Disease uncertainty experienced by patients during [18F]FDG PET/CT imaging: implications for clinical nursing
As a core hybrid imaging modality, 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) plays a vital role in diagnosing and managing a wide range of diseases. During the scanning process, many patients experience clinically significant illness uncertainty, which adversely impacts their overall medical experience and adherence to treatment plans. This research intended to quantify the level of illness uncertainty in [18F]FDG PET/CT recipients and clarify the factors associated with this condition. This cross-sectional investigation recruited 386 consecutive patients undergoing [18F]FDG PET/CT scans at a hospital’s nuclear medicine unit between August and December 2025 using a convenience sampling strategy. A structured questionnaire was applied to collect data, aiming to assess illness uncertainty and its potential correlates in the study population. The average total score of illness uncertainty was 30.22 ± 4.06 (range 9–36), corresponding to an upper-moderate level. Correlation analysis showed that better knowledge of [18F]FDG PET/CT was strongly linked to lower illness uncertainty ( r = − 0.623, P < 0.001). Conversely, examination-related psychological distress and unaddressed needs and worries were significantly positively correlated with higher uncertainty ( r = 0.587, 0.498; all P < 0.001). Six independent factors were confirmed to predict increased illness uncertainty through multiple linear regression ( P < 0.05 for all): female sex, age below 40 years, low education level, divorced or widowed status, initial [18F]FDG PET/CT examination, and poor procedural knowledge. The model explained 55.7% of the variance in illness uncertainty. Elevated illness uncertainty is highly prevalent among patients receiving [18F]FDG PET/CT examinations and is determined by various demographic and clinical variables. Accordingly, we recommend conducting stratified health education and one-on-one psychological interventions. These strategies may help patients fully understand the examination process and relieve illness-related uncertainty in clinical settings.
Authors
- Jun Cai (ORCID: https://orcid.org/0000-0002-8928-9570)
- Jihui Li (ORCID: https://orcid.org/0000-0002-4748-0692)
- Li Du (ORCID: https://orcid.org/0009-0002-0334-192X)
- Bin Zhang
- Yufeng Feng
Institutions
- Soochow University (CN)
- First Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2026-08-26
- DOI
- https://doi.org/10.1186/s12885-026-16839-w
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00