New Model for Predicting Breast Cancer Types

Breast cancer is one of the most common types of tumors that affect women. In recent years, more women are being diagnosed with breast cancer, and younger patients are increasingly affected. This condition poses serious risks to health and quality of life. Breast cancer is influenced by hormones, particularly estrogen. Changes in estrogen levels can increase the risk of developing this cancer.

Doctors classify breast cancer based on certain Hormone Receptors present in the tumor. These are called estrogen receptors (ER) and progesterone receptors (PR). Depending on the levels of these receptors, breast cancer can be categorized into different types. The two main categories are Luminal types, which test positive for either ER or PR, and Non-luminal types, which test negative for both. This classification helps guide treatment options and predict patient outcomes.

Developing accurate methods to diagnose different types of breast cancer is crucial. Recent advancements in molecular biology have led to the discovery of more markers that can indicate breast cancer. These markers can be measured in the blood and help provide important information about the cancer's characteristics, spread, and potential for recurrence. For example, markers like CA153, CEA, and CA125 are useful for screening and monitoring treatment effectiveness. Among these, CA153 is found to be particularly valuable due to its high sensitivity and specificity.

However, relying solely on CA153 may not give a complete picture of the cancer. To complement this, a technique called contrast-enhanced Ultrasound (CEUS) is used. This imaging method allows doctors to see how blood flows within the breast tumors, providing detailed images of both the tumor and surrounding tissues.

#Study Purpose

The goal of the study was to create a model that combines data from CEUS and the serum marker CA153 to better predict the type of breast cancer.

#Methods

In this study, 120 women diagnosed with breast cancer between January 2020 and January 2023 were selected for examination. These patients all underwent ultrasound tests and tests to check hormone receptor status. The study focused on patients who had invasive breast cancer confirmed by biopsy and had complete medical records. Certain patients were excluded, including those with serious other health issues or who were pregnant.

The ultrasound procedure used a specific machine and probe to examine the breasts. First, standard ultrasound was performed to locate tumors. Then, a contrast agent was injected to observe the blood flow in the tumors. Measurements were taken at several points within the tumors to analyze how they responded to the contrast agent.

The hormone receptors, HER-2 levels, and a proliferation marker called Ki-67 were also measured in tissue samples. A blood sample was taken to check the level of CA153, using a special automated system. If the CA153 level was above a certain threshold, it was considered positive for indicating breast cancer.

The data collected was analyzed using specific statistical software. Various statistical tests were used to compare data from different types of breast cancer patients.

#Results

The study included women with ages ranging from 24 to 80 years. Among the 120 patients, 72 had Luminal breast cancer and 48 had non-Luminal breast cancer. The key data collected included age, tumor size, menopausal status, and cancer type.

The ultrasound results showed differences between the two groups of breast cancer types. In women with Luminal cancer, the ultrasound showed a specific pattern of blood flow, while the non-Luminal patients exhibited different characteristics. This suggests that there are identifiable features on ultrasound that can help distinguish between the types.

When comparing quantitative measurements from CEUS and CA153 levels, significant differences were observed. The study found that the contrast parameters (peak intensity and area under the curve) and serum CA153 levels can help predict the type of breast cancer.

Furthermore, a statistical analysis indicated that these measurements are independent predictors of breast cancer types. This means that they can serve as reliable indicators for distinguishing between Luminal and non-Luminal breast cancer.

#Prediction Model

Using the independent predictors identified in the analysis, a prediction model was developed. This model combined blood test results and ultrasound measurements to assess the likelihood of a patient having non-Luminal breast cancer. The accuracy of this model was evaluated using specific statistical methods, indicating that it provides good accuracy for predicting the risk of this type of breast cancer.

The model was represented visually in a nomogram. This graphical representation allows doctors to input individual patient data and quickly assess the likelihood of non-Luminal breast cancer. The accuracy of the model was validated through various tests, showing strong predictive capabilities.

#Importance of Findings

Breast cancer is a complex disease linked to various factors, including hormones and genetic makeup. Different molecular subtypes of breast cancer behave differently, respond to treatments variably, and have distinct outcomes. Among them, Luminal types are more common, while non-Luminal types tend to be more aggressive and challenging to treat.

The study highlights the significance of early detection and precise classification of breast cancer. Understanding the cancer’s molecular type can greatly influence treatment decisions and patient care. The findings emphasize the importance of combining serum markers and imaging techniques to improve diagnosis.

Furthermore, this study acknowledges that breast cancer relies heavily on the growth of new blood vessels for tumor progression. This makes imaging methods like CEUS valuable for understanding tumor behavior. CEUS can provide insights into blood flow dynamics within tumors, which is crucial for assessing aggressiveness and guiding treatment strategies.

While the study presents promising results, it also points out limitations. The research was conducted in a single center with a limited number of patients. This means that further research is needed to confirm these findings in larger, more diverse populations.

#Conclusion

In summary, the characteristics observed in breast cancer through ultrasound can reveal important information about the disease. The study successfully constructed a model using CEUS parameters and CA153 levels to predict breast cancer subtypes. This approach can help doctors make more informed decisions regarding diagnosis and individualized treatment plans.

As more research is conducted in this area, it will potentially lead to improvements in breast cancer management and outcomes for patients. Early detection and proper classification remain critical for maximizing survival rates and enhancing quality of life for those affected by breast cancer.