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

April 19, 2025 updated by: Second Affiliated Hospital, School of Medicine, Zhejiang University

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

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

This study aims to explore the use of deep learning models for classifying patients based on Raman spectroscopy analysis of blood samples, distinguishing between individuals in physiological conditions and patients with various types of precancerous conditions or malignant tumors. The study is conducted through a multi-center collaboration, where blood samples are collected from both healthy participants and patients with histopathologically diagnosed precancerous conditions or primary malignant tumors.

All blood samples are obtained from patients' routine clinical blood tests conducted during hospital admission or other necessary medical evaluations. The spectral data undergo a rigorous preprocessing pipeline, which includes alignment resampling to standardize the data, baseline removal to eliminate unwanted variations, and normalization to ensure uniformity across all samples. The data is optimized for deep learning model training.

Various deep-learning models are then employed to analyze the processed Raman spectra and develop a classification system to distinguish between pan-cancer cases and healthy controls. The preprocessed dataset is partitioned into three subsets for model training and performance evaluation: 80% for training, 10% for validation, and 10% for testing. These datasets are used for model training to identify patterns in the spectral data that correlate with the presence of specific cancers or a healthy state, enabling accurate classification.

To enhance the interpretability of deep learning models, Grad-CAM (Gradient-weighted Class Activation Mapping) is used to visualize the models' decision-making processes. This allows the identification of the Raman spectra regions that are more influential in the model's classification decision, providing a transparent understanding of how the model differentiates between the various classes.

Ultimately, this study aims to demonstrate the potential of Raman spectroscopy combined with deep learning techniques as a non-invasive, accurate, and interpretable method for cancer detection and classification, with implications for early diagnosis and personalized treatment strategies.

Study Type

Observational

Enrollment (Estimated)

600

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Jiangxi
      • Nanchang, Jiangxi, China, 330006
        • Recruiting
        • The First Affiliated Hospital to Nanchang University
        • Contact:
      • Nanchang, Jiangxi, China, 330008
        • Recruiting
        • The Second Affiliated Hospital to Nanchang University
        • Contact:
    • Shanghai
      • Shanghai, Shanghai, China, 200040
        • Recruiting
        • Huashan Hospital Affiliated to Fudan University
        • Contact:
    • Zhejiang
      • Hangzhou, Zhejiang, China, 310009
        • Recruiting
        • The Second Affiliated Hospital of Zhejiang University School of Medicine
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Probability Sample

Study Population

Pan-cancer patients from Zhejiang, Jiangxi and Shanghai province

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Normal Physiology
Patients without cancers or precancerous lesion
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Colorectal Cancer
Patients diagnosed with colorectal cancer (Pre-intervention)
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Gastric Cancer
Patients diagnosed with gastric cancer (Pre-intervention)
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Hepatic Cancer
Patients diagnosed with hepatic cancer (Pre-intervention)
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Oesophageal
Patients diagnosed with oesophageal cancer (Pre-intervention)
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Pancreatic Cancer
Patients diagnosed with pancreatic cancer (Pre-intervention)
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Gastric Ulcer
Patients with gastric ulcers without any cancer
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Colorectal Adenoma
Patients with colorectal adenoma without any cancer
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Liver Cirrhosis
Patients with liver cirrhosis without any cancer
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Pancreatitis
Patients with pancreatitis without any cancer
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.
Oesophagitis
Patients with oesophagitis without any cancer
Other: No Interventions
All blood samples from participating patients were obtained from routine clinical blood tests conducted during hospital admission or other necessary medical evaluations, followed by serum extraction.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
A Deep Learning Model for High-Accuracy Pan-Cancer Classification
Time Frame: From patient enrollment to the completion of model construction, expected to be finalized within two months after data collection.
Establish deep learning models with high specificity and sensitivity for pan-cancer classification, capable of distinguishing different pan-cancer types (Distinguish between patients in physiological conditions, precancerous lesion and malignant tumour) based on Raman spectroscopy.
From patient enrollment to the completion of model construction, expected to be finalized within two months after data collection.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Raman Shift Characteristics for Model Decision Interpretation and Visualization
Time Frame: From the end of model construction to the end of model interpretable analysis - expected 2 months after model construction
Performing interpretable analysis of the diagnosis derived from the primary outcome using Grad-CAM to visualize and illustrate the model's decision-making process.
From the end of model construction to the end of model interpretable analysis - expected 2 months after model construction

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Second Affiliated Hospital, School of Medicine, Zhejiang University

Collaborators

The First Affiliated Hospital of Nanchang University
Huashan Hospital
Second Affiliated Hospital of Nanchang University

Investigators

  • Study Chair: Kefeng Ding, MD, Department of Colorectal Surgery, The Second Hospital of Zhejiang University School of Medicine

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 1, 2022

Primary Completion (Estimated)

May 15, 2025

Study Completion (Estimated)

July 28, 2025

Study Registration Dates

First Submitted

February 5, 2025

First Submitted That Met QC Criteria

February 5, 2025

First Posted (Actual)

February 12, 2025

Study Record Updates

Last Update Posted (Actual)

April 24, 2025

Last Update Submitted That Met QC Criteria

April 19, 2025

Last Verified

January 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2024-1060

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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