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RT-SAM: Visual-Prompt Fusion and Uncertainty Enhancement for Nasopharyngeal Carcinoma Radiotherapy Target

Hee Guan Khor, Xin Yang, Yihua Sun

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2026
    PubMed
    Summary
    This summary is machine-generated.

    RT-SAM automates clinical target volume (CTV) and nodal CTV contouring for nasopharyngeal carcinoma (NPC) radiotherapy. This AI framework significantly reduces inter-observer variability, offering a clinically viable solution for precise treatment planning.

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    Area of Science:

    • Medical Imaging and Radiation Oncology
    • Artificial Intelligence in Healthcare
    • Computational Anatomy

    Background:

    • Accurate clinical target volume (CTV) and nodal CTV (CTV$_{nd}$) delineation is critical for nasopharyngeal carcinoma (NPC) radiotherapy.
    • Manual contouring is time-consuming and prone to significant inter-observer variability due to complex anatomy.

    Purpose of the Study:

    • To introduce RT-SAM, a novel framework for automated CTV and CTV$_{nd}$ contouring in NPC CT images.
    • To leverage the Medical Segment Anything Model 2 (MedSAM-2) integrated with a specialist network for enhanced contouring accuracy.

    Main Methods:

    • Developed RT-SAM by integrating MedSAM-2 with a 2D U-Net specialist network.
    • Implemented automated multi-modal prompt generation (mask, bounding box, point) to guide MedSAM-2.
    • Introduced Visual-Prompt Fusion Attention (ViPFA) and Uncertainty-Enhanced Prediction Adjustment (UEPA) mechanisms for improved robustness and accuracy.

    Main Results:

    • RT-SAM achieved a mean DICE coefficient of 0.796 ± 0.033 on a multi-center dataset.
    • Clinical validation showed RT-SAM contours were clinically indistinguishable from expert delineations.
    • RT-SAM achieved superior quality ratings in 75% of comparisons and was rated clinically acceptable in over 97% of cases.

    Conclusions:

    • RT-SAM offers a clinically feasible solution for automated CTV and CTV$_{nd}$ contouring in NPC radiotherapy.
    • The framework has strong potential to standardize treatment planning and reduce inter-observer variability.
    • RT-SAM represents a significant advancement in AI-driven radiotherapy planning for NPC.