基于 PET/CT 的计算机辅助诊断模型，用于分类颈部淋巴结肿大患者的淋巴结转移和淋巴瘤累及。
Computer-aided diagnostic models to classify lymph node metastasis and lymphoma involvement in enlarged cervical lymph nodes using PET/CT.
机构信息
West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Department of Biotherapy and Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
出版信息
Med Phys. 2023 Jan;50(1):152-162. doi: 10.1002/mp.15901. Epub 2022 Aug 17.
BACKGROUND
It is a clinical problem to identify histological component in enlarged cervical lymph nodes, particularly in differentiation between lymph node metastasis and lymphoma involvement.
PURPOSE
To construct two kinds of deep learning (DL)-based computer-aided diagnosis (CAD) systems including DL-convolutional neural networks (DL-CNN) and DL-machine learning for pathological diagnosis of cervical lymph nodes by positron emission tomography (PET)/computed tomography (CT) images.
METHODS
We collected CT, PET, and PET/CT images series from 165 patients with enlarged cervical lymph nodes receiving examinations from January 2014 to June 2018. Six CNNs pretrained on ImageNet as DL architectures were used for two kinds of DL-based CAD models, including DL-CNN and DL-machine learning models. The DL-CNN models were constructed via transfer learning for classification of lymphomatous and metastatic lymph nodes. The DL-machine learning models were developed by DL-based features extractors and support vector machine (SVM) classifier. As for DL-SVM models, we also evaluate the effect of handcrafted radiomics features in combination of DL-based features.
RESULTS
The DL-CNN model with ResNet50 architecture on PET/CT images had the best diagnostic performance among all six algorithms with an area under the receiver operating characteristic curve (AUC) of 0.845 and accuracy of 78.13% in the testing cohort. The DL-SVM model on ResNet50 extractor showed great performance for the testing cohort with an AUC of 0.901, accuracy of 86.96%, sensitivity of 76.09%, and specificity of 94.20%. The combination of DL-based and handcrafted features yielded the improvement of diagnostic performance.
CONCLUSIONS
Our DL-based CAD systems on PET/CT images were developed for classifying metastatic and lymphomatous involvement with favorable diagnostic performance in enlarged cervical lymph nodes. Further clinical practice of our systems may improve quality of the following therapeutic interventions and optimize patients' outcomes.
背景
在鉴别增大的颈部淋巴结的组织学成分方面，尤其是在区分淋巴结转移和淋巴瘤累及方面，这是一个临床问题。
目的
构建两种基于深度学习（DL）的计算机辅助诊断（CAD）系统，包括基于 DL-卷积神经网络（DL-CNN）和 DL-机器学习的用于正电子发射断层扫描（PET）/计算机断层扫描（CT）图像的颈部淋巴结病理诊断的 CAD 系统。
方法
我们收集了 2014 年 1 月至 2018 年 6 月期间 165 例接受颈部淋巴结肿大检查的患者的 CT、PET 和 PET/CT 图像系列。使用 6 种在 ImageNet 上进行预训练的 CNN 作为 DL 架构，用于两种基于 DL 的 CAD 模型，包括 DL-CNN 和 DL-机器学习模型。DL-CNN 模型通过迁移学习构建，用于分类淋巴瘤性和转移性淋巴结。DL-机器学习模型由基于 DL 的特征提取器和支持向量机（SVM）分类器开发。对于 DL-SVM 模型，我们还评估了结合基于 DL 的特征的手工制作的放射组学特征的效果。
结果
在测试队列中，基于 ResNet50 架构的 PET/CT 图像的 DL-CNN 模型在所有 6 种算法中具有最佳诊断性能，受试者工作特征曲线（ROC）下面积（AUC）为 0.845，准确率为 78.13%。基于 ResNet50 提取器的 DL-SVM 模型在测试队列中表现出色，AUC 为 0.901，准确率为 86.96%，敏感度为 76.09%，特异性为 94.20%。基于 DL 的特征与手工制作的特征的结合提高了诊断性能。
结论
我们基于 PET/CT 图像的基于 DL 的 CAD 系统是为分类转移性和淋巴瘤性累及而开发的，在增大的颈部淋巴结中具有良好的诊断性能。我们的系统在临床上的进一步应用可能会提高后续治疗干预的质量，并优化患者的结局。
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