Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis
Trial Parameters
Brief Summary
The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening. The study includes healthy individuals, patients at risk of cancer, and patients with diagnosed cancers. The main questions it aims to answer are: * Evaluating the deep-learning model's accuracy and specificity in identifying cancer-specific features in Raman spectral data and determining whether this method can accurately classify patients based on risk. * Identifying which model is more adaptable to the Raman spectrum * Providing an interpretable analysis of the model-generated diagnosis Participants are already being diagnosed and follow-up to determine the type of cancer.
Eligibility Criteria
Inclusion Criteria: * Histopathological diagnosis of malignant tumors, including colorectal cancer, gastric cancer, hepatic cancer, pancreatic cancer, and esophageal cancer. * Patients in normal physiological conditions without any malignant tumors or precancerous lesions. * Patients with malignant tumor without recieving any interventions, including chemotherapy, surgery, radiotherapy, immunotherapy or other anti-tumor treatments. * Patients with a histopathological diagnosis of any precancerous lesions or non-malignant disease. Exclusion Criteria: * Patients with metastatic tumors or in the condition with two or more kinds of malignant tumors at the same time * Post-cancer treatment patients.