[論文レビュー] Severity Assessment of Coronavirus Disease 2019 (COVID-19) Using Quantitative Features from Chest CT Images

Zhenyu Tang, Wei Zhao+5 more|arXiv (Cornell University)|Mar 26, 2020

COVID-19 diagnosis using AI参考文献 19被引用数 149

ひとこと要約

本論文は、胸部CT定量特徴からCOVID-19の重症度（非重症 vs 重症）を自動的に分類するランダムフォレストモデルを開発し、176例の3-foldクロスバリデーションで高いAUCと精度を達成した。

ABSTRACT

Background: Chest computed tomography (CT) is recognized as an important tool for COVID-19 severity assessment. As the number of affected patients increase rapidly, manual severity assessment becomes a labor-intensive task, and may lead to delayed treatment. Purpose: Using machine learning method to realize automatic severity assessment (non-severe or severe) of COVID-19 based on chest CT images, and to explore the severity-related features from the resulting assessment model. Materials and Method: Chest CT images of 176 patients (age 45.3$\pm$16.5 years, 96 male and 80 female) with confirmed COVID-19 are used, from which 63 quantitative features, e.g., the infection volume/ratio of the whole lung and the volume of ground-glass opacity (GGO) regions, are calculated. A random forest (RF) model is trained to assess the severity (non-severe or severe) based on quantitative features. Importance of each quantitative feature, which reflects the correlation to the severity of COVID-19, is calculated from the RF model. Results: Using three-fold cross validation, the RF model shows promising results, i.e., 0.933 of true positive rate, 0.745 of true negative rate, 0.875 of accuracy, and 0.91 of area under receiver operating characteristic curve (AUC). The resulting importance of quantitative features shows that the volume and its ratio (with respect to the whole lung volume) of ground glass opacity (GGO) regions are highly related to the severity of COVID-19, and the quantitative features calculated from the right lung are more related to the severity assessment than those of the left lung. Conclusion: The RF based model can achieve automatic severity assessment (non-severe or severe) of COVID-19 infection, and the performance is promising. Several quantitative features, which have the potential to reflect the severity of COVID-19, were revealed.

研究の動機と目的

• COVID-19患者の迅速かつ自動的な重症度評価を促進し、適切な治療をタイムリーに支援する。
• 病変の重症度を反映する定量的CT由来特徴を調査する。
• 画像特徴に基づいて非重症と重症を識別する機械学習モデルを構築・評価する。

提案手法

• 全肺の感染体積を含む63の定量的CT特徴およびground-glass opacity (GGO)指標を抽出する。
• 重症度を予測するランダムフォレスト分類器を訓練する（非重症 vs 重症）。
• RFモデルから特徴の重要度を算出し、重症度に関連する特徴を明らかにする。
• 3-foldクロスバリデーションを用いて評価し、性能指標を報告する。

実験結果

リサーチクエスチョン

• RQ1 定量的CT由来特徴は非重症と重症のCOVID-19を識別できるか？
• RQ2 どのCT特徴（および肺側の寄与）が重症度を最も予測するか？
• RQ3 提供データセット上で自動重症度評価におけるランダムフォレストモデルの性能はどの程度か？

主な発見

• RFモデルは0.933の真陽性率（TPR）を達成。
• RFモデルは0.745の真陰性率（TNR）を達成。
• RFモデルは0.875の全体精度を達成。
• RFモデルは0.91のROC曲線下面積（AUC）を達成。
• ground-glass opacity (GGO)領域の体積と体積比は重症度と強く関連する。
• 右肺からの特徴は左肺からの特徴よりも重症度との関連が強い。