Title
AI based prediction of chemotherapy response in cancer patients: A cross-sectional study
Authors
Vipin Kharade1,*, Tapesh Pounikar2 & Chetna Devkar3
Affiliation
1Department of Radiation Oncology, All India Institute of Medical Sciences Bhopal, Madhya Pradesh, India; 2Department of Radiation Oncology, Chhindwara Institute of Medical Sciences, Chinaware, Madhya Pradesh, India; 3Department of Computer Sciences & Engineering, National Institute of Technology, Bhopal, Madhya Pradesh, India; *Corresponding author
Vipin Kharade - E-mail: vipin.radiotherapy@aiimsbhopal.edu.in
Tapesh Pounikar - E-mail: tapeshpounikar@gmail.com
Chetna Devkar - E-mail: rgtu.chetna@gmail.com
Article Type
Research Article
Date
Received March 1, 2026; Revised March 31, 2026; Accepted March 31, 2026, Published March 31, 2026
Abstract
Predicting chemotherapy response remains challenging due to the variability in tumor biology, genetics and host factors. Therefore, it is of interest to evaluate the effectiveness of AI-based predictive models in assessing chemotherapy response in cancer patients. AI models demonstrated high accuracy in distinguishing responders from non-responders, with those integrating multiple data types outperforming single-domain models. The integration of clinical, radiological and laboratory data significantly enhanced prediction accuracy. Therefore, this study advances knowledge by highlighting the potential of AI in revolutionizing chemotherapy response prediction and contributing to more targeted, effective cancer treatments
Keywords
Artificial intelligence, chemotherapy response, machine learning, oncology, personalized medicine
Citation
Kharade et al. Bioinformation 22(3): 1515-1517 (2026)
Edited by
Ritik Kashwani
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
