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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.
The main aims are as follows:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Lung cancer group | Participants with lung cancer/pulmonary nodules | ||
| Pulmonary tuberculosis group | Participants with pulmonary tuberculosis | ||
| COIVD-19 group | Participants with COIVD-19 |
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| 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). |
| Measure | Description | Time Frame |
|---|---|---|
| sex (male/female) | sex of patients(male/female). | 2021-2024 |
| age (years) | age of patients (years). | 2021-2024 |
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Inclusion Criteria:
Exclusion Criteria:
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Common malignant tumors and major infectious diseases in lung, including lung cancer, pulmonary tuberculosis, and COVID-19.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| wei geng, Phd | Contact | 18696152606 | wguh116@hust.edu.cn |
| Name | Affiliation | Role |
|---|---|---|
| Yang Jin, Professor | union hospital, Tongji Medical college, Huazhonguniversity of science and technology | Study Director |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Union Hospital, Tongji Medical College, Huazhong University of Science and Technology | Recruiting | Wuhan | Hubei | 430000 | China |
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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 |
| 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 |
| ID | Term |
|---|---|
| D008175 | Lung Neoplasms |
| D014397 | Tuberculosis, Pulmonary |
| D000086382 | COVID-19 |
| ID | Term |
|---|---|
| D012142 | Respiratory Tract Neoplasms |
| D013899 | Thoracic Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
| D014376 | Tuberculosis |
| D009164 | Mycobacterium Infections |
| D000193 | Actinomycetales Infections |
| D016908 | Gram-Positive Bacterial Infections |
| D001424 | Bacterial Infections |
| D001423 | Bacterial Infections and Mycoses |
| D007239 | Infections |
| D012141 | Respiratory Tract Infections |
| D011024 | Pneumonia, Viral |
| D011014 | Pneumonia |
| D014777 | Virus Diseases |
| D018352 | Coronavirus Infections |
| D003333 | Coronaviridae Infections |
| D030341 | Nidovirales Infections |
| D012327 | RNA Virus Infections |
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