Clinical trial NCT06253065: Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer
Official title: Prospective Validation of Pathology-based Artificial Intelligence Diagnostic Model for Lymph Node Metastasis in Prostate Cancer
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Prostatic Neoplasms
Lymphatic Metastasis
Keywords: artificial intelligence
lymph node metastasis
prostate cancer
whole slide image
Lead sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Sponsor class: OTHER
First posted: 2024-02-12
Enrollment: 100
Sex: MALE
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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.

Brief 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.

Eligibility 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.

Primary outcomes:
measure: sensitivity
timeFrame: For each enrolled patient, the diagnosis results of AI model will be obtained in not long after pelvic lymph node dissection, and the sensitivity of the AI model will be evaluated through study completion, an average of 2 year.
Secondary outcomes:
measure: specificity
timeFrame: For each enrolled patient, the diagnosis results of AI model will be obtained in not long after pelvic lymph node dissection, and the specificity of the AI model will be evaluated through study completion, an average of 2 year.
Other outcomes:
Locations:
city: Guangzhou
country: China
facility: Sun Yat-sen Memorial Hospital of Sun Yat-sen University
state: Guangdong
Study officials:
affiliation: Department of Urology of Sun Yat-sen Memorial Hospital of Sun Yat-sen University
name: Tianxin Lin, Ph.D
role: STUDY_CHAIR
References: