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Artificial Intelligence-Based Sentinel Lymph Node Metastasis Detection in Cervical Cancer.

Ilse G T Baeten1, Jacob P Hoogendam1, Nikolas Stathonikos2

  • 1Department of Gynecologic Oncology, Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, The Netherlands.

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Summary

A deep learning algorithm showed 100% sensitivity for detecting sentinel lymph node (SLN) metastases in early-stage cervical cancer. This AI tool could reduce pathologist workload and costs for cancer staging.

Keywords:
artificial intelligencecervical cancerdeep learningsentinel lymph nodeultrastaging

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Area of Science:

  • Oncology
  • Pathology
  • Artificial Intelligence

Background:

  • Sentinel lymph node (SLN) mapping is crucial for staging early-stage cervical cancer.
  • Pathological ultrastaging, involving serial sectioning and IHC, is labor-intensive and costly.
  • Deep learning (DL) offers potential for efficient metastasis detection, reducing workload and cost.

Purpose of the Study:

  • To evaluate the effectiveness of a DL algorithm for SLN metastasis detection in early-stage cervical cancer.
  • To assess the sensitivity of a commercially available DL algorithm, originally for breast and colon cancer, in cervical cancer.
  • To demonstrate the potential of leveraging existing AI tools for cervical cancer pathology.

Main Methods:

  • Retrospective analysis of whole slide images (WSIs) of H&E-stained SLNs from 21 early-stage cervical cancer patients.
  • Application of a CE-IVD certified DL algorithm (off-label) to 47 SLN specimens.
  • Evaluation of algorithm performance on H&E slides with known macro- and micrometastases.

Main Results:

  • The study included 21 patients with varying cervical cancer subtypes and metastasis sizes (10 macro, 11 micro).
  • The DL algorithm achieved 100% sensitivity in detecting all macro- and micrometastases on H&E stained SLN WSIs.
  • The algorithm successfully evaluated 47 SLN specimens, including 22 negative cases.

Conclusions:

  • A DL algorithm demonstrated high sensitivity for detecting clinically relevant SLN metastases in early-stage cervical cancer.
  • The findings support the potential of using existing AI tools, developed for other cancers, in cervical cancer pathology.
  • Further prospective validation in larger patient cohorts is warranted to confirm these promising results.