AI-ready rectal cancer MR imaging: a workflow for tumor detection and segmentation.

Journal: BMC medical imaging
PMID: 40087634

Abstract

BACKGROUND: Magnetic Resonance (MR) imaging is the preferred modality for staging in rectal cancer; however, despite its exceptional soft tissue contrast, segmenting rectal tumors on MR images remains challenging due to the overlapping appearance of tumor and normal tissues, variability in imaging parameters, and the inherent subjectivity of reader interpretation. For studies requiring accurate segmentation, reviews by multiple independent radiologists remain the gold standard, albeit at a substantial cost. The emergence of Artificial Intelligence (AI) offers promising solutions to semi- or fully-automatic segmentation, but the lack of publicly available, high-quality MR imaging datasets for rectal cancer remains a significant barrier to developing robust AI models.

Authors

  • Heather M Selby
    S-SPIRE Center, Department of Surgery, Stanford University School of Medicine, Palo Alto, USA. selbyh@stanford.edu.
  • Yewon A Son
    S-SPIRE Center, Department of Surgery, Stanford University School of Medicine, Palo Alto, USA.
  • Vipul R Sheth
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Todd H Wagner
    S-SPIRE Center, Department of Surgery, Stanford University School of Medicine, Palo Alto, USA.
  • Erqi L Pollom
    Department of Radiation Oncology, Stanford University School of Medicine, 875 Blake Wilbur Drive, Stanford, CA, 94305, USA. erqiliu@stanford.edu.
  • Arden M Morris
    S-SPIRE Center, Department of Surgery, Stanford University School of Medicine, Palo Alto, USA.

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