A Bayesian Network for Differentiating Benign From Malignant Thyroid Nodules Using Sonographic and Demographic Features
A Bayesian Network for Differentiating Benign From Malignant Thyroid Nodules Using Sonographic and Demographic Features
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DOI：
10.2214/ajr.09.4037
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发表时间：
2011-05-01
影响因子：
5
通讯作者：
Rubin, Daniel L.
中科院分区：
文献类型：
--
作者：
Liu, Yueyi I.;Kamaya, Aya;Rubin, Daniel L.
OBJECTIVE.我们研究的目的是建立一个贝叶斯网络（BN），它结合了大量的影像学特征和患者的人口统计学特征，以指导放射科医生评估可疑甲状腺结节恶性肿瘤的可能性。我们建立了一个BN，联合收割机了甲状腺结节恶性潜力的多个指标，包括成像和人口统计学因素。从广泛的文献综述中汇编了影像学特征和与这些特征相关的诊断条件概率。为了评估我们的网络，我们从93名接受超声引导活检的成人患者中随机选择了54个良性和45个恶性结节。每例病例的最终诊断均通过病理学确定。我们将我们的网络的性能与两名放射科医生的性能进行了比较，这两名放射科医生在怀疑恶性肿瘤的5分制上独立评估每个病例。采用受试者工作特征（ROC）分析比较BN和放射科医师对恶性肿瘤的概率估计。该网络对两位放射科专家进行了调查。使用每个放射科医师对成像特征的评估作为网络的输入，BN和放射科医师的ROC曲线下面积（A（z））之间的差异为-0.03（BN vs放射科医师1，0.85 vs 0.88）和-0.01（BN vs放射科医师2，0.76 vs 0.77）。我们创建了一个BN，它结合了一系列超声和人口统计学特征，并提供了甲状腺结节是良性还是恶性的概率。BN区分良性和恶性甲状腺结节以及放射科专家。
OBJECTIVE. The objective of our study was to create a Bayesian network (BN) that incorporates a multitude of imaging features and patient demographic characteristics to guide radiologists in assessing the likelihood of malignancy in suspicious-appearing thyroid nodules.MATERIALS AND METHODS. We built a BN to combine multiple indicators of the malignant potential of thyroid nodules including both imaging and demographic factors. The imaging features and conditional probabilities relating those features to diagnoses were compiled from an extensive literature review. To evaluate our network, we randomly selected 54 benign and 45 malignant nodules from 93 adult patients who underwent ultrasound-guided biopsy. The final diagnosis in each case was pathologically established. We compared the performance of our network with that of two radiologists who independently evaluated each case on a 5-point scale of suspicion for malignancy. Probability estimates of malignancy from the BN and radiologists were compared using receiver operating characteristic (ROC) analysis.RESULTS. The network performed comparably to the two expert radiologists. Using each radiologist's assessment of the imaging features as input to the network, the differences between the area under the ROC curve (A(z)) for the BN and for the radiologists were -0.03 (BN vs radiologist 1, 0.85 vs 0.88) and -0.01 (BN vs radiologist 2, 0.76 vs 0.77).CONCLUSION. We created a BN that incorporates a range of sonographic and demographic features and provides a probability about whether a thyroid nodule is benign or malignant. The BN distinguished between benign and malignant thyroid nodules as well as the expert radiologists did.