This project focuses on leveraging natural language processing (NLP) techniques to extract critical information from pathology reports. Participants will gain hands-on experience with a wide range of text classification methods, from traditional tf-idf analysis to cutting-edge large language models. The Cancer Genome Atlas (TCGA) pathology report corpus used in this project offers a unique opportunity for the development of advanced NLP technologies that can ultimately enhance patient diagnosis, treatment selection, and many other aspects of cancer care.

Datasets

• GDC Data Portal: https://portal.gdc.cancer.gov/
• TCGA pathology reports corpus: https://github.com/tatonetti-lab/tcga-path-reports/blob/main/TCGA_Reports.csv.zip
• TCGA cancer type: https://github.com/tatonetti-lab/tcga-path-reports/blob/main/data/tcga_metadata/tcga_patient_to_cancer_type.csv
• TNM stage: https://github.com/tatonetti-lab/tnm-stage-classifier/tree/main/TCGA_Metadata
• TCGA clinical endpoints: https://xenabrowser.net/datapages/?dataset=Survival_SupplementalTable_S1_20171025_xena_sp&host=https%3A%2F%2Fpancanatlas.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443
• TCGA Pan-Cancer: https://xenabrowser.net/datapages/?cohort=TCGA Pan-Cancer (PANCAN)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443

Code

• Code repository for this project: https://github.com/guilopgar/AI-Campus-Project-7-NLP
• TNM stage classifier: https://github.com/tatonetti-lab/tnm-stage-classifier/tree/main
• Cancer type classifier: https://github.com/tatonetti-lab/tcga-path-reports

Publications

• Kefeli, J., Berkowitz, J., Acitores Cortina, J.M. et al. Generalizable and automated classification of TNM stage from pathology reports with external validation. Nat Commun 15, 8916 (2024). https://doi.org/10.1038/s41467-024-53190-9
• Kefeli, J., Tatonetti, N. TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models. Patterns 5(3), 100933 (2024). https://doi.org/10.1016/j.patter.2024.100933

Project Prepared By:
Guillermo Lopez Garcia – Guillermo.LopezGarcia@cshs.org
Takeshi Onishi – Takeshi.Onishi@cshs.org