Clinical trial NCT04959656: Multimodal Imaging-assisted Diagnosis Model for Cervical Spine Tumors
Official title: Based on a Small Sample Deep Learning Multi-modal Image-assisted Diagnosis Model of Cervical Spine Tumors Clinical Application Research
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Spine Tumor
Lead sponsor: Peking University Third Hospital
Sponsor class: OTHER
First posted: 2021-07-13
Enrollment: 600
Sex: ALL
Minimum age (years): 18
Maximum age (years): 50
Standard ages: ADULT
Accepts healthy volunteers: no
Observational model: CASE_ONLY
Time perspective: RETROSPECTIVE
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.

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.

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: tumor detection
timeFrame: 2022-2023
Secondary outcomes:
measure: cervical spine detection
timeFrame: 2022-2023
Other outcomes:
Locations:
city: Beijing
country: China
facility: Peking University Third Hospital
Study officials:
affiliation: Peking University Third Hospital
name: hanqiang ouyang
role: STUDY_CHAIR
References: