Fantechi Lorenzo, Barbarossa Federico, Cecchini Sara, Zoppi Lorenzo, Amabili Giulio, Di Rosa Mirko, Paci Enrico, Fornarelli Daniela, Bonfigli Anna Rita, Lattanzio Fabrizia, Maranesi Elvira, Bevilacqua Roberta
Unit of Nuclear Medicine, IRCCS INRCA, 60127 Ancona, Italy.
Scientific Direction, IRCCS INRCA, 60124 Ancona, Italy.
Bioengineering (Basel). 2025 Mar 31;12(4):368. doi: 10.3390/bioengineering12040368.
(1) Background: Predicting hospitalization length for COVID-19 patients is crucial for optimizing resource allocation and patient management. Radiomics, combined with machine learning (ML), offers a promising approach by extracting quantitative imaging features from CT scans. The aim of the present study is to use and adapt machine learning (ML) architectures, exploiting CT radiomics information, and analyze algorithms' capability to predict hospitalization at the time of patient admission. (2) Methods: The original CT lung images of 168 COVID-19 patients underwent two segmentations, isolating the ground glass area of the lung parenchyma. After an isotropic voxel resampling and wavelet and Laplacian of Gaussian filtering, 92 intensity and texture radiomics features were extracted. Feature reduction was conducted by applying a last absolute shrinkage and selection operator (LASSO) to the radiomic features set. Three ML classification algorithms, linear support vector machine (LSVM), medium neural network (MNN), and ensemble subspace discriminant (ESD), were trained and validated through a 5-fold cross-validation technique. Model performance was assessed using accuracy, sensitivity, specificity, precision, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). (3) Results: The LSVM classifier achieved the highest predictive performance, with an accuracy of 86.0% and an AUC of 0.93. However, reliable outcomes are also registered when MNN and ESD architecture are used. (4) Conclusions: The study shows that radiomic features can be used to build a machine learning framework for predicting patient hospitalization duration. The findings suggest that radiomics-based ML models can accurately predict COVID-19 hospitalization length.
（1）背景：预测新冠病毒疾病（COVID-19）患者的住院时长对于优化资源分配和患者管理至关重要。放射组学与机器学习（ML）相结合，通过从CT扫描中提取定量影像特征，提供了一种很有前景的方法。本研究的目的是使用并调整机器学习（ML）架构，利用CT放射组学信息，并分析算法在患者入院时预测住院情况的能力。（2）方法：对168例COVID-19患者的原始胸部CT图像进行了两次分割，分离出肺实质的磨玻璃区域。经过各向同性体素重采样以及小波和高斯拉普拉斯滤波后，提取了92个强度和纹理放射组学特征。通过对放射组学特征集应用最小绝对收缩和选择算子（LASSO）进行特征约简。使用5折交叉验证技术对三种ML分类算法，即线性支持向量机（LSVM）、中型神经网络（MNN）和集成子空间判别（ESD）进行训练和验证。使用准确率、灵敏度、特异性、精确率、F1分数以及受试者工作特征曲线下面积（AUC-ROC）评估模型性能。（3）结果：LSVM分类器取得了最高的预测性能，准确率为86.0%，AUC为0.93。然而，使用MNN和ESD架构时也获得了可靠的结果。（4）结论：该研究表明，放射组学特征可用于构建一个预测患者住院时长的机器学习框架。研究结果表明，基于放射组学的ML模型可以准确预测COVID-19的住院时长。
