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

作者：Heather M. Selby, Yewon A. Son, Vipul Sheth, Todd H. Wagner, Erqi L. Pollom, Arden M. Morris · 发表于：BMC Medical Imaging · 年份：2025 · DOI：10.1186/s12880-025-01614-3 · 被引用次数：4 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Artificial Intelligence in Healthcare and Education、Colorectal Cancer Surgical Treatments

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. OBJECTIVE: This study aimed to foster collaboration between a radiologist and two data scientists in the detection and segmentation of rectal tumors on T2- and diffusion-weighted MR images. By combining the radiologist's clinical expertise with the data scientists' imaging analysis skills, we sought to establish a foundation for future AI-driven approaches that streamline rectal tumor detection and segmentation, and optimize workflow efficiency. METHODS: A total of 37 patients with rectal cancer were included in this study. Through radiologist-led training, attendance at Stanford's weekly Colorectal Cancer Multidisciplinary Tumor Board (CRC MDTB), and the use of radiologist annotations and clinical notes in Epic E...