CST 499 Week 3

 Onco-Logic: Week 3 - Unlocking Pathology Insights

This week, our Onco-Logic capstone project took a significant step forward as our team began extracting structured insights from free-text pathology reports. Our main objective is to transform these complex, unstructured narratives into organized, clinically actionable data, which will be crucial for identifying patients for clinical studies, flagging high-risk individuals, and enabling broader clinical analysis.

Initial Data Handling and Exploration

We successfully integrated and explored the key datasets for our project, all of which were found to be complete with no missing values. This strong foundation allows us to proceed confidently with our analysis. We examined the characteristics of the pathology reports, noting a wide range in text lengths, indicating the diverse nature of the information contained within them. We also analyzed the distribution of the cancer staging (TNM) labels, gaining a preliminary understanding of the common classifications within our dataset. After initial data cleaning and normalization, we successfully merged these datasets, creating a comprehensive view of the patient reports alongside their associated cancer types and staging information.

Early NLP Observations

Our initial exploration into the textual data involved generating a word cloud, which visually highlighted the most frequently occurring terms in the pathology reports. This provided an immediate sense of the prevalent medical terminology. We also conducted preliminary evaluations using a logistic regression model on text features to classify cancer types. The results demonstrated that increasing the complexity of the text features led to improved classification accuracy, underscoring the rich information embedded within these reports. Further analysis using dimensionality reduction techniques like PCA and t-SNE helped us visualize the inherent structure and clustering of different cancer types based on their textual descriptions, revealing the high dimensionality of the data and the need for advanced methods to capture its full variance.

Next Steps: Advancing to Domain-Specific NLP

Moving forward, our core focus is to develop a robust natural language processing pipeline. While our team is considering various strategies, including the use of large language models like Llama for retrieval-augmented generation, I am particularly keen on implementing a solution using a domain-specific S-BERT model. Models such as ClinicalBERT or Path-BERT, pre-trained on extensive biomedical and pathology texts, are exceptionally well-suited for understanding the nuances of medical language. This specialized training allows them to interpret terminology and context with greater accuracy than general-purpose models, which is vital for extracting precise clinical insights.

Our next steps will involve:

• S-BERT Pipeline Development: Constructing and fine-tuning an S-BERT-based pipeline for tasks like identifying key medical entities and their relationships within the pathology reports.

• Structured Data Generation: Transforming the extracted information into clear, structured formats that are readily usable for clinical applications.

• Methodology Evaluation: Continuously evaluating and comparing the performance of our chosen NLP approaches to ensure we are deploying the most effective solutions for our project goals.

We are enthusiastic about the potential of these sophisticated NLP techniques to extract actionable intelligence from unstructured pathology reports, thereby supporting enhanced clinical decision-making and patient care.