Multimodal Imaging-assisted Diagnosis Model for Cervical Spine Tumors

NCT04959656 | Completed DMC

• 1 other identifier: IRB00006761-M2020255
• Study Type: observational
• Target: 600
• Locations: 1 country
• Sites: 1

Brief Summary

Cervical spine tumor is a small sample of tumor disease with low incidence, great harm, and complex anatomical structure. It is very difficult to identify and classify benign and malignant cervical spine tumors clinically. The deep learning model we constructed in the early stage has a higher accuracy rate for the image diagnosis of cervical spondylosis with a large number of cases, and a better clinical application effect, but the accuracy rate for cervical spine tumors with a small number of cases is lower. The reason may be the amount of data. With limited tasks, the traditional deep learning model is difficult to play an effective role. Based on this, we propose to build a small sample-oriented deep learning model to assist clinicians in the diagnosis of cervical spine tumors with multimodal images, and to evaluate the benign and malignant tumors.

Conditions

Spine Tumor

Outcome Measures

Primary Outcomes (1)

• tumor detection

  On the basis of the cervical spine structure, it is the modeling of the tumor. The model based on weakly supervised learning recognizes the morphological features such as the size of the tumor lesion, and uses the fast-adapted meta-learning method to achieve a fast model under a small amount of training. Optimize, and finally evaluate the benignity, borderline and malignant probability of the tumor and use it as an output.

  2022-2023

Secondary Outcomes (1)

• cervical spine detection

  2022-2023

Study Arms (3)

X-ray

This study completed the manual labeling of preoperative multi-modal images of cervical spine structures and tumor lesions. On the normal cervical spine, six target areas were labeled: cervical spinal cord (MRI), cervical spine alignment (MRI), cervical intervertebral discs ( MRI), cervical spinal canal area (MRI), cervical cobb angle (X-ray) and cervical posterior longitudinal ligament ossification (CT). For cervical tumor lesions, complete MR and CT as well as orthopedic, axial and coronal positions. The label on the lateral X-ray image.

CT

This study completed the manual labeling of preoperative multi-modal images of cervical spine structures and tumor lesions. On the normal cervical spine, six target areas were labeled: cervical spinal cord (MRI), cervical spine alignment (MRI), cervical intervertebral discs ( MRI), cervical spinal canal area (MRI), cervical cobb angle (X-ray) and cervical posterior longitudinal ligament ossification (CT). For cervical tumor lesions, complete MR and CT as well as orthopedic, axial and coronal positions. The label on the lateral X-ray image.

MRI

This study completed the manual labeling of preoperative multi-modal images of cervical spine structures and tumor lesions. On the normal cervical spine, six target areas were labeled: cervical spinal cord (MRI), cervical spine alignment (MRI), cervical intervertebral discs ( MRI), cervical spinal canal area (MRI), cervical cobb angle (X-ray) and cervical posterior longitudinal ligament ossification (CT). For cervical tumor lesions, complete MR and CT as well as orthopedic, axial and coronal positions. The label on the lateral X-ray image.

Eligibility Criteria

• Age: 18 Years - 50 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

Inclusion criteria: clinically suspected cervical spine tumors, multi-modality (X-ray, CT, MR) imaging, followed by needle biopsy or surgery to confirm the tumor, and pathology report. Exclusion criteria: surgery or radiotherapy before imaging, cervical spine Those who have fractures, deformities, infections, etc. who cannot cooperate with imaging examinations, and those who have not signed an informed consent.

You may qualify if:

• years old, about 300 males and females; in the orthopedics outpatient and emergency department of our hospital, the imaging scans (X-ray, CT, MR) showed no obvious abnormalities.

You may not qualify if:

• have had surgery before acquiring the images, Those who have cervical spine fractures, deformities, infections, etc. who cannot cooperate with imaging examinations, and those who have not signed the informed consent. The normal control group" includes about 600 patients with normal or slightly degenerated cervical spine, as a standard for training computers to recognize cervical spine structures Images and control images for detecting tumor lesions.

Sponsors & Collaborators

• Peking University Third Hospital: lead

Study Sites (1)

Peking University Third Hospital

Beijing, China

Location

Study Officials

• hanqiang ouyang

  Peking University Third Hospital

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 4, 2021
• First Posted: July 13, 2021
• Study Start: January 1, 2020
• Primary Completion: June 1, 2020
• Study Completion: June 1, 2021
• Last Updated: July 13, 2021

Record last verified: 2021-07

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)