End‐to‐end automated radiotherapy planning using transfer learning to overcome data scarcity

Biomedical Imaging CFB-GBM v2.0: An Augmented Longitudinal Dataset for Multi-Modal Glioblastoma Segmentation, Radiomics, and RANO Progression Tracking

Transformer‐Based Deep Learning Integrating Multi‐Regional MRI and Clinical Features for Predicting Biochemical Recurrence After Radical Prostatectomy: A Multicenter Study

Existing deep learning models for predicting biochemical recurrence (BCR) after radical prostatectomy often ignore the biological context beyond the tumor. This study aims to develop and validate a transformer‐based model combining multi‐regional magnetic resonance imaging (MRI) features and clinical data to improve BC...

Editorial: Radiomics and AI-driven deep learning for cancer diagnosis and treatment

, recurrence, treatment response, and treatment-related risks. Several studies also illustrate a broader shift toward automation, multimodal data fusion, and clinically scalable arti fi cial intelligence (AI) pipelines, including applications to histological images and the integration of histopathology with RNA sequenci...

End‐to‐end differentiable volumetric modulated arc therapy optimization via computation graph–driven automatic differentiation

Conventional volumetric modulated arc therapy (VMAT) optimization relies on gradient‐based methods in which analytical gradients must be explicitly derived for each objective function and machine constraint. This dedicated mathematical derivation imposes substantial barriers to extending the optimization framework with...

A hybrid deep learning framework for automated treatment planning in cervical cancer radiotherapy

The clinical feasibility of the proposed HALO framework for cervical cancer radiotherapy in producing high‐quality and deliverable IMRT plans is demonstrated, demonstrating that the proposed automated paradigm can serve as a stand‐alone platform for treatment planning.

Clinical adoption of radiobiological models and artificial intelligence in radiotherapy treatment planning: a cross-sectional practice survey

Introduction: Radiobiological models optimising tumour control probability (TCP) and normal tissue complication probability (NTCP) underpin effective radiotherapy treatment planning; however, real-world adoption – particularly in low-and middle-income country (LMIC) settings and for plan evaluation – remains poorly cha...