A Post-Neoadjuvant Treatment MRI Based AI System to Predict Pathologic Complete Response for Patients With Rectal Cancer: A Multicenter, Prospective Clinical Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 205
- 试验地点
- 3
- 主要终点
- The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response
研究概览
简要总结
In this study, investigators seek for a better way to identify the potential pathologic complete response (pCR) patients form non-pCR patients with locally advanced rectal cancer (LARC), based on their post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data.
Previously, a post neoadjuvant treatment MRI based radiomics AI model had been constructed and trained. Here, the predictive power of this artificial intelligence system and expert radiologist to identify pCR patients from non-pCR LARC patients will be compared in this prospective, multicenter, back-to-back clinical study
详细描述
This is a multicenter, prospective, observational clinical study for seeking out a better way to predict the pathologic complete response (pCR) in patients with locally advanced rectal cancer (LARC) based on the post-neoadjuvant treatment Magnetic Resonance Imaging (MRI) data. Patients who have been pathologically diagnosed as rectal adenocarcinoma and defined as clinical II-III stage will be enrolled from the Sixth Affiliated Hospital of Sun Yat-sen University, Sir Run Run Shaw Hospital and the Third Affiliated Hospital of Kunming Medical College. All participants should follow a standard treatment protocol, including neoadjuvant treatment, total mesorectum excision (TME) surgery. Patients with LARC who received neoadjuvant treatment will be enrolled and their post-neoadjuvant treatment MRI images will be used to predict their pathologic response (pCR vs. non-pCR). The artificial intelligence prediction system and the expert radiologist will define the pathologic response as pCR or non-pCR, respectively. The pathologist will provide the final pathology report of TME surgery specimen (pCR or non-pCR) as a standard. The predictive efficacy of these two back-to-back approaches generated will be compared in this multicenter, prospective clinical study.
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 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •pathologically diagnosed as rectal adenocarcinoma
- •defined as clinical II-III staging (≥T3, and/or positive nodal status) without distant metastasis
- •receive neoadjuvant chemoradiotherapy or chemotherapy
- •pre- and post-neoadjuvant treatment MRI data obtained
- •receive total mesorectum excision (TME) surgery after neoadjuvant therapy and get the pathologic assessment of tumor response
排除标准
- •with history of other cancer
- •insufficient imaging quality of MRI to delineate tumor volume or obtain measurements (e.g., lack of sequence, motion artifacts)
- •not completing neoadjuvant chemotherapy or chemoradiotherapy
- •tumor recurrence or distant metastasis during neoadjuvant treatment
- •not undergoing surgery resulting in lack of pathologic assessment of tumor response
结局指标
主要结局
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in prediction tumor response
时间窗: baseline
The area under curve (AUC) of Receiver Operating Characteristic (ROC) curves of AI prediction system and expert radiologists in identifying the pCR candidates from non-pCR individuals among neoadjuvant chemotherapy or chemoradiotherapy treated LARC patients will be calculated respectively.
次要结局
- The sensitivity of AI prediction system and expert radiologists in prediction tumor response(baseline)
- The specificity of AI prediction system and expert radiologists in prediction tumor response(baseline)
研究者
wanxiangbo
professor of Radiation Oncology, Vice Director, Department of Radiation Oncology
Sixth Affiliated Hospital, Sun Yat-sen University