Targeted Chemotherapy with Personalized Immunotherapy

An AI Approach

Abhishek Kumar, Prasenjit Das, Pramod Singh Rathore, Sachin Ahuja, Chetan Sharma

• Publisher: Wiley-Scrivener
• Year: 2025
• Print ISBN: 9781394270583
• eBook ISBN: 9781394270606
• Edition: 1
• Topic: Medicine
• Subtopic: Immunology

Targeted Chemotherapy with Personalized Immunotherapy: An AI Approach is an essential guide for healthcare teams, offering groundbreaking insights into novel immunotherapies and personalized treatments to improve cancer patient care and quality of life.

In the last 20 years, there have been significant leaps forward in the treatment of cancer. We now have a far better understanding of how our cells interact with one another, how cancer suppresses and hides from the immune system, and how to support the body in reacting to stop the spread of cancer. Nevertheless, there is still a great deal more to learn in this field. Researchers are working to develop methods that will help pinpoint the most effective treatment for patients. Through this research, they have discovered that, for certain patients, the best results may be reached by combining precisely targeted chemotherapy with personalized immunotherapy.

Instead of treating patients with medications that are detrimental to the body as a whole, researchers now aim to identify the molecules that play an essential part in the communication that takes place between cells. This study will help pave the way for the development of novel immunotherapies that will help the body in its fight against cancer. In order to accurately plan cancer treatment, participation from a number of different members of the healthcare team is essential. This book is a comprehensive guide for all members of this team, providing insights into groundbreaking new treatments to cure more patients and improve quality of life.

Table of contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Contents
• Foreword
• Preface
• Chapter 1 Assessing Predictive Accuracy: Model Validation in Cancer Diagnostics
• Chapter 2 Applying Transfer Learning to Accelerate Cancer Classification and Prediction
• Chapter 3 Artificial Intelligence in Cancer Screening: Innovations in Early Detection
• Chapter 4 Comprehensive Approaches to Survival Analysis and Prognostic Modeling in Cancer Research: Integrating Statistical Techniques, and Clinical Variables
• Chapter 5 Exploring Cancer Therapeutics: A Collection of Case Studies
• Chapter 6 Predicting Cancer Outcomes Using Transfer Learning: Harnessing Pre-Trained Models and Cross-Domain Knowledge for Enhanced Prognosis and Personalized Treatment Strategies
• Chapter 7 Predicting Cancer Outcomes with RNNs: A Time Series Approach
• Chapter 8 AI in Cancer Screening and Early Detection
• Chapter 9 Challenges and Limitations of AI in Oncology
• Chapter 10 Predictive Models for Cancer-Related Lymphedema: Enhancing Telerehabilitation and Physiotherapy Management
• Chapter 11 Role of AI in the Prediction of Leukemia and AI-Driven Predictive Models for Rehabilitation Outcomes in Acute Lymphoblastic Leukemia
• Chapter 12 Data Privacy and Ethical Challenges in AI-Driven Cancer Care
• Chapter 13 Cancer Rehabilitation in the Era of Targeted Chemotherapy and Personalized Immunotherapy
• Chapter 14 Role of AI in Cancer Screening and Its Detection
• Chapter 15 Automated 3D U-Net Framework for Brain Tumor Segmentation and Classification with Insights Into AI-Driven Cancer Research Applications
• Chapter 16 Early Prediction of Bone Cancer: Integrating Deep Learning Models
• Chapter 17 Machine Learning Techniques for Predicting Epileptic Seizures: A Data-Driven Analysis Using EEG Signals
• Chapter 18 Transfer Learning in Cancer Research
• Chapter 19 Machine Learning Approaches for Early Detection of Cervical Cancer: A Comparative Study of Classification Models
• Chapter 20 Interactive Data Management for Cancer Care: Leveraging Electronic Health Records and Proteomic Data
• Chapter 21 Artificial Intelligence–Driven Personalized Cancer Treatment
• Chapter 22 Revolutionizing Breast Cancer Detection: Emerging Trends and Future Technologies
• Chapter 23 Future of Neurological Research: Leveraging Artificial Intelligence for Precision and Discovery
• Chapter 24 Cervical Cancer Detection Using Machine Learning
• Chapter 25 Deep Learning Techniques–Based Medical Image Segmentation in Cervical Cancer
• Index
• Also of Interest
• EULA