Martina Iezzi

Adaptive skin radiotherapy (ASRT): Clinical outcomes and dosimetric evaluation of a time-efficient approach for online adaptive MRI-guided radiotherapy in thorax

Francesco Catucci, Lana Smiljanic, Francesco Preziosi, Luca Vellini et autres

Introduction: The thorax presents unique challenges in radiotherapy: although stereotactic ablative radiotherapy (SABR) achieves high local control in NSCLC and pulmonary metastases, further improvements are desirable, particularly dose escalation in radioresistant metastases and planning target volume (PTV) margin reduction to minimize toxicity. …

nl, it, us (code pays fourni par la source)

Optimizing thoracic synthetic computed tomography generation from magnetic resonance imaging: the role of Fourier transform and other key factors

Alessandro Bombini, Luca Vellini, Flaviovincenzo Quaranta, Jacopo Lenkowicz et autres

Background and purpose: Magnetic Resonance Imaging-only (MRI-only) workflows are an emerging strategy in radiotherapy, with artificial intelligence (AI) playing a central role in generating synthetic computed tomography (sCT) images. The thorax remains a particularly difficult region due to marked electron density (ED) …

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AI-driven online adaptive radiotherapy in prostate cancer treatment: considerations on activity time and dosimetric benefits

Francesco Preziosi, Althea Boschetti, Francesco Catucci, Claudio Votta et autres

AIMS: Recent advances in Radiotherapy have led to the development of online adaptive RT (oART), a procedure addressing inter-fraction anatomical variations. Integrating artificial intelligence (AI) into the oART procedure speeds up the process and reduces user dependency. This study investigates the dosimetric …

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703 Stereotactic Body Radiation therapy (SBRT) with MRI-Linac in oligometastatic gynaecological cancer patients: a systematic review

Donato Pezzulla, Alessia Re, Francesco Catucci, Martina Iezzi et autres

it, us (code pays fourni par la source)

3840 Dosimetric and Clinical Outcomes of MRI-Linac Online Adaptive Radiotherapy for Prostate Cancer: A Single- Center Experience

Francesco Preziosi, Francesco Catucci, Althea Boschetti, Martina Iezzi et autres

3790 Implementation of a Novel Adaptive Strategy for Thoracic Tumors Using MRI-Linac

Francesco Catucci, Francesco Preziosi, Althea Boschetti, Alessia Re et autres

A deep learning algorithm to generate synthetic computed tomography images for brain treatments from 0.35 T magnetic resonance imaging

Luca Vellini, Flaviovincenzo Quaranta, Sebastiano Menna, Elisa Pilloni et autres

Background and Purpose: The development of Magnetic Resonance Imaging (MRI)-only Radiotherapy (RT) represents a significant advancement in the field. This study introduces a Deep Learning (DL) algorithm designed to quickly generate synthetic CT (sCT) images from low-field MR images in the brain, …

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Adaptive Skin Radiotherapy (Asrt): Clinical Outcomes and Dosimetric Evaluation of a Novel Simplified Approach for Thoracic Mri-Guided Radiotherapy

Francesco Catucci, Lana Smiljanic, Francesco Preziosi, Luca Vellini et autres

it, nl, us (code pays fourni par la source)

Integrating Interventional Oncology with Other Cancer Treatment Options for Bone Tumors: Radiotherapy

Gian Carlo Mattiucci, Francesco Preziosi, P. Foro, Martina Iezzi

it, es (code pays fourni par la source)

A Deep Learning Approach for the Fast Generation of Synthetic Computed Tomography from Low-Dose Cone Beam Computed Tomography Images on a Linear Accelerator Equipped with Artificial Intelligence

Luca Vellini, Sergio Zucca, Jacopo Lenkowicz, Sebastiano Menna et autres

Artificial Intelligence (AI) is revolutionising many aspects of radiotherapy (RT), opening scenarios that were unimaginable just a few years ago. The aim of this study is to propose a Deep Leaning (DL) approach able to quickly generate synthetic Computed Tomography (CT) images …

it (code pays fourni par la source)

1816: MR-only workflow in the brain: a DL algorithm to generate synthetic CT starting from 0.35T MRI

Luca Vellini, Jacopo Lenkowicz, Claudio Votta, Sebastiano Menna et autres

Clinical Validation of a Deep-Learning Segmentation Software in Head and Neck: An Early Analysis in a Developing Radiation Oncology Center

Andrea D’Aviero, Alessia Re, Francesco Catucci, Danila Piccari et autres

BACKGROUND: Organs at risk (OARs) delineation is a crucial step of radiotherapy (RT) treatment planning workflow. Time-consuming and inter-observer variability are main issues in manual OAR delineation, mainly in the head and neck (H & N) district. Deep-learning based auto-segmentation is a …

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