Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Artificial Intelligence, Deep Learning, Lung Cancer, Lung; Node. Basic parameters: 18 years — 75 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Development of an Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment Based on Multimodal Data Fusion Using Deep Learning Technology
Overview
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.).
Primary outcome measures
- The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule) [Time frame: 2022-2026]
- The outcome of clinical diagnosis of suspected patients with stomach cancer or lesion (Benign/Malignant). [Time frame: 2022-2026]
- The outcome of clinical diagnosis of suspected patients with colorectal cancer or lesion (Benign/Malignant). [Time frame: 2022-2026]
- Treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer (CR, PR, PD, SD). [Time frame: 2022-2026]
Eligibility criteria
Inclusion criteria
- Participants with the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision
- Participants that have signed informed consent.
- 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/node, or stomach cancer/lesion, or colorectal cancer/leision.
Exclusion criteria
- 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
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology — Wuhan
Identifiers
NCT: NCT05426135 · Jin_cancer risk