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Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer

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Sun Yat-sen University

Status

Enrolling

Conditions

Lymphatic Metastasis
Prostatic Neoplasms

Treatments

Diagnostic Test: Artificial intelligence (AI)-based diagnostic model (developed)

Study type

Observational

Funder types

Other

Identifiers

NCT06253065
SYSKY-2023-1281-01

Details and patient eligibility

About

The goal of this diagnostic test is to prospectively test the performance of pre-developed artificial intelligence (AI) diagnostic model for detecting pathological lymph node metastasis (LNM) of prostate cancer. Investigators had developed this AI model based on deep learning algorithms in preliminary research, and it performed well in retrospective tests.

Investigators will compare the diagnostic performance (sensitivity, specificity, etc.) of the AI model and routine pathological report issued by pathologists, to see if the AI model can improve the clinical workflow of pathological evaluation of LNM in prostate cancer in the real world.

Full description

Lymph node metastasis (LNM) is a common mode of metastasis in prostate cancer, and accurate postoperative pathological lymph node staging is of great significance for further treatment and prognosis assessment. However, the current pathological evaluation of lymph nodes relies on manual examination by pathologists, which has a relatively low diagnostic efficiency and is prone to missed-diagnosis for micro metastatic lesions. Therefore, investigators developed an AI diagnostic model for detecting pathological lymph node metastasis of prostate cancer based on deep learning algorithms in preliminary research, and it performed well in retrospective tests.

This study is a diagnostic test with no intervention measures, planning to collect pathological slides of formalin-fixed, paraffin-embedded lymph nodes resected from the enrolled patients and digitise them into whole-slide images (WSIs). The AI model will analyse the WSIs and generate pixel-level heatmaps and slide-level diagnostic results (with or without LNM). The routine pathological examination will be performed as usual. These two processes will not interfere with each other. And if there are inconsistency in slide-level classification between AI and routine pathological examination, investigators would convene senior pathologists for discussion to make the final decision (immunohistochemistry would be performed if necessary). The final result will be presented to the patient in the form of a pathological report.

Enrollment

100 estimated patients

Sex

Male

Volunteers

No Healthy Volunteers

Inclusion criteria

  • Patients with prostate cancer, undergoing radical prostatectomy and pelvic lymph node dissection.
  • Patients with complete clinical and pathological information.

Exclusion criteria

  • Patients with other tumors that metastasized to pelvic lymph nodes.
  • The patient refused to participate in this diagnostic test.

Trial design

100 participants in 1 patient group

Patients undergoing PLND
Description:
Patients (will) undergo radical prostatectomy and pelvic lymph node dissection
Treatment:
Diagnostic Test: Artificial intelligence (AI)-based diagnostic model (developed)

Trial documents
2

Trial contacts and locations

1

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Central trial contact

Tianxin Lin, Ph.D; Shaoxu Wu, MD

Data sourced from clinicaltrials.gov

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