Clinical trial Recruiting at 4 sites Observational

Using deep learning and Raman spectroscopy for early cancer diagnosis

Official title: A Novel Raman Spectroscopy-Based Method for Pan-Cancers Early Diagnosis Supported by Deep Learning: A Prospective, Single-Arm, Multicentre Study

Run by Second Affiliated Hospital, Zhejiang University, School of Medicine · NCT06822413 · Data updated 2026-11-12

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 typeObservational
SexAll
Enrollment600 (estimated)
SponsorSecond Affiliated Hospital, Zhejiang University, School of Medicine
Academic / other
Drugs / interventionschemotherapy, immunotherapy
Locations4 sites (Nanchang, Jiangxi and 3 other locations)
StatusRecruiting
StartedSeptember 1, 2022
Main results expectedMay 15, 2025
Study endsJuly 28, 2025
Registry record updatedApril 24, 2025
Trial IDNCT06822413 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Ideal candidates include individuals with histopathologically diagnosed malignant tumors, those with precancerous lesions, and healthy individuals without any malignancies.

Probably not a fit

Patients with metastatic tumors or those who have received any form of cancer treatment may not benefit from this study.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

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.

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 gone: 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.

Who can join: the full rules

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 it's running

  • 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

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.