Validation of High-Throughput Large-Format Tissue Preprocessing for Lung and Colorectal Cancer

Nanfang Hospital, Southern Medical University1,000 enrolled

Overview

This project aims to employ a sample preprocessing system in conjunction with three-dimensional imaging techniques to generate morphologically more complete, high-resolution datasets for lung and colorectal cancers. Building on systematic experimental optimization of the preprocessing system, the investigators will establish tissue-clearing workflows and transparency assessment criteria specifically for lung and colorectal cancer specimens, and develop and validate an efficient 3D immunofluorescent iterative staining protocol adapted for these tumor types to achieve robust three-dimensional imaging. Successful implementation of this project will enable an in-depth characterization of the spatial morphological features of lung and colorectal cancer pathology, facilitate identification of more effective and precise interventional strategies, and ultimately contribute to improved overall survival for cancer patients. Additionally, the resulting datasets will support prospective validation of two-dimensional pathological models.

Study Type

OBSERVATIONAL

Enrollment

1,000

Conditions

CRC (Colorectal Cancer)Lung Cancer (Diagnosis)

Eligibility

Sex: ALL

Medical Language ↔ Plain English

Inclusion Criteria: 1. Patients who have been pathologically diagnosed as having lung cancer or colorectal cancer. 2. Patients with complete clinical data and tumor tissue materials, including H\&E slides, paraffin blocks, and discarded ex vivo specimens. Exclusion Criteria: 1.Patients with missing data or specimens not meeting quality control requirements for analysis.

Outcomes

Primary Outcomes

Area under ROC curve (AUC)

Area under the curve

Time frame: Diagnostic evaluation will be performed within 1 week when the WSIs are obtained

Secondary Outcomes

Specificity

The true negative rate (TNR) of the diagnostic platform, which is the ratio between the number of negative individuals correctly categorized by platform and the total number of actual negative individuals (%).