Artificial Intelligence System for Assessment of Tumor Risk and Diagnosis and Treatment

Lung Cancer Clinical Trial

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05426135
• Other study ID #: Jin_cancer risk
• Status: Recruiting
• Start date: June 1, 2022
• Est. completion date: October 2026

Study information

• Verified date: June 2022
• Source: Wuhan Union Hospital, China
• Contact: Yang Jin
• Phone: 15107177084
• Email: whuhjy[@]126.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.).

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 3000
• Est. completion date: October 2026
• Est. primary completion date: October 2025
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

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

Related Conditions & MeSH terms

• Artificial Intelligence
• Cancer Risk
• Cancer Screening
• Cancer, Treatment-Related
• Colon Cancer
• Deep Learning
• Lung Cancer
• Lung; Node
• Neoplasms, Second Primary
• Stomach Cancer
• Stomach Neoplasms

Locations

Country	Name	City	State
China	Union Hospital, Tongji Medical College, Huazhong University of Science and Technology	Wuhan	Hubei

Sponsors (1)

Lead Sponsor	Collaborator
Wuhan Union Hospital, China

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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).; ? Benign nodule; ? Malignant neoplasm/nodule: squamous cell carcinoma, adenocarcinoma, small cell carcinoma, and large cell carcinoma.	2022-2026
Primary	The outcome of clinical diagnosis of suspected patients with stomach cancer or lesion (Benign/Malignant).	? Benign; ? Malignant	2022-2026
Primary	The outcome of clinical diagnosis of suspected patients with colorectal cancer or lesion (Benign/Malignant).	? Benign; ? Malignant	2022-2026
Primary	Treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer (CR, PR, PD, SD).	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.	2022-2026