Abstract PO5-12-07: Analyzing Patient Experiences with Adjuvant Hormone Therapy for Early Breast Cancer Treatment via NLP/ML on Social Media: Symptoms and Their Mitigation
Authors: Sameet Sreenivasan, Chao Fang, Emuella Flood, Natasha Markuzon, Jasmine Sze
Journal: Cancer Research (2024)

Abstract Understanding the early breast cancer (eBC) patient experience of disease and treatment is critical to addressing unmet patient needs, improving outcomes, and informing the next wave of drug development. Traditional approaches, such as interviews or focus group studies, are resource intensive and have limitations in reflecting the diverse experiences of patients with different disease characteristics, treatments, and sociodemographic backgrounds, and in detecting rare severe events that could potentially lead to therapy discontinuation. To overcome these limitations, we have developed a semi-automated approach utilizing Natural Language Processing (NLP) and Machine Learning (ML) methods in application to public patient forums to extract patient insights. Specifically, we analyzed 500,000 anonymized posts from the BreastCancer.org forum, utilizing Transformer-based methods to identify posts related to eBC patient experiences treated with standard hormonal therapies (HT). First, a novel ML-based model identified posts related to eBC adjuvant HT. For those, patient demographics, symptoms and their severity, and symptom management strategies such as exercise and yoga were subject to further analysis. Out of 362,074 relevant eBC posts, 32 symptoms and 9 impacts were significantly associated with at least one of the six hormonal therapies we considered. Hot flushes (relative risk [RR]: 6.70; 95% CI: 3.36–13.36), arthralgia (RR: 6.67; 95% CI: 3.53–12.59), weight increase (RR: 4.83; 95% CI: 3.20–7.28), mood swings (RR: 7.36; 95% CI: 5.75–9.42), insomnia (RR: 4.76; 95% CI: 3.14–7.22), and depression (RR: 3.05; 95% CI: 1.71–5.44) demonstrated the strongest associations. Severe headache, dizziness, back pain, and muscle spasms showed significant associations with ≥1 HT despite their low overall prevalence in eBC posts. Notably, posts contained a wide range of symptoms severity, including rarely seen symptoms in smaller patient pools, such as severe night hot flashes leading to treatment discontinuation. Symptoms/impacts mentioned in association with exercise and yoga as a way for symptom mitigation included hot flashes, arthralgia, fatigue, anxiety, depression, and pain. Of those posts, 53.3% to 82.5% reported positive or neutral sentiments for exercise, and 22.2% to 100.0% for yoga. For posts specifically mentioning the management of side effects of HT, between 50.0% and 63.5% had a positive sentiment associated with exercise concerning each of the symptoms/impacts with a lower positive impact related to yoga (between 11.1% and 43.0% of posts). Posts discussing pain management and exercise included a higher proportion of negative feedback, versus positive sentiment for depression. Posts related to yoga as an HT symptom mitigation strategy had a higher prevalence of negative sentiments for anxiety and depression. Our proposed ML/NLP-based approach to collecting patient feedback from publicly available forums complements standard qualitative methods by enabling the scalable extraction of insights from hundreds of thousands of online conversations that could span a broad diversity of symptoms and well-being reflections. Furthermore, our findings suggest the potential value of informing clinical outcome assessment (COA) measurement for clinical trials, alone and in combination with standard qualitative research approaches to guide the next clinical development. Overall, our study highlights the significant potential of NLP and ML to address the critical need to better understand the eBC patient experience of disease and treatment to improve outcomes for patients. Citation Format: Sameet Sreenivasan, Chao Fang, Emuella Flood, Natasha Markuzon, Jasmine Sze. Analyzing Patient Experiences with Adjuvant Hormone Therapy for Early Breast Cancer Treatment via NLP/ML on Social Media: Symptoms and Their Mitigation [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-12-07.