结合机器学习和深度传输学习评估肺癌患者的纵隔淋巴结

Q1 Computer Science
Hui XIE , Jianfang ZHANG , Lijuan DING , Tao TAN , Qing LI
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引用次数: 0

摘要

背景肺癌患者的预后和生存率很可能随着转移而恶化。利用深度学习检测淋巴结转移可以无创计算淋巴结转移的可能性,从而为临床医生提供关键信息,提高诊断精度,最终改善患者的生存和预后。利用七个深度学习模型,即 Alex、GoogLeNet、Resnet18、Resnet101、Vgg16、Vgg19 和 MobileNetv3(小型),提取深度图像组织学特征。然后使用斯皮尔曼相关系数(r ≥ 0.9)和最小绝对收缩与选择操作符对提取的特征进行降维。采用了 11 种机器学习方法,即支持向量机、K-近邻、随机森林、额外树、XGBoost、LightGBM、Naive Bayes、AdaBoost、梯度提升决策树、线性回归和多层感知器,为过滤后的最终特征构建分类预测模型。使用各种指标评估了模型的诊断性能,包括准确率、接收者操作特征曲线下面积、灵敏度、特异性、阳性预测值和阴性预测值。结果本研究表明,使用从 Vgg16 提取的深度放射学特征,结合通过线性回归算法构建的预测模型,可以有效区分肺癌患者纵隔淋巴结的状态。该模型的性能评估基于各种指标,包括准确率、接收者工作特征曲线下面积、灵敏度、特异性、阳性预测值和阴性预测值,其值分别为 0.808、0.834、0.851、0.745、0.829 和 0.776。结论本研究从计算机断层扫描图像中获取了 Vgg16 的深部放射组学信息,并利用线性回归方法准确诊断了肺癌患者的纵隔淋巴结转移。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining machine and deep transfer learning for mediastinal lymph node evaluation in patients with lung cancer

Background

The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis. Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis, thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis

Methods

In total, 623 eligible patients were recruited from two medical institutions. Seven deep learning models, namely Alex, GoogLeNet, Resnet18, Resnet101, Vgg16, Vgg19, and MobileNetv3 (small), were utilized to extract deep image histological features. The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient (r ≥ 0.9) and Least Absolute Shrinkage and Selection Operator. Eleven machine learning methods, namely Support Vector Machine, K-nearest neighbor, Random Forest, Extra Trees, XGBoost, LightGBM, Naive Bayes, AdaBoost, Gradient Boosting Decision Tree, Linear Regression, and Multilayer Perceptron, were employed to construct classification prediction models for the filtered final features. The diagnostic performances of the models were assessed using various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value. Calibration and decision-curve analyses were also performed.

Results

The present study demonstrated that using deep radiomic features extracted from Vgg16, in conjunction with a prediction model constructed via a linear regression algorithm, effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer. The performance of the model was evaluated based on various metrics, including accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, which yielded values of 0.808, 0.834, 0.851, 0.745, 0.829, and 0.776, respectively. The validation set of the model was assessed using clinical decision curves, calibration curves, and confusion matrices, which collectively demonstrated the model's stability and accuracy

Conclusion

In this study, information on the deep radiomics of Vgg16 was obtained from computed tomography images, and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.

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来源期刊
Virtual Reality  Intelligent Hardware
Virtual Reality Intelligent Hardware Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.40
自引率
0.00%
发文量
35
审稿时长
12 weeks
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