Deep learning-based segmentation of peritoneal cancer index regions from CT imaging - Takeaways - MDSpire
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Automated Segmentation of Peritoneal Cancer Index Areas Using Deep Learning from CT Scans

  • By

  • Pieter C. Gort

  • Lotte J. S. Fleurkens-Ewals

  • Lenah D. Kampmeijer

  • Anna F. van Herwijnen

  • Marion W. Tops-Welten

  • Cris H. B. Claessens

  • Joost Nederend

  • Ignace H. J. T. De Hingh

  • Max J. Lahaye

  • Misha D. P. Luyer

  • Fons van der Sommen

  • September 27, 2026

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  • 1

    Peritoneal metastases (PM) primarily arise from gastric, colorectal, and ovarian cancers, historically associated with poor prognosis.

  • 2

    The Sugarbaker’s Peritoneal Cancer Index (PCI) is the standard method for grading PM severity, ranging from 0 to 39.

  • 3

    Imaging evaluation of PM via CT scans is challenging, often leading to missed small lesions and underestimation of disease burden.

  • 4

    This study introduces a deep learning method for automated segmentation of rPCI regions, aiming to enhance assessment accuracy.

  • 5

    The research compares convolutional and transformer-based architectures for segmentation and evaluates performance against interobserver agreement.

Original Source(s)

Deep learning-based segmentation of peritoneal cancer index regions from CT imaging

  • by Pieter C. Gort, Lotte J. S. Fleurkens-Ewals, Lenah D. Kampmeijer, Anna F. van Herwijnen, Marion W. Tops-Welten, Cris H. B. Claessens, Joost Nederend, Ignace H. J. T. De Hingh, Max J. Lahaye, Misha D. P. Luyer, Fons van der Sommen

  • September 27, 2026

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