JMIR Publications

ABSTRACT

Background:

Cancer patients often experience diverse psychosocial stressors that profoundly affect disease trajectories, treatment adherence, and overall quality of life. Understanding how patients experience and articulate these issues is critical for designing patient-centered interventions. Conventional data collection methods such as surveys and interviews provide depth but are constrained by recall bias, scalability, and may overlook sensitive or underreported concerns. Patient-authored narratives in online health communities (OHCs) present a valuable opportunity to identify prevalent and underserved issues. However, critical analytic challenges remain in generating coherent and interpretable insights due to their unstructured and large-scale nature.

Objective:

This study aims to leverage TopicGPT, a prompt-based topic modeling framework powered by large language models (LLMs), in combination with network analysis for interpretable topic discovery and interrelationship analysis in cancer patient narratives.

Methods:

Patient-authored posts describing psychosocial challenges about cancer experience were collected from four OHCs. Eligible posts were pre-processed and analyzed using TopicGPT, wherein topics were generated hierarchically and mapped at sentence level. Comparison analyses were conducted among three state-of-the-art LLMs through cosine similarity and manual evaluation. Results from the best-performing model were used to construct the network subsequently. Topic co-occurrence was examined using the Pointwise Mutual Information (PMI) algorithm and centrality metrics to reveal influential topics and thematic interconnections across narratives.

Results:

A total of 11306 posts were collected from Reddit, Macmillan, Mijian, and Douban between Dec 6, 2006 and Apr 24, 2025. Of these, 3169 posts were retained for topic modeling and network analysis. DeepSeek-V3.2 consistently outperformed Gemini-2.5-Flash and GPT-4o, with similarity scores of 0.6295, 0.5342, and 0.5247, respectively. "Fear of cancer recurrence" and "psychological distress" emerged as both most frequent and bridging topics across a hierarchy comprising 42 top-level and 58 subtopics. Strong connections were observed among "sexual health concerns", "reproductive concerns", and "quality of life impact"; "family communication concerns" frequently co-occurred with "employment concerns", " diagnostic delays and misdiagnosis", and "social support".

Conclusions:

This study demonstrates the potential of LLM-based topic modeling for large-scale, context-sensitive analysis of patient-authored narratives. The proposed integrated, domain-adaptable pipeline enables the identification of high-fidelity topics and their interrelationships, offering a scalable and interpretable approach to qualitative data in healthcare. Importantly, our findings reveal substantial concerns and unmet needs among cancer patients, emphasizing the necessity for patient-tailored interventions and enhanced support strategies to improve their overall well-being.