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Completed

NCT Number: NCT05576506

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

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.

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Key information

Conditions

Colorectal Neoplasms Colonic Diseases Colorectal Adenoma Colorectal Polyp Colorectal SSA Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Intestinal Diseases Intestinal Neoplasms Neoplasms Neoplasms by Site Rectal Diseases

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Qilu hosipital

Jinan, Shandong, 250012, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

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

    Time frame: 1 year

    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%

  2. Sensitivity

    Time frame: 1 year

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

  3. Specificity

    Time frame: 1 year

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

  4. Negative predictive values(NPV)

    Time frame: 1 year

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

  5. AUC (95% CI)

    Time frame: 1 year

    area under the receiver operating characteristic curve (AUC)

Secondary outcomes

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

    Time frame: 1 year

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

Sponsors and collaborators

Lead sponsor

Shandong University

Other

Registry information

Important dates

Study start
2022
Primary completion
2022
Study completion
2022
First posted
Oct 12, 2022
Registry last updated
Jul 31, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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