Developing a MRI-based Deep Learning Model to Predict MMR Status of Endometrial Carcinoma
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 600
- 主要终点
- Area under receiver operating characteristic curve (AUROC)
研究概览
简要总结
In order to develop a convenient, cheap and comprehensive method to preoperatively predict dMMR and reduce the number of people requiring dMMR-related immunohistochemical or genetic testing after surgery, this study aims to establish a deep learning model based on MRI to predict the MMR status of endometrial cancer. Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery were collected. Deep learning was used to combine the clinical model with MR Image data to build the model. ROC curves were constructed for the testing group, internal verification group and external verification group, and the area under ROC curves were calculated to evaluate the diagnostic effect and stability of the model.
The dual threshold triage strategy was used to screen out the pMMR population (below the lower threshold), dMMR population (above the upper threshold) and the uncertain part of the population (between the thresholds).
详细描述
In this study, patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery were collected from 2017 to 2022. It is expected to collect 500 cases in our hospital, which are divided into 375 cases (experimental group) and 125 cases (internal verification group).
100 cases of Sun Yat-sen University Cancer Center for external verification. Clinical data (age, gender, BMI, CA125, CA19-9, MR-T staging, immunohistochemical results of MMR-related proteins) of the study population were collected and logistics regression analysis was conducted to establish clinical models. Extract, segment, integrate and enhance MR Image data.
Deep learning was used to combine the clinical model with MR Image data to build the model. ROC curves were constructed for the testing group, internal verification group and external verification group, and the area under ROC curves were calculated to evaluate the diagnostic effect and stability of the model.
The dual threshold triage strategy was used to screen out the pMMR population (below the lower threshold), dMMR population (above the upper threshold) and the uncertain part of the population (between the thresholds). If the predictive score is above the lower threshold, the patient is advised to undergo further immunohistochemical or genetic testing to confirm MMR status or dMMR type
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Retrospective
入排标准
- 性别
- Female
- 接受健康志愿者
- 否
入选标准
- •Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery from 2017 to 2022
排除标准
- •(1) There was no immunohistochemical detection result of MMR-related protein; (2) Radiotherapy and chemotherapy before MRI; (3) small tumors that are difficult to identify on the image (<5mm) ; (4) The T2-weighted imaging quality is insufficient to plot ROI, such as obvious motion artifacts; (5) There are other gynecological malignancies
结局指标
主要结局
Area under receiver operating characteristic curve (AUROC)
时间窗: one year
The area under receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models
次要结局
未报告次要终点