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<p>Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Radiation therapy (RT) plays a main role in GBM treatment, with precise tumor volume delineation. Tumor contouring is important in RT treatment planning, followed by successful treatment. Recent advances in artificial intelligence (AI), particularly deep learning (DL) techniques, have introduced automated contouring methods to improve accuracy and reduce interobserver variability. This review considers different architectures such as CNN, U-Net, nnU-Net, and PKMI-Net. In addition, a comparison was made between recent advances in DL models for the automatic segmentation of GBM target volumes, focusing on more applicable architectures and their characteristics, challenges, and limitations. This review study was performed on the recent literature from scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science by the end of January 2025. The results of this review study among the analyzed models, nnU-Net architecture emerged as the strongest, offering superior segmentation accuracy due to its self-configuring capabilities and adaptability to different imaging modalities.</p>

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Evaluation of CNN-based deep learning models for auto-contouring in glioblastoma radiotherapy: a review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月13日

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  • Amirmohammad Soltaninejad,
  • Daryoush Shahbazi-Gahrouei,
  • Amir Khorasani,
  • Simin Hemati

摘要

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Radiation therapy (RT) plays a main role in GBM treatment, with precise tumor volume delineation. Tumor contouring is important in RT treatment planning, followed by successful treatment. Recent advances in artificial intelligence (AI), particularly deep learning (DL) techniques, have introduced automated contouring methods to improve accuracy and reduce interobserver variability. This review considers different architectures such as CNN, U-Net, nnU-Net, and PKMI-Net. In addition, a comparison was made between recent advances in DL models for the automatic segmentation of GBM target volumes, focusing on more applicable architectures and their characteristics, challenges, and limitations. This review study was performed on the recent literature from scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science by the end of January 2025. The results of this review study among the analyzed models, nnU-Net architecture emerged as the strongest, offering superior segmentation accuracy due to its self-configuring capabilities and adaptability to different imaging modalities.