Clinical trial NCT05046366: 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.
Official title: 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.
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Artificial Intelligence
Deep Learning
Pathology, Molecular
Medical Informatics
Database
Lung Cancer
Pulmonary Tuberculosis
Covid19
Lead sponsor: Wuhan Union Hospital, China
Sponsor class: OTHER
First posted: 2021-09-16
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Common malignant tumors and major infectious diseases in lung, including lung cancer, pulmonary tuberculosis, and COVID-19.

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.

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.).

Eligibility criteria: 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.

Primary outcomes:
measure: The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule).
timeFrame: 2021-2024
measure: The outcome of clinical diagnosis of suspected patients with pulmonary tuberculosis (Positive/Negative).
timeFrame: 2021-2024
measure: The outcome of clinical diagnosis of suspected patients with COVID-19 (Positive/Negative).
timeFrame: 2021-2024
measure: Treatment response of anti-cancer therapy at first evaluation in patients with lung cancer/pulmonary nodules (CR, PR, PD, SD).
timeFrame: 2021-2024
measure: Treatment response of anti-inflammation and antiviral therapy at first evaluation in patients with COVID-19 (effective/ineffective treatment).
timeFrame: 2021-2024
measure: Treatment response of antituberculous bacilli and anti-inflammation therapy at first evaluation in patients with pulmonary tuberculosis.
timeFrame: 2021-2024
measure: Progression free survival
timeFrame: 2021-2024
measure: Overall survival
timeFrame: 2021-2024
measure: Whole genome sequencing of blood samples
timeFrame: 2021-2024
measure: Whole-genome sequencing of tissue samples
timeFrame: 2021-2024
measure: Whole genome sequencing of exhaled air condensate samples
timeFrame: 2021-2024
measure: Whole genome sequencing of urine samples
timeFrame: 2021-2024
measure: Transcriptome sequencing of blood samples
timeFrame: 2021-2024
measure: Transcriptome sequencing of tissue samples
timeFrame: 2021-2024
measure: Transcriptome sequencing of exhaled air condensate samples
timeFrame: 2021-2024
measure: Transcriptome sequencing of urine samples
timeFrame: 2021-2024
measure: Metabolomics of blood samples
timeFrame: 2021-2024
measure: Metabolomics of tissue samples
timeFrame: 2021-2024
measure: Metabolomics of exhaled air condensate samples
timeFrame: 2021-2024
measure: Metabolomics of urine samples
timeFrame: 2021-2024
measure: Proteomics of blood samples
timeFrame: 2021-2024
measure: Proteomics of tissue samples
timeFrame: 2021-2024
measure: Proteomics of exhaled air condensate samples
timeFrame: 2021-2024
measure: Proteomics of urine samples
timeFrame: 2021-2024
Secondary outcomes:
measure: sex (male/female)
timeFrame: 2021-2024
measure: age (years)
timeFrame: 2021-2024
measure: weight (kilograms)
timeFrame: 2021-2024
measure: height (meters)
timeFrame: 2021-2024
measure: heart rate in each minute
timeFrame: 2021-2024
measure: blood pressure (mmHg)
timeFrame: 2021-2024
measure: Forced vital capacity (FVC)
timeFrame: 2021-2024
measure: forced expiratory volume in one second (FEV1)
timeFrame: 2021-2024
measure: peak expiratory flow (PEF)
timeFrame: 2021-2024
measure: carbon monoxide diffusion capacity (DLCO)
timeFrame: 2021-2024
measure: St. George's Respiratory Questionnaire(SGRQ)
timeFrame: 2021-2024
measure: C-reactive protein in blood(mg/L)
timeFrame: 2021-2024
measure: total protein in blood(umol/L)
timeFrame: 2021-2024
measure: aspartate aminotransferase in blood(U/L)
timeFrame: 2021-2024
measure: glutamic-pyruvic transaminase in blood(U/L)
timeFrame: 2021-2024
measure: D-dimer in blood(ug/L)
timeFrame: 2021-2024
measure: fibrinogen in blood(g/L)
timeFrame: 2021-2024
measure: Active part thrombin time in blood(APTT)
timeFrame: 2021-2024
measure: prothrombin time in blood(PT)
timeFrame: 2021-2024
measure: thrombin time in blood (TT)
timeFrame: 2021-2024
measure: leucocytes in blood(×109/L)
timeFrame: 2021-2024
measure: neutrophils in blood(×109/L)
timeFrame: 2021-2024
measure: lymphocytes in blood(×109/L)
timeFrame: 2021-2024
measure: monocytes in blood(×109/L)
timeFrame: 2021-2024
measure: eosinophils in the blood(×109/L)
timeFrame: 2021-2024
measure: platelets in the blood(×109/L)
timeFrame: 2021-2024
measure: Carcinoembryonic Antigen (ug/L)
timeFrame: 2021-2024
measure: Cytokeratin 19 fragment (ug/L)
timeFrame: 2021-2024
measure: Squamous Cell Carcinoma Antigen(ug/L)
timeFrame: 2021-2024
measure: Nervous specific enolase (U/mL)
timeFrame: 2021-2024
measure: Tissue Polypeptide Specific Antigen(ug/L)
timeFrame: 2021-2024
measure: Cancer antigen 125 (U/mL)
timeFrame: 2021-2024
measure: Cancer antigen 15-3 (U/mL)
timeFrame: 2021-2024
measure: Bombesin (U/mL)
timeFrame: 2021-2024
measure: β2-microglobulin (U/mL)
timeFrame: 2021-2024
measure: the outcome of Etiological detection
timeFrame: 2021-2024
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
state: Hubei
Study officials:
affiliation: union hospital, Tongji Medical college, Huazhonguniversity of science and technology
name: Yang Jin, Professor
role: STUDY_DIRECTOR
References: