Radiation

Palliative radiotherapy (RT) is frequently delivered in time-sensitive clinical settings and requires rapid, patient-centred decision-making while balancing symptom burden, prognosis, comorbidities, treatment burden, and individual goals of care. Increasing demand for palliative RT and workforce pressures have stimulated the development of advanced practice models involving radiation therapy professionals. This review examines the evolving role of Advanced Practice Radiographers (APRs) in palliative RT, with particular attention to clinical practice, leadership, education, and research. APR-led models described internationally include patient assessment, treatment decision-making, treatment planning, treatment delivery, symptom and toxicity management, prescribing, follow-up, and coordination of supportive and palliative care. The available evidence indicates that appropriately trained APRs can undertake selected activities traditionally performed by medical staff within defined competency and governance frameworks, with reported benefits including increased service capacity, reduced waiting times, streamlined treatment pathways, continuity of care, and positive patient experience. However, the evidence base remains heterogeneous and is predominantly focused on feasibility, acceptability, workforce capacity, and service-level outcomes. Evidence regarding patient-centred outcomes, including symptom control, quality of life, treatment burden, safety, and concordance with patients’ goals of care, remains limited. Considerable international variation also exists in education, regulation, scope of practice, professional autonomy, and organisational support. Advanced practice radiography therefore represents a developing model for delivering palliative RT, but further prospective evaluation is needed to establish its impact on patient, clinical, service, workforce, and health-system outcomes, and to identify the organisational and governance conditions required for sustainable implementation.

Objectives: To assess the impact of magnetic resonance imaging (MRI) versus computed tomography (CT) on target delineation and dosimetry in adjuvant vaginal cuff brachytherapy (VBT) for endometrial cancer. Methods: Twenty-five consecutive patients receiving adjuvant high-dose-rate VBT after hysterectomy were prospectively included. All underwent standardized treatment of 4 × 5 Gy in 5 mm tissue depth using a vaginal cylinder with a fixed loading pattern; individualized optimization was permitted for suboptimal coverage or proximity of organs at risk (OARs). For each patient, clinical target volume (CTV) and OARs (bladder, rectum, sigmoid, bowel) were contoured on both CT and MRI of the first fraction. Dosimetric parameters (CTV D90, D98, D50; OAR D2cc) were compared. Treatment characteristics and outcomes were analyzed retrospectively. Results: Median CTV was smaller on MRI (9.8 cm³) than on CT (13.6 cm³). CTV coverage was comparable between modalities, with mean EQD2 D90 of 5.32 Gy per fraction on MRI versus 5.10 Gy per fraction on CT (p = 0.433) and mean EQD2 D98 of 3.95 Gy per fraction on MRI versus 3.80 Gy per fraction on CT (p = 0.491). In contrast, EQD2 D50 was significantly higher on MRI-based plans than on CT-based plans (9.12 vs. 8.62 Gy per fraction, p = 0.012). OAR D2cc values showed no significant differences between modalities. Most patients (80%) were treated with the institutional standard plan, and 20% required individualized optimization. After a median follow-up of 25 months (range 0–72), five patients (20%) recurred (one local, one regional, three distant), and one (4%) died. No grade ≥ 3 gastrointestinal or genitourinary toxicities occurred among patients with available data. Conclusions: In this small exploratory cohort, MRI-based planning delineated smaller CTVs, whereas the evaluated target-coverage and OAR-dose parameters were broadly similar between modalities. These findings suggest that CT-based planning may be adequate for many routine cases, with MRI potentially useful for selected, anatomically complex patients. Confirmation in larger multicenter studies is required.

Precision radiotherapy is becoming increasingly complex, requiring integration of multimodal imaging, treatment planning, adaptive radiotherapy, and diverse clinical data. This paper proposes agentic AI as a responsible clinical decision-support framework in which multiple specialized AI agents collaborate across imaging, contouring, treatment planning, quality assurance, adaptive radiotherapy, response monitoring, and multidisciplinary decision-making. Rather than replacing clinicians, these agents function within a coordinated ecosystem that emphasizes explainability, safety, fairness, privacy, regulatory compliance, and meaningful human oversight. Although challenges remain in clinical validation, interoperability, workflow integration, and governance, agentic AI offers a promising approach to transforming isolated AI applications into transparent, human-centered systems that support personalized cancer treatment.