Evaluation of Liquid Biopsy in Patients with HER2-Positive Breast Cancer.

Breast cancer is one of the common malignant tumors, and liquid biopsy has become a hot spot for clinical testing. To clarify the detection effect of liquid biopsy in breast cancer, we collected peripheral blood of HER2-positive (human epidermal growth factor receptor 2-positive) patients. Circulating tumor cells (CTCs) were isolated and analyzed. HER2 expression on CTCs was detected. The results showed that in the 198 HER2-positive samples, the CTC detection rate was 79.8% (158/198), and the mean number of CTCs was 21, ranging from 1 to 63/7.5 mL peripheral blood. Only 41.1% (65/158) of patients had histology and CTC HER2 status consistent with the remaining 58.9% (93/158) of patients, although their histological HER2 was positive, and CTC HER2 was negative. Our study confirmed the value of CTC HER2 real-time status testing in HER2-positive breast cancer patients. The inconsistency in HER2 status between CTCs and histology may be related to the time interval between CTCs and histological HER2 detection, suggesting that real-time HER2 detection is necessary for histological HER2-positive patients.

Diagnostic Regions Attention Network (DRA-Net) for Histopathology WSI Recommendation and Retrieval.

The development of whole slide imaging techniques and online digital pathology platforms have accelerated the popularization of telepathology for remote tumor diagnoses. During a diagnosis, the behavior information of the pathologist can be recorded by the platform and then archived with the digital case. The browsing path of the pathologist on the WSI is one of the valuable information in the digital database because the image content within the path is expected to be highly correlated with the diagnosis report of the pathologist. In this article, we proposed a novel approach for computer-assisted cancer diagnosis named session-based histopathology image recommendation (SHIR) based on the browsing paths on WSIs. To achieve the SHIR, we developed a novel diagnostic regions attention network (DRA-Net) to learn the pathology knowledge from the image content associated with the browsing paths. The DRA-Net does not rely on the pixel-level or region-level annotations of pathologists. All the data for training can be automatically collected by the digital pathology platform without interrupting the pathologists' diagnoses. The proposed approaches were evaluated on a gastric dataset containing 983 cases within 5 categories of gastric lesions. The quantitative and qualitative assessments on the dataset have demonstrated the proposed SHIR framework with the novel DRA-Net is effective in recommending diagnostically relevant cases for auxiliary diagnosis. The MRR and MAP for the recommendation are respectively 0.816 and 0.836 on the gastric dataset. The source code of the DRA-Net is available at https://github.com/zhengyushan/dpathnet.