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

ConditionsCancer DiagnosisLiver Cancer, AdultCancer ScreeningColorectal Cancer (CRC)Gastric CancersNormal PhysiologyPancreatic Cancer, AdultRaman SpectroscopyDeep Learning ModelEsophageal CancerMalignant TumoursPrecancerous ConditionsPancreatitisAdenoma Colon PolypGastric UlcerOesophagitisCirrhoses, Liver
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorSecond Affiliated Hospital, School of Medicine, Zhejiang University

About this trial

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

This trial accepts healthy volunteers

Qualifiers

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.

Disqualifiers

Patients with metastatic tumors or in the condition with two or more kinds of malignant tumors at the same time

Post-cancer treatment patients.

Trial population

Pan-cancer patients from Zhejiang, Jiangxi and Shanghai province

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • No Interventions

    Other intervention

    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.

Treatment groups

600 Participants
are divided into 11 treatment groups

11

Treatment groups

See each treatment group below.

Group A: Normal Physiology1 intervention
Group B: Colorectal Cancer1 intervention
Group C: Gastric Cancer1 intervention
Group D: Hepatic Cancer1 intervention
Group E: Oesophageal0 interventions
Group F: Pancreatic Cancer1 intervention
Group G: Gastric Ulcer1 intervention
Group H: Colorectal Adenoma1 intervention
Group I: Liver Cirrhosis1 intervention
Group J: Pancreatitis1 intervention
Group K: Oesophagitis1 intervention

Trial outcomes

Primary outcomes

1

A Deep Learning Model for High-Accuracy Pan-Cancer Classification

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.

Time frame
From patient enrollment to the completion of model construction, expected to be finalized within two months after data collection.

Secondary outcomes

1

Raman Shift Characteristics for Model Decision Interpretation and Visualization

Performing interpretable analysis of the diagnosis derived from the primary outcome using Grad-CAM to visualize and illustrate the model's decision-making process.

Time frame
From the end of model construction to the end of model interpretable analysis - expected 2 months after model construction

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Second Affiliated Hospital, School of Medicine, Zhejiang University

Lead sponsor

The First Affiliated Hospital of Nanchang University

Collaborator

Second Affiliated Hospital of Nanchang University

Collaborator

Huashan Hospital

Collaborator

Source ClinicalTrials.gov
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