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.

Research and Development of an Artificial Intelligence Technology System for Digital 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.

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.

Study Overview

• Status: Recruiting
• Conditions: Covid19; Lung Cancer; Artificial Intelligence; Pulmonary Tuberculosis; Medical Informatics; Deep Learning; Database; Pathology, Molecular

Detailed Description

The main aims are as follows:

1. To establish a medical big data platform for multi-modal information fusion of common tumors and major infectious diseases (lung cancer/pulmonary nodules, tuberculosis, and COVID-19) based on the existing pathological image features and clinical multi-omics information database: The medical big data platform supports the acquisition of the patient's clinical electronic medical records (including routine clinical detection), full view digital section of pathological image data, medical imaging (CT, MRI, ultrasound, nuclear medicine, etc.), multiple omics data (genome, transcriptome, and metabolome, proteomics) omics data, etiology, pathology, and associated graphic data reports and multimodal medical treatment data. We aim to realize the storage, sharing, fusion computing, privacy protection, and security supervision of multi-modal and cross-scale biomedical big data. Our work will open up key business processes and links across regions, across hospitals, between different terminals, between hospitals and doctors, and between departments, so as to promote continuous data accumulation and knowledge precipitation in hospitals and promote medical collaboration.
2. To create a multimodal information fusion database with pathologic features, imaging features, multi-omics (pathologic, genomic, transcriptome, metabolome, proteomics, etc.), and clinical information of patients at different stages of lung cancer/pulmonary nodules, tuberculosis, and COVID-19. The database scale includes multimodal data of at least 600 lung cancer/pulmonary nodules, 200 tuberculosis, and 200 COVID-19 patients. Moreover, there will be more than 10 biomarkers significantly related to the diagnosis and treatment of patients with lung cancer/pulmonary nodules, tuberculosis and COVID-19 were excavated through association analysis, providing parameters for artificial intelligence model construction.
3. We will make use of artificial intelligence technology to create the multi-modal medical big data cross-analysis technology and the above disease individualized accurate diagnosis and curative effect prediction models. In order to solve the three key problems of multi-modal data fusion mining, such as unbalanced, small sample size, and poor interpretability, we will establish an ARTIFICIAL intelligence recognition algorithm for image images and pathological images, and use image processing and deep learning technologies to mine multi-level depth visual features of image data and pathological data. In addition, we will use bioinformatics analysis algorithms to conduct molecular network mining and functional analysis of molecular markers at the level of multiple omics technologies (pathologic, genomic, transcriptome, metabolome, proteome, etc.).

• Study Type: Observational
• Enrollment (Anticipated): 1000

Contacts and Locations

• Study Contact: Name: wei geng, Phd; Phone Number: 18696152606; Email: wguh116@hust.edu.cn

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China, 430000
      • Recruiting
      • Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

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

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

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort
Lung cancer group; Participants with lung cancer/pulmonary nodules
Pulmonary tuberculosis group; Participants with pulmonary tuberculosis
COIVD-19 group; Participants with COIVD-19

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
sex (male/female)	sex of patients(male/female).	2021-2024
age (years)	age of patients (years).	2021-2024
weight (kilograms)	weight of patients (kilograms)	2021-2024
height (meters)	height of patients (meters).	2021-2024
heart rate in each minute	heart rate in each minute of patients.	2021-2024
blood pressure (mmHg)	blood pressure (mmHg) of patients.	2021-2024
Forced vital capacity (FVC)	Forced vital capacity (FVC) of patients	2021-2024
forced expiratory volume in one second (FEV1)	forced expiratory volume in one second (FEV1) for lung volume	2021-2024
peak expiratory flow (PEF)	peak expiratory flow (PEF) for velocity	2021-2024
carbon monoxide diffusion capacity (DLCO)	carbon monoxide diffusion capacity (DLCO) for pulmonary diffusion function.	2021-2024
St. George's Respiratory Questionnaire(SGRQ)	St. George's Respiratory Questionnaire total score(0-3989.4), St. George's Respiratory Questionnaire symptoms score(0-662.5); St. George's Respiratory Questionnaire impacts score(0-2117.8); St. George's Respiratory Questionnaire activity score(0-1209.1). The higher the score, the worse the lung.	2021-2024
C-reactive protein in blood(mg/L)	C-reactive protein (mg/L)	2021-2024
total protein in blood(umol/L)	total protein(umol/L)	2021-2024
aspartate aminotransferase in blood(U/L)	aspartate aminotransferase (U/L)	2021-2024
glutamic-pyruvic transaminase in blood(U/L)	glutamic-pyruvic transaminase (U/L)	2021-2024
D-dimer in blood(ug/L)	D-dimer (ug/L)	2021-2024
fibrinogen in blood(g/L)	fibrinogen(g/L)	2021-2024
Active part thrombin time in blood(APTT)	Active part thrombin time (APTT)	2021-2024
prothrombin time in blood(PT)	prothrombin time (PT)	2021-2024
thrombin time in blood (TT)	thrombin time (TT).	2021-2024
leucocytes in blood(×109/L)	leucocytes(×109/L)	2021-2024
neutrophils in blood(×109/L)	neutrophils in blood(×109/L)	2021-2024
lymphocytes in blood(×109/L)	lymphocytes in blood(×109/L)	2021-2024
monocytes in blood(×109/L)	monocytes in the blood(×109/L)	2021-2024
eosinophils in the blood(×109/L)	eosinophils in the blood(×109/L)	2021-2024
platelets in the blood(×109/L)	platelets in the blood(×109/L)	2021-2024
Carcinoembryonic Antigen (ug/L)	Serum tumor marker	2021-2024
Cytokeratin 19 fragment (ug/L)	Serum tumor marker	2021-2024
Squamous Cell Carcinoma Antigen(ug/L)	Serum tumor marker	2021-2024
Nervous specific enolase (U/mL)	Serum tumor marker	2021-2024
Tissue Polypeptide Specific Antigen(ug/L)	Serum tumor marker	2021-2024
Cancer antigen 125 (U/mL)	Serum tumor markers including Carcinoembryonic Antigen (ug/L), Cytokeratin 19 fragment , Squamous Cell Carcinoma Antigen(ug/L), Nervous specific enolase (U/mL), Tissue Polypeptide Specific Antigen(ug/L), Cancer antigen 125 (U/mL), Cancer antigen 15-3 (U/mL), Bombesin (U/mL), The stomach secrete ty (U/mL), β2-microglobulin (U/mL).	2021-2024
Cancer antigen 15-3 (U/mL)	Serum tumor marker	2021-2024
Bombesin (U/mL)	Serum tumor marker	2021-2024
β2-microglobulin (U/mL)	Serum tumor marker	2021-2024
the outcome of Etiological detection	Etiological detection including Mycoplasma, Chlamydia, Viruses, Bacteria (especially Mycobacterium tuberculosis), and Fungi. (Positive/Negative)	2021-2024

Collaborators and Investigators

• Sponsor: Wuhan Union Hospital, China
• Investigators: Study Director: Yang Jin, Professor, union hospital, Tongji Medical college, Huazhonguniversity of science and technology

Study record dates

Study Major Dates

• Study Start (Actual): October 1, 2021
• Primary Completion (Anticipated): December 1, 2023
• Study Completion (Anticipated): December 1, 2024

Study Registration Dates

• First Submitted: June 27, 2021
• First Submitted That Met QC Criteria: September 15, 2021
• First Posted (Actual): September 16, 2021

Study Record Updates

• Last Update Posted (Actual): November 16, 2021
• Last Update Submitted That Met QC Criteria: November 15, 2021
• Last Verified: November 1, 2021

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Respiratory Tract Infections; Respiratory Tract Diseases; Lung Diseases; Disease Attributes; Bacterial Infections; Bacterial Infections and Mycoses; Gram-Positive Bacterial Infections; Actinomycetales Infections; Mycobacterium Infections; Infections; Communicable Diseases; Tuberculosis; Tuberculosis, Pulmonary
• Other Study ID Numbers: [2021]IEC(491)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No