Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.16397 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929327341305856 |
|---|---|
| author | Gonçalves, Tiago Pulido-Arias, Dagoberto Willett, Julian Hoebel, Katharina V. Cleveland, Mason Ahmed, Syed Rakin Gerstner, Elizabeth Kalpathy-Cramer, Jayashree Cardoso, Jaime S. Bridge, Christopher P. Kim, Albert E. |
| author_facet | Gonçalves, Tiago Pulido-Arias, Dagoberto Willett, Julian Hoebel, Katharina V. Cleveland, Mason Ahmed, Syed Rakin Gerstner, Elizabeth Kalpathy-Cramer, Jayashree Cardoso, Jaime S. Bridge, Christopher P. Kim, Albert E. |
| contents | The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date, there is not a widely available method to reproducibly measure tumor and immune phenotypes for each patient's tumor. Given this unmet clinical need, we applied multiple instance learning (MIL) algorithms to assess activity of ten biologically relevant pathways from the hematoxylin and eosin (H&E) slide of primary breast tumors. We employed different feature extraction approaches and state-of-the-art model architectures. Using binary classification, our models attained area under the receiver operating characteristic (AUROC) scores above 0.70 for nearly all gene expression pathways and on some cases, exceeded 0.80. Attention maps suggest that our trained models recognize biologically relevant spatial patterns of cell sub-populations from H&E. These efforts represent a first step towards developing computational H&E biomarkers that reflect facets of the TME and hold promise for augmenting precision oncology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16397 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology Gonçalves, Tiago Pulido-Arias, Dagoberto Willett, Julian Hoebel, Katharina V. Cleveland, Mason Ahmed, Syed Rakin Gerstner, Elizabeth Kalpathy-Cramer, Jayashree Cardoso, Jaime S. Bridge, Christopher P. Kim, Albert E. Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantitative Methods 92C55 I.5.1; I.5.4; I.2.10; J.3 The interactions between tumor cells and the tumor microenvironment (TME) dictate therapeutic efficacy of radiation and many systemic therapies in breast cancer. However, to date, there is not a widely available method to reproducibly measure tumor and immune phenotypes for each patient's tumor. Given this unmet clinical need, we applied multiple instance learning (MIL) algorithms to assess activity of ten biologically relevant pathways from the hematoxylin and eosin (H&E) slide of primary breast tumors. We employed different feature extraction approaches and state-of-the-art model architectures. Using binary classification, our models attained area under the receiver operating characteristic (AUROC) scores above 0.70 for nearly all gene expression pathways and on some cases, exceeded 0.80. Attention maps suggest that our trained models recognize biologically relevant spatial patterns of cell sub-populations from H&E. These efforts represent a first step towards developing computational H&E biomarkers that reflect facets of the TME and hold promise for augmenting precision oncology. |
| title | Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantitative Methods 92C55 I.5.1; I.5.4; I.2.10; J.3 |
| url | https://arxiv.org/abs/2404.16397 |