Artificial IntelligenceNCT05046366
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.
An observational study of Artificial Intelligence and Deep Learning, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 1 site in China.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 90 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Deep Learning, Pathology, Molecular
- How many people
- 1,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Oct 2021to Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 90 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Common malignant tumors and major infectious diseases in lung, including lung cancer, pulmonary tuberculosis, and COVID-19.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430000
Yang Jin, MD
About this study
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.
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.
Read the detailed description
The main aims are as follows:
- 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.
- 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.
- 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.).
- Conditions
- Artificial Intelligence, Deep Learning, Pathology, Molecular, Medical Informatics, Database, Lung Cancer
- Medical subject headings
- Lung Neoplasms, Tuberculosis, Pulmonary, COVID-19
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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).
Measured: 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).
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 2021-2024
Overall survival
The time interval between the date of diagnosis and death (Months) of patients with lung cancer/pulmonary nodules.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: 2021-2024
Secondary outcomes (36)
sex (male/female)
sex of patients(male/female).
Measured: 2021-2024
age (years)
age of patients (years).
Measured: 2021-2024
weight (kilograms)
weight of patients (kilograms)
Measured: 2021-2024
height (meters)
height of patients (meters).
Measured: 2021-2024
heart rate in each minute
heart rate in each minute of patients.
Measured: 2021-2024
blood pressure (mmHg)
blood pressure (mmHg) of patients.
Measured: 2021-2024
Forced vital capacity (FVC)
Forced vital capacity (FVC) of patients
Measured: 2021-2024
forced expiratory volume in one second (FEV1)
forced expiratory volume in one second (FEV1) for lung volume
Measured: 2021-2024
peak expiratory flow (PEF)
peak expiratory flow (PEF) for velocity
Measured: 2021-2024
carbon monoxide diffusion capacity (DLCO)
carbon monoxide diffusion capacity (DLCO) for pulmonary diffusion function.
Measured: 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.
Measured: 2021-2024
C-reactive protein in blood(mg/L)
C-reactive protein (mg/L)
Measured: 2021-2024
total protein in blood(umol/L)
total protein(umol/L)
Measured: 2021-2024
aspartate aminotransferase in blood(U/L)
aspartate aminotransferase (U/L)
Measured: 2021-2024
glutamic-pyruvic transaminase in blood(U/L)
glutamic-pyruvic transaminase (U/L)
Measured: 2021-2024
D-dimer in blood(ug/L)
D-dimer (ug/L)
Measured: 2021-2024
fibrinogen in blood(g/L)
fibrinogen(g/L)
Measured: 2021-2024
Active part thrombin time in blood(APTT)
Active part thrombin time (APTT)
Measured: 2021-2024
prothrombin time in blood(PT)
prothrombin time (PT)
Measured: 2021-2024
thrombin time in blood (TT)
thrombin time (TT).
Measured: 2021-2024
leucocytes in blood(×109/L)
leucocytes(×109/L)
Measured: 2021-2024
neutrophils in blood(×109/L)
neutrophils in blood(×109/L)
Measured: 2021-2024
lymphocytes in blood(×109/L)
lymphocytes in blood(×109/L)
Measured: 2021-2024
monocytes in blood(×109/L)
monocytes in the blood(×109/L)
Measured: 2021-2024
eosinophils in the blood(×109/L)
eosinophils in the blood(×109/L)
Measured: 2021-2024
platelets in the blood(×109/L)
platelets in the blood(×109/L)
Measured: 2021-2024
Carcinoembryonic Antigen (ug/L)
Serum tumor marker
Measured: 2021-2024
Cytokeratin 19 fragment (ug/L)
Serum tumor marker
Measured: 2021-2024
Squamous Cell Carcinoma Antigen(ug/L)
Serum tumor marker
Measured: 2021-2024
Nervous specific enolase (U/mL)
Serum tumor marker
Measured: 2021-2024
Tissue Polypeptide Specific Antigen(ug/L)
Serum tumor marker
Measured: 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).
Measured: 2021-2024
Cancer antigen 15-3 (U/mL)
Serum tumor marker
Measured: 2021-2024
Bombesin (U/mL)
Serum tumor marker
Measured: 2021-2024
β2-microglobulin (U/mL)
Serum tumor marker
Measured: 2021-2024
the outcome of Etiological detection
Etiological detection including Mycoplasma, Chlamydia, Viruses, Bacteria (especially Mycobacterium tuberculosis), and Fungi. (Positive/Negative)
Measured: 2021-2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Sep 2021
- Study start1 Oct 2021
- Primary completionDec 2023 (estimated)
- Study completionDec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yang Jin, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Investigators
- Yang Jin, Professor, Study director, union hospital, Tongji Medical college, Huazhonguniversity of science and technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05046366
- Sponsor's protocol ID
- [2021]IEC(491)
- Status last verified
- Nov 2021
- First submitted
- 27 Jun 2021
- Last updated
- 16 Nov 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 16 Nov 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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.
- ClinicalTrials.gov ID
- NCT05046366
- Acronym / sponsor ID
- [2021]IEC(491)
- Conditions
- Artificial Intelligence, Deep Learning, Pathology, Molecular, Medical Informatics, Database, Lung Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyUniversity, hospital or other
- Study start
- 1 Oct 2021 (actual)
- Primary completion
- Dec 2023 (estimated)
- Study completion
- Dec 2024 (estimated)
- First posted
- 16 Sep 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- 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.
- 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.
- 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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Lung cancer group | – | –Participants with lung cancer/pulmonary nodules |
| Pulmonary tuberculosis group | – | –Participants with pulmonary tuberculosis |
| COIVD-19 group | – | –Participants with COIVD-19 |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Common malignant tumors and major infectious diseases in lung, including lung cancer, pulmonary tuberculosis, and COVID-19.
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule).
Time frame: 2021-2024
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.
The outcome of clinical diagnosis of suspected patients with pulmonary tuberculosis (Positive/Negative).
Time frame: 2021-2024
The outcome of clinical diagnosis of patients with pulmonary tuberculosis (Positive/Negative).
The outcome of clinical diagnosis of suspected patients with COVID-19 (Positive/Negative).
Time frame: 2021-2024
The outcome of clinical diagnosis of patients with COVID-19 (Positive/Negative).
Treatment response of anti-cancer therapy at first evaluation in patients with lung cancer/pulmonary nodules (CR, PR, PD, SD).
Time frame: 2021-2024
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.
Treatment response of anti-inflammation and antiviral therapy at first evaluation in patients with COVID-19 (effective/ineffective treatment).
Time frame: 2021-2024
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.
Treatment response of antituberculous bacilli and anti-inflammation therapy at first evaluation in patients with pulmonary tuberculosis.
Time frame: 2021-2024
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.
Progression free survival
Time frame: 2021-2024
The time interval between the date of treatment initiation and disease progression (Months) of patients with lung cancer/pulmonary nodules.
Overall survival
Time frame: 2021-2024
The time interval between the date of diagnosis and death (Months) of patients with lung cancer/pulmonary nodules.
Whole genome sequencing of blood samples
Time frame: 2021-2024
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.
Whole-genome sequencing of tissue samples
Time frame: 2021-2024
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.
Whole genome sequencing of exhaled air condensate samples
Time frame: 2021-2024
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.
Whole genome sequencing of urine samples
Time frame: 2021-2024
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.
Transcriptome sequencing of blood samples
Time frame: 2021-2024
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.
Transcriptome sequencing of tissue samples
Time frame: 2021-2024
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.
Transcriptome sequencing of exhaled air condensate samples
Time frame: 2021-2024
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.
Transcriptome sequencing of urine samples
Time frame: 2021-2024
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.
Metabolomics of blood samples
Time frame: 2021-2024
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.
Metabolomics of tissue samples
Time frame: 2021-2024
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.
Metabolomics of exhaled air condensate samples
Time frame: 2021-2024
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.
Metabolomics of urine samples
Time frame: 2021-2024
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.
Proteomics of blood samples
Time frame: 2021-2024
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.
Proteomics of tissue samples
Time frame: 2021-2024
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.
Proteomics of exhaled air condensate samples
Time frame: 2021-2024
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.
Proteomics of urine samples
Time frame: 2021-2024
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.
36 secondary outcomes
sex (male/female)
Time frame: 2021-2024
age (years)
Time frame: 2021-2024
weight (kilograms)
Time frame: 2021-2024
height (meters)
Time frame: 2021-2024
heart rate in each minute
Time frame: 2021-2024
blood pressure (mmHg)
Time frame: 2021-2024
Forced vital capacity (FVC)
Time frame: 2021-2024
forced expiratory volume in one second (FEV1)
Time frame: 2021-2024
peak expiratory flow (PEF)
Time frame: 2021-2024
carbon monoxide diffusion capacity (DLCO)
Time frame: 2021-2024
St. George's Respiratory Questionnaire(SGRQ)
Time frame: 2021-2024
C-reactive protein in blood(mg/L)
Time frame: 2021-2024
total protein in blood(umol/L)
Time frame: 2021-2024
aspartate aminotransferase in blood(U/L)
Time frame: 2021-2024
glutamic-pyruvic transaminase in blood(U/L)
Time frame: 2021-2024
D-dimer in blood(ug/L)
Time frame: 2021-2024
fibrinogen in blood(g/L)
Time frame: 2021-2024
Active part thrombin time in blood(APTT)
Time frame: 2021-2024
prothrombin time in blood(PT)
Time frame: 2021-2024
thrombin time in blood (TT)
Time frame: 2021-2024
leucocytes in blood(×109/L)
Time frame: 2021-2024
neutrophils in blood(×109/L)
Time frame: 2021-2024
lymphocytes in blood(×109/L)
Time frame: 2021-2024
monocytes in blood(×109/L)
Time frame: 2021-2024
eosinophils in the blood(×109/L)
Time frame: 2021-2024
platelets in the blood(×109/L)
Time frame: 2021-2024
Carcinoembryonic Antigen (ug/L)
Time frame: 2021-2024
Cytokeratin 19 fragment (ug/L)
Time frame: 2021-2024
Squamous Cell Carcinoma Antigen(ug/L)
Time frame: 2021-2024
Nervous specific enolase (U/mL)
Time frame: 2021-2024
Tissue Polypeptide Specific Antigen(ug/L)
Time frame: 2021-2024
Cancer antigen 125 (U/mL)
Time frame: 2021-2024
Cancer antigen 15-3 (U/mL)
Time frame: 2021-2024
Bombesin (U/mL)
Time frame: 2021-2024
β2-microglobulin (U/mL)
Time frame: 2021-2024
the outcome of Etiological detection
Time frame: 2021-2024
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 Nov 2021.
Related trials for Artificial Intelligence
RecruitingObservational study
The French Gut-colo
Patients Aged 50-75 and Requiring Colonoscopy
RecruitingObservational study
A Prospective Study of Anxiety, Depression, and Bipolar Disorder in People
Anxiety Disorders, Depressive Disorder
Active, not recruitingObservational study
Direct-to-patient Teledermatology and Computer-assisted Diagnosis
New Patient Use of Teledermatology Mobile App
RecruitingObservational study
Opportunistic Breast Cancer Screening Using Non-Contrast Chest CT
Breast Cancer
Not yet recruitingObservational study
Discovering Determinants of Food Intake by Application of Artificial Intelligence to Complex, High-Dimensional Data
Healthy Volunteer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Yang Jin, Professor study director
union hospital, Tongji Medical college, Huazhonguniversity of science and technology
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence and Deep Learning, run by Union Hospital, Tongji Medical College, Huazhong University of Science and Technology at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 16 Nov 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact wei geng, Phd (18696152606, wguh116@hust.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule). (time frame: 2021-2024). The study lists 24 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 Nov 2021.