基于CT图像的深度学习放射组学模型用于预测肝囊性棘球蚴病的生物活性

Mayidili Nijiati, Mireayi Tuerdi, Maihemitijiang Damola, Yasen Yimit, Jing Yang, Adilijiang Abulaiti, Aibibulajiang Mutailifu, Diliaremu Aihait, Yunling Wang, Xiaoguang Zou
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摘要

简介肝囊性棘球蚴病(HCE)是一种常见的寄生虫感染。生物活性对治疗计划至关重要。本研究旨在探索基于 CT 图像的深度学习放射组学(DLR)模型在预测肝囊性棘球蚴病生物活性分级方面的潜在应用:对160名肝棘球蚴病患者(训练集和验证集分别为127人和33人)进行了回顾性分析。绘制感兴趣区(VOI),提取放射组学特征和深度神经网络特征。对训练集进行特征选择,并计算放射组学得分(Rad Score)和深度学习得分(Deep Score)。利用所选的放射组学特征和两个深度模型特征,分别构建了七个生物活性分级诊断模型(基于逻辑回归算法)。使用接收者操作特征曲线对所有模型进行了评估,并计算了曲线下面积(AUC)。使用组合模型构建了一个提名图,并对其校准、判别能力和临床实用性进行了评估:从两个深度学习网络(DLN)特征中分别选取了12、6和10个最佳放射组学特征、深度学习特征。对于肝囊性棘球蚴病的生物活性分级,组合模型表现出很强的诊断性能,训练集的AUC值为0.888(95% CI:0.837-0.936),验证集的AUC值为0.876(0.761-0.964)。临床决策分析曲线显示了良好的结果,而校准曲线则显示了提名图的预测结果与实际结果高度吻合:结论:DLR模型可用于预测肝棘球蚴病的生物活性分级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A deep learning radiomics model based on CT images for predicting the biological activity of hepatic cystic echinococcosis
Introduction: Hepatic cystic echinococcosis (HCE) is a widely seen parasitic infection. Biological activity is crucial for treatment planning. This work aims to explore the potential applications of a deep learning radiomics (DLR) model, based on CT images, in predicting the biological activity grading of hepatic cystic echinococcosis.Methods: A retrospective analysis of 160 patients with hepatic echinococcosis was performed (127 and 33 in training and validation sets). Volume of interests (VOIs) were drawn, and radiomics features and deep neural network features were extracted. Feature selection was performed on the training set, and radiomics score (Rad Score) and deep learning score (Deep Score) were calculated. Seven diagnostics models (based on logistic regression algorithm) for the biological activity grading were constructed using the selected radiomics features and two deep model features respectively. All models were evaluated using the receiver operating characteristic curve, and the area under the curve (AUC) was calculated. A nomogram was constructed using the combined model, and its calibration, discriminatory ability, and clinical utility were assessed.Results: 12, 6 and 10 optimal radiomics features, deep learning features were selected from two deep learning network (DLN) features, respectively. For biological activity grading of hepatic cystic echinococcosis, the combined model demonstrated strong diagnostic performance, with an AUC value of 0.888 (95% CI: 0.837–0.936) in the training set and 0.876 (0.761–0.964) in the validation set. The clinical decision analysis curve indicated promising results, while the calibration curve revealed that the nomogram’s prediction result was highly compatible with the actual result.Conclusion: The DLR model can be used for predicting the biological activity grading of hepatic echinococcosis.
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