联合临床、CT 放射组学和深度学习模型预测侵袭性肺曲霉病。

Clinic, CT radiomics, and deep learning combined model for the prediction of invasive pulmonary aspergillosis.

机构信息

First Clinical Medical College of Guangdong Medical University, No. 2 Wenming East Road, Xiashan District, Zhanjiang, 524023, Guangdong, China.

Afffliated Hospital of Guangdong Medical University, No. 57 Renmin Avenue South, Xiashan District, Zhanjiang, 524001, Guangdong, China.

出版信息

BMC Med Imaging. 2024 Oct 7;24(1):264. doi: 10.1186/s12880-024-01442-x.

DOI:10.1186/s12880-024-01442-x

PMID:39375609

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11457327/

Abstract

BACKGROUND

Invasive pulmonary aspergillosis (IPA) is a serious fungal infection. However, current diagnostic methods have limitations. The purpose of this study was to use artificial intelligence to achieve a more accurate diagnosis of IPA.

METHODS

Totally 263 patients (148 cases of IPA, 115 cases of non-IPA) were retrospectively enrolled from a single institution and randomly divided into training and test sets at a ratio of 7:3. Clinic-radiological independent risk factors for IPA were screened using univariate analysis and multivariate logistic regression analysis, after which a clinic-radiological model was constructed. The optimal radiomics features were extracted and screened based on CT images to construct the radiomics label score (Rad-score) and radiomics model. The optimal DL features were extracted and screened using four pre-trained convolutional neural networks, respectively, followed by the construction of the DL label score (DL-score) and DL model. Then, the radiomics-DL model was constructed. Finally, the combined model was constructed based on clinic-radiological independent risk factors, the Rad-score, and the DL-score. LR was adopted as the classifier. Receiver operating characteristic (ROC) curves were drawn, and the areas under the curve (AUC) were calculated to evaluate the efficacy of each model in predicting IPA. Additionally, based on the best-performing model on the LR classifier, four other machine learning (ML) classifiers were constructed to evaluate the predictive value for IPA.

RESULTS

The AUC of the clinic-radiological model for predicting IPA in the training and test sets was 0.845 and 0.765, respectively. The AUC of the radiomics-DL and combined models in the training set was 0.871 and 0.932, while in the test set was 0.851 and 0.881, respectively. The combined model showed better predictive performance than all other models. DCA showed that taking 0.00-1.00 as the threshold, the clinical benefit of the combined model was higher than that of all other models. Then, the combined model was trained on four other machine learning classifiers, all of which achieved AUC values above 0.80 in the test set, showing good performance in predicting IPA.

CONCLUSION

Clinic, CT radiomics, and DL combined model could be used to predict IPA effectively.

摘要

背景

侵袭性肺曲霉病（IPA）是一种严重的真菌感染。然而，目前的诊断方法存在局限性。本研究旨在使用人工智能实现更准确的 IPA 诊断。

方法

回顾性纳入单一中心的 263 名患者（IPA 组 148 例，非 IPA 组 115 例），采用单因素分析和多因素逻辑回归分析筛选 IPA 的临床影像学独立危险因素，构建临床影像学模型。基于 CT 图像提取并筛选最佳放射组学特征，构建放射组学标签评分（Rad-score）和放射组学模型。分别采用四种预训练卷积神经网络提取并筛选最佳深度学习（DL）特征，构建 DL 标签评分（DL-score）和 DL 模型。然后构建放射组学-DL 模型。最后基于临床影像学独立危险因素、Rad-score 和 DL-score 构建联合模型。采用逻辑回归（LR）作为分类器。绘制受试者工作特征（ROC）曲线，计算曲线下面积（AUC），评估各模型预测 IPA 的效能。此外，基于 LR 分类器最佳模型，构建另外四种机器学习（ML）分类器，评估对 IPA 的预测价值。

结果

训练集和测试集中临床影像学模型预测 IPA 的 AUC 分别为 0.845 和 0.765。放射组学-DL 和联合模型在训练集的 AUC 分别为 0.871 和 0.932，在测试集的 AUC 分别为 0.851 和 0.881。联合模型的预测性能优于其他所有模型。决策曲线分析（DCA）显示，以 0.00-1.00 作为阈值，联合模型的临床获益高于其他所有模型。然后，将联合模型在另外四种机器学习分类器上进行训练，在测试集中均取得 AUC 值大于 0.80 的结果，在预测 IPA 方面表现出良好的性能。