Multimodal Imaging-assisted Diagnosis Model for Cervical Spine Tumors

Peking University

Status: Completed

Conditions: Spine Tumor

Study type: Observational

Funder types: Other

Identifiers

NCT04959656

IRB00006761-M2020255

Details and patient eligibility

About

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.

Full description

Cervical spine tumor is a small-sample 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 is suitable for the large number of cases. The imaging diagnosis of cervical spondylosis has a high accuracy rate and a good clinical application effect, but the accuracy rate is low for cervical spine tumors with a small number of cases. The reason may be that for tasks with limited amount of data, the traditional deep learning model is difficult to play an effective role. Based on this, we propose to construct a small sample-oriented deep learning model to assist clinicians in the diagnosis of cervical spine tumors in multi-modal imaging, and to evaluate the benign and malignant tumors. This research will not only improve the efficiency and efficiency of cervical spine tumor imaging diagnosis. Accuracy, to guide clinical personalized treatment, will also provide a basis for the clinical application of deep learning in the field of small samples, which has important clinical significance.

Enrollment: 600 patients

Sex: All

Ages: 18 to 50 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• 18-50 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.

Exclusion criteria

• 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.

Trial design

600 participants in 3 patient groups

X-ray

Description:

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

Description:

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

Description:

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.