Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran.
Faculty of Industrial Engineering and Systems Management, Amirkabir University of Technology, Tehran, Iran.
J Biomed Inform. 2021 Mar;115:103694. doi: 10.1016/j.jbi.2021.103694. Epub 2021 Feb 2.
Glioblastoma multiforme (GBM) is the most common and malignant type of primary brain tumors. Radiation therapy (RT) plus concomitant and adjuvant Temozolomide (TMZ) constitute standard treatment of GBM. Existing models for GBM growth do not consider the effect of different schedules on tumor growth and patient survival. However, clinical trials show that treatment schedule and drug dosage significantly affect patient survival. The goal is to provide a patient calibrated model for predicting survival according to the treatment schedule.
We propose a top-down method based on artificial neural networks (ANN) and genetic algorithm (GA) to predict survival of GBM patients. A feed forward undercomplete Autoencoder network is integrated with the neuro-evolutionary (NE) algorithm in order to extract a compressed representation of input clinical data. The proposed NE algorithm uses GA to obtain optimal architecture of a multi-layer perceptron (MLP). Taguchi L orthogonal design of experiments is used to tune parameters of the proposed NE algorithm. Finally, the optimal MLP is used to predict survival of GBM patients.
Data from 8 related clinical trials have been collected and integrated to train the model. From 847 evaluable cases, 719 were used for train and validation and the remaining 128 cases were used to test the model. Mean absolute error of the predictions on the test data is 0.087 months which shows excellent performance of the proposed model in predicting survival of the patients. Also, the results show that the proposed NE algorithm is superior to other existing models in both the mean and variability of the prediction error.
多形性胶质母细胞瘤（GBM）是最常见和恶性程度最高的原发性脑肿瘤。放射治疗（RT）联合替莫唑胺（TMZ）同期和辅助治疗是 GBM 的标准治疗方法。现有的 GBM 生长模型没有考虑不同方案对肿瘤生长和患者生存的影响。然而，临床试验表明，治疗方案和药物剂量显著影响患者的生存。本研究的目的是根据治疗方案为患者提供一种可预测生存的校准模型。
我们提出了一种基于人工神经网络（ANN）和遗传算法（GA）的自上而下的方法，用于预测 GBM 患者的生存。前馈欠完备自动编码器网络与神经进化（NE）算法相结合，以提取输入临床数据的压缩表示。所提出的 NE 算法使用 GA 获得多层感知器（MLP）的最优结构。田口 L 型正交设计实验用于调整所提出的 NE 算法的参数。最后，使用最优 MLP 预测 GBM 患者的生存。
收集并整合了 8 项相关临床试验的数据来训练模型。从 847 例可评估病例中，719 例用于训练和验证，其余 128 例用于测试模型。预测结果在测试数据上的平均绝对误差为 0.087 个月，表明所提出的模型在预测患者生存方面具有出色的性能。此外，结果表明，所提出的 NE 算法在预测误差的平均值和变异性方面均优于其他现有模型。
