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Background <p>Cervical 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.</p> Methods <p>In 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.</p> Results <p>Employing 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%.</p> Conclusion <p>The 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.</p> Trial registration <p>The 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月1日

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

摘要

Background

Cervical 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.

Methods

In 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.

Results

Employing 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%.

Conclusion

The 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 registration

The 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.