Zoulias Emmanouil A, Asvestas Pantelis A, Matsopoulos George K, Tseleni-Balafouta Sofia
Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
Anal Quant Cytol Histol. 2011 Aug;33(4):215-22.
To assist diagnosis of thyroid malignancy, implementing a decision support system (DSS) using fine needle aspiration biopsy (FNAB) data.
The set of 2,035 thyroid smears contained 1,886 smears of nonmalignancy (class 1) and 150 smears of malignancy (class 2) verified histologically. For each smear, 67 medical features were considered by the expert, forming 2,036 feature vectors, which were fed into a DSS for discriminating between malignant and nonmalignant smears. The DSS comprised a feature selection and classification module using a combination of three classifiers, the artificial neural network, the support vector machines, and the k-nearest neighbor, under the majority vote procedure.
The overall classification accuracy of the DSS was 98.6%, marginally better than the FNAB (97.3%). The DSS had lower sensitivity (89.1%) and better specificity (99.4%) compared to the FNAB. Regarding the smears characterized as "suspicious" by FNAB, a significant improvement of overall accuracy was obtained by the proposed DSS system (84.6%) compared to the FNAB (50.0%).
The proposed DSS provides significant improvement compared to FNAB regarding discrimination of smears characterized by an expert as "suspicious," reducing the number of patients undergoing surgical procedures.
利用细针穿刺活检（FNAB）数据实施决策支持系统（DSS），以辅助甲状腺恶性肿瘤的诊断。
2035份甲状腺涂片样本中，有1886份经组织学证实为非恶性（1类）涂片，150份为恶性（2类）涂片。专家针对每份涂片考虑了67个医学特征，形成2036个特征向量，并将其输入DSS以区分恶性和非恶性涂片。DSS包括一个特征选择和分类模块，该模块在多数投票程序下结合了三种分类器，即人工神经网络、支持向量机和k近邻算法。
DSS的总体分类准确率为98.6%，略高于FNAB（97.3%）。与FNAB相比，DSS的敏感性较低（89.1%），特异性较高（99.4%）。对于FNAB判定为“可疑”的涂片，与FNAB（50.0%）相比，所提出的DSS系统在总体准确率上有显著提高（84.6%）。
与FNAB相比，所提出的DSS在鉴别专家判定为“可疑”的涂片方面有显著改进，减少了接受手术的患者数量。
