Multimodal Imaging-assisted Diagnosis Model for Cervical Spine Tumors
2021年7月4日 更新者：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.
調査の概要
状態
完了
条件
詳細な説明
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.
研究の種類
観察的
入学 (実際)
600
連絡先と場所
このセクションには、調査を実施する担当者の連絡先の詳細と、この調査が実施されている場所に関する情報が記載されています。
研究場所
-
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Beijing、中国
- Peking University Third Hospital
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参加基準
研究者は、適格基準と呼ばれる特定の説明に適合する人を探します。これらの基準のいくつかの例は、人の一般的な健康状態または以前の治療です。
適格基準
就学可能な年齢
18年～50年 (大人)
健康ボランティアの受け入れ
いいえ
受講資格のある性別
全て
サンプリング方法
非確率サンプル
調査対象母集団
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.
説明
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.
研究計画
このセクションでは、研究がどのように設計され、研究が何を測定しているかなど、研究計画の詳細を提供します。
研究はどのように設計されていますか？
デザインの詳細
コホートと介入
グループ/コホート |
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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.
|
|
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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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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tumor detection
時間枠：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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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cervical spine detection
時間枠：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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協力者と研究者
ここでは、この調査に関係する人々や組織を見つけることができます。
捜査官
- スタディチェア：hanqiang ouyang、Peking University Third Hospital
研究記録日
これらの日付は、ClinicalTrials.gov への研究記録と要約結果の提出の進捗状況を追跡します。研究記録と報告された結果は、国立医学図書館 (NLM) によって審査され、公開 Web サイトに掲載される前に、特定の品質管理基準を満たしていることが確認されます。
主要日程の研究
研究開始 (実際)
2020年1月1日
一次修了 (実際)
2020年6月1日
研究の完了 (実際)
2021年6月1日
試験登録日
最初に提出
2021年7月4日
QC基準を満たした最初の提出物
2021年7月4日
最初の投稿 (実際)
2021年7月13日
学習記録の更新
投稿された最後の更新 (実際)
2021年7月13日
QC基準を満たした最後の更新が送信されました
2021年7月4日
最終確認日
2021年7月1日
詳しくは
本研究に関する用語
その他の研究ID番号
- IRB00006761-M2020255
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
いいえ
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
いいえ
米国FDA規制機器製品の研究
いいえ
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