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Multimodal Imaging-assisted Diagnosis Model for Cervical Spine Tumors

4. juli 2021 opdateret af: 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.

Studieoversigt

Status

Afsluttet

Betingelser

Detaljeret beskrivelse

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.

Undersøgelsestype

Observationel

Tilmelding (Faktiske)

600

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

  • Kina
      • Beijing, Kina
        • Peking University Third Hospital

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

18 år til 50 år (Voksen)

Tager imod sunde frivillige

Ingen

Køn, der er berettiget til at studere

Alle

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

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.

Beskrivelse

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.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Kohorter og interventioner

Gruppe / kohorte
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.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
tumor detection
Tidsramme: 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.
2022-2023

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
cervical spine detection
Tidsramme: 2022-2023
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.
2022-2023

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Peking University Third Hospital

Efterforskere

  • Studiestol: hanqiang ouyang, Peking University Third Hospital

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Faktiske)

1. januar 2020

Primær færdiggørelse (Faktiske)

1. juni 2020

Studieafslutning (Faktiske)

1. juni 2021

Datoer for studieregistrering

Først indsendt

4. juli 2021

Først indsendt, der opfyldte QC-kriterier

4. juli 2021

Først opslået (Faktiske)

13. juli 2021

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

13. juli 2021

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

4. juli 2021

Sidst verificeret

1. juli 2021

Mere information

Begreber relateret til denne undersøgelse

Andre undersøgelses-id-numre

  • IRB00006761-M2020255

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

INGEN

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Ingen

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Kliniske forsøg med Spine Tumor

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Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

CROs by country

CROs in Mozambique

Betingelser

Sjældne sygdomme

Narkotikainterventioner

Kosttilskud

Sponsor / samarbejdspartnere

Placeringer

3
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