Accurate Prediction and Treatment of Prostate Cancer by Artificial Intelligence Model-based Whole Slide Images and MRIs
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- Prediction of postradical prostate cancer pathology after radical prostatectomy using the 'AUC' comprehensive assessment model
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Triple (Participant, Care Provider, Outcomes Assessor)
入排标准
- 年龄范围
- 30 Years 至 —（Adult, Older Adult）
- 性别
- Male
- 接受健康志愿者
- 是
入选标准
- •Patients with suspected PCa (elevated PSA or suspicious positive lesions on ultrasound or MRI results);
排除标准
- •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;
结局指标
主要结局
Prediction of postradical prostate cancer pathology after radical prostatectomy using the 'AUC' comprehensive assessment model
时间窗: 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
时间窗: 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
时间窗: 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.
次要结局
- Assess the amount of cost difference between the predictive model and the clinical approach by "economic cost savings"(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.(From subject enrolment to initial post-surgery/puncture recovery, usually 30-90 days.)
研究者
Shao Pengfei
Chief physician
The First Affiliated Hospital with Nanjing Medical University