Computed Tomography for COVID-19 Diagnosis (STOIC)
Computed Tomography for Coronavirus Disease 19 Diagnosis
調査の概要
状態
詳細な説明
The outbreak of the novel coronavirus SARS-CoV-2, initially epicentred in China and responsible for COVID-19 pneumonia has now spread to France, with 7730 confirmed cases and 175 deaths as on March 17th. Diagnosis relies on the identification of viral RNA by reverse-transcription polymerase chain reaction (RT-PCR), but its positivity can be delayed. A series based on 1014 chinese patients reported higher sensitivity for CT, with a mean interval time between the initial negative to positive RT-PCR results of 5.1 ± 1.5 days (PMID: 32101510). Moreover, obtaining RT-PCR results requires several hours, which is problematic for patients triage.
Chest CT can allow early depiction of COVID-19, especially when performed more than 3 days after symptoms onset. It is important to distinguish between COVID-19 and bacterial causes of pulmonary infection, which requires expertise in thoracic imaging. Thus, it is important to identify reliable CT diagnostic criteria based on visual assessment, and also develop deep-learning based solutions for early positive diagnosis which could be used by less experienced readers, in a context of large epidemic.
Several risk factors for poor outcome are already identified, such as older age, comorbidities, or an elevated d-dimer level at presentation (PMID: 32171076). Extensive CT abnormalities are linked to poor outcome, but some patients secondarily worsen despite non extensive abnormalities at first assessment, highlighting the need for worsening prediction based on initial imaging findings. Lastly, there is currently no drug with a proven efficacy for patients with acute respiratory distress syndrome, who for management relies on mechanical ventilation and supportive care. Some hypothesized that Remdesivir, an antiviral therapy could be effective (PMID: 32147516), with ongoing randomized trials conducted in China and the US. Automated tools allowing quantifying the disease extent on CT would be desirable in order to evaluate the efficacy of new treatments.
Building a large dataset of CT images is needed for identification of accurate CT criteria and development of deep learning-based solutions for diagnosis, quantification and prognostic estimation.
The aim of this project is three fold: (i) create a multi-centric open database repository on CT scans relative to COVID-19, (ii) create a multi-expert annotation protocol with different level of annotations depicting the severity of the disease, (iii) allow the development of non-proprietary computer aided solutions (academia & industry) for automatic quantification of the diseases and prognosis through the use of the latest advances in the field of artificial intelligence.
For patients, the validation of reliable diagnostic criteria will allow early detection of the disease, and better distinction with other potential cause of acute respiratory symptoms, requiring a specific treatment, such as bacterial bronchopneumonia. It will contribute to a standardization of care as well as an equal access to diagnosis and treatment for the ensemble of the population.
Public health benefit will be an access to CT diagnosis of COVID-19 independently from the availability of local expertise in thoracic imaging. The possibility to anticipate the need for ventilation, based on the developed CT severity scores, will also positively impact the management of patients in particular in the context of a massive flow of patients as expected at the epidemic peak. This project will allow evaluating the proportion of patients likely to present respiratory sequelae, based on the severity and extent of lung abnormalities at the acute phase of the disease.
The availability of automated quantification tools will help evaluating treatment efficacy if new therapeutic approaches are developed.
Lastly, the developed tools for early diagnosis, evaluation of severity and prediction of outcomes could prove useful if other viral pandemic occurs in the future. Indeed SARS-Cov2 outbreak has been preceded by SARS and MERS outbreaks due to other coronavirus.
研究の種類
入学 (実際)
連絡先と場所
研究場所
-
-
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Paris、フランス、75014
- Cochin Hospital
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参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
サンプリング方法
調査対象母集団
説明
Inclusion Criteria:
- Age>18 years
- CT examination performed for suspicion or follow-up of COVID-19
- Non opposition for use of data
Exclusion Criteria:
- Unavailability of RT-PCR results for SARS-Cov-2
- Failure of CT image anonymized export
研究計画
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 |
介入・治療 |
---|---|
Patients with suspicions of COVID-19 pneumonia
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Chest computed tomography (CT) examination
Identification of viral RNA by reverse-transcription polymerase chain reaction
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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 |
---|---|---|
Predictive values of CT criteria
時間枠:1 month
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Sensibility specificity positive and negative predictive values of CT criteria with RT-PCR results as standard of reference.
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1 month
|
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
---|---|---|
Accuracy of CT composite severity score
時間枠:1 month
|
Accuracy (ROC curve analysis) of CT visual composite score to predict ventilation requirement and 1-month mortality
|
1 month
|
Accuracy of deep-learning based score
時間枠:1 month
|
Accuracy (ROC curve analysis) of deep-learning based score to predict ventilation requirement and 1-month mortality
|
1 month
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Predictive values of deep-learning based diagnostic algorithms
時間枠:1 month
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Sensibility specificity Positive and Negative predictive values of deep-learning based diagnostic algorithms
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1 month
|
Dice similarity coefficient between manual and automated segmentation of lung disease abnormalities
時間枠:1 month
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1 month
|
協力者と研究者
協力者
捜査官
- 主任研究者:Marie-Pierre REVEL, MD,PhD、Assistance Publique - Hôpitaux de Paris
出版物と役立つリンク
一般刊行物
- Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, Xiang J, Wang Y, Song B, Gu X, Guan L, Wei Y, Li H, Wu X, Xu J, Tu S, Zhang Y, Chen H, Cao B. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020 Mar 28;395(10229):1054-1062. doi: 10.1016/S0140-6736(20)30566-3. Epub 2020 Mar 11. Erratum In: Lancet. 2020 Mar 28;395(10229):1038. Lancet. 2020 Mar 28;395(10229):1038.
- Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L. Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology. 2020 Aug;296(2):E32-E40. doi: 10.1148/radiol.2020200642. Epub 2020 Feb 26.
- Ko WC, Rolain JM, Lee NY, Chen PL, Huang CT, Lee PI, Hsueh PR. Arguments in favour of remdesivir for treating SARS-CoV-2 infections. Int J Antimicrob Agents. 2020 Apr;55(4):105933. doi: 10.1016/j.ijantimicag.2020.105933. Epub 2020 Mar 6. No abstract available.
- Revel MP, Boussouar S, de Margerie-Mellon C, Saab I, Lapotre T, Mompoint D, Chassagnon G, Milon A, Lederlin M, Bennani S, Moliere S, Debray MP, Bompard F, Dangeard S, Hani C, Ohana M, Bommart S, Jalaber C, El Hajjam M, Petit I, Fournier L, Khalil A, Brillet PY, Bellin MF, Redheuil A, Rocher L, Bousson V, Rousset P, Gregory J, Deux JF, Dion E, Valeyre D, Porcher R, Jilet L, Abdoul H. Study of Thoracic CT in COVID-19: The STOIC Project. Radiology. 2021 Oct;301(1):E361-E370. doi: 10.1148/radiol.2021210384. Epub 2021 Jun 29.
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (実際)
研究の完了 (実際)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
追加の関連 MeSH 用語
その他の研究ID番号
- APHP200434
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?
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
米国FDA規制機器製品の研究
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。
COVID-19(新型コロナウイルス感染症)の臨床試験
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Massachusetts General Hospital募集
-
First Affiliated Hospital Xi'an Jiaotong UniversityShangluo Central Hospital; Ankang Central Hospital; Hanzhong Central Hospital; Yulin No.2 Hospital; Nuclear 215 Hospital of Shaanxi Province と他の協力者募集COVID-19(新型コロナウイルス感染症) | COVID-19後症候群 | ポスト急性COVID-19 | 急性 COVID-19中国
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Endourage, LLC募集長い COVID | ロング Covid19 | ポスト急性COVID-19 | 長距離COVID | 長距離COVID-19 | ポスト急性COVID-19症候群アメリカ
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Sheba Medical Centerわからない
Chest computed tomography (CT)の臨床試験
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Emory UniversityNational Cancer Institute (NCI); Blue Earth Diagnostics積極的、募集していない