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

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.

Study Overview

• Status: Completed
• Conditions: Idiopathic Interstitial Pneumonia
• Intervention / Treatment: Radiation: high resolution CT

Detailed Description

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.

• Study Type: Observational
• Enrollment (Actual): 150

Contacts and Locations

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 100191
      • Peking University Third Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 90 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

From June 1, 2012 to December 31, 2020, all inpatients with IIP were admitted to the respiratory and critical care department of the Third Hospital of Peking University. Total patients are 150, 45 patients using hormone, average age 62 years old, 21 male.

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Hormone sensitive group; Prednisone, 0.5mg/kgqd, 3-6months	Radiation: high resolution CT; Ground glass，honeycomb，reticulation, consolidation
Hormone insensitivity group; Prednisone, 0.5mg/kgqd, 3-6months	Radiation: high resolution CT; Ground glass，honeycomb，reticulation, consolidation

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
clinical data and imaging feature ratios in both groups	clinical data including ages，gender，symptoms,signs,smoking history,complications,laboratory examination，lung function. Imaging feature including ground-glass opacity, reticulation, honeycomb and consolidation.	3-6 months after medication

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
the relationship between imaging feature ratios and hormone sensibility	Logistic regression analyzing the relationship between imaging feature ratios and hormone sensibility.	3-6 months after medication

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
development of artificial intelligence algorithm model	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.	3-6 months after medication

Collaborators and Investigators

• Sponsor: Peking University Third Hospital
• Investigators: Principal Investigator: Bei He, Peking University Third Hospital Respiratory and critical care department

Study record dates

Study Major Dates

• Study Start (Actual): December 30, 2019
• Primary Completion (Actual): June 1, 2021
• Study Completion (Actual): June 30, 2021

Study Registration Dates

• First Submitted: June 30, 2021
• First Submitted That Met QC Criteria: November 23, 2021
• First Posted (Actual): December 7, 2021

Study Record Updates

• Last Update Posted (Actual): December 7, 2021
• Last Update Submitted That Met QC Criteria: November 23, 2021
• Last Verified: June 1, 2021

More Information

Terms related to this study

• Keywords: artificial intelligence; imaging features; Hormone sensitivity
• Additional Relevant MeSH Terms: Infections; Respiratory Tract Infections; Respiratory Tract Diseases; Immune System Diseases; Lung Diseases; Idiopathic Pulmonary Fibrosis; Hypersensitivity; Pneumonia; Pulmonary Fibrosis; Lung Diseases, Interstitial; Idiopathic Interstitial Pneumonias; Hamman-Rich Syndrome
• Other Study ID Numbers: LM2019173

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