Positron emission tomography-guided radiation therapy: An overview of the RefleXion SCINTIX biology-guided platform

PubMed Central · 2026-01-01

Defining Stereotactic Body Radiation Therapy: A (Hi)story of Precision and Accuracy.

Open Access CRIS of the University of Bern · 2026-03-23

Positron emission tomography-guided radiation therapy: An overview of the RefleXion SCINTIX biology-guided platform.

PubMed · 2026-01-01

Purpose: To present an introduction and overview of the RefleXion™ X1 system using SCINTIX, a radiation therapy platform guided by positron emission tomography (PET) designed for biology-guided radiation therapy (BgRT). Methods: The RefleXion X1 system integrates a 6 MV flattening-filter-free linear accelerator, onboard kilovoltage computed tomography (kVCT), and dual PET arcs. The system enables both conventional kVCT-based image-guided radiation therapy (IGRT) and PET-guided treatment with SCINTIX. Compared to the conventional IGRT radiation treatment, the SCINTIX workflow includes a planning PET session prior to treatment planning and a PET pre-scan before each fraction. Fluorodeoxyglucose (FDG) was used as the PET tracer. An activity concentration (AC) ≥5 kBq/ml) and normalized target signal (NTS) ≥2.7 for planning PET session and an NTS ≥2.0 for treatment) are evaluated to decide whether the patient is eligible to treat with the SCINTIX. Results: The RefleXion X1 with SCINTIX treatment system was successfully installed for PET-guided radiation therapy. Detailed SCINTIX and SBRT/IGRT treatment workflows are described. SCINTIX-related shielding design, including uptake room specifications, is presented. Representative clinical cases include lung and rib lesions treated with SCINTIX, and a liver case treated with SBRT. Conclusion: SCINTIX enables PET-guided radiation therapy, offering a novel approach for BgRT. The integration of high-resolution kVCT further supports conventional IGRT workflows. Initial clinical experience demonstrates feasibility, accuracy, and potential for expanded adoption in personalized radiation therapy.

Defining Stereotactic Body Radiation Therapy: A (Hi)story of Precision and Accuracy

International Journal of Radiation Oncology*Biology*Physics · 2026-03-17

Radiobiology for Brachytherapy

2026-04-01

The interaction of radiation with biological tissue is influenced by a multitude of factors. These may be related to the host, for example, intrinsic radiosensitivity, oxygenation status, proliferation rate and damage repair capacity, or to the radiation field (e.g., dose, temporal and spatial pattern of dose delivery and radiation quality). The effect of a given radiation schedule is the result of a complex interplay among these parameters. Radiobiological models provide a mathematical link between these factors and the endpoint of interest.

18F-FDG PET Image Metric Analysis of Clinical Patients Treated Using Biological-Guided Radiotherapy (BgRT)

International Journal of Radiation Oncology*Biology*Physics · 2025-09-01

Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT

Precision Radiation Oncology · 2025-03-01 · 21 citations

Background: We hypothesize generative adversarial networks (GAN) combined with self-attention (SA) and aggregated residual transformations (ResNeXt) perform better than conventional deep learning models in differentiating hepatocellular carcinoma (HCC). Attention modules facilitate concentrating on salient features and suppressing redundancies, while residual transformations can reuse relevant features. Therefore, we aim to propose a GAN+SA+ResNeXt deep learning model to improve HCC prediction accuracy. Methods: 228 multiphase CTs from 57 patients were retrospectively analyzed with local IRB's approval, where 30 patients were pathologically confirmed with HCC and the rest 27 were non-HCC. Pre-processing of automatic liver segmentation and Hounsfield unit (HU) normalization was performed, followed by deep learning training with five-fold cross validation in a conventional 3D GAN, a 3D GAN+A, and a 3D GAN+A+ ResNeXt, respectively (training: testing ∼ 4:1). Area under receiver operating characteristics curves (AUROC), accuracy, sensitivity and specificity of HCC prediction were evaluated. Results: Results showed the proposed method had larger AUROC (95%), better accuracy (91%) and sensitivity (93%) with acceptable specificity (88%) and prediction time (0.04s). Deep GAN with attentions and residual transformations for HCC diagnosis using multiphase CT is feasible and favorable with improved accuracy and efficiency, which harbors clinical potentials in differentiating HCC from other benign or malignant liver lesions.

Recent Innovations to Foster Personalized Adaptive Radiation Therapy

International Journal of Radiation Oncology*Biology*Physics · 2025-06-06 · 1 citations

Commissioning of the RefleXion X1 Linear Accelerator and Development of a Consensus Reference Beam Model

Medical Physics · 2025-11-26 · 1 citations

BACKGROUND: Consistent commissioning of novel radiation therapy systems remains a critical step to ensure safety and accuracy of radiation delivery. The initial installations of the Reflexion X1 linear accelerator (linac) and Treatment Planning System (TPS) allowed for development of a consistent method for beam model commissioning and convergence towards a consensus reference beam model. PURPOSE: To define reference commissioning, quality assurance (QA), and quality control (QC) data for the RefleXion X1 linac and TPS. METHODS: Acquired beam measurements and TPS model data for the first seven installed X1 linear accelerators were compared. Each site's beam model was optimized based on (1) measurements of static beam geometry in liquid water and (2) treatment beam geometry in various phantoms using diodes, ion chambers, film, and array detectors. Treatment plans were generated in a consistent manner, and their measured doses were compared across all institutions. The observed differences in machine-specific parameters as measured during QC, and the corresponding differences in beam models are reported. Evaluation of a candidate consensus model for all systems was also performed including cross comparison of measurements and different site-specific beam models. RESULTS: Static beam data showed agreement for all X1s in percent depth dose, lateral beam profiles, and output factors, with inter-linac differences generally < 2% in all metrics considered. Notable differences in longitudinal field widths and beam centering implies a consensus beam model is not ideal for the X1. Treatment planning and delivery also demonstrated larger differences which required unique beam model optimization for all treatment sites. CONCLUSIONS: A systematic method for Reflexion planning and dosimetric commissioning was developed including a standardized set of treatment plans for model validation. Inter-institution differences between measurements and beam models suggest a gold beam reference dataset may be achievable with improved QC at installation, but at this time individual, site-specific beam models are necessary to encompass machine-specific variations.

4098 Case report of the first-in-human two modality radiotherapy treatment in a single plan using biology-guided radiotherapy

Radiotherapy and Oncology · 2025-05-01