Huhdanpaa Hannu, Douville Christopher, Baum Kerry, Krishnamurthy Venkat N, Holcombe Sven, Enchakalody Binu, Wang Lu, Wang Stewart C, Su Grace L
Department of Radiology, University of Minnesota, Minneapolis, MN, USA.
Scand J Gastroenterol. 2011 Dec;46(12):1468-77. doi: 10.3109/00365521.2011.613946. Epub 2011 Oct 13.
To develop a novel non-invasive, quantitative approach utilizing computed tomography scans to predict cirrhosis.
A total of 105 patients (54 cirrhosis and 51 normal) who had CT scans within 6 months of a liver biopsy or were identified through a Trauma registry were included in this study. Patients were randomly divided into the training set (n = 81) and the validation set (n = 24). Each liver was segmented in a semi-automated fashion from a computed tomography scan using Mimics software. The resulting liver surfaces were saved as a stereo lithography mesh into an Oracle database, and analyzed in MATLAB(®) for morphological markers of cirrhosis.
The best predictive model for diagnosing cirrhosis consisted of liver slice-bounding box slice ratio, the dimensions of the liver bounding box, liver slice area, slice perimeter, surface volume and adjusted surface area. With this model, we calculated an area under the receiver operating characteristic curve of 0.90 for the training set, and 0.91 for the validation set. For comparison, we calculated an area under the receiver operating characteristic curve of 0.70 for our dataset when we used the lab value based aspartate aminotransferase-platelet ratio index, another reported model for predicting cirrhosis. Last, by combining the aspartate aminotransferase-platelet ratio index and our model, we obtained an area under the receiving operating characteristic of 0.95.
This study shows "proof of concept" that quantitative image analysis of livers on computed tomography scans may be utilized to predict cirrhosis in the absence of a liver biopsy.
开发一种利用计算机断层扫描进行预测肝硬化的新型非侵入性定量方法。
本研究纳入了105例患者（54例肝硬化患者和51例正常患者），这些患者在肝活检6个月内进行了CT扫描，或者是通过创伤登记系统识别出来的。患者被随机分为训练集（n = 81）和验证集（n = 24）。使用Mimics软件从计算机断层扫描中以半自动方式分割每个肝脏。将得到的肝脏表面保存为立体光刻网格，存入Oracle数据库，并在MATLAB(®)中分析肝硬化的形态学标志物。
诊断肝硬化的最佳预测模型包括肝脏切片边界框切片比率、肝脏边界框尺寸、肝脏切片面积、切片周长、表面体积和调整后的表面积。使用该模型，我们计算出训练集的受试者操作特征曲线下面积为0.90，验证集为0.91。作为比较，当我们使用基于实验室值的天冬氨酸氨基转移酶 - 血小板比率指数（另一种报道的预测肝硬化的模型）时，计算出我们数据集的受试者操作特征曲线下面积为0.70。最后，通过将天冬氨酸氨基转移酶 - 血小板比率指数与我们的模型相结合，我们获得了受试者操作特征曲线下面积为0.95。
本研究表明“概念验证”，即在无需肝活检的情况下，计算机断层扫描对肝脏进行定量图像分析可用于预测肝硬化。
