Based on a Small Sample Deep Learning Multi-modal Image-assisted Diagnosis Model of Cervical Spine Tumors Clinical Application Research
Sponsor study identifier
observational
600
1 country
1
Study Start
January 1, 2020
Actual (reported)Primary Completion
June 1, 2020
Actual (reported)Study Completion
June 1, 2021
Actual (reported)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.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
June 1, 2021
Actual (reported)Click on a node to explore related trials.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
January 1, 2020
Actual (reported)June 1, 2020
Actual (reported)June 1, 2021
Actual (reported)July 4, 2021
Actual (reported)July 13, 2021
Actual (reported)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
cervical spine detection
2022-2023
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.
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.
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.
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.
Peking University Third Hospital
Beijing, China
hanqiang ouyang
Peking University Third Hospital
600 participants (actual enrollment reported by the registry).
First Submitted
July 4, 2021
First Posted
July 13, 2021
Study Start
January 1, 2020
Actual (reported)Spine Oncology Registry
Contact the study team to confirm eligibility.
Primary Completion
June 1, 2020
Actual (reported)Study Completion
June 1, 2021
Actual (reported)Last Updated
July 13, 2021
Record last verified: 2021-07
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