Radiotherapy treatment planning is a vital component in modern cancer management, requiring precise delivery of radiation to tumour regions while preserving surrounding healthy tissues. Conventional planning approaches are often manual, time-intensive, and limited in their ability to adapt to patient-specific variations. To address these challenges, this study explores a machine learning-driven framework for intelligent radiotherapy planning. The proposed approach leverages advanced deep learning architectures, particularly Convolutional Neural Networks (CNNs), along with classical machine learning models such as Support Vector Machines (SVM) and Random Forests (RF), to enhance tumour segmentation, dose estimation, and treatment optimization. By utilizing multimodal medical imaging data, including CT, MRI, and PET scans, the system enables accurate identification of tumour boundaries and supports data-driven clinical decisions. Furthermore, the integration of techniques such as multimodal learning and reinforcement-based optimization improves the adaptability and precision of treatment planning. The results demonstrate that the proposed framework achieves high segmentation accuracy and reliable dose prediction, contributing to improved treatment effectiveness and reduced adverse effects. This work highlights the transformative potential of machine learning in enabling personalized, efficient, and intelligent radiotherapy solutions.
Chamundeswari et al. (Thu,) studied this question.