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NCT Number: NCT05426135

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

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.

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Key information

Conditions

Artificial Intelligence Cancer Risk Cancer Screening Cancer, Treatment-Related Colon Cancer Colonic Diseases Colonic Neoplasms Colorectal Neoplasms Deep Learning Digestive System Diseases Digestive System Neoplasms Gastrointestinal Diseases Gastrointestinal Neoplasms Intestinal Diseases Intestinal Neoplasms Lung Cancer Lung Diseases Lung Neoplasms Lung; Node Neoplasms Neoplasms by Site Neoplasms, Second Primary Respiratory Tract Diseases Respiratory Tract Neoplasms Stomach Cancer Stomach Diseases Stomach Neoplasms Thoracic Neoplasms

Age range

18 year–75 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430000, China

Location status: Recruiting

Location contact

Yang Jin, MD

CONTACT

About this study

The main aims are as follows:

  • 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.
  • 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.).

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. 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 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.

  2. The outcome of clinical diagnosis of suspected patients with stomach cancer or lesion (Benign/Malignant).

    Time frame: 2022-2026

    ① Benign

    ② Malignant

  3. The outcome of clinical diagnosis of suspected patients with colorectal cancer or lesion (Benign/Malignant).

    Time frame: 2022-2026

    ① Benign

    ② Malignant

  4. Treatment response of anti-cancer therapy at first evaluation in patients with lung/stomach/colorectal cancer (CR, PR, PD, SD).

    Time frame: 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.

Study contacts

Contact information is provided by the study sponsor or research team.

Yang Jin

CONTACT

[email protected]

15107177084

Sponsors and collaborators

Lead sponsor

Union Hospital, Tongji Medical College, Huazhong University of Science and Technology

Other

Registry information

Official study 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

Important dates

Study start
2022
Primary completion
2025
Study completion
2026
First posted
Jun 21, 2022
Registry last updated
Jun 21, 2022

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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