Manogaran Nalini, Panabakam Nirupama, Selvaraj Durai, Seerangan Koteeswaran, Khan Firoz, Selvarajan Shitharth
Department of CSE, S.A. Engineering College (Autonomous), Chennai, 600077, Tamil Nadu, India.
Department of CSE, VEMU Institute of Technology, Chitoor, 517112, Andhra Pradesh, India.
Sci Rep. 2025 May 5;15(1):15713. doi: 10.1038/s41598-025-00401-y.
The forecasting of a patient's response to radiotherapy and the likelihood of experiencing harmful long-term health impacts would considerably enhance individual treatment plans. Due to the continuous exposure to radiation, cardiovascular disease and pulmonary fibrosis might occur. For forecasting the response of patients to chemotherapy, the Convolutional Neural Networks (CNN) technique is widely used. With the help of radiotherapy, cancer diseases are diagnosed, but some patients suffer from side effects. The toxicity of radiotherapy and chemotherapy should be estimated. For validating the patient's improvement in treatments, a patient response prediction system is essential. In this paper, a Deep Learning (DL) based patient response prediction system is developed to effectively predict the response of patients, predict prognosis and inform the treatment plans in the early stage. The necessary data for the response prediction are collected manually. The collected data are then processed through the feature selection segment. The Repeated Exploration and Exploitation-based Coati Optimization Algorithm (REE-COA) is employed to select the features. The selected weight features are input into the prediction process. Here, the prediction is performed by Multi-scale Dilated Ensemble Network (MDEN), where we integrated Long-Short term Memory (LSTM), Recurrent Neural Network (RNN) and One-dimensional Convolutional Neural Networks (1DCNN). The final prediction scores are averaged to develop an effective MDEN-based model to predict the patient's response. The proposed MDEN-based patient's response prediction scheme is 0.79%, 2.98%, 2.21% and 1.40% finer than RAN, RNN, LSTM and 1DCNN, respectively. Hence, the proposed system minimizes error rates and enhances accuracy using a weight optimization technique.
预测患者对放疗的反应以及出现长期有害健康影响的可能性，将大大改进个性化治疗方案。由于持续暴露于辐射，可能会发生心血管疾病和肺纤维化。为了预测患者对化疗的反应，卷积神经网络（CNN）技术被广泛使用。借助放疗可以诊断癌症疾病，但一些患者会出现副作用。应该评估放疗和化疗的毒性。为了验证患者在治疗中的改善情况，患者反应预测系统至关重要。在本文中，开发了一种基于深度学习（DL）的患者反应预测系统，以有效预测患者的反应、预测预后并在早期为治疗方案提供参考。反应预测所需的数据是手动收集的。然后通过特征选择部分对收集到的数据进行处理。采用基于重复探索与利用的浣熊优化算法（REE-COA）来选择特征。将所选的权重特征输入到预测过程中。在这里，预测由多尺度扩张集成网络（MDEN）执行，我们在其中集成了长短期记忆（LSTM）、递归神经网络（RNN）和一维卷积神经网络（1DCNN）。对最终预测分数进行平均，以开发一个基于MDEN的有效模型来预测患者的反应。所提出的基于MDEN的患者反应预测方案分别比随机网络（RAN）、RNN、LSTM和1DCNN精确0.79%、2.98%、2.21%和1.40%。因此，所提出的系统使用权重优化技术将错误率降至最低并提高了准确性。
