An automatic Darknet-based immunohistochemical scoring system for IL-24 in lung cancer

Abstract

Immunohistochemical (IHC) detection is of critical importance in the pathological diagnosis of lung cancer. Interleukin-24 (IL-24) is a significant predictive and prognostic marker in IHC detection, which can help to characterize the tumor and predict the clinical course. To determine the score of the IL-24 expression, pathologists will assess the positive area and staining intensity through a microscope and then use a semi-quantitative assessment method to assign a score for each IHC image of lung cancer. However, this process is a time-consuming, imprecise, and subjective process, which can result in inter- and intra-observer discrepancies. Meanwhile, The performance of computer-aided diagnosis (CAD) systems for IHC scoring depends on the quality of manually extracted features. For example, many advanced methods require precise extraction of features of cell nuclei and membranes. However, the complex background and overlapping cells in lung cancer IHC images present significant challenges for segmentation, which can affect the accuracy of the final system's predicted scoring results. In this paper, an automatic Darknet-based IHC scoring system for IL-24 in lung cancer is proposed. Firstly, the original IHC images of lung cancer are blocked and the features are concentrated by a “block attention mechanism, which can reduce the computational burden of the analysis of millions of pixels in IHC images. The blocked images are then inputted into a Darknet-based scoring network, which incorporates a novel feature extraction backbone and loss function to obtain the final scores. To the best of our knowledge, this is the first end-to-end system that directly outputs a clinical score using lung cancer IL-24 IHC images as input. We have constructed a dataset of 5000 manually annotated IL-24 IHC images of lung cancer obtained from the Institute of Life Science and Bioengineering at Beijing Jiaotong University. We will present experimental results to demonstrate the feasibility of our proposed method, which can greatly assist with the clinical diagnosis and treatment of lung cancer.