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.
Trial ID: NCT05046366
Status: UNKNOWN
Study type: OBSERVATIONAL
Start date: 2021-10-01
Completion date: 2024-12
Sponsor: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Conditions: Artificial Intelligence; Deep Learning; Pathology, Molecular; Medical Informatics; Database; Lung Cancer; Pulmonary Tuberculosis; Covid19
Locations: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Countries: China
Enrollment: 1000
Age range: 18 Years - 90 Years
Sex: ALL

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.

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.

Outcomes:
The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule).; The outcome of clinical diagnosis of suspected patients with pulmonary tuberculosis (Positive/Negative).; The outcome of clinical diagnosis of suspected 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).; Treatment response of anti-inflammation and antiviral therapy at first evaluation in patients with COVID-19 (effective/ineffective treatment).; Treatment response of antituberculous bacilli and anti-inflammation therapy at first evaluation in patients with pulmonary tuberculosis.; Progression free survival; Overall survival; Whole genome sequencing of blood samples; Whole-genome sequencing of tissue samples; Whole genome sequencing of exhaled air condensate samples; Whole genome sequencing of urine samples; Transcriptome sequencing of blood samples; Transcriptome sequencing of tissue samples; Transcriptome sequencing of exhaled air condensate samples; Transcriptome sequencing of urine samples; Metabolomics of blood samples; Metabolomics of tissue samples; Metabolomics of exhaled air condensate samples; Metabolomics of urine samples; Proteomics of blood samples; Proteomics of tissue samples; Proteomics of exhaled air condensate samples; Proteomics of urine samples