Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis

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

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.

Conditions20

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