Clinical trial NCT05426135: Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment
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
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Artificial Intelligence
Deep Learning
Lung Cancer
Lung; Node
Stomach Cancer
Colon Cancer
Cancer Risk
Cancer Screening
Cancer, Treatment-Related
Lead sponsor: Wuhan Union Hospital, China
Sponsor class: OTHER
First posted: 2022-06-21
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients the suspected of lung cancer/node, or stomach cancer/lesion, or colorectal cancer/leision

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.).

Eligibility criteria: 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

Primary outcomes:
measure: The outcome of clinical diagnosis of suspected patients with lung cancer/pulmonary nodular (Benign/Malignant nodule)
timeFrame: 2022-2026
measure: The outcome of clinical diagnosis of suspected patients with stomach cancer or lesion (Benign/Malignant).
timeFrame: 2022-2026
measure: The outcome of clinical diagnosis of suspected patients with colorectal cancer or lesion (Benign/Malignant).
timeFrame: 2022-2026
measure: Treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer (CR, PR, PD, SD).
timeFrame: 2022-2026
Secondary outcomes:
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
state: Hubei
Study officials:
References: