Evidence map›Paper›PMID 41327229›Full record

ArticleBMC medical informatics and decision making2025

Predictive framework for cervical cancer brachytherapy fractionation mode integrating generative model and dynamic feature aggregation GNNs.

Xueping Liu, Xingya Liu, Youru Li, Shi Bai, Na Li, Tianyi Gong, Silu Ding

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Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xueping LiuCollege of Artificial Intelligence, Shenyang Aerospace University, No. 37 Daoyi South Avenue, Shenyang, Liaoning, 110136, China.
Xingya LiuCollege of Artificial Intelligence, Shenyang Aerospace University, No. 37 Daoyi South Avenue, Shenyang, Liaoning, 110136, China.
Youru LiCollege of Computer Science, Beijing University of Technology, No. 100 Ping Le Yuan, Beijing, 100124, China.
Shi BaiSchool of Information Science and Engineering, Shenyang University of Technology, No. 111 Shenliao West Road, Shenyang, Liaoning, 110870, China.
Na LiDepartment of Medical Imaging, Liaoning Cancer Institute and Hospital, No. 44 Xiaoheyan Road, Shenyang, Liaoning, 110042, China.
Tianyi GongCollege of Artificial Intelligence, Shenyang Aerospace University, No. 37 Daoyi South Avenue, Shenyang, Liaoning, 110136, China.
Silu DingDepartment of Radiation Oncology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning, 110002, China. slding@cmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCervical cancer is among the top four most prevalent cancers in women globally. Its treatment strategy necessitates a combination of external beam radiation therapy (EBRT) and brachytherapy, demanding precise treatment decisions from doctors. However, current clinical guidelines often provide only principled implementation standards, leaving significant freedom in clinical treatment operations. This study aims to develop a predictive framework to assist in determining cervical cancer brachytherapy fractionation modes.

methodsIn response to the intricate dynamics between patient characteristics and inter-patient relationships, we introduce a novel approach: the dynamic feature aggregation graph neural network (DyFAGNN). This model incorporates a phase-aware module to capture nuanced connections between patient characteristics and leverages graph neural network technology to illustrate interactions among patients. We collected data from 1271 real clinical patients and employed a GPT-2 based generative model to simulate clinically realistic treatment data to overcome data scarcity.

resultsEmploying the generated datasets, we conducted comparative analyses across eight models. The results demonstrated that the DyFAGNN model, when trained on synthetic data balanced with the ADASYN technique, achieved the highest performance, with an accuracy of 82.55%, macro-precision of 82.91%, macro-recall of 82.59%, and macro-F1 score of 82.68%.

conclusionThe proposed framework, integrating a generative model with DyFAGNN, provides a robust and accurate method for predicting cervical cancer brachytherapy fractionation modes. This approach can serve as a valuable tool to support clinical decision-making and personalize treatment strategies.

trial registrationThe study was conducted following the Declaration of Helsinki and was approved by the Ethics Committee of The First Hospital of China Medical University. As it was a retrospective study with anonymized data, informed consent was waived.

Indexed as

BrachytherapyDose Fractionation, RadiationGraph Neural NetworksUterine Cervical NeoplasmsFemaleGenerative Artificial IntelligenceHumansCervical cancer brachytherapyGraph neural networksImbalanced dataSynthetic data

Identifiers

PMID41327229
PMCPMC12771814

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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.