Artificial Intelligence-Aided Multiple Tumor Detection Method Based on Immunohistochemistry-Enhanced Dark-Field Imaging

The immunohistochemical method serves as one of the most practical tools in clinical cancer detection and thus has great application value to overcome the existing limits of the conventional method and further improve the detecting efficiency and sensitivity. This study employed 3,3'-diaminobenzidine (DAB), a conventional color indicator for immunohistochemistry, as a novel high-sensitive scattering reagent to provide a multidimensional image signal varying with the overexpression rate of tumor markers. Based on the scattering properties of DAB aggregates, an efficient and robust artificial intelligence-aided immunohistochemical method based on dark-field imaging has been established, with improvement in both the imaging quality and interpretation efficiency in comparison with the conventional manual-operated immunohistochemical method. Referencing the diagnosis from three independent pathologists, this method succeeded in detecting HER2 overexpressed breast tumors with a sensitivity of 95.2% and a specificity of 100.0%; meanwhile, it was found to be applicable for non-small-cell lung tumors and malignant lymphoma as well. As demonstrated, this study provided an effective and reliable means for making diagnostic suggestions, which exhibited great potential in multiple tumor pathological detection at low cost.