RESUMO
AIMS: The automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting the tumor area into distinct ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) regions. Moreover, the quantitative analysis of immunohistochemistry requires a specific focus on invasive carcinoma regions. METHODS AND RESULTS: In this study, we propose an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images (WSIs). Our method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional H&E and P63 staining slides. In addition, we introduced an advanced semi-supervised learning algorithm, allowing efficient training of the model using unlabeled data. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 618 IHC-stained WSIs from 170 cases, including four types of staining (ER, PR, HER2, and Ki-67). Notably, the model demonstrated an impressive intersection over union (IoU) score exceeding 80% on the test set. Furthermore, to ascertain the practical utility of our model in IHC quantitative evaluation, we constructed a fully automated Ki-67 scoring system based on the model's predictions. Comparative experiments convincingly demonstrated that our system exhibited high consistency with the scores given by experienced pathologists. CONCLUSIONS: Our developed model excels in accurately distinguishing between DCIS and invasive carcinoma regions in breast cancer immunohistochemistry WSIs. This method paves the way for a clinically available, fully automated immunohistochemistry quantitative scoring system.
RESUMO
Emerging infectious diseases pose a serious threat to human health and affect social stability. In recent years, the epidemic situation of emerging infectious diseases is very serious; among these infectious diseases, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected many countries and regions in a short time. The prevention and treatment of these diseases require rapid on-site detection methods. However, the common detection method, RT-PCR, requires expensive instruments, complex operations, and professional operators. Here, we developed a portable low-cost assay for rapid on-site detection of viral nucleic acid using reverse transcription-loop-mediated isothermal amplification (RT-LAMP). The SARS-CoV-2 RNA can be successfully amplified within 15 min in a thermos, and the detection result is read rapidly in a portable low-cost device with a sensitivity of 100 copies/µL. The portable low-cost device consists of a black box, a laser or LED and a filter, costing only a few cents. The rapid on-site detection method can provide strong support for the control of biological threats such as infectious diseases. It is also an emergency detection method for low-resource settings, relieving the huge pressure on health care.