Development of an Artificial Intelligence System for Intelligent Pathological Diagnosis and Therapeutic Effect Prediction Based on Multimodal Data Fusion of Common Tumors and Major Infectious Diseases in the Respiratory System Using Deep Learning Technology.

NCT05046366 | Unknown

• 1 other identifier: [2021]IEC(491)
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

To improve accurate diagnosis and treatment of common malignant tumors and major infectious diseases in the respiratory system, we aim to establish a large medical database that includes standardized and structured clinical diagnosis and treatment information such as electronic medical records, image features, pathological features, and multi-omics information, and to develop a multi-modal data fusion-based technology system for individualized intelligent pathological diagnosis and therapeutic effect prediction using artificial intelligence technology.

Conditions

Artificial Intelligence; Deep Learning; Pathology, Molecular; Medical Informatics; Database; Lung Cancer; Pulmonary Tuberculosis; Covid19

Outcome Measures

Primary Outcomes (24)

• The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule).

  The outcome of clinical diagnosis of patients with lung cancer/pulmonary nodular (Benign/Malignant nodule). ① Benign nodule ② Malignant neoplasm/nodule: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and large cell carcinoma.

  2021-2024

• The outcome of clinical diagnosis of suspected patients with pulmonary tuberculosis (Positive/Negative).

  The outcome of clinical diagnosis of patients with pulmonary tuberculosis (Positive/Negative).

  2021-2024

• The outcome of clinical diagnosis of suspected patients with COVID-19 (Positive/Negative).

  The outcome of clinical diagnosis of patients with COVID-19 (Positive/Negative).

  2021-2024

• Treatment response of anti-cancer therapy at first evaluation in patients with lung cancer/pulmonary nodules (CR, PR, PD, SD).

  The treatment response of anti-cancer therapy at first evaluation in patients with lung cancer/pulmonary nodules follows The Response Evaluation Criteria In Solid Tumors (RECIST version 1.1) from the World Health Organization (WHO). The evaluation index is as follows. CR (complete response): Disappearance of all target lesions and reduction in the short axis measurement of all pathologic lymph nodes to ≤10 mm. PR (partial response): 30% decrease in the sum of the longest diameter of the target lesions compared with baseline. PD (progressive disease):≥20% increase of at least 5 mm in the sum of the longest diameter of the target lesions compared with the smallest sum of the longest diameter recorded OR The appearance of new lesions, including those detected by FDG-PET (fludeoxyglucose positron emission tomography). SD (stable disease): Neither PR nor PD.

  2021-2024

• Treatment response of anti-inflammation and antiviral therapy at first evaluation in patients with COVID-19 (effective/ineffective treatment).

  Treatment response of anti-inflammation and antiviral therapy at first evaluation in patients with COVID-19 (effective/ineffective treatment). effective treatment： Improved total time to recovery, resolution of fever, cough remission, and pneumonia severity. ineffective treatment： The above conditions have not improved or patients go die.

  2021-2024

• Treatment response of antituberculous bacilli and anti-inflammation therapy at first evaluation in patients with pulmonary tuberculosis.

  Treatment cure： patients with bacteriologically confirmed TB at the beginning of treatment who were smear- or culture-negative in the last month of treatment and on at least one previous occasion. Treatment completer: patients who completed treatment without evidence of failure but with no record to show that sputum smear or culture results in the last month of treatment and on at least one previous occasion were negative. Treatment success: The sum of cured and treatment completed. Treatment failure: patients whose sputum smear or culture is positive at month 5 or later during treatment. Treatment relapse: Patients who were declared cured or treatment completed at the end of their most recent course of TB treatment, and are now diagnosed with a recurrent episode of TB. This can be either a true relapse or a new episode of TB caused by reinfection. Patient died.

  2021-2024

• Progression free survival

  The time interval between the date of treatment initiation and disease progression (Months) of patients with lung cancer/pulmonary nodules.

  2021-2024

• Overall survival

  The time interval between the date of diagnosis and death (Months) of patients with lung cancer/pulmonary nodules.

  2021-2024

• Whole genome sequencing of blood samples

  Whole-genome sequencing of blood samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

  2021-2024

• Whole-genome sequencing of tissue samples

  Whole-genome sequencing of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

  2021-2024

• Whole genome sequencing of exhaled air condensate samples

  Whole-genome sequencing of exhaled air condensate samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

  2021-2024

• Whole genome sequencing of urine samples

  Whole-genome sequencing of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Whole-genome sequencing is mainly used to find single nucleotide polymorphisms (SNPs), copy number variations, and insertions/deletions.

  2021-2024

• Transcriptome sequencing of blood samples

  Transcriptome sequencing of blood samples before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

  2021-2024

• Transcriptome sequencing of tissue samples

  Transcriptome sequencing of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

  2021-2024

• Transcriptome sequencing of exhaled air condensate samples

  Transcriptome sequencing of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

  2021-2024

• Transcriptome sequencing of urine samples

  Transcriptome sequencing of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. The collection of all transcripts, including messenger RNA, ribosomal RNA, transport RNA, and non-coding RNA.

  2021-2024

• Metabolomics of blood samples

  Metabolomics of blood specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

  2021-2024

• Metabolomics of tissue samples

  Metabolomics of tissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

  2021-2024

• Metabolomics of exhaled air condensate samples

  Metabolomics of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

  2021-2024

• Metabolomics of urine samples

  Metabolomics of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Non-target metabolites are generally analyzed qualitatively and quantitatively based on LC-MS technology for metabolites in samples, and identified by matching primary and secondary information with local self-built databases and commercial standard databases.

  2021-2024

• Proteomics of blood samples

  Proteomics of blood specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

  2021-2024

• Proteomics of tissue samples

  Proteomicstissue samples after surgery in patients with lung cancer/pulmonary nodular and tuberculosis. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

  2021-2024

• Proteomics of exhaled air condensate samples

  Proteomics of exhaled air condensate specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

  2021-2024

• Proteomics of urine samples

  Proteomics of urine specimens before and after treatment in patients with lung cancer/pulmonary nodular, tuberculosis, and COVID-19. Unlabeled proteomics technology based on the timsTOF Pro ion mobility platform for differential quantitative proteomics analysis using data-dependent acquisition - Synchronous cumulative continuous fragmentation (ddaPASEF) scan mode.

  2021-2024

Secondary Outcomes (36)

• sex (male/female)

  2021-2024

• age (years)

  2021-2024

• weight (kilograms)

  2021-2024

• height (meters)

  2021-2024

• heart rate in each minute

  2021-2024

Study Arms (3)

Lung cancer group

Participants with lung cancer/pulmonary nodules

Pulmonary tuberculosis group

Participants with pulmonary tuberculosis

COIVD-19 group

Participants with COIVD-19

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Common malignant tumors and major infectious diseases in lung, including lung cancer, pulmonary tuberculosis, and COVID-19.

You may qualify if:

• Participants with the clinical diagnosis of lung cancer, pulmonary tuberculosis, and COVID-19.
• Participants that have signed informed consent.
• Participants \>= 18 years old and \< 90 years old.
• Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information.
• Healthy participants with no clinical diagnosis of lung cancer, pulmonary tuberculosis, and COVID-19.

You may not qualify if:

• Participants \< 18 years old.
• Participants with primary clinical and pathological data missing.
• Participants lost to follow-up.
• Participants with too poor medical image quality to perform segment and mark ROI accurately.

Sponsors & Collaborators

• Union Hospital, Tongji Medical College, Huazhong University of Science and Technology: lead

Study Sites (1)

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430000, China

RECRUITING

MeSH Terms

Conditions

Lung Neoplasms; Tuberculosis, Pulmonary; COVID-19

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Tuberculosis; Mycobacterium Infections; Actinomycetales Infections; Gram-Positive Bacterial Infections; Bacterial Infections; Bacterial Infections and Mycoses; Infections; Respiratory Tract Infections; Pneumonia, Viral; Pneumonia; Virus Diseases; Coronavirus Infections; Coronaviridae Infections; Nidovirales Infections; RNA Virus Infections

Study Officials

• Yang Jin, Professor

  union hospital, Tongji Medical college, Huazhonguniversity of science and technology

  STUDY DIRECTOR

Central Study Contacts

wei geng, Phd

CONTACT

18696152606; wguh116@hust.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Department of Respiratory and Critical Care Medicine

Study Record Dates

• First Submitted: June 27, 2021
• First Posted: September 16, 2021
• Study Start: October 1, 2021
• Primary Completion: December 1, 2023
• Study Completion: December 1, 2024
• Last Updated: November 16, 2021

Record last verified: 2021-11

Locations

CN (1)