Smoleń Agata, Czekierdowski Artur, Stachowicz Norbert, Kotarski Jan
Zakładu Matematyki i Biostatystyki Medycznej AM w Lublinie.
Ginekol Pol. 2003 Sep;74(9):855-62.
Advanced statistical methods are currently are more often used in the prediction of ovarian malignancy when adnexal tumor is detected. These methods include logistic regression analysis and artificial neural networks (ANN's), i.e. computer programs which are capable of learning from presented data and further predict various events, such as clinical diagnosis or outcome of a given treatment.
We have analyzed data of 307 women with adnexal tumors who were operated in the Ist Dept. of Gynecology, Medical University in Lublin between 2000-2002. Following clinical and sonographic variables were included: age, menopausal status, serum CA-125, bilaterality, tumor size and volume, papillary projections, septa, solid parts presence, Doppler blood flow indices (PI, RI, Vmax), and subjective-color Doppler score. A multiple layer perceptron (MLP) neural network with 13 input variables, 11 hidden neurons and one output variable was constructed to assess probability of malignancy in each women (Statistica v. 6.0 for Windows, Statsoft, USA). Sensitivity, specificity and accuracy of the model were calculated. Receiver-Operating Characteristics curves were generated and corresponding Areas Under ROC Curves (AUROC's) for all diagnostic tests were compared.
Final histologic examination revealed 228 (74.3%) benign tumors and 79 (25.7%) malignant masses including 21 women with FIGO stage I ovarian cancer. With a 75% cut-off probability of malignancy level the sensitivity and specificity of the best network in the testing set was 96.7% and 100%, respectively. In the validation set the corresponding values of sensitivity and specificity were 82.3% and 97.5%. The highest of all used tests AUROC equal to 0.9749 was found for the ANN predictive model.
ANN may help in the extraction of the most useful predictive clinical and ultrasound data. The sensitivity and specificity of the ANN's generated model were higher than currently used single clinical and diagnostic tests. However, a prospective testing in a new, much larger group of women with adnexal tumors is essential for the clinical usefulness of the proposed statistical model.
目前，在检测到附件肿瘤时，先进的统计方法更常用于预测卵巢恶性肿瘤。这些方法包括逻辑回归分析和人工神经网络（ANN），即能够从呈现的数据中学习并进一步预测各种事件的计算机程序，如临床诊断或特定治疗的结果。
我们分析了2000年至2002年间在卢布林医科大学第一妇科接受手术的307例附件肿瘤女性的数据。纳入以下临床和超声变量：年龄、绝经状态、血清CA-125、双侧性、肿瘤大小和体积、乳头状突起、隔膜、实性部分的存在、多普勒血流指数（PI、RI、Vmax）以及主观彩色多普勒评分。构建了一个具有13个输入变量、11个隐藏神经元和1个输出变量的多层感知器（MLP）神经网络，以评估每位女性的恶性概率（美国Statsoft公司的Windows版Statistica v. 6.0）。计算了模型的敏感性、特异性和准确性。生成了受试者操作特征曲线，并比较了所有诊断测试的相应曲线下面积（AUROC）。
最终组织学检查显示228例（74.3%）为良性肿瘤，79例（25.7%）为恶性肿块，其中包括21例FIGO I期卵巢癌女性。在测试集中，以75%的恶性概率截断水平，最佳网络的敏感性和特异性分别为96.7%和100%。在验证集中，敏感性和特异性的相应值分别为82.3%和97.5%。ANN预测模型的AUROC在所有使用的测试中最高，等于0.9749。
ANN可能有助于提取最有用的预测性临床和超声数据。ANN生成模型的敏感性和特异性高于目前使用的单一临床和诊断测试。然而，对于所提出的统计模型在临床上的实用性而言，在一组新的、规模大得多的附件肿瘤女性中进行前瞻性测试至关重要。
