Using deep learning and Raman spectroscopy for early cancer diagnosis
A Novel Raman Spectroscopy-Based Method for Pan-Cancers Early Diagnosis Supported by Deep Learning: A Prospective, Single-Arm, Multicentre Study
This study is trying to see if a new deep learning tool using blood samples can help spot early signs of different types of cancer, like colorectal and gastric cancers, in both healthy people and those already diagnosed.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 600 (estimated) |
| Sex | All |
| Sponsor | Second Affiliated Hospital, School of Medicine, Zhejiang University Academic / other |
| Drugs / interventions | chemotherapy, immunotherapy |
| Locations | 4 sites (Nanchang, Jiangxi and 3 other locations) |
| Trial ID | NCT06822413 on ClinicalTrials.gov |
What this trial studies
This observational study aims to develop a deep learning model that utilizes Raman spectroscopy to enhance early cancer screening across multiple types of cancer, including colorectal and gastric cancers. It involves collecting blood samples from healthy individuals and patients with diagnosed cancers or precancerous conditions. The study focuses on evaluating the model's accuracy in identifying cancer-specific features in the spectral data and classifying patients based on their risk. The data undergoes preprocessing to standardize and optimize it for deep learning analysis, allowing for the identification of patterns associated with cancer presence.
Who should consider this trial
Good fit: Ideal candidates include individuals with histopathologically diagnosed malignant tumors, those with precancerous lesions, and healthy individuals without any malignancies.
Not a fit: Patients with metastatic tumors or those who have received any form of cancer treatment may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to earlier and more accurate cancer diagnoses, improving patient outcomes.
How similar studies have performed: While the use of deep learning in cancer diagnosis is gaining traction, this specific application of Raman spectroscopy combined with deep learning is relatively novel and has not been extensively tested in prior studies.
Eligibility criteria
Show full inclusion / exclusion 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.
Where this trial is running
Nanchang, Jiangxi and 3 other locations
- The First Affiliated Hospital to Nanchang University — Nanchang, Jiangxi, China (Recruiting)
- The Second Affiliated Hospital to Nanchang University — Nanchang, Jiangxi, China (Recruiting)
- Huashan Hospital Affiliated to Fudan University — Shanghai, Shanghai Municipality, China (Recruiting)
- The Second Affiliated Hospital of Zhejiang University School of Medicine — Hangzhou, Zhejiang, China (Recruiting)
Study contacts
- Study coordinator: Jiasheng Xu, MD
- Email: 1821286450@qq.com
- Phone: +86 18720996980
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.