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
Conditions: Prostate Cancer, Lymph Node Cancer Metastatic, Artificial Intelligence (AI)
Sponsor: Anhui Medical University

1 
 
 
 
 
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 
NCT number: NCT07112599 
Document date: August 11, 2025 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

2 
 
Project name: 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                   
Sponsor:              The First Affiliated Hospital of Anhui Medical University                                     
Responsible departments: Urology                                  
Main researchers: Sheng Tai                         
Contact number: 18355159268                        
Participating units: Anhui University                               
Participating units: Nanjing Drum Tower Hospital  
Participating units: Cancer Hospital of Chinese Academy of Medical Sciences 
Participating units: The First Affiliated Hospital of Bengbu Medical University 
Participating units: Hospital General University Gregorii Maran 
Study period: August 2025-July 2026 
Version number: V1.0   Version date: August 6.2025 

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Program summary 
Project name 
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 
Research 
purpose 
Based on artificial intelligence technology and combining clinical, 
pathological, and imaging data to build a prediction model for lymph 
node metastasis of high-risk prostate cancer 
research 
design 
Retrospective, prospective, multicenter, diagnostic accuracy study 
study 
population 
Patients with high-risk prostate cancer (PSA≥20ng/ml or 
Gleason≥8） 
Total number 
of cases 
2000 cases 
 
 

4 
 
Case 
selection 
Inclusion criteria 
● 
Age≥50 years old 
● 
Patients must have histologically or cytologically confirmed 
prostate adenocarcinoma 
● 
PSA≥20 of/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 ≥90 g/L; white 
blood cell count ≥ 3.0×109/L 
● 
Normal liver function: alanine aminotransferase (ALT) or 
aspartate aminotransferase (AST) ≤ 2.5 times the upper limit of 
normal (ULN); for patients with liver metastases, ALT/AST can 
be ≤ 5 times the ULN 
● 
Total bilirubin ≤ 1.5 times ULN or total bilirubin > 1.5 times 
ULN and direct bilirubin ≤ ULN; 
● 
Normal coagulation function: 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%; QTc for men <450ms, for women <470ms, serum 
potassium ≥3.5mmol/L 
● 
Normal blood pressure: systolic blood pressure <160mmHg, 
diastolic blood pressure <95mmHg; 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 be willing to sign the 
informed consent form 
● 
Ability to adhere to study visit schedules and other protocol 
requirements 

5 
 
Exclusion criteria 
● 
There are contraindications for MRI examination, such as metal 
implants in the body, claustrophobia, etc. 
● 
Patients with any missing items in baseline clinical and 
pathological information 
● 
Have a clear history of neurological or mental disorders, such as 
dementia, epilepsy, or seizure proneness 
● 
In the researcher's judgment, there are factors that seriously 
endanger the safety of the subjects, or concomitant diseases that 
affect the subjects' completion of this study (such as severe 
diabetes, thyroid disease, and mental illness, etc.), or there are 
factors that affect the safety of the patients or serious and/or 
unstable medical, mental, or other conditions (including 
abnormal laboratory tests) that affect the patient's ability to 
provide informed consent, or there are any psychological, 
family, sociological, or geographical conditions that affect the 
research plan and follow-up plan. 
● 
The researcher believes that he is not suitable to participate in 
this clinical trial for any reason 
● 
Unable to provide informed consent 
intervention 
plan 
1. Retrospective patient analysis and training set construction: 
600 patients who underwent radical prostatectomy plus pelvic 
lymph node dissection in the First Affiliated Hospital of Anhui 
Medical University were collected as a training set (360 cases) and 
an internal validation set (240 cases) in a ratio of 3:2. MRI images of 
the patients' primary lesions, pathological images of puncture tissues, 
and clinical data (such as age, BMI, pre-puncture PSA, and 
preoperative imaging magnetic resonance PI-RADS score, whether 
neoadjuvant treatment was performed before surgery, postoperative 
Gleason score, TNM stage, whether the resection margin was 
positive, whether seminal vesicle invasion, whether lymph node 
metastasis, number of lymph node metastases (left/right), whether 
nerve invasion and other data, whether the patient had biochemical 

6 
 
recurrence after surgery, survival period, etc.), combined with 
artificial intelligence deep learning to build a prediction model for 
lymph node metastasis in high-risk prostate cancer patients. A multi-
omics lymph node metastasis prediction model for prostate cancer 
patients was constructed by combining pathomics, radiomics, and 
clinical data. 
2. Retrospective patient validation set validation: 
The constructed multi-omics lymph node metastasis prediction 
model for prostate cancer patients will be externally validated in the 
internal validation cohort of the First Affiliated Hospital of Anhui 
Medical University (300 cases), the external validation cohort of 
Nanjing Drum Tower Hospital (400 cases), the external validation 
cohort of the Cancer Hospital of the Chinese Academy of Medical 
Sciences (400 cases), and the external validation cohort of the First 
Affiliated Hospital of Bengbu Medical University (300 cases). 
3. Prospective patient validation set validation: 
     A prospective cohort (300 cases) of high-risk prostate cancer who 
underwent radical surgery plus lymph node dissection in the First 
Affiliated Hospital of Anhui Medical University was established. 
The multi-omics lymph node metastasis prediction model of prostate 
cancer patients was used to analyze whether the patients had lymph 
node metastasis before surgery. Radical prostatectomy plus pelvic 
lymph node dissection was performed on the patients. Primary tumor 
specimens and Transcriptome and single-cell omics sequencing of 
pelvic lymph nodes were performed in some cases, combined with 
postoperative pathologists' reading of lymph node specimen tissue to 
determine whether there was lymph node metastasis, the accuracy of 
the artificial intelligence deep learning model in predicting lymph 
node metastasis was analyzed, and the effectiveness of the multi-
omics artificial intelligence deep learning model combined with 
genomics in predicting lymph node metastasis was analyzed. 

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Evaluation 
index 
Effectiveness evaluation index 
● 
Diagnostic accuracy: sensitivity, specificity, positive predictive 
value, negative predictive value, overall diagnostic accuracy, 
etc. 
● 
Clinical efficacy: progression-free survival (PFS), overall 
survival (OS), biochemical recurrence rate, and meaningless 
lymph node dissection rate of prostate cancer patients. 
Safety evaluation index 
● 
The incidence of postoperative complications, such as the 
incidence of postoperative lymphadenopathy due to lymph node 
dissection 
● 
Incidence of puncture- and surgery-related adverse events (AE) 
and serious adverse events (SAE) 
Statistical 
methods 
Descriptive statistical analysis, hypothesis testing, non-parametric 
testing, regression analysis, survival analysis, covariance analysis, 
missing data processing 
Study period 
August 2025 - July 2026 
 

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Chapter 1 
Research background 
Prostate cancer is the most common cancer in men, and most patients present with 
low-risk characteristics[1, 2], the global incidence rate is close to 20%[3]. Prostate cancer 
has the highest five-year survival rate of any cancer[4]. According to statistics, high-risk 
prostate cancer accounts for 12% to 15% of prostate cancer detected abroad.[5]. At 
present, the incidence rate of prostate cancer has surpassed that of bladder cancer, 
ranking first among malignant tumors of the male genitourinary system in my country. 
There are obvious regional differences in the composition of prostate cancer in my 
country, with urban areas being higher than rural areas, and eastern areas being higher 
than central and western areas.[6]. But overall, the proportion of high-risk prostate 
cancer is still relatively high[7], and the prognosis of high-risk prostate cancer patients 
is poor. As the population ages, the incidence of prostate cancer is on the rise. In 2020, 
the incidence and mortality rates of prostate cancer in my country will be as high as 
8.92% and 13.37% respectively, ranking first among malignant tumors.[8], has become 
one of the important diseases threatening men's health. 
According to the National Comprehensive Cancer Network guidelines and 
D’Amico risk stratification definition, one of the following criteria is met: tumor stage 
T2c~T4, serum prostate-specific antigen (PSA) >20ng/ml, Gleason score≥ a score of 8 
falls into the category of high-risk prostate cancer.[9]. Most patients with low-risk 
prostate cancer can benefit from radical prostatectomy, but a subset of high-risk patients 
do not appear to benefit from radical prostatectomy.[10]; and are more likely to die from 
disease due to metastasis[5]. Lymph node dissection (LND) has been a routine part of 
cancer surgery for more than a century[11], the current view is that the treatment of high-
risk prostate cancer should adopt a comprehensive treatment plan with radical 
prostatectomy combined with extended pelvic lymph node dissection as the initial 
treatment; this plan can improve the accuracy of staging and reduce the long-term risk 
of prostate cancer-specific death.[12, 13], there are also studies showing that pelvic lymph 
node dissection can reduce the probability of biochemical recurrence[14], has some 
oncological benefit; another study showed that patients who had pelvic lymph node 
dissection had a 9% lower 10-year risk of death compared with patients who had no 
pelvic lymph node dissection or limited lymph node dissection.[15], therefore the 
diagnostic value of pelvic lymph node dissection is undeniable. However, the positive 
rate of lymph nodes in clinical patients with high-risk prostate cancer is low. The 
positive rate of lymph node dissection in patients with high-risk prostate cancer is about 

9 
 
12%.[16], meaning that most patients undergoing this procedure have pointless 
dissections[17]. And pelvic lymph node dissection may lead to some poor perioperative 
complications, including intraoperative ureteral injury, vascular injury, obturator nerve 
injury, postoperative asymptomatic lymphocele and deep vein thrombosis, 
postoperative lymphatic leakage leading to delayed drainage tube removal, etc.[18]. 
Asymptomatic lymphocele is the longest postoperative complication[19, 20]. According 
to the Briganti 2017 nomogram prediction model recommendation, lymph node 
dissection should be performed in patients with lymph node infiltration index greater 
than 5%.[21], has higher prediction accuracy than Briganti 2012 and MSKCC, but it does 
not incorporate imaging data and genomic data. 
In recent years, artificial intelligence (AI) technology has been increasingly used 
in the medical field, especially in disease diagnosis, treatment decision-making, 
efficacy evaluation, etc., showing great potential[22]. Since the 1970s, medical imaging 
technology has made new breakthroughs, various medical imaging models have 
gradually matured, and medical image recognition has become an important research 
direction for artificial intelligence diagnosis and treatment of diseases.[23]. Current 
methods for diagnosing prostate cancer mainly include serum prostate-specific antigen 
(PSA) testing[24], prostate multi-parameter magnetic resonance PI-RADS score, 
prostate biopsy tissue pathological section examination, etc.[25]; Conventional images 
such as computed tomography or magnetic resonance imaging (MRI) have poor 
accuracy in identifying lymph node invasion[26]. Although lymph node size can help 
identify lymph node invasion, its sensitivity is low because normal-sized lymph nodes 
may contain smaller metastatic disease[27]. Currently, artificial intelligence shows 
potential benefits in developing automatic or semi-automatic diagnostic tools and has 
been applied in many medical scenarios[28-30]. There are currently studies that combine 
prostate cancer pathology slide images with artificial intelligence to predict prostate 
cancer lymph node metastasis.[31]. However, this method still relies on postoperative 
pathology to help doctors determine whether lymph node metastasis has occurred. It 
cannot improve the accuracy of doctors' preoperative judgment of whether lymph node 
metastasis has occurred in patients and help patients avoid meaningless pelvic lymph 
node dissection. By combining artificial intelligence with imaging data, the research 
team has successfully built a lymph node pathology prediction model for high-risk 
prostate cancer patients based on clinical data, imaging data, pathology data and other 
multi-omics data of approximately 460 patients in the early stage, combined with 

10 
 
artificial intelligence deep learning, and achieved good prediction performance in the 
internal validation set. On this basis, this study plans to take the lead in conducting a 
retrospective combined with prospective multi-center study to further confirm the 
performance of the prediction model in an external validation set, and combine it with 
multi-omics artificial intelligence deep learning of prospective genomics to build a 
more accurate prediction model for lymph node metastasis. The developed prostate 
cancer lymph node metastasis prediction model was compared with pathological results 
to explore its clinical utility. 
 
Chapter 2 
Research purpose 
2.1 Main purpose: 
 Based on artificial intelligence technology, by delineating the quantitative 
mapping correlation between prostate cancer tissue whole-section annotation images, 
MRI and pelvic lymph node metastasis, clarify common characteristics and establish a 
prediction model for lymph node metastasis in patients with high-risk prostate cancer. 
First, the model can accurately predict whether pelvic lymph nodes have metastasized 
before surgery, eliminating unnecessary pelvic lymph node dissection and avoiding 
postoperative complications caused by pelvic lymph node dissection, such as lymph 
leakage, lymph fluid infection, etc.; second, the established artificial intelligence 
prediction model can accurately diagnose the probability of lymph node metastasis, 
invasiveness, grade, subtype, and biochemical recurrence of prostate cancer by 
predicting the subject's MRI images and puncture pathological tissue images before 
surgery or puncture. 
2.2 Secondary purpose:  
Establish a model based on artificial intelligence technology to develop a 
personalized treatment plan for the patient before surgery or puncture; perform three-
dimensional reconstruction of the prostate and lesions to clarify the exact location of 
the lesions and guide puncture or surgical treatment. 
 

11 
 
Chapter 3 
Research methods 
3.1 Research design 
This study adopts a retrospective, prospective, multi-center, diagnostic accuracy 
research design, and is planned to be mainly conducted at the First Affiliated Hospital 
of Anhui Medical University. It is expected to cooperate with Anhui University, 
Nanjing Drum Tower Hospital, Cancer Hospital of the Chinese Academy of Medical 
Sciences, and the First Affiliated Hospital of Bengbu Medical University. Departments 
include urology, medical imaging, and pathology for multidisciplinary collaborative 
research. 
600 patients who underwent radical prostatectomy plus pelvic lymph node 
dissection in the First Affiliated Hospital of Anhui Medical University were used as a 
training set (360 cases) and an internal validation set (240 cases) in a ratio of 3:2. MRI 
images of the patients' primary prostate lesions were collected, including multi-
parameter magnetic resonance T1-weighted, T2-weighted, DWI diffusion-weighted 
images and other multi-parameter data, pathological images of punctured tissues and 
clinical data (such as age, BMI, pre-puncture) PSA, preoperative MRI PI-RADS score, 
whether neoadjuvant treatment was performed before surgery, postoperative Gleason 
score, TNM stage, whether the resection margin is positive, whether seminal vesicle 
invasion, whether lymph node metastasis, number of lymph node metastases 
(left/right), whether nerve invasion and other data, whether the patient has biochemical 
recurrence after surgery, survival time, etc.), combined with artificial intelligence deep 
learning to build a prediction model for lymph node metastasis in high-risk prostate 
cancer patients. A multi-omics lymph node metastasis prediction model for prostate 
cancer patients was constructed by combining pathomics, radiomics, and clinical data. 
Research evaluation is specifically divided into the following parts: 
(1) Evaluation of pathological diagnosis accuracy: Compare the diagnostic results 
of prostate cancer lymph node metastasis of the artificial intelligence model with 
pathological examination (gold standard) to evaluate the accuracy of the model in 
diagnosing postoperative pathology of lymph node metastasis in patients with prostate 
cancer. 
(2) Prediction model evaluation: Evaluate the accuracy of the artificial intelligence 
model in predicting lymph node metastasis, lymph node metastasis, invasiveness, grade 
and subtype of prostate cancer based on preoperative MRI. 

12 
 
(3) Personalized evaluation of treatment options: Compare differences in treatment 
options, treatment effects, and patient prognosis among patients with high-risk prostate 
cancer. 
(4) Three-dimensional reconstruction accuracy evaluation: Evaluate the accuracy 
of the artificial intelligence model in three-dimensional reconstruction of the prostate 
and lesions, including the accuracy of lesion location, size and shape. 
(5) Clinical utility evaluation: Evaluate the feasibility, efficiency, and cost-
effectiveness of artificial intelligence models in actual clinical applications. Assess 
medical staff's acceptance and satisfaction with artificial intelligence models through 
questionnaires, interviews, etc. 
 
3.2 Study population 
This study plans to include retrospectively before June 2025 and prospectively 
between June 2025 and May 2026 patients with high-risk prostate cancer who 
underwent radical prostatectomy plus pelvic lymph node dissection. Patients with high-
risk prostate cancer from the Department of Urology of our hospital and cooperative 
units who met the enrollment conditions and signed informed consent forms will be 
included. Due to the heterogeneity of prostate cancer and the robustness of the model, 
in order to achieve the expected test performance and statistical power, the total number 
of subjects planned to be included is 2,000. 
3.2.1 Inclusion criteria 
Inclusion criteria 
● 
Age≥50 years old 
● 
Patients must have histologically or cytologically confirmed prostate 
adenocarcinoma 
● 
PSA≥20 of/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 ≥90 g/L; white blood cell count ≥ 3.0×109/L 
● 
Normal 
liver 
function: 
alanine 
aminotransferase 
(ALT) 
or 
aspartate 

13 
 
aminotransferase (AST) ≤ 2.5 times the upper limit of normal (ULN); for patients 
with liver metastases, ALT/AST can be ≤ 5 times the ULN 
● 
Total bilirubin ≤ 1.5 times ULN or total bilirubin > 1.5 times ULN and direct 
bilirubin ≤ ULN; 
● 
Normal coagulation function: 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%; QTc for 
men <450ms, for women <470ms, serum potassium ≥3.5mmol/L 
● 
Normal blood pressure: systolic blood pressure <160mmHg, diastolic blood 
pressure <95mmHg; 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 be willing to sign the informed consent 
form 
● 
Ability to adhere to study visit schedules and other protocol requirements 
3.2.2 Exclusion criteria 
● 
There are contraindications for MRI examination, such as metal implants in the 
body, claustrophobia, etc. 
● 
Patients with any missing items in baseline clinical and pathological information 
● 
Have a clear history of neurological or mental disorders, such as dementia, 
epilepsy, or seizure proneness 
● 
In the researcher's judgment, there are factors that seriously endanger the safety of 
the subjects, or concomitant diseases that affect the subjects' completion of this 
study (such as severe diabetes, thyroid disease, and mental illness, etc.), or there 
are factors that affect the safety of the patients or serious and/or unstable medical, 
mental, or other conditions (including abnormal laboratory tests) that affect the 
patient's ability to provide informed consent, or there are any psychological, 
family, sociological, or geographical conditions that affect the research plan and 
follow-up plan. 
● 
The researcher believes that he is not suitable to participate in this clinical trial for 
any reason 

14 
 
Unable to provide informed consent 
3.2.3 Exit criteria 
● 
Subject withdraws informed consent and voluntarily withdraws 
● 
Serious adverse events occur, and the researcher determines that withdrawal is to 
protect the subjects. 
● 
Lack of efficacy or disease progression, and the investigator determines that 
withdrawal is beneficial to the subject 
● 
If the subject is lost to follow-up, the researcher needs to record the contact process 
● 
Subject died 
● 
Other reasonable reasons (the occurrence of concurrent diseases or the need for 
treatment with banned drugs, etc.) 
3.2.4 Termination/suspension criteria 
● 
If a serious adverse event occurs, there is a risk that the subject will suffer serious 
harm, and the trial protocol needs to be suspended. 
● 
Serious protocol violations occurred, affecting the safety of subjects 
● 
Ethics committee calls for suspension 
● 
Suspension requested by the Food and Drug Administration or national competent 
authority 
● 
The sponsor terminates the trial study or other reasonable reasons 
 
3.3 Research interventions 
3.3.1 Research interventions 
Intervention was carried out for patients in the experimental group, and the 
intervention measures were as follows: 
(1) Retrospective patient analysis and training set construction: 
600 patients who underwent radical prostatectomy plus pelvic lymph node 
dissection in the First Affiliated Hospital of Anhui Medical University were collected 
as a training set (360 cases) and an internal validation set (240 cases) in a ratio of 3:2. 
MRI images of the patients' primary lesions, pathological images of puncture tissues, 
and clinical data (such as age, BMI, pre-puncture PSA, and preoperative imaging 
magnetic resonance PI-RADS score, whether neoadjuvant treatment was performed 

15 
 
before surgery, postoperative Gleason score, TNM stage, whether the resection margin 
was positive, whether seminal vesicle invasion, whether lymph node metastasis, 
number of lymph node metastases (left/right), whether nerve invasion and other data, 
whether the patient had biochemical recurrence after surgery, survival period, etc.), 
combined with artificial intelligence deep learning to build a prediction model for 
lymph node metastasis in high-risk prostate cancer patients. A multi-omics lymph node 
metastasis prediction model for prostate cancer patients was constructed by combining 
pathomics, radiomics, and clinical data. 
(2) Retrospective patient validation set verification: 
The constructed multi-omics lymph node metastasis prediction model for prostate 
cancer patients will be externally validated in the internal validation cohort of the First 
Affiliated Hospital of Anhui Medical University (300 cases), the external validation 
cohort of Nanjing Drum Tower Hospital (400 cases), the external validation cohort of 
the Cancer Hospital of the Chinese Academy of Medical Sciences (400 cases), and the 
external validation cohort of the First Affiliated Hospital of Bengbu Medical University 
(300 cases). 
(3) Prospective patient validation set verification: 
A prospective cohort (300 cases) of high-risk prostate cancer who underwent 
radical surgery plus lymph node dissection in the First Affiliated Hospital of Anhui 
Medical University was established. The multi-omics lymph node metastasis prediction 
model of prostate cancer patients was used to analyze whether the patients had lymph 
node metastasis before surgery. Radical prostatectomy plus pelvic lymph node 
dissection was performed on the patients. Primary tumor specimens and Transcriptome 
and single-cell omics sequencing of pelvic lymph nodes were performed in some cases, 
combined with postoperative pathologists' reading of lymph node specimen tissue to 
determine whether there was lymph node metastasis, the accuracy of the artificial 
intelligence deep learning model in predicting lymph node metastasis was analyzed, 
and the effectiveness of the multi-omics artificial intelligence deep learning model 
combined with genomics in predicting lymph node metastasis was analyzed.   
The control group will follow the routine clinical diagnosis and treatment process: 
radical prostatectomy + pelvic lymph node dissection based on the patient's 
preoperative Gleason score or PSA, and immunohistochemistry will be performed 
based on the postoperative pathological conditions. And continue to follow up the 
patient's prognosis. 

16 
 
3.3.2 Combined medication 
In clinical trials, clarifying the drugs that can be used together and the drugs that 
are prohibited from being used together is crucial to ensure the safety of the study and 
the validity of the results. The following are concomitant medication requirements 
based on research on prostate cancer diagnosis and treatment: 
(1) Drugs that can be used together 
1  
Conventional drug treatment: 
● 
Anti-infective drugs: used to treat or prevent infections. 
● 
Painkillers: Used to relieve mild to moderate pain. 
● 
Antacids: Used to treat symptoms related to hyperacidity in the stomach. 
2  
Chronic Disease Management Medications: 
● 
Hypertension drugs: such as ACE inhibitors, ARBs, calcium channel blockers, etc. 
● 
Diabetes drugs: such as insulin, oral hypoglycemic drugs, etc. 
● 
Heart disease drugs: such as beta-blockers, antiplatelet drugs, etc. 
3  
Supportive treatment: 
● 
Vitamin and mineral supplements: Use only as recommended by your doctor. 
● 
Hormone replacement therapy: For example, used to treat symptoms associated 
with hormone deficiency. 
(2) Drugs that are not allowed to be used together 
1  
Medications that may affect study results: 
● 
Other anti-tumor drugs: may affect tumor growth and assessment of treatment 
efficacy. 
● 
Drugs with strong immunomodulatory effects: may affect the natural course of the 
disease and the effectiveness of treatment. 
● 
Drugs that may cause serious adverse reactions: 
2  
Unapproved medicines: 
● 
Any medicine not approved by the drug regulatory authority. 
(3) Special instructions 
Before the start of the study, all medications the subject is taking will be 
documented in detail and the possibility of continued use will be discussed with the 
study physician. Any new medications or dosage changes during the study will be 
reported to the study physician and their impact on the study will be assessed. Subjects 
should avoid self-medication during the study, and the use of all drugs should be under 

17 
 
the guidance of the study doctor. 
 
3.4 Evaluation indicators/research endpoints 
3.4.1 Study Endpoints/Effectiveness Evaluation—Diagnostic Accuracy: 
● 
Model sensitivity (true positive rate): The model's ability to correctly identify 
lymph node metastases in patients with high-risk prostate cancer. 
● 
Model specificity (true negative rate): The model's ability to correctly identify non-
metastatic lymph nodes in patients with high-risk prostate cancer. 
● 
Positive predictive value: The proportion of positive predictions predicted by the 
model that are actually positive. 
● 
Negative predictive value: The proportion of negative predictions predicted by the 
model that are actually negative. 
● 
Overall diagnostic accuracy: the ratio of the total number of cases correctly 
diagnosed by the model to the total number of cases. 
3.4.2 Secondary research endpoints/comprehensive efficacy evaluation 
and safety evaluation: 
1  
Clinical efficacy: 
● 
Progression-free survival (PFS) in prostate cancer patients. 
● 
Overall survival (OS). 
● 
Disease recurrence rate. 
2  
Treatment safety: 
● 
Postoperative complication rates. 
● 
Surgery-related serious adverse events (SAEs). 
3  
Patient satisfaction: 
● 
Patient ratings of satisfaction with treatment options. 
3.4.3 Follow-up time: 
● 
Short-term observation: from subject enrollment to the initial recovery period after 
surgery/puncture, usually 7-30 days. Evaluate surgical recovery and record 
pathological results and complications. 
● 
Mid-term observation: 1 year after surgery/puncture (1 month, 3 months, 6 months, 
12 months after surgery): Regular follow-up of PSA level, imaging examination, 

18 
 
liver and kidney function, and blood routine to evaluate the long-term effect and 
safety of treatment. 
● 
Long-term observation: From 1 year to 5 years or more, PSA level testing, imaging 
examinations, and liver and kidney function tests should be performed every 6 
months to evaluate long-term survival and disease control. 
3.4.4 Record and analyze 
1  
Record 
● 
Basic patient information: including age, gender, weight, prostate-specific antigen 
(PSA) level, etc. 
● 
Diagnosis and treatment data: including MRI results, artificial intelligence model 
diagnosis results, treatment plans, surgery or puncture details, pathology reports, 
etc. 
● 
Follow-up data: recurrence status (clinical indicators, including PSA level, DRE 
results, imaging examinations (MRI, CT, bone scan, etc.), survival status (quality 
of life assessment, assessing the patient's quality of life through questionnaires or 
other methods), complications and adverse event records. 
2  
analyze 
(1) Effectiveness evaluation: 
● 
Compare the coincidence rate of artificial intelligence model diagnosis results with 
pathological examination. 
● 
Analyze the consistency between the treatment plan recommended by the model 
and the actual treatment plan. 
● 
Use statistical methods (such as Kaplan-Meier curves, Cox regression analysis) to 
evaluate clinical efficacy indicators. 
(2) Safety evaluation: 
● 
The incidence rates of postoperative complications and SAEs were counted. 
● 
Analyze the relationship between adverse events and AI model diagnosis and 
treatment. 
(3) Comprehensive efficacy evaluation: 
● 
Combined with diagnostic accuracy, treatment plan compliance rate, clinical 
efficacy and safety data, the value of artificial intelligence models in the diagnosis 
and treatment of prostate cancer is comprehensively evaluated. 

19 
 
● 
Conduct a cost-benefit analysis to assess the economic impact of AI models. 
 
3.5 Schedule of visits and data collection during the study 
3.5.1 Main efficacy endpoints 
1  
Diagnostic accuracy (for prostate cancer diagnosis) 
● 
Precise prediction results such as whether prostate cancer has lymph node 
metastasis, grade, subtype, etc. 
● 
Detailed pathological information results after radical prostatectomy. 
● 
The accuracy of the treatment plan recommended by the artificial intelligence 
model and the actual treatment plan implemented. 
 
3.5.2 secondary efficacy endpoints 
1  
clinical efficacy 
● 
Progression-free survival (PFS) 
● 
Overall survival (OS) 
● 
disease recurrence rate 
● 
Postoperative review of PSA indicators 
● 
Imaging tests (such as MRI, CT, bone scan) to evaluate tumor progression or 
recurrence 
2  
patient satisfaction 
● 
Assessing patient satisfaction through questionnaires 
 
3.5.3 Security evaluation indicators 
1  
Postoperative complications (such as urinary incontinence, erectile 
dysfunction, urinary tract infection, etc.) rate 
2  
Incidence of serious adverse events (SAEs) 
 
3.5.4 Detection time point 
1  
Baseline (at enrollment): 
● 
PSA level. 

20 
 
● 
DRE results. 
● 
Imaging examinations (MRI, CT, etc.). 
● 
Blood routine, liver and kidney function, electrocardiogram. 
2  
Before making a treatment decision: 
● 
Check the PSA level again. 
● 
Additional imaging studies as needed. 
3  
After treatment: 
● 
One week after surgery: Evaluate surgical recovery and record pathological results 
and complications. 
● 
One month after surgery: PSA level, imaging examination to evaluate treatment 
effect. 
● 
3 months, 6 months, and 12 months after surgery: Regular follow-up of PSA level, 
imaging examination, liver and kidney function, and blood routine to evaluate the 
long-term effect and safety of treatment. 
4  
Long-term follow-up: 
● 
PSA level testing, imaging studies, and liver and kidney function tests are 
performed every 6 months for 5 years or more to assess long-term survival and 
disease control. 
 
Chapter 4 
Statistical processing 
4.1 Statistical software: 
● 
R: A free, open source statistical programming language and software environment 
for complex statistical analysis and data visualization. Versions are constantly 
updated, such as R 4.0.x, R 4.1.x, etc. 
● 
STATA: A comprehensive statistical software for data analysis, data management, 
graphing, simulation and custom programming. Common versions include STATA 
15, STATA 16, etc. 
4.2 Statistical analysis method content 
1  
Descriptive statistical analysis: 
● 
Measurement data: Calculate mean, standard deviation, median, minimum value, 
maximum value, quartile, etc. 

21 
 
● 
Counting data: Calculate frequencies, percentages, etc. 
2  
Hypothesis testing: 
● 
t test: used to compare the difference in means between two groups, suitable for 
continuous variables. 
● 
ANOVA (Analysis of Variance): Used to compare differences in means of two or 
more groups. 
● 
Chi-square test: used to compare the association between two or more categorical 
variables. 
3  
Non-parametric test: 
● 
It is used when the data does not meet the prerequisites of parametric tests, such as 
Mann-Whitney U test, Kruskal-Wallis H test, etc. 
4  
Multiple comparisons: 
● 
When comparing multiple groups, use methods such as Tukey’s HSD and 
Bonferroni correction to control the Type I error rate. 
5  
Regression analysis: 
● 
Linear Regression: Used to predict continuous dependent variables. 
● 
Logistic regression: used to predict binary dependent variables. 
6  
Survival analysis: 
● 
Used to analyze data from time to event, such as Kaplan-Meier curves and Cox 
proportional hazards models. 
7  
Analysis of covariance (ANCOVA): 
● 
Used to compare the difference in means between two or more groups while 
controlling for one or more covariates. 
8  
Missing data handling: 
● 
Use methods such as multiple imputation and maximum likelihood estimation to 
handle missing data. 
 
Chapter 5 
Reporting and handling of adverse events and emergency plans 
An adverse event refers to any adverse medical event that occurs to a subject during 
a clinical trial, regardless of whether there is a causal relationship with the trial 
intervention. 
Regarding the diagnosis of lymph node metastasis of high-risk prostate cancer in 

22 
 
this study, there may be the following expected adverse events and corresponding 
emergency treatments: 
1． 
Prediction errors of artificial intelligence models lead to misdiagnosis or 
misdiagnosis: Due to poor image quality, software errors, or operator errors in 
pathological slide annotation, MR, and pathological mapping, at the same time, 
artificial intelligence models may cause errors in the prediction of lymph node 
metastasis of high-risk prostate cancer due to algorithm limitations, data bias, or 
insufficient training, leading to harm to subjects caused by errors in the selection and 
implementation of treatment plans. 
 Emergency treatment: ① Case re-evaluation: We will quickly organize a 
professional medical team to re-evaluate the subject's case to ensure accurate diagnosis, 
and formulate or adjust treatment plans based on the latest evaluation results. ②Medical 
intervention: For injuries to subjects caused by misjudgment, we will take necessary 
medical intervention measures, including but not limited to drug treatment, surgical 
treatment, psychological counseling, etc., to protect the physical and mental health of 
the subjects. ③Compensation mechanism: On the premise of ensuring that the subjects 
receive proper treatment, we will activate the compensation mechanism and 
compensate the subjects in accordance with the law based on the actual degree of 
damage suffered by the subjects. The scope of compensation includes medical 
expenses, lost work expenses, nursing expenses, transportation expenses, 
accommodation expenses, mental damage solatium, etc. ④ Model adjustment and 
optimization: In response to this incident, we will conduct an in-depth analysis of the 
artificial intelligence model to find out the root cause of the prediction error and make 
corresponding adjustments and optimizations. At the same time, data quality 
management should be strengthened to ensure the accuracy and diversity of training 
data and improve the prediction accuracy of the model. ⑤Supervision and tracking: 
After the compensation matters are processed, we will also conduct long-term tracking 
of the subjects to ensure that their health status receives continuous attention. At the 
same time, supervision of artificial intelligence models will be strengthened to prevent 
similar incidents from happening again. 
2． 
Information leakage or privacy invasion: In clinical trials, due to 
negligence in data processing or transmission, subjects' personal information, medical 
records or imaging data may be accessed or leaked by unauthorized third parties. 

23 
 
 Emergency handling: Stop data transmission immediately, investigate the cause 
of the leak, notify affected subjects, and take legal measures. 
3． 
Adverse reactions of patients to MR imaging: Some patients may have 
adverse reactions to MR imaging, such as allergic reactions or other systemic reactions. 
 Emergency treatment: Stop the examination immediately, take emergency 
medical measures, and closely monitor the subjects. 
4． 
Pathological slide processing errors or analysis errors: Technical 
problems may occur during the production, staining, or analysis of pathological slides, 
such as incomplete sections, uneven staining, or reading errors. 
 Emergency treatment: Reanalyze the slices, retake samples if necessary, and 
notify the subject. 
For adverse events that have occurred, assessment tools will be used: such as Karch 
Lasagna rating scale, Naranjo rating scale, and independent experts or data monitoring 
committees (DMC) will be invited to review adverse events: 
1． 
Chronology: Whether the adverse event occurred after the intervention 
and is temporally reasonable. 
2． 
Consistency: Whether an adverse event occurs repeatedly across 
multiple subjects and is related to the intervention. 
3． 
Specificity: Whether the adverse event is consistent with known side 
effects of the intervention. 
4． 
Dose-response relationship: whether the severity of an adverse event is 
related to the dose of the intervention. 
5． 
Dechallenge and rechallenge: whether the adverse event disappears or 
diminishes after the intervention is discontinued, and whether it reappears after the 
intervention is reintroduced. 
This study will record the occurrence time, nature, severity, duration, treatment 
measures and results of adverse events in detail, and save all original records related to 
adverse events, including medical records, laboratory reports, imaging data, etc. 
When a serious adverse event occurs, this study will promptly report it through the 
electronic system or paper in accordance with the regulations of the regulatory agency 
(no more than 24 hours), and take necessary medical measures for the serious adverse 
event to protect the health and safety of the subjects. Adverse events will continue to 

24 
 
be monitored until the incident is resolved. In the follow-up, subjects will be followed 
up regularly to prevent recurrence or deterioration. Adverse events that occurred this 
year will be summarized and analyzed in the annual report. 
 
Chapter 6 
Quality control and quality assurance of research 
6.1 Laboratory indicator testing: 
Pathological slice quality control: Establish a standardized pathological slice 
preparation process to ensure that the quality of the slices meets research requirements. 
Pathologists undergo unified training to ensure accuracy and consistency in slide 
reading. 
MRI image quality control: Develop a standardized process for MRI image 
acquisition and transmission to ensure that image quality meets the needs of artificial 
intelligence model training and prediction. Radiologists undergo uniform training to 
ensure consistency in image acquisition. 
6.2 Execute relevant SOPs: 
Develop and implement detailed research protocols and operating procedures 
(SOPs): Ensure that all research steps are carried out according to established 
procedures to avoid human errors. 
Regularly conduct SOP training for researchers: Ensure that researchers are 
familiar with and master SOP, and ensure the standardization and consistency of 
research operations. 
6.3 Researcher training: 
Provide unified training to all researchers involved in the study: including the use 
of artificial intelligence models, data analysis methods, ethical knowledge, etc., to 
ensure that researchers have the skills and knowledge required to conduct research. 
Regularly organize seminars and exchange meetings: Promote communication and 
exchanges among researchers and solve problems encountered in research in a timely 
manner. 
6.4 Subject compliance: 
Establish a complete subject follow-up mechanism: ensure that subjects participate 

25 
 
in follow-up visits on time and collect follow-up data in a timely manner. 
Regularly communicate and educate subjects: improve subjects’ awareness and 
understanding of the study and enhance subjects’ compliance. 
6.5 Research monitoring: 
Establish an independent Data Monitoring Committee (DMC): Regularly monitor 
research data to ensure data integrity and accuracy. 
Conduct regular on-site inspections: Ensure that the implementation of the research 
plan meets the requirements, and promptly discover and solve problems existing in the 
research. 
6.6 Other quality control measures: 
Establish a data quality control system: including data entry, data cleaning, data 
backup, etc. to ensure data integrity and security. 
Establish an adverse event reporting and handling mechanism: timely discover and 
handle adverse events that occur during research to ensure the safety of subjects. 
 
Chapter 7 
Data Security Monitoring 
Clinical research will develop corresponding data safety monitoring plans based 
on the level of risk. All adverse events need to be recorded, processed and tracked until 
they are properly resolved or the condition is stable. Serious adverse events and 
unexpected events, etc., should be reported to the ethics committee, competent 
authorities, sponsors and drug regulatory authorities in accordance with regulations and 
time; the principal investigator will regularly conduct a cumulative review of all 
adverse events, and convene an investigator meeting when necessary to evaluate the 
risks and risks of the study. Benefits; double-blind trials can be unblinded urgently 
when necessary to ensure the safety and rights of subjects; low-risk studies will arrange 
independent data monitors to monitor the research data, and high-risk studies will 
establish an independent data safety monitoring committee to monitor the accumulated 
safety data and effectiveness data to make recommendations on whether the study 
should continue. 
 

26 
 
Chapter 8 
Reporting format of research results 
Research data will be integrated through a data collection form, and an annual 
report for that year will be written at the end of each year (specifically including trial 
progress and a summary of adverse events); 
Subsequent research results will be presented in the form of articles. 
 
Chapter 9 
Ethics of clinical research 
Clinical research will comply with relevant regulations such as the Declaration of 
Helsinki of the World Medical Assembly. Before the start of the study, the clinical 
study will be implemented after the ethics committee approves the research protocol. 
Before each subject is enrolled in this study, the researcher is responsible for fully and 
comprehensively introducing the purpose, procedures and possible risks of this study 
to the subject or his/her legal guardian, and signing a written informed consent form. 
Subjects should be informed that they have the right to withdraw from this study at any 
time, and the informed consent should be retained as a clinical research document for 
future reference. The personal privacy and data confidentiality of the subjects will be 
protected during the research process. 
 
Chapter 10 
Supplementary instructions for sample collection and biobanking 
10.1 Sample type and processing: 
This study will collect prostate magnetic resonance imaging (MRI) images and 
prostate tissue pathological sections from subjects. MRI images will be used to build 
artificial intelligence models to predict lymph node metastasis, aggressiveness, grade 
and subtype of prostate cancer. Pathological sections of prostate tissue will be used to 
map with MRI images to verify the accuracy of the artificial intelligence model and 
assist in clinical immunohistochemical diagnosis. 
This study plans to include 2,000 subjects. Each patient will have one MR image 
and one pathology section of prostate tissue. It is expected to collect 2,000 MRI images 
and 2,000 pathology sections. Each MRI image will contain a complete image 
sequence, including T1-weighted, T2-weighted, DWI and ADC image sequences. Each 
pathological section will contain complete tissue sections and undergo HE staining and 

27 
 
immunohistochemical staining. 
10.2 Sample source and testing unit: 
The samples for this study are from planned collection, which plans to collect MR 
images of patients after admission and pathological sections of prostate tissue after 
surgery. All collected samples will be processed and stored at the First Affiliated 
Hospital of Anhui Medical University or cooperative units, MRI images will be 
collected and processed in the Radiology Department or Nuclear Medicine Department, 
and prostate tissue pathological sections will be stained and pathologically diagnosed 
in the Pathology Department. The MR images and pathological slide scans will be 
subsequently collected and sent to the First Affiliated Hospital of Anhui Medical 
University, Anhui, and part of the image information will be sent to Anhui University 
to build or optimize artificial intelligence models. 
10.3 Processing and destruction of remaining samples: 
After the study is completed, according to the wishes of the subjects in the signed 
informed consent form, some permissionImages and digital information may be reused; 
the restMRI images and prostate tissue pathology sections will be destroyed uniformly. 
The destruction method will be implemented in accordance with the relevant 
regulations of the cooperative unit or the First Affiliated Hospital of Anhui Medical 
University, and the samples will be processed safely and harmlessly. 
 
Chapter 11 
Research Progress 
June 2025-December 2025: Recruitment starts, patients are enrolled and mid-term 
evaluation is completed 
June 2025-May 2026: Complete enrollment, final follow-up and data collection 
April 2026 - May 2026: Data cleaning and quality control, statistical analysis, 
writing relevant research papers 
         
Chapter 12 
Participants 
Name 
Professional title/major
Task 
GCP training 

28 
 
(time) 
Sheng Tai  
chief physician 
Project design, 
evaluation, revision 
April 1, 2021 
 
Chapter 13 
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