Breast cancer
Breast cancer (BC) is the leading cause of cancer-related mortality in women worldwide. The identification of effective markers for early diagnosis and the prognosis is important for reducing mortality and ensuring that therapy for BC is effective.
Control of breast-to-brain metastasis remains an urgent unmet clinical need. While chemotherapy is essential in reducing systemic tumor burden, they have been shown to promote non-brain metastatic invasiveness and drug-driven neurocognitive deficits through the formation of neurofibrillary tangles (NFT), independently. Now, in this study, we investigated the effect of chemotherapy on brain metastatic progression and promoting tumor-mediated NFT. Results show chemotherapies increase brain-barrier permeability and facilitate enhanced tumor infiltration, particularly through the blood-cerebrospinal fluid barrier (BCSFB). This is attributed to increased expression of matrix metalloproteinase 9 (MMP9) which, in turn, mediates loss of Claudin-6 within the choroid plexus cells of the BCSFB. Importantly, increased MMP9 activity in the choroid epithelium following chemotherapy results in cleavage and release of Tau from breast cancer cells. This cleaved Tau forms tumor-derived NFT that further destabilize the BCSFB. Our results underline for the first time the importance of the BCSFB as a vulnerable point of entry for brain-seeking tumor cells post-chemotherapy and indicate that tumor cells themselves contribute to Alzheimer's-like tauopathy 1).
Classification
Diagnosis
Ca15-3 is the protein product of the MUC1 gene and is the most widely used serum marker in BC
Physical Exam: Palpation of the axillary lymph nodes is part of a routine physical examination, especially when investigating infections, breast abnormalities, or lymphadenopathy. Imaging: Ultrasound, mammography, CT scans, and MRI can be used to assess the axillary lymph nodes for size, consistency, and other characteristics indicative of disease. Biopsy: A biopsy may be performed if there is suspicion of malignancy or to diagnose the cause of lymphadenopathy. Understanding the anatomy and function of axillary lymph nodes is vital for diagnosing and treating various medical conditions, particularly those related to infections and cancer.
The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status.
Materials and methods: Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model.
Results: Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature.
Conclusion: This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option 2).