Artificial Intelligence, Cancer Risk, Cancer Screening, Cancer, Treatment-Related, Colon Cancer, Deep Learning, Lung Cancer, Lung; Node, Stomach Cancer
Conditions
Brief summary
To improve the accuracy of risk prediction, screening and treatment outcome of cancer, we aim to establish a medical database that includes standardized and structured clinical diagnosis and treatment information, image features, pathological features, and multi-omics information and to develop a multi-modal data fusion-based technology system using artificial intelligence technology based on database.
Detailed description
The main aims are as follows: 1. To establish a data platform for multi-modal information of common tumors (lung cancer/pulmonary nodules, stomach and colorectal cancers) : electronic medical records (including routine clinical detection, treatment, outcome), pathological image data, medical imaging (CT, MRI, ultrasound, nuclear medicine, etc.), multiple omics data (genome, transcriptome, and metabolome, proteomics) omics data, etiology and carcinogenic exposure information. 2. 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.).
Interventions
OTHERNo Intervention
No Intervention
Sponsors
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
Yes
Inclusion criteria
1. Participants with the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision 2. Participants that have signed informed consent. 3. Participants with detailed electronic medical records, image records, pathological records, multi-omics information, and other important clinical diagnostic information. 4. Healthy participants with no clinical diagnosis of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision.
Exclusion criteria
1. Participants with primary clinical and pathological data missing. 2. Participants lost to follow-up. 3. Participants with too poor medical image quality to perform segment and mark ROI accurately
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule) | 2022-2026 | 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 colorectal cancer or lesion (Benign/Malignant). | 2022-2026 | ① Benign ② Malignant |
| Treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer (CR, PR, PD, SD). | 2022-2026 | The treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer 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. |
Countries
China
Contacts
Primary ContactJuanjuan Xu
Backup ContactYang Jin
Outcome results
None listed