Spine TumorNCT04959656
Multimodal Imaging-assisted Diagnosis Model for Cervical Spine Tumors
An observational study of Spine Tumor, run by Peking University Third Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 50 years
- Conditions the study is for
- Spine Tumor
- How many people
- 600 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2020to 1 Jun 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 50 years
- All sexes
- No healthy volunteers
- Adult
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.
Who may be able to join
- 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.
Who cannot join
- 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 locations and contacts
1 site in 1 country.
About this study
Official title: Based on a Small Sample Deep Learning Multi-modal Image-assisted Diagnosis Model of Cervical Spine Tumors Clinical Application Research
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.
Read the 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.
- Conditions
- Spine Tumor
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 2022-2023
Secondary outcomes (1)
cervical spine detection
Taking the postoperative pathology report of cancer patients as the audit standard, testing the sensitivity and accuracy of the model, and integrating it into a complete deep learning model.
Measured: 2022-2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Jul 2021
- Study start1 Jan 2020
- Primary completion1 Jun 2020
- Study completion1 Jun 2021
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Spine TumorStudies of the same condition, open and under way.
- More trials run by Peking University Third HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 600 participants (actual)
Sponsor and investigators
- Lead sponsor
- Peking University Third Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- hanqiang ouyang, Study chair, Peking University Third Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04959656
- Sponsor's protocol ID
- IRB00006761-M2020255
- Status last verified
- Jul 2021
- First submitted
- 4 Jul 2021
- Last updated
- 13 Jul 2021
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Based on a Small Sample Deep Learning Multi-modal Image-assisted Diagnosis Model of Cervical Spine Tumors Clinical Application Research
- ClinicalTrials.gov ID
- NCT04959656
- Acronym / sponsor ID
- IRB00006761-M2020255
- Conditions
- Spine Tumor
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 600 participants (actual)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- 1 Jun 2020 (actual)
- Study completion
- 1 Jun 2021 (actual)
- First posted
- 13 Jul 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 to 50 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- 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.
Key inclusion criteria (1 listed)
- 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.
Key exclusion criteria (1 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
tumor detection
Time frame: 2022-2023
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.
1 secondary outcome
cervical spine detection
Time frame: 2022-2023
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Jul 2021.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
hanqiang ouyang study chair
Peking University Third Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Spine Tumor, run by Peking University Third Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 Jul 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
tumor detection (time frame: 2022-2023). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Jul 2021.