Kaiserman Igor, Rosner Mordechai, Pe'er Jacob
Department of Ophthalmology, Hadassah University Hospital, Jerusalem, Israel.
Ophthalmology. 2005 Sep;112(9):1608. doi: 10.1016/j.ophtha.2005.04.008.
To develop an artificial neural network (ANN) that will forecast the 5-year mortality from choroidal melanoma.
Retrospective, comparative, observational cohort study.
One hundred fifty-three eyes of 153 consecutive patients with choroidal melanoma (age, 58.4+/-14.6 years) who were treated with ruthenium 106 brachytherapy between 1988 and 1998 at the Department of Ophthalmology, Hadassah University Hospital, Jerusalem, Israel.
Patients were observed clinically and ultrasonographically (A- and B-mode standardized ultrasonography). Metastatic screening included liver function tests and liver imaging. Backpropagation ANNs composed of 3 or 4 layers of neurons with various types of transfer functions and training protocols were assessed for their ability to predict the 5-year mortality. The ANNs were trained on 77 randomly selected patients and tested on a different set of 76 patients. Artificial neural networks were compared based on their sensitivity, specificity, forecasting accuracy, area under the receiver operating curves, and likelihood ratios (LRs). The best ANN was compared with the results of logistic regression and the performance of an ocular oncologist.
The ability of the ANNs to forecast the 5-year mortality from choroidal melanoma.
Thirty-one patients died during the follow-up period of metastatic choroidal melanoma. The best ANN (one hidden layer of 16 neurons) had 84% forecasting accuracy and an LR of 31.5. The number of hidden neurons significantly influenced the ANNs' performance (P<0.001). The performance of the ANNs was not significantly influenced by the training protocol, the number of hidden layers, or the type of transfer function. In comparison, logistic regression reached 86% forecasting accuracy, with a very low LR (0.8), whereas the human expert forecasting ability was <70% (LR, 1.85).
Artificial neural networks can be used for forecasting the prognosis of choroidal melanoma and may support decision-making in treating this malignancy.
开发一种人工神经网络（ANN），用于预测脉络膜黑色素瘤的5年死亡率。
回顾性、对比性、观察性队列研究。
1988年至1998年期间在以色列耶路撒冷哈达萨大学医院眼科接受钌106近距离放射治疗的153例脉络膜黑色素瘤患者的153只眼（年龄58.4±14.6岁）。
对患者进行临床和超声检查（A模式和B模式标准化超声检查）。转移筛查包括肝功能检查和肝脏成像。评估由3层或4层神经元组成、具有不同类型传递函数和训练方案的反向传播人工神经网络预测5年死亡率的能力。人工神经网络在77例随机选择的患者身上进行训练，并在另一组76例患者身上进行测试。根据人工神经网络的敏感性、特异性、预测准确性、受试者操作曲线下面积和似然比（LRs）进行比较。将最佳人工神经网络与逻辑回归结果及眼科肿瘤学家的表现进行比较。
人工神经网络预测脉络膜黑色素瘤5年死亡率的能力。
31例患者在脉络膜黑色素瘤转移的随访期内死亡。最佳人工神经网络（一个包含16个神经元的隐藏层）的预测准确率为84%，似然比为31.5。隐藏神经元数量显著影响人工神经网络的性能（P<0.001）。训练方案、隐藏层数或传递函数类型对人工神经网络的性能没有显著影响。相比之下，逻辑回归的预测准确率达到86%，似然比非常低（LR，0.8），而人类专家的预测能力<70%（LR，1.85）。
人工神经网络可用于预测脉络膜黑色素瘤的预后，并可能支持对这种恶性肿瘤的治疗决策。
