About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Patients who have been pathologically diagnosed as having lung cancer or colorectal cancer.
Patients with complete clinical data and tumor tissue materials, including H&E slides, paraffin blocks, and discarded ex vivo specimens.
Disqualifiers
None
Trial population
The study population will consist of patients with a confirmed pathological diagnosis of colorectal cancer or lung cancer. Eligible participants include adults who have undergone diagnostic biopsy or surgical resection yielding tumor tissue available for analysis. Clinical data and corresponding tumor specimens (including H\&E slides, paraffin blocks, and discarded ex vivo tissue) will be collected. All cases were prospectively collected from Nanfang Hospital, Southern Medical University.
Trial design
Cohort
Prospective
Treatments tested in this trial
Not listed
Trial groups
Trial outcomes
Primary outcomes
Area under ROC curve (AUC)
Area under the curve
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 (%).
Sponsors and contacts
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