An efficient patient’s response predicting system using multi-scale dilated ensemble network framework with optimization strategy

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 t...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 15713 - 21
Hauptverfasser: Manogaran, Nalini, Panabakam, Nirupama, Selvaraj, Durai, Seerangan, Koteeswaran, Khan, Firoz, Selvarajan, Shitharth
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 05.05.2025
Nature Publishing Group
Nature Portfolio
Schlagworte:
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639/705
Algorithms
Cancer
Cardiovascular diseases
Chemotherapy
Deep Learning
Fibrosis
Humanities and Social Sciences
Humans
Long-short term memory
Lung diseases
Multi-scale dilated ensemble network
multidisciplinary
Neoplasms - radiotherapy
Neural networks
Neural Networks, Computer
One-dimensional convolutional neural networks
Optimization techniques
Patients
Patient’s response prediction
Predictions
Prognosis
Radiation therapy
Recurrent neural network
Repeated exploration and exploitation-based coati optimization algorithm
Science
Science (multidisciplinary)
Short term memory
Toxicity
Multi-scale dilated ensemble network
Patient’s response prediction
Long-short term memory
Recurrent neural network
Repeated exploration and exploitation-based coati optimization algorithm
One-dimensional convolutional neural networks
ISSN:2045-2322, 2045-2322
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Zusammenfassung: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-00401-y