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| Name | Class |
|---|---|
| Institute of Automation, Chinese Academy of Sciences | OTHER |
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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:
Participants will:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Experimental group | Experimental | This group of patients will receive predictions assisted by artificial intelligence models. |
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| Control Group | No Intervention | This group of patients will not receive predictions assisted by artificial intelligence models. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Accurate Prediction Artificial Intelligence Models | Diagnostic 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 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. | 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. | From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Pengfei Shao, Professor | Contact | 13851925825 | spf032@hotmail.com | |
| Pan Zang, Postgraduate | Contact | 18914730216 | 896381164@qq.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| The First Affiliated Hospital of Nanjing Medical University (Jiangsu Provincial People's Hospital) | Nanjing | Jiangsu | 210036 | China |
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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
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| "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. | From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days. |
| ID | Term |
|---|---|
| D011471 | Prostatic Neoplasms |
| C565201 | Prostate Cancer, Hereditary, 7 |
| ID | Term |
|---|---|
| D005834 | Genital Neoplasms, Male |
| D014565 | Urogenital Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D005832 | Genital Diseases, Male |
| D000091662 | Genital Diseases |
| D000091642 | Urogenital Diseases |
| D011469 | Prostatic Diseases |
| D052801 | Male Urogenital Diseases |
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