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. 2025 Sep 2:36:100340.
doi: 10.1016/j.tipsro.2025.100340. eCollection 2025 Dec.

AI based sentiment analysis of online discussions related to cervical brachytherapy

R Kouzy  1 , M K Rooney  1 , E E Cha  1 , S Vinjamuri  2 , H Wu  1 , Z El Kouzi  1 , O Mohamad  1 , T T Sims  3 , C R Weil  1 , N Taku  1 , L L Lin  1 , A Jhingran  1 , P Eifel  1 , M Joyner  1 , L E Colbert  1 , A H Klopp  1
Affiliations

AI based sentiment analysis of online discussions related to cervical brachytherapy

R Kouzy et al. Tech Innov Patient Support Radiat Oncol. .

Abstract

Purpose/objectives: Few studies have documented the experiences of patients receiving cervical brachytherapy. While evidence regarding quality of life issues in this population has emerged, traditional structured questionnaires often fail to capture the full range of patient perspectives. We hypothesized that analyzing unfiltered patient discussions from online forums would reveal unique insights into patient experiences, including previously unidentified emotional responses, concerns, and coping strategies. This study applied an artificial intelligence (AI) workflow to analyze cervical cancer and brachytherapy discussions from an online forum.

Materials/methods: We extracted posts and comments from the subreddit r/cervicalcancer, focusing on discussions about brachytherapy between November 2020 and January 2024. We applied a processing pipeline to prepare the data for analysis. The content was analyzed using RoBERTa, a pre-trained deep learning model, to categorize sentiments as positive, negative, or neutral. We further evaluated posts using pre-defined keyword tagging to identify dominant topics within conversations based on recent literature.

Results: The analysis encompassed 898 unique posts and comments from an initial dataset of 1075 entries. Overall sentiments were categorized as 40.4% positive, 29.9% negative, and 29.7% neutral. Discussions related to "Bowel Domain" showed the highest proportion of negative sentiments (51.2%) among all topics. "Urinary Domain" (46.8%), "Pain" (43.4%), "Fatigue" (42.4%), and "Anesthesia" (41.4%) discussions also reflected predominantly negative sentiments. In contrast, "Recovery" and "Survivorship" discussions were predominantly positive. The sentiments on "Sex" and "Mental Health" related topics displayed a more balanced distribution between positive and negative perspectives.

Conclusion: Our study demonstrates the value of analyzing unstructured patient narratives from online forums related to cervical brachytherapy. We identified patterns of concerns that can inform clinical practice, particularly regarding patient education about bowel and urinary side effects. These findings can improve informed consent discussions and help clinicians better address patients' significant concerns. Further work will focus on developing automated systems to bridge the gap between clinicians' understanding and patients' lived experiences.

Keywords: Artificial Intelligence; Brachytherapy; Cervical Cancer; Natural Language Processing.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

None
Graphical abstract
Fig. 1
Fig. 1
Workflow Pipeline for Sentiment Analysis and Keyword Tagging of Online Patient Discussions on Brachytherapy.
Fig. 2
Fig. 2
Bubble Chart Depicting the Distribution of Keywords in Online Discussions Related to Brachytherapy.
Fig. 3
Fig. 3
Sentiment Distribution Across Keywords in Online Discussions Related to Brachytherapy.
See this image and copyright information in PMC

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