Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

28
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
28

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Retraction Note: Prediction of malnutrition in kids by integrating ResNet-50-based deep learning technique using facial images.

Scientific reports·2026
Same author

Fuzzy min-max dual motif-guided heterogeneous Dandelion graph multi-scale residual attention network for healthcare AI misclassification reduction.

Scientific reports·2026
Same author

Quantitative investigation on working memory patterns through EEG based on visual attention task for children with learning disability.

Frontiers in systems neuroscience·2026
Same author

Path informed adaptive trend analyzer using Hilbert Huang transform for electric vehicle driving range prediction.

Scientific reports·2026
Same author

Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.

Scientific reports·2026
Same author

ZuraNet: a hybrid rule-based intrusion detection system with deep learning for securing SCADA-driven cyber-physical systems.

Scientific reports·2026

Related Experiment Video

Updated: May 12, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

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

Nalini Manogaran1, Nirupama Panabakam2, Durai Selvaraj3

  • 1Department of CSE, S.A. Engineering College (Autonomous), Chennai, 600077, Tamil Nadu, India.

Scientific Reports
|May 5, 2025
PubMed
Summary

This study introduces a Deep Learning (DL) model for predicting patient response to radiotherapy and chemotherapy, improving treatment plans and reducing side effects. The Multi-scale Dilated Ensemble Network (MDEN) enhances prediction accuracy and minimizes errors.

Keywords:
Long-short term memoryMulti-scale dilated ensemble networkOne-dimensional convolutional neural networksPatient’s response predictionRecurrent neural networkRepeated exploration and exploitation-based coati optimization algorithm

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

907
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.4K

Related Experiment Videos

Last Updated: May 12, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

907
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.4K

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Radiotherapy and chemotherapy are vital cancer treatments but can cause severe side effects like cardiovascular disease and pulmonary fibrosis.
  • Accurate prediction of patient response and toxicity is crucial for personalized treatment planning and improved outcomes.
  • Existing methods, such as Convolutional Neural Networks (CNN), show promise but require further enhancement for precise forecasting.

Purpose of the Study:

  • To develop a Deep Learning (DL) based patient response prediction system for radiotherapy and chemotherapy.
  • To accurately predict patient response and prognosis, enabling early-stage informed treatment decisions.
  • To minimize treatment-related toxicities and enhance overall patient care through precise forecasting.

Main Methods:

  • A Deep Learning (DL) model integrating Long-Short term Memory (LSTM), Recurrent Neural Network (RNN), and One-dimensional Convolutional Neural Networks (1DCNN) was developed.
  • The Repeated Exploration and Exploitation-based Coati Optimization Algorithm (REE-COA) was utilized for optimal feature selection from manually collected patient data.
  • A Multi-scale Dilated Ensemble Network (MDEN) was employed for prediction, with final scores averaged to create a robust predictive model.

Main Results:

  • The proposed MDEN-based model demonstrated superior performance compared to existing methods.
  • The MDEN scheme achieved improvements of 0.79%, 2.98%, 2.21%, and 1.40% over RAN, RNN, LSTM, and 1DCNN, respectively.
  • The system effectively minimizes error rates and enhances prediction accuracy through optimized feature weighting.

Conclusions:

  • The developed DL-based MDEN system offers a significant advancement in predicting patient response to cancer therapies.
  • This approach holds the potential to personalize radiotherapy and chemotherapy, leading to better patient outcomes and reduced adverse effects.
  • The study highlights the efficacy of integrating advanced DL techniques and optimization algorithms for robust clinical decision support.