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Completed

NCT Number: NCT05146934

The Relationship Between Hormone Sensitivity and Imaging of Idiopathic Interstitial Pneumonia by Artificial Intelligence

Application of artificial intelligence deep learning algorithm to analyze the relationship between hormone sensitivity of idiopathic interstitial pneumonia and imaging features of high resolution CT.

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Key information

Conditions

Idiopathic Interstitial Pneumonia Idiopathic Interstitial Pneumonias Idiopathic Pulmonary Fibrosis Lung Diseases Lung Diseases, Interstitial Pulmonary Fibrosis Respiratory Tract Diseases

Age range

18 year–90 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Peking University Third Hospital

Beijing, Beijing Municipality, 100191, China

About this study

Methods: the medical records and chest high-resolution CT images of patients with idiopathic interstitial pneumonia admitted to the respiratory department of the Third Hospital of Peking University from June 1, 2012 to December 31, 2020 were retrospectively analyzed.Application of artificial intelligence deep learning neural convolution network method to create recognition technology of different imaging features.Including ground glass, mesh, honeycomb, nodule or consolidation, the model was established. IIP patients were divided into hormone sensitive group and hormone insensitive group according to whether the use of hormone was effective or not.Logistic regression analysis was used to analyze the correlation between statistically significant parameters and hormone sensitivity.Artificial intelligence was used to establish the correlation model between imaging features and clinical data and hormone sensitivity.

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

Clinical-pathological-radiology diagnosis of idiopathic interstitial pneumonia Hormone therapy was used; The follow-up data were complete, and the effect of hormone use could be judged.

Exclusion criteria

Lung infection disease; Heart failure; Connective tissue disease; IIP Without hormone therapy ; IIP but the follow-up data were incomplete, and the effect of hormone use could not be judged.

Treatment and study plan

high resolution CT

Radiation

Ground glass，honeycomb，reticulation, consolidation

Primary outcomes

  1. clinical data and imaging feature ratios in both groups

    Time frame: 3-6 months after medication

    clinical data including ages，gender，symptoms,signs,smoking history,complications,laboratory examination，lung function. Imaging feature including ground-glass opacity, reticulation, honeycomb and consolidation.

Secondary outcomes

  1. the relationship between imaging feature ratios and hormone sensibility

    Time frame: 3-6 months after medication

    Logistic regression analyzing the relationship between imaging feature ratios and hormone sensibility.

Other outcomes

  1. development of artificial intelligence algorithm model

    Time frame: 3-6 months after medication

    The U-net method of deep learning convolutional neural network (CNN) was used to create the recognition model of different imaging features. Imaging features include ground-glass opacity, reticulation, honeycomb and consolidation. With the area ratio of imaging features of the two groups as the input and hormone efficacy as the output, the correlation model between imaging features and hormone sensitivity was established by using artificial intelligence k nearest neighbor (KNN) algorithm and support vector machine (SVM) algorithm.

Sponsors and collaborators

Lead sponsor

Peking University Third Hospital

Other

Registry information

Acronym: IIP

Important dates

Study start
2019
Primary completion
2021
Study completion
2021
First posted
Dec 7, 2021
Registry last updated
Dec 7, 2021

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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