Breast cancer remains one of the leading causes of cancer-related deaths globally, affecting millions of women each year. Accurate diagnosis and classification are crucial for guiding treatment, especially for subtypes such as Luminal A, Luminal B, Triple-Negative Breast Cancer (TNBC), and HER2-positive cancers. These subtypes differ in their biology and response to therapies, making precise identification critical to determining the most effective treatment plans, whether that be hormone therapy, chemotherapy, or targeted therapies like anti-HER2 agents. Misclassification or delays in diagnosis can lead to suboptimal treatment outcomes, underscoring the need for advanced diagnostic tools.

AI-Driven Precision in Breast Cancer Diagnosis

At PAICON, we are developing an AI-based solution designed to deduce breast cancer subtypes directly from H&E-stained slides. This innovation aims to reduce the dependence on additional diagnostic tests like immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH), which are typically required for definitive subtype classification. By leveraging a large and diverse dataset, our AI-driven approach has demonstrated the potential to classify breast cancer subtypes with a high degree of accuracy based solely on H&E slides. This breakthrough could be particularly advantageous in regions where access to advanced diagnostic tools such as IHC and FISH is limited or where these methods can be time-consuming and resource-intensive.

Our AI model is trained to recognize subtle morphological features in H&E slides that correlate with the molecular characteristics of each subtype. For example, it can distinguish between Luminal A and Luminal B tumors, which have different prognoses and treatment approaches, as well as detect more aggressive subtypes like TNBC and HER2-positive cancers. By providing these insights earlier in the diagnostic process, we aim to facilitate faster and more informed clinical decisions.

Automated IHC for Faster Pathology

In parallel, PAICON is advancing the automation of IHC result interpretation, further enhancing the efficiency of pathology workflows. Our automated tools are capable of analyzing and grading key biomarkers such as estrogen receptor (ER), progesterone receptor (PR), Ki-67, and HER2 with high precision. This automation not only accelerates the diagnostic process but also ensures consistency and objectivity, addressing the variability that can sometimes occur in manual interpretations. By integrating AI into IHC analysis, we can reduce the burden on pathologists while maintaining high standards of diagnostic accuracy.

At PAICON, we are dedicated to advancing AI technologies that not only enhance the expertise of pathologists but also deliver clear, actionable, and interpretable insights. Our goal is to seamlessly integrate these AI-driven tools into clinical workflows, empowering healthcare professionals to make more informed decisions faster and with greater confidence. As we continue to expand our efforts, we are committed to making a meaningful impact on personalized cancer treatment worldwide, with a particular focus on improving outcomes in underserved areas where diagnostic resources are scarce.

Through these innovations, PAICON is driving the future of AI-powered pathology, transforming how breast cancer is diagnosed and treated. By reducing the reliance on traditional diagnostic methods and streamlining pathology workflows, we are making personalized medicine more accessible, efficient, and impactful for patients everywhere.

Schedule a Demo

Want to learn more about how our AI-powered tools can transform breast cancer diagnostics in your practice? Contact us today to schedule a demo and explore how our cutting-edge technology can support your team in delivering precise, personalized cancer care.