Computer vision detects morphological correlates of HER2 positive breast cancer in H&E stained histological images
Published in Proceedings of the San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX., 2018
The determination of HER2-positivity by IHC or FISH is critical for identifying breast cancer patients most likely to benefit from anti-HER2 therapy. However, these methods do not always provide an accurate indication of HER2 overactivity, which can occur without gene amplification or overexpression of the HER2 protein. Our study objective was to determine if a deep learning convolutional neural network (CNN) could be trained, using IHC HER2 staining, to learn a morphological signature for HER2 positivity in H&E stained slides.
Citation
‘Dhage S, Anand D, Kumar N, Gann PH, Sethi A. (2018). "Computer vision detects morphological correlates of HER2 positive breast cancer in H&E stained histological images." Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-02-11.’