Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images

• URL: http://arxiv.org/abs/2409.09053v1
• Date: Fri, 30 Aug 2024 13:57:33 GMT
• Title: Deep learning-based classification of breast cancer molecular subtypes from H&E whole-slide images
• Authors: Masoud Tafavvoghi, Anders Sildnes, Mehrdad Rakaee, Nikita Shvetsov, Lars Ailo Bongo, Lill-Tove Rasmussen Busund, Kajsa Møllersen,
• Abstract summary: We investigated whether H&E-stained whole slide images could be leveraged to predict breast cancer molecular subtypes. We used 1,433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping. The pipeline was tested on 221 hold-out WSIs, achieving an overall macro F1 score of 0.95 for tumor detection and 0.73 for molecular subtyping.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application of deep learning models on H&E-stained whole slide images (WSI) for molecular subtyping, but these efforts vary in their methods, datasets, and reported performance. In this work, we investigated whether H&E-stained WSIs could be solely leveraged to predict breast cancer molecular subtypes (luminal A, B, HER2-enriched, and Basal). We used 1,433 WSIs of breast cancer in a two-step pipeline: first, classifying tumor and non-tumor tiles to use only the tumor regions for molecular subtyping; and second, employing a One-vs-Rest (OvR) strategy to train four binary OvR classifiers and aggregating their results using an eXtreme Gradient Boosting (XGBoost) model. The pipeline was tested on 221 hold-out WSIs, achieving an overall macro F1 score of 0.95 for tumor detection and 0.73 for molecular subtyping. Our findings suggest that, with further validation, supervised deep learning models could serve as supportive tools for molecular subtyping in breast cancer. Our codes are made available to facilitate ongoing research and development.

Related papers

• CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H\&E stained images [45.32169712547367]
  We introduce CIMIL-CRC', a framework that solves the MSI/MSS MIL problem by efficiently combining a pre-trained feature extraction model with principal component analysis (PCA) to aggregate information from all patches. We assessed our CIMIL-CRC method using the average area under the curve (AUC) from a 5-fold cross-validation experimental setup for model development on the TCGA-CRC-DX cohort.
  arXiv  Detail & Related papers  (2024-01-29T12:56:11Z)
• One-dimensional convolutional neural network model for breast cancer subtypes classification and biochemical content evaluation using micro-FTIR hyperspectral images [0.0]
  This study created a 1D deep learning tool for breast cancer subtype evaluation and biochemical contribution. CaReNet-V1, a novel 1D convolutional neural network, was developed to classify breast cancer (CA) and adjacent tissue (AT) A 1D adaptation of Grad-CAM was applied to assess the biochemical impact to the classifications.
  arXiv  Detail & Related papers  (2023-10-23T16:58:34Z)
• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
  The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
  arXiv  Detail & Related papers  (2023-04-12T15:08:34Z)
• 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)
• Subtype-Former: a deep learning approach for cancer subtype discovery with multi-omics data [17.36619699329539]
  This study proposed Subtype-Former, a deep learning method based on Transformer and Block. We found that Subtype-Former can perform better on the benchmark datasets of more than 5000 tumors based on the survival analysis. We identified 50 essential biomarkers, which can be used to study targeted cancer drugs.
  arXiv  Detail & Related papers  (2022-07-28T08:15:06Z)
• Deep Learning Based Model for Breast Cancer Subtype Classification [3.419451872918847]
  This paper focuses on the use of gene expression data for the classification of breast cancer into four subtypes, Basal, Her2, LumA, and LumB. The size of the feature set is reduced from 20,530 gene expression values to 500 by using an autoencoder. By deploying the combined network of stages 1 and 2, we have been able to attain a mean 10-fold test accuracy of 0.907 on the TCGA breast cancer dataset.
  arXiv  Detail & Related papers  (2021-11-06T17:15:35Z)
• Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network [93.22587316229954]
  We propose a graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E) We evaluate the performance of the proposed algorithm on H&E slides obtained from an SVM K-rasG12D lung cancer mouse model using precision/recall rates, F-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC)
  arXiv  Detail & Related papers  (2021-10-27T19:28:36Z)
• An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization [45.00998416720726]
  We propose a framework to address the unique properties of medical images. This model first uses a low-capacity, yet memory-efficient, network on the whole image to identify the most informative regions. It then applies another higher-capacity network to collect details from chosen regions. Finally, it employs a fusion module that aggregates global and local information to make a final prediction.
  arXiv  Detail & Related papers  (2020-02-13T15:28:42Z)
• Stan: Small tumor-aware network for breast ultrasound image segmentation [68.8204255655161]
  We propose a novel deep learning architecture called Small Tumor-Aware Network (STAN) to improve the performance of segmenting tumors with different size. The proposed approach outperformed the state-of-the-art approaches in segmenting small breast tumors.
  arXiv  Detail & Related papers  (2020-02-03T22:25:01Z)
• Deep learning generates custom-made logistic regression models for explaining how breast cancer subtypes are classified [0.2529563359433233]
  We developed an explainable deep learning model called a point-wise linear (PWL) model that generates a custom-made logistic regression for each patient. We trained the PWL model with RNA-seq data to predict PAM50 intrinsic subtypes and applied it to the 41/50 genes of PAM50 through the subtype prediction task.
  arXiv  Detail & Related papers  (2020-01-20T05:56:32Z)

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.