Spots Global Cancer Trial Database for Dynamic Multi-omics Integration Model to Predict Neoadjuvant Therapy Response in Locally Advanced Rectal Cancer

The following info and data is provided "as is" to help patients around the globe.
We do not endorse or review these studies in any way.

Trial Identification

Brief Title: Dynamic Multi-omics Integration Model to Predict Neoadjuvant Therapy Response in Locally Advanced Rectal Cancer

Official Title: A Study of the Clinical Value of Dynamic Multi-omics Integration Model to Predict Neoadjuvant Therapy Response in Locally Advanced Rectal Cancer (T3-4NxM0)

Study ID: NCT06364371

Conditions

Predictive Cancer Model

Pathologic Complete Response

Interventions

Medical examination

Study Description

Brief Summary: The goal of this observational study is to establish a dynamic multi-omics integration model for predicting pathological complete response (pCR) after neoadjuvant treatment in locally advanced (T3-4NxM0) rectal cancer, providing support for subsequent patient selection for the watch-and-wait strategy. The main question it aims to answer is: What is the predictive value of this model to assess individual achievement of pathological complete response (pCR) after neoadjuvant treatment? Eligible patients will be prospectively enrolled, and the clinical features of their pre-neoadjuvant treatment, during-treatment, and post-treatment preoperative will be collected and annotated.

Detailed Description: This is a single-center, prospective, observational phase II clinical study aimed at validating a dynamic multi-omics (imaging, pathology, molecular biomarkers) integration model for predicting pathological complete response (pCR) after neoadjuvant treatment in locally advanced (T3-4NxM0) rectal cancer. Specifically, the study aims to validate the predictive accuracy of the dynamic multi-omics prediction model and determine whether it outperforms other conventional prediction models based on single-modality imaging, pathology, and molecular biomarkers. Eligible patients will be prospectively enrolled, and images of their pre-neoadjuvant treatment, during-treatment, and post-treatment preoperative magnetic resonance imaging (MRI) scans, histopathology slides stained with hematoxylin and eosin (H\&E), carcinoembryonic antigen (CEA), and circulating tumor DNA (ctDNA) will be collected and annotated. MRI, H\&E images, CEA, ctDNA, and their change features will be applied to the prediction model to assess individual achievement of pathological complete response (pCR) after neoadjuvant treatment. The predictive results will be further compared with the pathological tumor response obtained from resected specimens.