Timmerman D, Verrelst H, Bourne T H, De Moor B, Collins W P, Vergote I, Vandewalle J
Department of Obstetrics and Gynecology, University Hospitals Leuven, Belgium.
Ultrasound Obstet Gynecol. 1999 Jan;13(1):17-25. doi: 10.1046/j.1469-0705.1999.13010017.x.
The aim of this study was to generate and evaluate artificial neural network (ANN) models from simple clinical and ultrasound-derived criteria to predict whether or not an adnexal mass will have histological evidence of malignancy.
The data were collected prospectively from 173 consecutive patients who were scheduled to undergo surgical investigations at the University Hospitals, Leuven, between August 1994 and August 1996. The outcome measure was the histological classification of excised tissues as malignant (including borderline) or benign.
Age, menopausal status and serum CA 125 levels and sonographic features of the adnexal mass were encoded as variables. The ANNs were trained on a randomly selected set of 116 patient records and tested on the remainder (n = 57). The performance of each model was evaluated using receiver operating characteristic (ROC) curves and compared with corresponding data from an established risk of malignancy index (RMI) and a logistic regression model.
There were 124 benign masses, five of borderline malignancy and 44 invasive cancers (of which 29% were metastatic); 37% of patients with a malignant or borderline tumor had stage I disease. The best ANN gave an area under the ROC curve of 0.979 for the whole dataset, a sensitivity of 95.9% and specificity of 93.5%. The corresponding values for the RMI were 0.882, 67.3% and 91.1%, and for the logistic regression model 0.956, 95.9% and 85.5%, respectively.
An ANN can be trained to provide clinically accurate information, on whether or not an adnexal mass is malignant, from the patient's menopausal status, serum CA 125 levels, and some simple ultrasonographic criteria.
本研究旨在根据简单的临床和超声检查标准生成并评估人工神经网络（ANN）模型，以预测附件包块是否具有恶性组织学证据。
前瞻性收集了1994年8月至1996年8月期间在鲁汶大学医院计划接受手术检查的173例连续患者的数据。结局指标是切除组织的组织学分类，分为恶性（包括交界性）或良性。
将年龄、绝经状态、血清CA 125水平和附件包块的超声特征编码为变量。ANN在随机选择的116例患者记录集上进行训练，并在其余患者（n = 57）上进行测试。使用受试者操作特征（ROC）曲线评估每个模型的性能，并与既定的恶性风险指数（RMI）和逻辑回归模型的相应数据进行比较。
有124个良性包块，5个交界性恶性包块和44个浸润性癌（其中29%为转移性）；37%的恶性或交界性肿瘤患者为I期疾病。最佳的ANN在整个数据集上的ROC曲线下面积为0.979，敏感性为95.9%，特异性为93.5%。RMI的相应值分别为0.882、67.3%和91.1%，逻辑回归模型的相应值分别为0.956、95.9%和85.5%。
可以训练ANN根据患者的绝经状态、血清CA 125水平和一些简单的超声检查标准，提供关于附件包块是否为恶性的临床准确信息。
