Prostate CancerNCT07112599
Study of Predicting Lymph Node Metastasis of High-risk Prostate Cancer by Artificial Intelligence Multi-omics Analysis
An observational study of Prostate Cancer and Lymph Node Cancer Metastatic, run by Anhui Medical University at 1 site in China.
Trial summary
The pathological-omics and imaging-omics in this study are combined to construct an artificial intelligence (AI) model that can predict whether high-risk prostate cancer patients may have lymph node metastasis. Read more about this study
- Who is it for?
- Ages 50 years and older, men only
- Conditions the study is for
- Prostate Cancer, Lymph Node Cancer Metastatic, Artificial Intelligence (AI)
- How many people
- 2,000 participants
- Where
- 1 siteChina
- Study dates
- 25 Jan 2026to 30 Jun 2027
- How long will I take part?
- 1 Month
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- Men only
- No healthy volunteers
- Adult, Older adult
Study population: Patients with high-risk prostate cancer (PSA ≥ 20ng/ml or Gleason ≥ 8)
Who may be able to join
- Age ≥ 50 years
- Patients must have histologically or cytologically confirmed prostate adenocarcinoma
- PSA ≥ 20ng/ml or Gleason ≥ 8
- Eastern Cooperative Oncology Group (ECOG) performance status (PS) score of 0-2
- Life expectancy ≥ 6 months
- Normal bone marrow function: absolute neutrophil count ≥ 1.5×109/L; platelets ≥ 75×109/L; hemoglobin ≥ 90g/L; white blood cell count ≥ 3.0×109/L
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- Normal liver function: alanine aminotransferase (ALT) or aspartate aminotransferase (AST) ≤ 2.5 times the upper limit of normal (ULN); for patients with liver metastasis, ALT/AST can be ≤ 5 times ULN
- Total bilirubin ≤ 1.5 times ULN or total bilirubin > 1.5 times ULN and direct bilirubin ≤ ULN;
- Normal coagulation function: International Normalized Ratio（INR） ≤ 1.5, partial thromboplastin time (APTT) ≤ 1.5 times ULN, prothrombin time (PT) < ULN + 4 seconds
- Normal heart function: left ventricular ejection fraction (LVEF) ≥ 50%; corrected QT interval male < 450ms, female < 470ms, serum potassium ≥ 3.5mmol/L
- Normal blood pressure: systolic blood pressure < 140mmHg, diastolic blood pressure < 90mmHg; patients with stable blood pressure assessment after appropriate clinical treatment can be enrolled
- Normal renal function: serum creatinine ≤ 1.5 times ULN, and creatinine clearance ≥ 50 mL/min
- Prospective subjects can understand and are willing to sign the informed consent form
- Able to comply with the study visit schedule and other protocol requirements
Who cannot join
- Patients with contraindications to MRI examination, such as metal implants in the body, claustrophobia, etc.
- Patients with any missing baseline clinical and pathological information
- Patients with a clear history of neurological and psychiatric disorders, such as dementia, epilepsy, or seizures
- In the judgment of the investigator, there are serious concomitant diseases that endanger the safety of the subjects or affect the subjects' completion of this study (such as severe diabetes, thyroid disease, and mental illness, etc.), or factors that affect the safety of the patients or affect the patients' provision of informed consent (including laboratory abnormalities), or any psychological, family, sociological or geographical conditions that affect the study plan and follow-up plan
- The investigator believes that it is not suitable to participate in this clinical trial for any reason
- Unable to provide informed consent
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Science and Technology Institute, Anhui Medical University
Hefei, Anhui, 230022
Yihao Chen, Doctor, +86-551-62922831, ayfykyc@126.com
About this study
Official title: Clinical Study on Predicting Lymph Node Metastasis of High-risk Prostate Cancer Based on Artificial Intelligence Multi-omics Analysis：A Multicenter, Prospective and Observational Clinical Study
The pathological-omics and imaging-omics in this study are combined to construct an artificial intelligence (AI) model that can predict whether high-risk prostate cancer patients may have lymph node metastasis. The model determines whether the patient has lymph node metastasis based on the MRI results and the pathological section image information of the case combined with clinical data before radical resection of the prostate. This study is a multicenter, prospective clinical study to verify the model's ability to predict whether high-risk prostate cancer patients may have lymph node metastasis.
Read the detailed description
This is a multicenter, prospective clinical study designed to validate the radiopathology artificial intelligence (AI) model. The study will recruit patients with prostate cancer from the First Affiliated Hospital of Anhui Medical University, Nanjing Drum Tower Hospital, Cancer Hospital of Chinese Academy of Medical Sciences, Hospital General University Gregorii Maran and the First Affiliated Hospital of Bengbu Medical University, with Gleason score ≥8 or prostate specific antigen (PSA)≥20ng/ml. In addition, MRI examinations are required before prostate biopsy, and pathological sections are scanned after radical prostatectomy. Experienced radiologists and pathologists manually outline the tumor region of interest (ROI) on the image. The outlined MRI information and pathological section scan information are input into the model to obtain the probability of lymph node metastasis in the patient. Whether lymph node metastasis occurs is determined by pelvic lymph node dissection specimens. By comparing the probability of lymph node metastasis predicted by the model with the actual situation, the researchers calculate the predicted sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy. This study verifies the high accuracy of the radiopathology AI model in predicting lymph node metastasis in patients with high-risk prostate cancer, and provides a basis for the precise treatment of high-risk prostate cancer patients.
- Keywords
- prostate cancer, AI
- Medical subject headings
- Prostatic Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathology artificial intelligence model
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiomics and pathomics artificial intelligence model for predicting lymph node metastasis in high-risk prostate cancer patients
Measured: baseline
Secondary outcomes (1)
The specificity of the radiopathology artificial intelligence model
The specificity of the radiomics and pathomics artificial intelligence model for predicting lymph node metastasis in high-risk prostate cancer patients.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Aug 2025
- Study start25 Jan 2026 (estimated)
- Primary completion30 Dec 2026 (estimated)
- Study completion30 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prostate CancerStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 2,000 participants (estimated)
- Target duration
- 1 Month
Sponsor and investigators
- Lead sponsor
- Anhui Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Sheng Tai, Anhui Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07112599
- Sponsor's protocol ID
- PJ 2025-04-44
- Status last verified
- Jan 2026
- First submitted
- 28 Jul 2025
- Last updated
- 9 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Clinical Study on Predicting Lymph Node Metastasis of High-risk Prostate Cancer Based on Artificial Intelligence Multi-omics Analysis：A Multicenter, Prospective and Observational Clinical Study
- ClinicalTrials.gov ID
- NCT07112599
- Acronym / sponsor ID
- PJ 2025-04-44
- Keywords
- prostate cancer, AI
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Anhui Medical UniversityUniversity, hospital or other
- Study start
- 25 Jan 2026 (estimated)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 30 Jun 2027 (estimated)
- First posted
- 8 Aug 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The pathological-omics and imaging-omics in this study are combined to construct an artificial intelligence (AI) model that can predict whether high-risk prostate cancer patients may have lymph node metastasis. The model determines whether the patient has lymph node metastasis based on the MRI results and the pathological section image information of the case combined with clinical data before radical resection of the prostate. This study is a multicenter, prospective clinical study to verify the model's ability to predict whether high-risk prostate cancer patients may have lymph node metastasis.
Detailed description
This is a multicenter, prospective clinical study designed to validate the radiopathology artificial intelligence (AI) model. The study will recruit patients with prostate cancer from the First Affiliated Hospital of Anhui Medical University, Nanjing Drum Tower Hospital, Cancer Hospital of Chinese Academy of Medical Sciences, Hospital General University Gregorii Maran and the First Affiliated Hospital of Bengbu Medical University, with Gleason score ≥8 or prostate specific antigen (PSA)≥20ng/ml. In addition, MRI examinations are required before prostate biopsy, and pathological sections are scanned after radical prostatectomy. Experienced radiologists and pathologists manually outline the tumor region of interest (ROI) on the image. The outlined MRI information and pathological section scan information are input into the model to obtain the probability of lymph node metastasis in the patient. Whether lymph node metastasis occurs is determined by pelvic lymph node dissection specimens. By comparing the probability of lymph node metastasis predicted by the model with the actual situation, the researchers calculate the predicted sensitivity, specificity, positive predictive value, negative predictive value, and overall diagnostic accuracy. This study verifies the high accuracy of the radiopathology AI model in predicting lymph node metastasis in patients with high-risk prostate cancer, and provides a basis for the precise treatment of high-risk prostate cancer patients.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Target follow-up
- 1 Month
Who may qualify
- Ages
- 50 years and older
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with high-risk prostate cancer (PSA ≥ 20ng/ml or Gleason ≥ 8)
Key inclusion criteria (14 listed)
- Age ≥ 50 years
- Patients must have histologically or cytologically confirmed prostate adenocarcinoma
- PSA ≥ 20ng/ml or Gleason ≥ 8
- Eastern Cooperative Oncology Group (ECOG) performance status (PS) score of 0-2
- Life expectancy ≥ 6 months
Key exclusion criteria (6 listed)
- Patients with contraindications to MRI examination, such as metal implants in the body, claustrophobia, etc.
- Patients with any missing baseline clinical and pathological information
- Patients with a clear history of neurological and psychiatric disorders, such as dementia, epilepsy, or seizures
- In the judgment of the investigator, there are serious concomitant diseases that endanger the safety of the subjects or affect the subjects' completion of this study (such as severe diabetes, thyroid disease, and mental illness, etc.), or factors that affect the safety of the patients or affect the patients' provision of informed consent (including laboratory abnormalities), or any psychological, family, sociological or geographical conditions that affect the study plan and follow-up plan
- The investigator believes that it is not suitable to participate in this clinical trial for any reason
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathology artificial intelligence model
Time frame: baseline
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiomics and pathomics artificial intelligence model for predicting lymph node metastasis in high-risk prostate cancer patients
1 secondary outcome
The specificity of the radiopathology artificial intelligence model
Time frame: baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Jan 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Prostate Cancer and Lymph Node Cancer Metastatic, run by Anhui Medical University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 14 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 9 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Sheng Tai (+86 18355159268, taisheng@ahmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of the radiopathology artificial intelligence model (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Jan 2026.