Multimodal Imaging-assisted Diagnosis Model for Cervical Spine Tumors
4 juli 2021 bijgewerkt door: Peking University Third Hospital
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.
Studie Overzicht
Toestand
Voltooid
Conditie
Gedetailleerde beschrijving
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.
Studietype
Observationeel
Inschrijving (Werkelijk)
600
Contacten en locaties
In dit gedeelte vindt u de contactgegevens van degenen die het onderzoek uitvoeren en informatie over waar dit onderzoek wordt uitgevoerd.
Studie Locaties
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Beijing, China
- Peking University Third Hospital
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Deelname Criteria
Onderzoekers zoeken naar mensen die aan een bepaalde beschrijving voldoen, de zogenaamde geschiktheidscriteria. Enkele voorbeelden van deze criteria zijn iemands algemene gezondheidstoestand of eerdere behandelingen.
Geschiktheidscriteria
Leeftijden die in aanmerking komen voor studie
18 jaar tot 50 jaar (Volwassen)
Accepteert gezonde vrijwilligers
Nee
Geslachten die in aanmerking komen voor studie
Allemaal
Bemonsteringsmethode
Niet-waarschijnlijkheidssteekproef
Studie Bevolking
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.
Beschrijving
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.
Studie plan
Dit gedeelte bevat details van het studieplan, inclusief hoe de studie is opgezet en wat de studie meet.
Hoe is de studie opgezet?
Ontwerpdetails
Cohorten en interventies
Groep / Cohort |
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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.
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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.
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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.
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Wat meet het onderzoek?
Primaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
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tumor detection
Tijdsspanne: 2022-2023
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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.
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2022-2023
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Secundaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
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cervical spine detection
Tijdsspanne: 2022-2023
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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.
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2022-2023
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Medewerkers en onderzoekers
Hier vindt u mensen en organisaties die betrokken zijn bij dit onderzoek.
Sponsor
Onderzoekers
- Studie stoel: hanqiang ouyang, Peking University Third Hospital
Studie record data
Deze datums volgen de voortgang van het onderzoeksdossier en de samenvatting van de ingediende resultaten bij ClinicalTrials.gov. Studieverslagen en gerapporteerde resultaten worden beoordeeld door de National Library of Medicine (NLM) om er zeker van te zijn dat ze voldoen aan specifieke kwaliteitscontrolenormen voordat ze op de openbare website worden geplaatst.
Bestudeer belangrijke data
Studie start (Werkelijk)
1 januari 2020
Primaire voltooiing (Werkelijk)
1 juni 2020
Studie voltooiing (Werkelijk)
1 juni 2021
Studieregistratiedata
Eerst ingediend
4 juli 2021
Eerst ingediend dat voldeed aan de QC-criteria
4 juli 2021
Eerst geplaatst (Werkelijk)
13 juli 2021
Updates van studierecords
Laatste update geplaatst (Werkelijk)
13 juli 2021
Laatste update ingediend die voldeed aan QC-criteria
4 juli 2021
Laatst geverifieerd
1 juli 2021
Meer informatie
Termen gerelateerd aan deze studie
Andere studie-ID-nummers
- IRB00006761-M2020255
Plan Individuele Deelnemersgegevens (IPD)
Bent u van plan om gegevens van individuele deelnemers (IPD) te delen?
NEE
Informatie over medicijnen en apparaten, studiedocumenten
Bestudeert een door de Amerikaanse FDA gereguleerd geneesmiddel
Nee
Bestudeert een door de Amerikaanse FDA gereguleerd apparaatproduct
Nee
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