人工智能-深度学习-基于深度学习的颈部淋巴结病变诊断的初步研究.pdf

摘要

摘要

背景和目的：

颈部淋巴结肿大是颈部乃至全身许多疾病的常见临床表现，其良恶性鉴别

不仅仅对疾病定性诊断有帮助，而且是恶性肿瘤患者分级、分期的重要依据，

同时对肿瘤患者的生存、局部复发和远处转移的判断有重要价值。深度学习

（Deep learning，DL）是人工智能（Artificial intelligence，AI）的方法之一，它

第一部分 基于深度学习模型的颈部淋巴结定性诊断的初步研究

目的：

探讨人工智能深度学习模型在颈部淋巴结定性诊断中的应用价值。

材料与方法：

收集经组织病理学证实的 115 例病人的颈部增强

CT

轴位图像，包括恶性淋

巴结 207 枚和良性淋巴结 359 枚，按照就诊时间顺序将前 486 枚（恶性 169 枚，

良性 317 枚）作为训练组，后 80 枚（恶性 38 枚，良性 42 枚）作为验证组。训

练组采用

DenseNet

摘要

为 0.842。

结论：

基于深度学习算法的 DenseNet 模型可以用于颈部淋巴结良恶性的鉴别诊

断，其诊断效能优于常规影像方法的经验诊断，且可以节省时间。

关键词：颈部淋巴结；病理性质；深度学习；体层摄影技术，X 线计算机；

目的：

探讨人工智能深度学习模型能否对恶性肿瘤颈部转移淋巴结的来源进行初

步判断。

计算其准确率、敏感度、特异性、阳性预测值（PPV）、阴性预测值（NPV），

并绘制受试者工作特征曲线（ROC）。

结果：

摘要

特异性、PPV、NPV 分别为 67.5%、59.1%、77.8%、76.5%、60.9%，曲线下面积

（AUC）为 0.747。

结论：

初步研究表明，基于深度学习算法的 DenseNet 模型可以在一定程度上对颈

部转移淋巴结来源进行预测，其诊断效能尚需要在更大规模的数据集上进一步

验证。

关键词：颈部淋巴结；转移；深度学习；体层摄影技术，X 线计算机；

Abstract

tumors. At the same time, it is of great value for the judgment of the survival, local

recurrence and distant metastasis of the patients with tumors.Deep learning (DL) is

one of the methods of artificial intelligence (AI). It is a convolutional neural network

with neurons as the basic unit. It is one of the most advanced machine learning

methods in the medical field.The model has been applied to the identification and

differentiation of pulmonary nodules, the identification and location of lymph node

metastasis of lung cancer, the classification of liver cancer, the stratification of risk

factors of cardiovascular disease and the establishment of prognosis model of tumor

patients, and has achieved high diagnostic efficiency.Therefore, the purpose of this

study was to investigate whether the deep learning model could make qualitative

lesions and predict the source of cervical metastatic lymph nodes.

Abstract

order of consultation time, the first 486 (169 malignant, 317 benign) were used as the

training group, and the last 80 (malignant 38, benign 42) as the verification group.

The training group used DenseNet network for model training. The trained model was

used to test the lymph nodes in the verification group. The accuracy, sensitivity,

specificity, positive predictive value (PPV), and negative predictive value (NPV)

were calculated based on the confusion matrix. The receiver operating characteristic

curve (ROC) was drawn.

The accuracy, sensitivity, specificity, PPV and NPV of qualitative diagnosis of

and it can save time.

computed;Pathological properties;