Establishing a Strategy for Selecting Watchful Waiting and Determining the Optimal Timing for Surgery Following Neoadjuvant Therapy
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 869
- 试验地点
- 1
研究概览
简要总结
This study aims to utilise a real-world data platform to integrate multi-omics data-including radiomics, gut microbiota, pathological quality control and liquid biopsy-to construct a multidimensional predictive model for the efficacy of rectal cancer treatment following neoadjuvant therapy. By integrating multimodal data, the study aims to accurately assess the efficacy of neoadjuvant therapy and identify patients suitable for a 'watch-and-wait' strategy, thereby achieving tumour control and preserving organ function without the need for surgery. Furthermore, it seeks to provide scientific evidence for the efficacy of the 'watch-and-wait' strategy and the selection of optimal timing for surgery, whilst validating the model's effectiveness and assessing its clinical feasibility through prospective clinical trials.
详细描述
- Optimisation and predictive modelling of immunotherapy combined with neoadjuvant chemoradiotherapy Through real-world studies, we will evaluate the efficacy and safety of immunotherapy combined with neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer, focusing on comparing outcomes with traditional chemoradiotherapy regimens to optimise neoadjuvant treatment protocols. By integrating radiomics and molecular subtyping data, we will develop a deep learning model to predict the rate of pathological complete response, thereby accurately forecasting treatment outcomes for patients. This model can further optimise personalised treatment decisions, enhance the effectiveness of organ-preservation strategies, and ultimately reduce surgical trauma for patients.
- A Treatment Efficacy Assessment System Combining Multi-omics Data with Artificial Intelligence Integrate multi-omics data (such as molecular subtyping, radiomics, and pathological assessment) with artificial intelligence technology to establish a treatment efficacy assessment system. Utilise deep learning models to analyse pre- and post-treatment imaging data and pathological samples, thereby achieving precise efficacy assessment.
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Other
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
- •The patient is informed and has provided written informed consent;
- •Rectal adenocarcinoma confirmed by colonoscopic biopsy and pathology, meeting the following criteria:
- •Clinical stage II/III locally advanced rectal cancer (LARC): cT1-4aN0-2M0;
- •The distal edge of the tumor is ≤ 10 cm from the anal verge (measured by MRI);
- •No distant metastasis;
- •Scheduled to receive neoadjuvant therapy;
- •Age ≥ 18 years, male or female。
排除标准
- •Presence of distant organ metastasis;
- •Multiple primary colorectal cancers;
- •History of prior malignancy (except completely cured carcinoma in situ of the cervix, basal cell carcinoma, or squamous cell carcinoma of the skin).
研究者
Yao Hongwei
Consultant, Professor
Beijing Friendship Hospital