Prostate CancerNCT06662708
Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer
A study testing Accurate Prediction Artificial Intelligence Models in people with Prostate Cancer and Prostate Intraductal Carcinoma, run by Shao Pengfei at 1 site in China.
Trial summary
The aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the predictions made by the artificial intelligence models.The main target questions addressed by… Read more about this study
- Who is it for?
- Ages 30 years and older, men onlyHealthy volunteers can take part
- Conditions the study is for
- Prostate Cancer, Prostate Intraductal Carcinoma, Prostate Cancer Aggressiveness
- What is being tested?
- Accurate Prediction Artificial Intelligence Models
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Dec 2024to 31 Dec 2030
- Phase
- Interventional
- 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 30 years and older
- Men only
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Patients with suspected PCa (elevated PSA or suspicious positive lesions on ultrasound or MRI results);
Who cannot join
- Previous treatment of the prostate in any form, including surgery, radiotherapy/chemotherapy, endocrine therapy, targeted therapy and immunotherapy;
- Patients with any item missing from the baseline clinical and pathological information;
- Patients with a history of other malignancies, serious comorbidities or other health problems;
- Unable to provide/sign an informed consent form;
- Patients who, in the judgement of the investigator, are deemed unfit to participate in this clinical trial;
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
The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital)
Nanjing, Jiangsu, 210036
About this study
Official title: Accurate Prediction and Treatment of Prostate Cancer by Artificial Intelligence Model-based Whole Slide Images and MRIs
The aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the predictions made by the artificial intelligence models.The main target questions addressed by the model building are:
- whether the AI model can learn from preoperative MRI and postoperative Whole Slide Images so as to accurately predict information such as benignness or malignancy, aggressiveness, grading, subtypes, genes, etc. for participants suspected of having prostate cancer preoperatively/puncturally.
- whether the AI model is capable of learning postoperative macropathology slides to enable outcome diagnosis of surgical pathology slides in new participants.
Read more
Participants will:
- complete an MRI examination and have their MRI images analysed by the established AI model to make an accurate diagnosis of them.
- Based on the diagnosis, if prostate cancer is predicted, they will undergo radical prostate cancer surgery and refine their surgical pathology.
Read the detailed description
Based on artificial intelligence technology, the prediction model is built by outlining the quantitative mapping correlation between annotated prostate cancer Whole Slide Images and MRI, and clarifying the common features. Firstly, the model can accurately diagnose the radical pathology of prostate cancer, which can be exempted from immunohistochemistry to obtain detailed pathological information; secondly, the established AI prediction model can accurately diagnose the benign/malignant, invasiveness, grade and subtype of prostate cancer by predicting the participant's MRI images before surgery or puncture, so that a personalised treatment plan can be formulated for the patient before operation or puncture. Finally, based on AI technology, the model learns from the MRI images and performs 3D reconstruction of the prostate and lesions before surgery/puncture, thus clarifying the exact location of the lesions and guiding puncture or surgical treatment.
- Conditions
- Prostate Cancer, Prostate Intraductal Carcinoma, Prostate Cancer Aggressiveness, Prostate Cancer Stage, Pathology
- Being tested
- Accurate Prediction Artificial Intelligence Models
- Keywords
- Articicial Intelligence, Whole Slide Image, mp-MRI, Prostate Cancer, Prediction Model
- Medical subject headings
- Prostatic Neoplasms, Prostate Cancer, Hereditary, 7
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Accurate Prediction Artificial Intelligence ModelsDiagnostic test
Diagnostic Test: Accurate Prediction Artificial Intelligence Models Post-operative pathology, precise pre-operative diagnosis (including benign and malignant, invasive, grading, subtypes) or 3D lesion modelling will be predicted based on the AI predictive model in response to the information provided
| Study group | Role | What they receive |
|---|---|---|
| Experimental group | Experimental | This group of patients will receive predictions assisted by artificial intelligence models. Receives: Diagnostic Test: Accurate Prediction Artificial Intelligence Models |
| Control Group | No intervention | This group of patients will not receive predictions assisted by artificial intelligence models. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediction of postradical prostate cancer pathology after radical prostatectomy using the 'AUC' comprehensive assessment model
'AUC' refers to the area under the ROC (Receiver Operating Characteristic) curve, which indicates the performance of the model in predicting immunohistochemistry-related pathological information of prostate cancer after surgery, and the AUC ranges from 0-1, with the larger value indicating the better prediction effect.
Measured: From subject enrolment to initial post-surgery, usually 30-90 days.
Predicting the performance of post-radical pathology by the 'AUC' comprehensive assessment model
'AUC' refers to the area under the ROC (Receiver Operating Characteristic) curve, indicating the level of performance of the model in predicting prostate cancer in the preoperative period, with AUC ranging from 0-1, with larger values indicating better prediction results.
Measured: From subject enrolment to initial post-surgery, usually 30-90 days.
'F1 Score' to assess performance of preoperative 3D modelling
A reconciled average of the preoperative 3D modelling precision and recall assessed through the 'F1 score', which represents the match to the real situation.
Measured: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
Secondary outcomes (2)
Assess the amount of cost difference between the predictive model and the clinical approach by "economic cost savings"
Compare the difference in costs incurred using a predictive model with those predicted using a clinical approach, the difference will be in yuan.
Measured: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
"Diagnostic Time" evaluate the time taken to predict immunohistochemistry-related pathology in the postoperative period.
The time spent postoperatively predicting or assisting the pathologist in obtaining immunohistochemistry-related pathology information is assessed by "Diagnostic Time" and will be measured in minutes.
Measured: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted29 Oct 2024
- Study start1 Dec 2024 (estimated)
- Primary completion1 Jan 2030 (estimated)
- Study completion31 Dec 2030 (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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Inclusion of enrolled patients in an artificial intelligence predictive model that predicts postoperative pathology, precise preoperative diagnosis (including benign and malignant, invasive, grading, and subtypes) or 3D lesion modelling based on the information provided
- Masking
- Triple blind
- Who is masked
- Participant, Care provider, Outcomes assessor
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shao Pengfei
- Sponsor type
- University, hospital or other
- Collaborators
- Institute of Automation, Chinese Academy of Sciences
- Responsible party
- Sponsor-investigator, Shao Pengfei, The First Affiliated Hospital with Nanjing Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06662708
- Sponsor's protocol ID
- Shao Pengfei
- Status last verified
- Oct 2024
- First submitted
- 13 Aug 2024
- Last updated
- 29 Oct 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 29 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Accurate Prediction and Treatment of Prostate Cancer by Artificial Intelligence Model-based Whole Slide Images and MRIs
- ClinicalTrials.gov ID
- NCT06662708
- Acronym / sponsor ID
- Shao Pengfei
- Conditions
- Prostate Cancer, Prostate Intraductal Carcinoma, Prostate Cancer Aggressiveness, Prostate Cancer Stage, Pathology
- Keywords
- Articicial Intelligence, Whole Slide Image, mp-MRI, Prostate Cancer, Prediction Model
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Shao PengfeiUniversity, hospital or other
- Collaborators
- Institute of Automation, Chinese Academy of Sciences
- Study start
- 1 Dec 2024 (estimated)
- Primary completion
- 1 Jan 2030 (estimated)
- Study completion
- 31 Dec 2030 (estimated)
- First posted
- 29 Oct 2024 (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 aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the predictions made by the artificial intelligence models.The main target questions addressed by the model building are:
- whether the AI model can learn from preoperative MRI and postoperative Whole Slide Images so as to accurately predict information such as benignness or malignancy, aggressiveness, grading, subtypes, genes, etc. for participants suspected of having prostate cancer preoperatively/puncturally.
- whether the AI model is capable of learning postoperative macropathology slides to enable outcome diagnosis of surgical pathology slides in new participants.
Participants will:
- complete an MRI examination and have their MRI images analysed by the established AI model to make an accurate diagnosis of them.
- Based on the diagnosis, if prostate cancer is predicted, they will undergo radical prostate cancer surgery and refine their surgical pathology.
Detailed description
Based on artificial intelligence technology, the prediction model is built by outlining the quantitative mapping correlation between annotated prostate cancer Whole Slide Images and MRI, and clarifying the common features. Firstly, the model can accurately diagnose the radical pathology of prostate cancer, which can be exempted from immunohistochemistry to obtain detailed pathological information; secondly, the established AI prediction model can accurately diagnose the benign/malignant, invasiveness, grade and subtype of prostate cancer by predicting the participant's MRI images before surgery or puncture, so that a personalised treatment plan can be formulated for the patient before operation or puncture. Finally, based on AI technology, the model learns from the MRI images and performs 3D reconstruction of the prostate and lesions before surgery/puncture, thus clarifying the exact location of the lesions and guiding puncture or surgical treatment.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Triple blind (participant, care provider, outcomes assessor)
- Number of arms
- 2
- Model description
- Inclusion of enrolled patients in an artificial intelligence predictive model that predicts postoperative pathology, precise preoperative diagnosis (including benign and malignant, invasive, grading, and subtypes) or 3D lesion modelling based on the information provided
| Arm | Type | Receives |
|---|---|---|
| Experimental group | Experimental | Diagnostic Test: Accurate Prediction Artificial Intelligence ModelsThis group of patients will receive predictions assisted by artificial intelligence models. |
| Control Group | No intervention | –This group of patients will not receive predictions assisted by artificial intelligence models. |
Who may qualify
- Ages
- 30 years and older
- Sex
- Male only
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Patients with suspected PCa (elevated PSA or suspicious positive lesions on ultrasound or MRI results);
Key exclusion criteria (5 listed)
- Previous treatment of the prostate in any form, including surgery, radiotherapy/chemotherapy, endocrine therapy, targeted therapy and immunotherapy;
- Patients with any item missing from the baseline clinical and pathological information;
- Patients with a history of other malignancies, serious comorbidities or other health problems;
- Unable to provide/sign an informed consent form;
- Patients who, in the judgement of the investigator, are deemed unfit to participate in this clinical trial;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Accurate Prediction Artificial Intelligence ModelsDiagnostic Test: Accurate Prediction Artificial Intelligence Models Post-operative pathology, precise pre-operative diagnosis (including benign and malignant, invasive, grading, subtypes) or 3D lesion modelling will be predicted based on the AI predictive model in response to the information provided | Diagnostic test | Experimental group |
Primary outcome
Prediction of postradical prostate cancer pathology after radical prostatectomy using the 'AUC' comprehensive assessment model
Time frame: From subject enrolment to initial post-surgery, usually 30-90 days.
'AUC' refers to the area under the ROC (Receiver Operating Characteristic) curve, which indicates the performance of the model in predicting immunohistochemistry-related pathological information of prostate cancer after surgery, and the AUC ranges from 0-1, with the larger value indicating the better prediction effect.
Predicting the performance of post-radical pathology by the 'AUC' comprehensive assessment model
Time frame: From subject enrolment to initial post-surgery, usually 30-90 days.
'AUC' refers to the area under the ROC (Receiver Operating Characteristic) curve, indicating the level of performance of the model in predicting prostate cancer in the preoperative period, with AUC ranging from 0-1, with larger values indicating better prediction results.
'F1 Score' to assess performance of preoperative 3D modelling
Time frame: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
A reconciled average of the preoperative 3D modelling precision and recall assessed through the 'F1 score', which represents the match to the real situation.
2 secondary outcomes
Assess the amount of cost difference between the predictive model and the clinical approach by "economic cost savings"
Time frame: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
"Diagnostic Time" evaluate the time taken to predict immunohistochemistry-related pathology in the postoperative period.
Time frame: From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.
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 29 Oct 2024.
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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?
A study testing Accurate Prediction Artificial Intelligence Models in people with Prostate Cancer and Prostate Intraductal Carcinoma, run by Shao Pengfei 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 1 inclusion and 5 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: opening soon, as of the sponsor's last update on 29 Oct 2024. 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 Pengfei Shao, Professor (13851925825, spf032@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of postradical prostate cancer pathology after radical prostatectomy using the 'AUC' comprehensive assessment model (time frame: From subject enrolment to initial post-surgery, usually 30-90 days.). The study lists 3 primary outcomes in all. 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 29 Oct 2024.