecular Tumor Biomarkers from H&E: A Practical Review [0.0]
  Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors. Applying machine learning to H&E images can provide a more cost-effective screening method. This article reviews the diverse applications across cancer types and the methodology to train and validate these models.
  arXiv  Detail & Related papers  (2022-11-27T14:57:41Z)
• Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology: AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
  Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate. We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
  arXiv  Detail & Related papers  (2022-02-17T08:33:52Z)
• Cancer Gene Profiling through Unsupervised Discovery [49.28556294619424]
  We introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. Our signature reports promising results on distinguishing immune inflammatory and immune desert tumors.
  arXiv  Detail & Related papers  (2021-02-11T09:04:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.