Rectal Cancer Neoadjuvant Therapy-Real World Study

Official title

Establishing a Strategy for Selecting Watchful Waiting and Determining the Optimal Timing for Surgery Following Neoadjuvant Therapy

Overview

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.

Detailed description

1. 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. 2. 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.

Interventions

• Other This study is a multicenter, real-world, non-interventional study.
  non-interventional study

Primary outcome measures

• To compare the complete response rates across different treatments. [Time frame: Post-neoadjuvant Therapy Efficacy Assessment Time Point (within 8-12 weeks after radiotherapy completion)]

Secondary outcome measures (3)

• Safety profile [Time frame: 20 weeks after the first radiotherapy session]
• Therapy Tolerability [Time frame: 20 weeks after the first radiotherapy session]
• Disease-Free Survival (DFS) [Time frame: within 5 years after completion of neoadjuvant therapy]

Eligibility criteria

Inclusion criteria

• 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。

Exclusion criteria

• 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).

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Other

Study locations

China · 1 center

• Beijing Friendship Hospital — Beijing

Identifiers

NCT: NCT07566559 · MR-11-26-025202 · MR-11-26-025202

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗