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Microsatellite instability (MSI) results from deficient mismatch repair (dMMR) and plays a crucial role in tumorigenesis and treatment, particularly in colon cancer. The gold standard for MSI classification relies on multiplex fluorescent PCR with capillary electrophoresis (CE); however, manual interpretation is time-consuming and subjective due to panel variability. No dedicated tools currently exist for MSI classification from CE profiles. We developed Automated Classification of Microsatellite Instability (ACMSI) to streamline fragment analysis and automate MSI classification. ACMSI includes size calling, manual calibration, and automated classification (Fig. 1). Evaluated on 322 electrophoresis profiles (129 tumor-normal pairs, 774 markers) using the 2B3D NCI Panel (BAT25, BAT26, D2S123, D5S346, D17S250), ACMSI achieved a 99.07% automated alignment success rate. Compared to manual classification, it demonstrated 96.3% sensitivity and 99.64% specificity per marker and 100% accuracy per patient. ACMSI provides a robust, objective solution for MSI classification and is available on GitHub: https://github.com/OpenGene/ACMSI .

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ACMSI: An Innovative Automated Analysis Application Utilizing Computer Vision for Accurate Microsatellite Instability Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Jiale Wen,
  • Xiao Lan,
  • Yaru Chen,
  • Kamen Ivanov,
  • Jia Gu,
  • Shifu Chen

摘要

Microsatellite instability (MSI) results from deficient mismatch repair (dMMR) and plays a crucial role in tumorigenesis and treatment, particularly in colon cancer. The gold standard for MSI classification relies on multiplex fluorescent PCR with capillary electrophoresis (CE); however, manual interpretation is time-consuming and subjective due to panel variability. No dedicated tools currently exist for MSI classification from CE profiles. We developed Automated Classification of Microsatellite Instability (ACMSI) to streamline fragment analysis and automate MSI classification. ACMSI includes size calling, manual calibration, and automated classification (Fig. 1). Evaluated on 322 electrophoresis profiles (129 tumor-normal pairs, 774 markers) using the 2B3D NCI Panel (BAT25, BAT26, D2S123, D5S346, D17S250), ACMSI achieved a 99.07% automated alignment success rate. Compared to manual classification, it demonstrated 96.3% sensitivity and 99.64% specificity per marker and 100% accuracy per patient. ACMSI provides a robust, objective solution for MSI classification and is available on GitHub: https://github.com/OpenGene/ACMSI .