Evidence map›Paper›PMID 39794280›Full record

ArticleMedical physics2025

Risk assessment and quality management in AIO based on CT-linac for nasopharyngeal carcinoma: An improved FMEA and FTA approach.

Guangyu Wang, Shouliang Ding, Xin Yang, Sijuan Huang, Guanqun Zhou, Lu Liu, Hua Li, Lecheng Jia, Wenchao Diao, Ying Sun and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Guangyu WangState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Shouliang DingState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Xin YangState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Sijuan HuangState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Guanqun ZhouState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Lu LiuState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Hua LiRadiotherapy Laboratory, Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China.
Lecheng JiaRadiotherapy Laboratory, Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China.
Wenchao DiaoState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Ying SunState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Yanfei LiuRadiotherapy Laboratory, Shenzhen United Imaging Research Institute of Innovative Medical Equipment, Shenzhen, China.
Zun PiaoState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Chendi XuState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Li ChenState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
Xiaoyan HuangState Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAll-in-one radiotherapy workflow (AIO) is a novel one-stop solution that integrates the multiple conventional radiotherapy steps from simulation, contouring, planning, image guidance, beam delivery, and in vivo dosimetry into a single device (integrated computed tomography linac, the uRT-linac 506c), making the treatment process more efficient and convenient while reducing errors for cancer patients' initial radiotherapy. Despite its numerous advantages, the implementation of AIO faces challenges such as interdisciplinary coordination, software and hardware complexity, and reliance on artificial intelligence. To ensure its safety and effectiveness, it is necessary to conduct a risk assessment and identify appropriate quality management measures. PURPOSE: To perform risk assessment on the AIO for nasopharyngeal carcinoma using failure mode and effects analysis (FMEA) and fault tree analysis (FTA), and to validate the effectiveness of the quality management measures.

methodsA flowchart was established for the AIO of nasopharyngeal carcinoma. FMEA analysis was conducted based on the flowchart, and quantitative assessments of each failure mode (FM) were performed to obtain

resultsA flowchart of AIO for nasopharyngeal carcinoma was established, consisting of 5 main steps and 28 sub-steps. After FMEA analysis, 86 FMs were identified. In the group without implementing QM measures (QM-free group), the RPN of FMs ranged from 13.5 to 186.2, with a threshold of 94.6 for the top 20% RPN scores, resulting in 17 high-risk FMs. Additionally, 21 FMs had

conclusionThe improved FMEA and FTA analysis methods are practical and operational, allowing for a comprehensive analysis of potential failures and risks in the AIO for nasopharyngeal carcinoma. They can effectively assist in establishing and evaluating QM standards for AIO of nasopharyngeal carcinoma. Moreover, the analytical methods and QM measures of this study can be effectively applied to AIO for tumors in other sites.

Indexed as

Nasopharyngeal CarcinomaNasopharyngeal NeoplasmsParticle AcceleratorsTomography, X-Ray ComputedWorkflowHumansQuality ControlRisk Assessmentall‐in‐one radiotherapy workflowfailure mode and effects analysisfault tree analysisquality managementsimilarity aggregation method

Identifiers

PMID39794280
PMCPMC11972045

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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.