TNM staging (Tumor, Node, Metastasis) is a fundamental standard in oncology for classifying cancer extent. Recently, within the framework of the sixth SMM4H-HeaRD 2026 workshop, a challenge was proposed for independent prediction of these labels from pathology reports of The Cancer Genome Atlas (TCGA). The problem was approached as a multi-label classification, combining classical techniques such as TF-IDF with biomedical language models (ClinicalBERT, BioBERT, PubMedBERT) and machine learning algorithms. The results showed promising performance, although with challenges in generalization and class balance. This type of research underscores the potential of artificial intelligence to streamline oncological diagnoses, but also highlights the need for robust and customized solutions.
In this context, digital transformation in the healthcare sector demands custom applications that integrate predictive models securely and efficiently. Companies like Q2BSTUDIO offer custom software development services, artificial intelligence for businesses, and cloud solutions that enable deploying these systems on AWS or Azure with high cybersecurity standards. The ability to process extensive clinical documents and handle data imbalances requires scalable infrastructure and specifically trained AI agents.
Furthermore, the combination of models such as LightGBM with biomedical embeddings shows that hybrid architectures can improve accuracy. To implement these techniques in real-world environments, it is key to have business intelligence services that allow visualizing results through tools like Power BI, facilitating clinical decision-making. Q2BSTUDIO also provides process automation solutions and AI for businesses tailored to each need, from creating reproducible pipelines to implementing machine learning models. TNM research is an example of how technology can transform medicine, but it requires strategic partnerships with expert developers.
