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Radiomics-Guided Automatic Delineation for Clinical Target Volume of Endometrial Cancer: Limited Sample Multicenter

Ang Qu1, Xile Zhang1, Yipeng Song2

  • 1Department of Radiation Oncology, Peking University Third Hospital, Beijing, China.

International Journal of Radiation Oncology, Biology, Physics
|April 17, 2026
PubMed
Summary

This study developed a novel radiomics-guided meta-learning approach to improve clinical target volume (CTV) segmentation in endometrial cancer radiotherapy. The method effectively addresses multi-institutional variations and data scarcity, enhancing segmentation accuracy and efficiency.

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Area of Science:

  • Medical Imaging
  • Radiotherapy Oncology
  • Artificial Intelligence in Medicine

Background:

  • Inconsistent clinical target volume (CTV) delineation in endometrial carcinoma pelvic radiotherapy poses challenges for deep learning models.
  • Variations in internal target area definitions across institutions hinder accurate segmentation.

Purpose of the Study:

  • To develop an effective method for addressing multi-institutional variations in CTV delineation for endometrial cancer.
  • To overcome challenges posed by scarce data availability in few-shot, multi-centric segmentation tasks.

Main Methods:

  • Utilized 207 simulated CT cases from five centers, employing a support, query, and test set split within each center.
  • Extracted radiomics features to identify significant differences in CTV delineation and images across institutions.
  • Applied Model-Agnostic Meta-Learning (MAML) strategy with a 3D U-Net model (MAML-r), guided by important radiomics features, for pre-training and fine-tuning.

Main Results:

  • Identified 8 key radiomics features showing significant inter-center differences (p < 0.01).
  • MAML-r achieved a mean Dice Similarity Coefficient (DSC) of 0.818, outperforming other methods.
  • Demonstrated superior performance on an external test cohort (DSC 0.886) and reduced CTV modification time to 3.8±1.2 minutes.

Conclusions:

  • Introduced a novel radiomics-guided MAML framework for few-shot, multi-centric CTV segmentation in endometrial cancer radiotherapy.
  • The approach effectively mitigates performance degradation due to inter-institutional variations and data scarcity.
  • Offers a promising solution for persistent clinical challenges in radiotherapy planning.