3716 - Artificial Intelligence Enabled Analysis of Reddit r/Breastcancer Posts about Radiation
Presenter(s)
J. Runnels1, A. C. Segura2,3, M. Cohen1, V. A. Dumane1, and S. Green1; 1Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, 2Department of Biomedical Engineering, University of New Mexico School of Engineering, Albuquerque, NM, 3Department of Internal Medicine, Division of Radiation Oncology, University of New Mexico School of Medicine, Albuquerque, NM
Purpose/Objective(s): Breast cancer patients potentially requiring radiation therapy (RT) receive extensive information from Radiation Oncologists, yet many also turn to the internet. Reddit’s r/breastcancer is a popular forum with over 32,000 subscribers. Users post questions, share experiences, and discuss topics related to breast cancer. Understanding what patients seek online can help Radiation Oncologists address information gaps, correct misconceptions, and enhance communication. This study uses artificial intelligence (AI) to analyze the content and sentiment of radiation-related Reddit discussions.
Materials/Methods: We conducted sentiment analysis using natural language processing on r/breastcancer posts containing “radiation” or “radiotherapy” (RT) from January 2024 to January 2025. Using Python-based BERTopic, posts were clustered into thematic categories. Sentiment analysis, performed using Python’s VADER library, classified text in posts and corresponding comments as positive, negative, or neutral. Descriptive statistics were reported, and Chi-Square tests assessed topic-sentiment associations.
Results: 167 posts were analyzed. Topics included toxicity (38.18%), preparation (19.39%), treatment experience (18.18%), management decisions (10.3%), radiation technique (9.7%), and reconstruction (4.25%). Toxicity posts centered on skin care, cosmesis, and heart/lung toxicity risks. Preparation posts sought guidance on clothing, jewelry, scheduling, and transportation. Treatment experience posts described sensations during RT. Management decision discussions involved whether to proceed with or decline RT. Radiation technique discussions covered dose-fractionation, partial breast treatment, breath-hold, and prone vs. supine positioning. Reconstruction posts debated timing relative to RT. Sentiment analysis revealed significant trends. Toxicity discussions were most likely negative (p < 0.001), reflecting concerns about side effects. Treatment experience and management decision discussions were significantly more positive (p < 0.05, p < 0.05), suggesting reassurance from shared experiences and decision-making. Reconstruction discussions were mostly neutral (p > 0.05), indicating a more factual tone.
Conclusion: This study highlights key trends in online discussions about radiation therapy for breast cancer. Toxicity is most frequently discussed and sentiments are typically negative, emphasizing the importance of patient education on side effect management and expectations. Many patients also seek logistical guidance on preparing for RT. Management decision-related discussions are often positive and clinicians can reinforce this with clear, evidence-based guidance. By understanding what patients seek, discover, and share online, we can anticipate concerns, dispel misinformation, and improve digital health literacy.