A novel deep classifier framework for automated molecular subtyping of breast carcinoma using immunohistochemistry image analysis. (July 2022)

• Record Type: Journal Article
• Authors: Mathew, Tojo; Niyas, S.; Johnpaul, C.I.; Kini, Jyoti R.; Rajan, Jeny
• Is Part Of: Biomedical signal processing and control. Volume 76(2022)
• Publication Date: 2022-07
• Subjects: Breast cancer -- Molecular subtyping -- Deep learning -- Immunohistochemistry -- Histopathology -- Molecular biomarkers; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103657
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An automated framework based on deep learning is proposed for molecular subtyping of breast cancer. The four tissue level biomarkers involved in molecular subtyping are assessed using separate deep learning based pipelines and the results are combined to determine the molecular subtype. The proposed method shows promising results in the extensive experimental analysis carried out to assess the individual biomarker response as well as for molecular subtyping. Abstract: Breast carcinoma has various subtypes based on the genetic factors involved in the pathogenesis of the malignancy. Identifying the exact subtype and providing targeted treatment to the patient can improve the survival chances. Molecular subtyping through immunohistochemistry analysis is a pathology procedure to determine the subtype of breast cancer. The existing manual procedure is tedious and involves assessing the status of the four vital molecular biomarkers present in the tumor tissues. In this paper, a deep learning-based framework for automated molecular subtyping of breast cancer is proposed. Digital slide images of the four biomarkers are separately processed by the proposed framework. In the preprocessing stage, the non-informative background regions from the images are separated. The patches extracted from the foreground regions are classified into target classes using convolutional neural network models trained for this purpose. Classification results are post-processed to predict the status of all the four biomarkers. The predictions for the individual biomarkers are finally consolidated as per clinical guidelines to determine the subtype of the cancer. The proposed system is evaluated for the performance of individual biomarker status prediction and patient-level subtype classification.For patient-level evaluation of biomarkers ER, PR, K67, and HER2, the proposed method gives F1 Scores 1.00, 1.00, 0.90, and 0.94 respectively, whereas for molecular subtyping an F1 score of 0.89 is obtained. In both these aspects, the proposed framework has given significant results that show the effectiveness of our approach.