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Trial Title:
Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer
NCT ID:
NCT06253065
Condition:
Prostatic Neoplasms
Lymphatic Metastasis
Conditions: Official terms:
Prostatic Neoplasms
Neoplasm Metastasis
Lymphatic Metastasis
Conditions: Keywords:
artificial intelligence
lymph node metastasis
prostate cancer
whole slide image
Study type:
Observational
Overall status:
Recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
Artificial intelligence (AI)-based diagnostic model (developed)
Description:
Collect pathological slides of resected lymph nodes of the enrolled patients. Digitise
these slides into whole-slide images (WSIs). Analyze the WSIs using the AI model to
generate diagnostic results (with or without lymphatic metastasis). No intervention to
patients would be performed in this diagnostic test study.
Arm group label:
Patients undergoing PLND
Summary:
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.
Detailed 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.
Criteria for eligibility:
Study pop:
Patients with prostate cancer, (will) undergo radical prostatectomy and pelvic lymph node
dissection between Jan, 2024 and Dec 2025 in Sun Yat-sen Memorial Hospital of Sun Yat-sen
University are planned to be enrolled in this prospective diagnostic test.
Histopathological slides of resected pelvic lymph nodes of enrolled patients will be
collected and digitised as whole-slide images (WSIs) for prospective validation of the AI
model.
Sampling method:
Non-Probability Sample
Criteria:
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.
Gender:
Male
Minimum age:
N/A
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Sun Yat-sen Memorial Hospital of Sun Yat-sen University
Address:
City:
Guangzhou
Zip:
510120
Country:
China
Status:
Recruiting
Contact:
Last name:
Cuimei Yao
Phone:
13450210603
Email:
syskyk02@163.com
Start date:
January 12, 2024
Completion date:
December 2025
Lead sponsor:
Agency:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Agency class:
Other
Source:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06253065
