Predicting the Efficacy of Neoadjuvant Therapy in Patients With Locally Advanced Rectal Cancer Using an AI Platform Based on Multi-parametric MRI
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 1,700
- 试验地点
- 4
- 主要终点
- The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response
研究概览
简要总结
Establish a deep learning model based on multi-parameter magnetic resonance imaging to predict the efficacy of neoadjuvant therapy for locally advanced rectal cancer.This study intends to combine DCE with conventional MRI images for DL, establish a multi-parameter MRI model for predicting the efficacy of CRT, and compare it with the DL and non-artificial quantitative MRI diagnostic model constructed by conventional MRI to evaluate the role of DL in MRI predicting CRT. And this study also tries to build a DL platform to assess the efficacy of LARC neoadjuvant radiotherapy and chemotherapy, accurately assess patients' complete respose (pCR) after CRT, and provide an important basis for guiding clinical decision-making.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Clinical suspicion or colonoscopic pathology of rectal cancer
- •Age over 18 years
- •Informed consent and signed informed consent form
排除标准
- •Poor magnetic resonance image quality, such as severe artifacts
- •Previous treatment for rectal cancer
- •History or combination of other malignant tumours
- •Not Locally Advanced Rectal Cancer (LARC)
- •Not received neoadjuvant therapy or not completed neoadjuvant therapy
- •No surgery
- •Time interval between MRI and surgery was more than 2 weeks
- •Patients were lost to follow-up and voluntarily withdrew from the study due to adverse reactions or other reasons
研究组 & 干预措施
complete response
Patients receiving neoadjuvant therapy achieved pathological complete response before LARC.
non complete response
Patients receiving neoadjuvant therapy did not achieve pathological complete response before LARC.
结局指标
主要结局
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in prediction tumor response
时间窗: baseline and pre-operation
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of models in identifying the pCR candidates from non-pCR individuals among neoadjuvant therapy treated LARC patients will be calculated.
次要结局
- The positive predictive value of models in prediction tumor response(baseline and pre-operation)
- The negative predictive value of models in prediction tumor response(baseline and pre-operation)
- The specificity of models in prediction tumor response(baseline and pre-operation)
- The sensitivity of models in prediction tumor response(baseline and pre-operation)
研究者
xiaochun meng
The Director of Diagnostic Radiology Department
Sixth Affiliated Hospital, Sun Yat-sen University