Huang Yu-Len, Chen Jeon-Hor, Shen Wu-Chung
Department of Computer Science & Information Engineering, Tunghai University, Taichung, Taiwan.
Acad Radiol. 2006 Jun;13(6):713-20. doi: 10.1016/j.acra.2005.07.014.
Computed tomography (CT) after iodinated contrast agent injection is highly accurate for diagnosis of hepatic tumors. However, iodinating may have problems of renal toxicity and allergic reaction. We aimed to evaluate the potential role of the computer-aided diagnosis (CAD) with texture analysis in the differential of hepatic tumors on nonenhanced CT.
This study evaluated 164 liver lesions (80 malignant tumors and 84 hemangiomas). The suspicious tumor region in the digitized CT image was manually selected and extracted as a circular subimage. Proposed preprocessing adjustments for subimages were used to equalize the information needed for a differential diagnosis. The autocovariance texture features of subimage were extracted and a support vector machine classifier identified the tumor as benign or malignant.
The accuracy of the proposed diagnosis system for classifying malignancies is 81.7%, the sensitivity is 75.0%, the specificity is 88.1%, the positive predictive value is 85.7%, and the negative predictive value is 78.7%.
This system differentiates benign from malignant hepatic tumors with relative high accuracy and is therefore clinically useful to reduce patients needed for iodinated contrast agent injection in CT examination. Because the support vector machine is trainable, it could be further optimized if a larger set of tumor images is to be supplied.
注射碘化造影剂后的计算机断层扫描（CT）对肝肿瘤的诊断具有高度准确性。然而，碘化可能存在肾毒性和过敏反应问题。我们旨在评估基于纹理分析的计算机辅助诊断（CAD）在非增强CT上鉴别肝肿瘤的潜在作用。
本研究评估了164个肝脏病变（80个恶性肿瘤和84个血管瘤）。在数字化CT图像中手动选择并提取可疑肿瘤区域作为圆形子图像。对这些子图像进行预处理调整以均衡鉴别诊断所需的信息。提取子图像的自协方差纹理特征，并使用支持向量机分类器将肿瘤鉴别为良性或恶性。
所提出的诊断系统对恶性肿瘤分类的准确率为81.7%，灵敏度为75.0%，特异度为88.1%，阳性预测值为85.7%，阴性预测值为78.7%。
该系统能够以相对较高的准确率区分肝肿瘤的良恶性，因此在临床上有助于减少CT检查中需要注射碘化造影剂的患者数量。由于支持向量机是可训练的，如果提供更大的肿瘤图像集，它可以进一步优化。
