Pharmacogenomics and Personalized Medicine (Feb 2024)
Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways
Abstract
Hui Lin,1,2 Lisa Ni,1 Christina Phuong,1 Julian C Hong1,3,4 1Department of Radiation Oncology, University of California San Francisco, San Francisco, CA, USA; 2UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, Berkeley and San Francisco, San Francisco, CA, USA; 3Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA; 4Joint Program in Computational Precision Health, University of California, Berkeley and San Francisco, Berkeley, CA, USACorrespondence: Julian C Hong, Email [email protected]: Natural language processing (NLP), a technology that translates human language into machine-readable data, is revolutionizing numerous sectors, including cancer care. This review outlines the evolution of NLP and its potential for crafting personalized treatment pathways for cancer patients. Leveraging NLP’s ability to transform unstructured medical data into structured learnable formats, researchers can tap into the potential of big data for clinical and research applications. Significant advancements in NLP have spurred interest in developing tools that automate information extraction from clinical text, potentially transforming medical research and clinical practices in radiation oncology. Applications discussed include symptom and toxicity monitoring, identification of social determinants of health, improving patient-physician communication, patient education, and predictive modeling. However, several challenges impede the full realization of NLP’s benefits, such as privacy and security concerns, biases in NLP models, and the interpretability and generalizability of these models. Overcoming these challenges necessitates a collaborative effort between computer scientists and the radiation oncology community. This paper serves as a comprehensive guide to understanding the intricacies of NLP algorithms, their performance assessment, past research contributions, and the future of NLP in radiation oncology research and clinics.Keywords: artificial intelligence, personalized medicine, radiation therapy, natural language processing
