![]() Furthermore, the inefficient diagnosis can be due to the error prone and subjectivity of visual judgment. In clinical practice, there are differences in the positive rate between different hospitals. If there is amplification, they are classified as HER2 positive, otherwise, the results are negative. IHC2+ cases are “equivocal” cases, needing further evaluation by fluorescence in situ hybridization (FISH) to finalize HER2 status. IHC1+ and IHC0 cases are directly determined as HER2 negative. IHC3+ cases are directly determined as HER2 positive. As Table 1 shows, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 IHC WSIs one by one under the microscope, and continuously switching the microscopic field of view magnification to find suspicious cancerous areas, which are classified as 0, 1+, 2+, and 3+ according to the percentage of tumor cell membrane staining and staining intensity score. However, the HER2 positive rate of gastric cancer in China is only 12–13% (Qiu, 2016).Ĭurrent evaluation of HER2 protein expression on HER2 immunohistochemically (IHC) stained sections is mainly performed manually, and the IHC method are still the preferred method for HER2 in gastric cancer (Li et al., 2016). The Trastuzumab for Gastric Cancer (ToGA) study clarified that proper detection and evaluation of HER2 protein expression and gene amplification status of gastric cancer are of great significance for the clinical diagnosis and treatment of gastric cancer (Bang et al., 2010). Human epidermal growth factor receptor 2 (HER2) positive gastric cancer is an important subtype of gastric cancer, and immunotherapy targeting HER2 significantly improves the prognosis of patients with advanced gastric cancer, which has become the first-line standard of care for advanced gastric cancer (Qiu, 2016). Experiment results have demonstrated the effectiveness of our proposed method with an accuracy of 0.94 for the HER2 scoring prediction.Ĭhina is a high incidence area of gastric cancer, accounting for 46% of new gastric cancer cases in the world (Alatab et al., 2020). To the best of our knowledge, this is the first study to provide a deep learning quantification algorithm for HER2 scoring of gastric cancer to assist the pathologist's diagnosis. In order to accelerate the computational process, we proposed to use the re-parameterization scheme to separate the training model from the deployment model, which significantly speedup the inference process. Different from other studies that use convolutional neural networks for extracting feature maps or pre-processing on WSIs, we proposed a novel automatic HER2 scoring framework in this study. This study proposed a deep learning algorithm for HER2 quantification evaluation of gastric cancer. Additionally, WSIs have billions of pixels in an image, which poses computational challenges to Computer-Aided Diagnosis (CAD) systems. It is a repetitive, tedious, and highly subjective process. However, pathologists usually use a semi-quantitative assessment method to assign HER2 scores for gastric cancer by repeatedly comparing hematoxylin and eosin (H&E) whole slide images (WSIs) with their HER2 immunohistochemical WSIs one by one under the microscope. Human epidermal growth factor receptor 2 (HER2) positive is an important subtype of gastric cancer, which can provide significant diagnostic information for gastric cancer pathologists. Gastric cancer is the third most common cause of cancer-related death in the world.
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