Hui  Xu

Tianjin Medical University Cancer Institute and Hospital, Hexi, 300210, Tianjin, China
Author    Correspondence author
Cancer Genetics and Epigenetics, 2024, Vol. 12, No. 3   
Received: 28 Apr., 2024    Accepted: 30 May, 2024    Published: 13 Jun., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

This study explores a comprehensive approach for predicting treatment response in advanced solid tumors, with a focus on the effectiveness of combining molecular, imaging, and clinical data to improve prediction accuracy. Emphasis was placed on the role of advanced computational models, including machine learning and artificial intelligence, in improving predictive capabilities. By integrating diverse data sources, these methods provide a comprehensive understanding of tumor biology and treatment response, ultimately leading to more personalized and effective treatment strategies. The significance of these comprehensive methods for research and clinical practice was also discussed, pointing out their potential to improve patient prognosis. The future directions include molecular analysis, advances in computational algorithms, collaborative research, and data sharing programs, all aimed at improving the accuracy and applicability of predictive models in precision oncology.

Integrative approaches; Predictive models; Advanced solid tumors; Precision oncology; Personalized treatment strategies