Evidence map›Paper›PMID 41639010›Full record

ArticleJNCI cancer spectrum2026

Natural language processing to identify documented pain preceding radiation therapy for bone metastases.

Jie Jane Chen, Jean Feng, Isabel D Friesner, Chichi Chang, Clodagh Kenny, Marianna V Elia, Lisa Ni, Felix Y Feng, Michael W Rabow, Steve E Braunstein and 2 more

Abstract read
In one paragraph

Article in JNCI cancer spectrum, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Jie Jane ChenDepartment of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States.ORCID 0000-0002-5380-4825
Jean FengDepartment of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, United States.
Isabel D FriesnerBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States.ORCID 0009-0000-0216-4031
Chichi ChangUCSF UC Berkeley Joint Program in Computational Health, San Francisco, CA, United States.ORCID 0009-0000-6301-7377
Clodagh KennyBakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, United States.ORCID 0009-0002-8861-3014
Marianna V EliaUCSF UC Berkeley Joint Program in Computational Health, San Francisco, CA, United States.ORCID 0009-0004-9204-2423
Lisa NiDepartment of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States.ORCID 0000-0001-9522-0900
Felix Y FengDepartment of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States.ORCID 0000-0001-9756-4037
Michael W RabowDepartment of Internal Medicine, Division of Palliative Medicine, University of California San Francisco, San Francisco, CA, United States.ORCID 0000-0002-0095-8951
Steve E BraunsteinDepartment of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States.ORCID 0000-0002-9841-9357
Lauren C BoretaDepartment of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States.ORCID 0000-0002-7286-9253
Julian C HongDepartment of Radiation Oncology, University of California San Francisco, San Francisco, CA, United States.ORCID 0000-0001-5172-6889

Funding

American Society of Clinical Oncology Conquer Cancer Foundation AwardRadiation Oncology Institute AwardUniversity of California San Francisco Computational Cancer Research AwardUniversity of California San Francisco Helen Diller Family Comprehensive Cancer Center Diversity, Equity, Inclusion and Accessibility
6 · The paper itself

Abstract

backgroundRadiation therapy (RT) plays a crucial role in managing cancer-related symptoms. This study characterized symptom documentation, especially pain, preceding bone metastasis diagnosis and initiation of RT for bone metastasis using natural language processing approaches.

methodsA deidentified cohort of patients who received RT for bone metastasis at a single tertiary-care institution (2013-2023) was created. Clinical data, notes, and metadata were computationally extracted. A previously validated natural language processing pipeline based on Apache clinical Text Analysis and Knowledge Extraction System was used to extract Common Terminology Criteria for Adverse Events-encoded symptoms from all notes in the 30 days preceding bone metastasis diagnosis and each course of RT for bone metastasis. Logistic regression analyses examined the association between clinical and demographic variables and pain documentation.

resultsIn all, 1061 patients (median [IQR] age, 64 [54-72] years; 582 [54.9%] men) received 1718 courses of RT for bone metastases. The most common documented symptoms before bone metastasis diagnosis and first RT for bone metastasis, respectively, were bone metastasis-related pain (52.5% vs 91.6%, P < .001), nausea (20.8% vs 48.9%, P < .001), and constipation (12.8% vs 34.2%, P < .001). Before bone metastasis diagnosis, multiracial or a race other than Asian, Black, or White (odds ratio = 0.61, 95% confidence interval = 0.38 to 0.99]; P = .045) was associated with decreased pain documentation compared with White race. Before RT for bone metastases, women (odds ratio = 1.48, 95% confidence interval = 1.02 to 2.15, P = .04) had increased pain documentation compared with men.

conclusionsWomen and patients who identified as multiracial or a race other than Asian, Black, or White experienced a relative increase in pain documentation from bone metastasis diagnosis to RT for bone metastases. This finding may reflect differential decision-making for which patients are offered RT for bone metastases sooner in the symptom trajectory. Interventions are needed to increase equitable distribution of RT.

Indexed as

Bone NeoplasmsCancer PainNatural Language ProcessingAgedDocumentationFemaleHumansLogistic ModelsMaleMiddle AgedNausea

Identifiers

PMID41639010
PMCPMC13032901

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.