Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy

Shandong University86 enrolled

Overview

The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.

Prospectively collect the hyperspectral image information of ordinary colonoscopic biopsy tissue. The colonoscopic biopsy tissue is from the Endoscopy Center of Qilu Hospital of Shandong University. The hyperspectral images are marked based on the biopsy pathological results, and the deep convolutional neural network (DCNN) model is used. With training and verification, develop the Hyperspectral Imaging Artificial Intelligence Diagnostic System (HSIAIDS) .A portion of colonoscopic biopsy tissue will be collected as a prospective test set to prospectively test the diagnostic performance of the HSIAIDS algorithm.

Study Type

OBSERVATIONAL

Enrollment

Conditions

Colorectal Neoplasms Colorectal Polyp Colorectal Adenoma Colorectal SSA

Eligibility

Sex: ALLMin age: 18 YearsMax age: 75 Years

Medical Language ↔ Plain English

Inclusion Criteria: * patients aged 18-75 years who undergo the colonoscopy examination and biopsy Exclusion Criteria: * patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in colonoscopy * patients with previous surgical procedures on the gastrointestinal tract. * patients with contraindications to biopsy * patients who refuse to sign the informed consent form

Locations (1)

Qilu hosipital

Jinan, Shandong, China

Outcomes

Primary Outcomes

Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer

Accuracy of hyperspectral imaging (HSI) artificial intelligence model to identify colorectal hyperplastic polyp, adenoma, SSL and colorectal cancer. Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects \* 100%

Time frame: 1 year

Sensitivity

Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) \* 100%.

Time frame: 1 year

Specificity

Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))\*100%

Time frame: 1 year

Negative predictive values(NPV)

Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)\*100%

Time frame: 1 year

AUC (95% CI)

area under the receiver operating characteristic curve (AUC)

Time frame: 1 year

Secondary Outcomes

To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition

Time frame: 1 year

Data from ClinicalTrials.gov

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