A Deep Learning Based Holistic Diagnosis System for Immunohistochemistry Interpretation and Molecular Subtyping

Purpose: Breast cancer in different molecular subtypes, which is determined by the overexpression rates of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), progesterone receptor (PR) and Ki67, exhibit distinct symptom characteristics and sensitivity to different treatment. Immunohistochemical method, one of the most common detecting tool for tumour markers, is heavily relied on artificial judgment and in clinical practice, with an inherent limitation in interpreting stability and operating efficiency. Here, a holistic intelligent breast tumour diagnosis system has been developed for tumour-markeromic analysis, combining the automatically interpretation and clinical suggestion. Methods: : The holistic intelligent breast tumour diagnosis system included two main modules. The interpreting modules were constructed based on convolutional neural network, for comprehensively extracting and analyzing the multi-features of immunostaining. Referring to the clinical classification criteria, the interpreting results were encoded in a low-dimensional feature representation in the subtyping module, to efficiently output a holistic detecting result of the critical tumour-markeromic with a diagnosis suggestions on molecular subtypes. Results: : The overexpression rates of HER2, ER, PR and Ki67, as well as an effective determination on molecular subtypes were successfully obtained by this diagnosis system, with an average sensitivity of 97.6% and an average specificity of 96.1%, among those, the sensitivity and specificity for interpreting HER2 were up to 99.8% and 96.9%. Conclusion: The holistic intelligent breast tumour diagnosis system shows improved performance in the interpretation of immunohistochemical images over pathologist-level, which can be expected to overcame the limitations of conventional manual interpretation in efficiency, precision and repeatability.