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Differentiating Radiotherapy-Specific Distress From General Cancer Distress: Natural Language Processing Analysis of Patient Narratives.

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Title: Differentiating Radiotherapy-Specific Distress From General Cancer Distress: Natural Language Processing Analysis of Patient Narratives.
Authors: Wang Y; Department of Radiation Oncology, The First Hospital of China Medical University, No. 210, Baita 1st Street, Hunnan District, Shenyang, Liaoning, 110167, China, +86-1384001728., Ji M; Department of Radiation Oncology, The First Hospital of China Medical University, No. 210, Baita 1st Street, Hunnan District, Shenyang, Liaoning, 110167, China, +86-1384001728., Bai X; Department of Radiation Oncology, The First Hospital of China Medical University, No. 210, Baita 1st Street, Hunnan District, Shenyang, Liaoning, 110167, China, +86-1384001728.
Source: JMIR formative research [JMIR Form Res] 2026 Jul 14; Vol. 10, pp. e100874. Date of Electronic Publication: 2026 Jul 14.
Publication Type: Journal Article; Observational Study
Language: English
Journal Info: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101726394 Publication Model: Electronic Cited Medium: Internet ISSN: 2561-326X (Electronic) Linking ISSN: 2561326X NLM ISO Abbreviation: JMIR Form Res Subsets: MEDLINE
Imprint Name(s): Original Publication: Toronto, ON, Canada : JMIR Publications, [2017]-
MeSH Terms: Neoplasms*/psychology , Neoplasms*/radiotherapy , Stress, Psychological*/etiology , Stress, Psychological*/psychology , Radiotherapy*/psychology , Radiotherapy*/adverse effects , Natural Language Processing* , Psychological Distress*, Humans ; Retrospective Studies ; Narration ; Female ; Male
Abstract: Background: Psychological distress is common among patients with cancer, and it negatively impacts treatment adherence and quality of life. Radiotherapy, with its unique procedures, such as daily sessions and physical immobilization, may induce distress distinct from general cancer anxiety. However, existing screening tools cannot differentiate these distress sources. This study leverages online patient narratives and natural language processing to distinguish radiotherapy-specific distress from general cancer distress.
Objective: This study aimed to systematically identify, differentiate, and compare the composition, structure, and emotional characteristics of general cancer distress and radiotherapy-specific distress through the analysis of large-scale online patient narratives.
Methods: Using a retrospective observational design, we screened 52,831 relevant posts published between 2015 and 2025 on the online health community Reddit, ultimately including 9860 first-person patient narratives meeting the inclusion criteria. Content-based extraction revealed that the most frequently represented cancer types were head and neck (1576/6625, 23.8%), breast (1418/6625, 21.4%), and prostate (1000/6625, 15.1%), with the majority of posts written during active treatment (3587/5748, 62.4% of phase-identifiable posts). We used latent Dirichlet allocation thematic modeling for topic identification, supplemented by manual qualitative coding for thematic classification. Structural relationships between topics were analyzed via correlation heatmaps, while emotional polarity and discrete sentiments for both distress categories were quantitatively compared using VADER (Valence Aware Dictionary and Sentiment Reasoner) and RoBERTa models.
Results: Radiotherapy-specific distress formed a thematically distinct and substantial domain, accounting for 50.4% (4969/9860) of all narratives, comparable to the proportion of 49.0% (4831/9860) attributed to general cancer distress. The remaining 0.6% (60/9860) was unclassifiable into either category. Thematic correlation analysis revealed that both categories exhibited high internal cohesion but weak intercategory associations, a pattern consistent with thematic differentiability between the 2 domains rather than a single undifferentiated construct. Sentiment analysis further revealed that radiotherapy-specific distress carried significantly stronger negative emotional intensity (Mann-Whitney U test, P<.001; rank-biserial correlation=0.34), with core emotions dominated by "fear" (2773/4969, 55.8%) and "anger/frustration" (1262/4969, 25.4%), whereas general cancer distress was more frequently expressed as "anxiety" (2183/4831, 45.2%) and "sadness" (1599/4831, 33.1%; χ² test, P<.001; Cramér V=0.22).
Conclusions: This study provides exploratory evidence that radiotherapy-specific distress is thematically and emotionally differentiable from general cancer distress, suggesting it may constitute a distinct domain warranting targeted assessment and intervention strategies. Developing targeted assessment and care strategies addressing radiotherapy-specific challenges is essential for achieving truly patient-centered, individualized psychosocial support in oncology.
(© Yike Wang, Meiyan Ji, Xinghua Bai. Originally published in JMIR Formative Research (https://formative.jmir.org).)
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Contributed Indexing: Keywords: natural language processing; patient experience; psychosocial distress; psychosocial oncology; radiotherapy; topic modeling
Entry Date(s): Date Created: 20260714 Date Completed: 20260715 Latest Revision: 20260813
Update Code: 20260814
PubMed Central ID: PMC13367937
DOI: 10.2196/100874
PMID: 42447465
Database: MEDLINE
Description
ISSN:2561-326X
DOI:10.2196/100874