MRIgRT real-time target tracking: TrackRAD2025 challenge report Tom Julius Blöcker, Pia A.W. Görts, Yiling Wang, Elia Lombardo, Adrian Thummerer, Yu Fan, Yue Zhao, Christianna Iris Papadopoulou, Coen Hurkmans, Rob H.N. Tijssen, Davide Cusumano, Martijn PW Intven, Pim Borman, Marco Riboldi, Denis Dudáš, Hilary L. Byrne, Lorenzo Placidi, Marco Fusella, Michael Jameson, Miguel A. Palacios, Paul Cobussen, Tobias Finazzi, Shyama U. Tetar, Cornelis J.A. Haasbeek, Paul Keall, Matteo Maspero, Christopher Kurz, Amparo Soeli Betancourt Tarifa, Kailin He, Shengqian Zhu, Ying Song, Guangjun Li, Junjie Hu, Felix Knispel, Sergios Gatidis, Hung Chu, Jiapan Guo, Maximilian Nielsen, Thilo Sentker, Valentin Boussot, Cédric Hémon, Jing Ni, Konstantinos Georgas, Theodoros P. Vagenas, George K. Matsopoulos, Guillaume Landry Medical Image Analysis, 2026 Magnetic resonance imaging (MRI)-guided radiotherapy (MRIgRT) integrates MRI with linear accelerators (MRI-linacs), enabling real-time motion management based on temporally resolved 2D MRI (cine-MRI). Current systems rely on template matching or deformable image registration for radiotherapy target (typically the gross tumor volume) localization, which allows beam gating. Further advances in localization could support more precise and efficient delivery methods. https://trackrad2025.grand-challenge.org/ was organized to provide a common dataset to benchmark algorithms for MRIgRT target tracking in 2D+t cine-MRI. Participants propagated target segmentation masks from an initialization frame across subsequent frames. The dataset comprised sagittal cine-MRI scans of 585 cancer patients undergoing radiotherapy at 0.35 T and 1.5 T MRI-linacs at six different institutions, with expert-annotated targets in 108 sequences. Target sites included the thorax (179 cases), abdomen (266 cases), and pelvis (140 cases). A total of 477 unlabeled and 50 labeled cases were provided for training purposes, 58 cases were kept private for preliminary testing (8) and final evaluation (50). The algorithms submitted by participants were executed on the challenge platform and assessed using metrics in three categories: geometric accuracy, surrogate dose accuracy and execution speed. Rankings were derived via a Rank-Then-Mean scheme. TrackRAD2025 attracted 148 registrations from 28 countries, 100 preliminary submissions and 24 final submissions from 14 teams. The top five methods achieved mean Dice similarity coefficients >0.87 and Euclidean center distances <2.1 mm, comparable to interobserver variability. Leading top five solutions featured foundation models with (4) or without (1) finetuning. Field strength had minimal effect on performance and tracking worked better for the pelvis with reduced motion amplitude compared to the thorax and abdomen cases, which achieved equivalent performance. TrackRAD2025 established a benchmark for MRIgRT tracking on multi-institutional cine-MRI data, highlighting foundation models as promising for clinical translation.
Stereotactic body radiotherapy combined with immunotherapy: a systematic review focus on timing and toxicity profile Carlo Gugliemo Cattaneo, Giuditta Chiloiro, Angela Romano, Giulia Panza, Matteo Galetto, Matteo Nardini, Lorenzo Placidi, Maria Antonietta Gambacorta, Luca Boldrini Technical Innovations and Patient Support in Radiation Oncology, 2026 Background: The combination of Stereotactic Body Radiotherapy (SBRT) with immune checkpoint inhibitors (ICIs) has gained increasing interest due to its potential to enhance antitumor immune responses. However, the optimal timing, dose, and safety profile of this combined approach remain unclear, and available clinical evidence is highly heterogeneous. Methods: A systematic review of the literature was conducted in accordance with PRISMA guidelines. PubMed/MEDLINE, Embase, and Cochrane Library databases were searched for clinical studies evaluating the combination of SBRT and ICIs. Studies were included if they reported toxicity outcomes and involved patients with solid tumors treated with SBRT in combination with ICIs. Data on study design, patient characteristics, SBRT dose and fractionation, treatment sequencing, and treatment-related toxicities were extracted and qualitatively analyzed. Results: values spanning approximately 43-113 Gy; detailed dosimetric data were lacking in about 38% of studies. The overall incidence of grade ≥ 3 treatment-related toxicity was comparable between concurrent and sequential approaches (approximately 12-15%). High-grade adverse events were predominantly immune-related, with pneumonitis more frequently reported in concurrent regimens, while gastrointestinal and dermatologic toxicities were slightly more common in sequential strategies. No consistent signal of increased severe toxicity attributable to the addition of SBRT was observed. Conclusions: Current clinical evidence suggests that the combination of SBRT and ICIs is generally feasible and does not appear to systematically increase the risk of severe toxicity compared with immunotherapy alone. However, substantial heterogeneity in study design, SBRT parameters, treatment sequencing, and toxicity reporting limits definitive conclusions regarding safety and efficacy. Future prospective trials with harmonized protocols, standardized toxicity attribution, and integrated translational endpoints are needed to define the optimal therapeutic window for SBRT-ICI combinations across different tumor types.
Standardizing MRI-only radiotherapy commissioning: Benchmark dataset and acceptance levels from the MESCAL initiative Davide Cusumano, Matteo Maspero, Luca Vellini, Emilie Alvarez-Michael, Anaïs Barateau, Igor Bessieres, Omar Bohoudi, Riccardo Dal Bello, Stéphane Dufreneix, Christopher Kurz, Guillaume Landry, Lisa Milan, Miguel A. Palacios, Gabriella Pastore, Charlotte Robert, Enrica Seravalli, Natalia Tejedor, Petra Trnkova, Fernanda Villegas, Laure Vieillevigne, Jonathan Wyatt, Poonam Yadav, Lorenzo Placidi, Marco Fusella Radiotherapy and Oncology, 2026
Terminology Proposal for Adaptive Particle Therapy Classification and Strategies Populaire Pieter, Simoes Rita, Nenoff Lena, Trnkova Petra, Placidi Lorenzo, Stützer Kristin, van Weerd Eva, Albertini Francesca International Journal of Particle Therapy, 2026 Adaptive radiotherapy (ART) is an increasingly prominent concept in modern radiation oncology, offering the ability to adjust treatment in response to patient-specific anatomical and physiological changes. The concept was first introduced in 1995 as a "closed-loop radiation treatment process where the treatment plan can be modified using a systematic feedback of measurements.<sup>"1</sup> In this manuscript, we use ART to denote a treatment paradigm in which the treatment plan is intentionally modified during the course of radiotherapy in response to observed or anticipated changes in patient geometry, anatomy, or biology, with the aim of maintaining or improving the therapeutic ratio. More recently, ART has gained momentum due to advances in imaging, computing, and automation, which enable such feedback to be implemented more systematically and at different timescales.2, 3, 4, 5 Therefore, ART could eventually become a standard of care for some traditionally difficult-to-treat indications. These developments are particularly significant for particle therapy (PT), which is highly sensitive to (anatomical) variations. While PT offers dosimetric advantages over conventional photon therapy, its precision also means that even small deviations from the planned density and/or geometry can translate into clinically relevant changes in water-equivalent path length and thus proton range, with substantial impact on the dose distribution.
Towards quantitative MRI Driving online adaptive MRgRT for lung tumors I. Moretti, M. Nardini, C. Mazzarella, A. Romano, G. Chiloiro, G. Panza, M. Galetto, H.E. Tran, K. Zormpas-Petridis, L. Boldrini, M.De Spirito, L. Placidi Physica Medica, 2026 INTRODUCTION: Successful delivery of lung cancer radiotherapy is hindered by respiratory motion, low soft-tissue contrast and anatomical variabilities, often compromising precision. Magnetic Resonance Image-guided Radiotherapy (MRgRT) has emerged as a promising approach, particularly with hybrid MR-Linac systems that offer superior soft-tissue visualization and enable online adaptive radiotherapy (online MRgART). PURPOSE: This review synthesizes current evidence for online MRgART in lung cancer and examines emerging roles for quantitative MRI (qMRI). A PubMed search covering the period from January 2020 to September 2025 identified 19 studies, 3 of which focused specifically on quantitative imaging. MAIN FINDINGS: Online MRgART consistently demonstrated workflow feasibility, frequent online adaptation, improved target coverage while respecting Organs-At-Risk (OARs) constraints and encouraging Local Control (LC) with low high-grade toxicity. qMRI on MR-Linacs, most commonly Diffusion-Weighted Imaging (DWI) and cine-MRI-derived ventilation/perfusion mapping, showed feasibility and early signals for treatment adaptation, toxicity prediction and response assessment. PRINCIPAL CONCLUSIONS: qMRI studies integrated in online MRgART for lungs are, at present, extremely limited; nevertheless, establishing a clear snapshot of the current state-of-the-art is essential, as this topic is expected to become highly prevalent and of particular interest in the near future. To our knowledge, this is the first review centered on online MRgART for lung tumors, with a dedicated subsection summarizing the nascent evidence on qMRI. Looking ahead, integrating AI-driven motion compensation, auto-segmentation and adaptive replanning with qMRI-enabled biomarkers could standardize workflows and accelerate truly personalized online MRgART. Prospective multi-center studies are needed to validate biomarkers and demonstrate clinical benefit.
GTV segmentation in MRI guided radiotherapy with promptable foundation models Tom Julius Blöcker, Nikolaos Delopoulos, Miguel A Palacios, Sebastian Klüter, Juliane Hörner-Rieber, Carolin Rippke, Lorenzo Placidi, Luca Boldrini, Vincenzo Frascino, Nicolaus Andratschke, Michael Baumgartl, Riccardo Dal Bello, Sebastian N Marschner, Claus Belka, Stefanie Corradini, Denis Dudas, Marco Riboldi, Christopher Kurz, Guillaume Landry Physics in Medicine and Biology, 2026 Objective . Magnetic resonance imaging (MRI) guided radiotherapy requires the delineation of gross tumor volumes (GTV) in daily MRI from MRI-linacs. Specialized models have been developed for this task for certain tumors. This study investigated an alternative, using promptable foundation models. Approach . Promptable foundation models were prompted with six different sparse geometric prompt types (points, boxes, 2D masks) to produce GTV segmentation masks, including Segment-anything 2 (SAM2), SAM2 fine-tuned for medical imaging (MedSAM2), and nnInteractive, an nnUnet-based promptable model for medical imaging. A diverse multi-institutional dataset of clinical GTV masks from the abdomen, lung, liver, pancreas, and pelvis sites on MRI scans from MRI-linacs was used to evaluate model outputs using various metrics, including the Dice similarity coefficient (DSC). Main results . The models produced segmentation masks comparable or superior to those from domain-specific models with median DSCs of up to 0.85 (nnInteractive-mask3 prompt). Prompts with more spatial information yielded better results with lower variance, with the effect reduced for nnInteractive and MedSAM2. These produced overall better results (median DSC over all prompt types 0.75 for nnInteractive, 0.70 for MedSAM2, 0.54 for SAM2). Significance . This investigation showed that promptable foundation models can in principle be used for GTV segmentation in MRI across multiple tumor types, although more research is necessary to reduce the variance and improve model performance.
Inverse consistency error for validating deformable image registration: an explorative study on computational phantoms Gianfranco Loi, Marco Fusella, Stefania Zara, Marica Vagni, Nicola Michielli, Orlando Zaccaria, Lorenzo Placidi, Pierfrancesco Franco, Filippo Molinari, Christian Fiandra Physics and Imaging in Radiation Oncology, 2026 Background and Purpose: Validation of deformable image registration (DIR) remains predominantly contourbased; this study evaluated inverse consistency error (ICE) as an automated voxelwise metric for DIR accuracy. Materials and Methods: Synthetic ground-truth DVFs were generated using geometric and head-and-neck (HN) digital phantoms undergoing controlled global and local deformations. DIR was performed with the ANACONDA algorithm in RayStation. ICE maps derived from clinical DVFs were compared with ground-truth registration error (GTRE), target registration error (TRE) from 20 anatomical landmarks, and mean distance to agreement (MDA) for 22 propagated ROIs. Results: Ground-truth DVFs showed negligible ICE values, confirming mathematical invertibility. In HN phantoms, median ICE and GTRE were 0.8 ± 0.2 mm and 1.6 ± 0.4 mm, respectively. ICE correlated strongly with GTRE (R = 0.85, p < 0.001) and moderately with TRE (R = 0.68, p < 0.001). No significant correlation was found with contourbased MDA (2.47 ± 0.18 mm). Voxel-wise analysis showed that ICE captured spatial patterns of uncertainty consistent with regions of higher GTRE, while underestimating error for global homogeneous deformations >15 mm due to DIR regularisation. Across all datasets, ICE correctly identified high-uncertainty subregions that were not detected by contour-based metrics. Conclusions: ICE enables automated voxel-wise quantification of DIR uncertainty directly from clinical DVFs. It complements traditional contour-based metrics and may support patient-specific QA and more reliable dose mapping in adaptive and re-irradiation radiotherapy workflows.
Assessing the agreement of radiomic tools for dosiomics: A multi-software comparative study Andrea Bettinelli, Francesca Marturano, Giovanni Pirrone, Eliana Gioscio, Michele Avanzo, Annarita Fanizzi, Cristina Garibaldi, Raffaella Massafra, Enrico Menghi, Lorenzo Placidi, Tiziana Rancati, Marta Paiusco Medical Physics, 2026 Background Radiomics involves extracting and analyzing quantitative imaging features to support medical decision‐making, particularly in radiology and oncology. When applied to radiotherapy dose distributions, this approach, referred to as ‘dosiomics’, aims to identify the spatial dose patterns associated with treatment outcomes. However, software discrepancies in feature extraction may hinder reproducibility and limit the clinical adoption of radiomic/dosiomic models. Purpose This study presents the first comprehensive evaluation of software agreement and feature reproducibility across tools in the field of dosiomics, assessing seven feature‐extraction tools. The evaluation focused on the impact of built‐in image pre‐processing steps (e.g., interpolation and discretization), feature‐extraction configurations (i.e., aggregation methods), and the morphological characteristics of the regions of interest (ROIs), such as the presence of holes or disconnected components. Materials and methods Five open‐source programs (MIRP, S‐IBEX, RaCaT, SERA, and PyRadiomics) and two proprietary tools (SPAARC and RadiomiCRO) were evaluated. The Image Biomarker Standardization Initiative (IBSI) digital phantom was used to preliminarily assess software IBSI‐compliance and to identify and exclude features with inconsistent implementation from subsequent analyses. Dosiomic features were then extracted from a digital dataset comprising eight Intensity Modulated Radiation Therapy (IMRT) dose distributions emulating a head and neck radiotherapy plan (available in both isotropic and anisotropic formats) and 10 ROIs, following a systematic feature extraction framework. The effects of pre‐processing parameters, feature‐extraction configurations, and ROI morphological characteristics were analyzed systematically. The evaluation metrics included the percentage of matching features across software to the third significant digit, the Agreement metric, and the coefficient of variation (CV) to quantify both software performance and dosiomic feature variability across them. Results The preliminary IBSI‐compliance evaluation showed that MIRP, S‐IBEX, RaCaT, and SERA achieved over 94% matching features with IBSI benchmark values. In contrast, SPAARC, RadiomiCRO, and PyRadiomics demonstrated lower compliance due to non‐computable features. On dose distributions, all tools exhibited high match percentages (>77%) for the isotropic dataset, which did not require software‐specific interpolation. However, discrepancies increased significantly with program‐specific interpolation for the anisotropic dose dataset, with match rates dropping to 14%. Agreement across software was consistently high for the isotropic dataset but notably lower for the anisotropic dataset. This trend was less evident when looking at the CV, which showed only a mild increase for the anisotropic format. Fixed bin size (FBS) discretization displayed lower Agreement and higher CV values, particularly in the cumulative intensity‐volume histogram (IVH) feature family. High CV values were predominantly observed for some feature family‐ROI combinations, including GLRLM, GLSZM, and NGLDM computed using 2.5D/3D aggregation methods. Additionally, we observed that some binary masks were incorrectly generated (e.g., without holes) when using the DICOM format, therefore, we relied on NRRD input files whenever possible, resulting in feature reproducibility remaining unaffected by this aspect. Conclusion The findings of this study indicate that, when properly configured, the tools show good overall agreement, with variability limited to specific features and pre‐processing choices. While variations in program‐specific resampling and FBS discretization implementation are present, their overall impact on dosiomic feature reproducibility remains minimal.
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, Sebastiano Menna, Elisa Pilloni, Francesco Catucci, Andrea D’Aviero, Claudio Votta, Giuditta Chiloiro, Martina Iezzi, Francesco Preziosi, Alessia Re, Althea Boschetti, Floranna Mauro, Sami Aburas, Lana Smiljanic, Antonio Piras, Carmela Di Dio, Lorenzo Placidi, Luca Boldrini, Maria Antonietta Gambacorta, Gian Carlo Mattiucci, Davide Cusumano Physics and Imaging in Radiation Oncology, 2026
TrackRAD2025 challenge dataset: real-time tumor tracking for MRI-guided radiotherapy Yiling Wang, Elia Lombardo, Adrian Thummerer, Tom Blöcker, Yu Fan, Yue Zhao, Christianna Iris Papadopoulou, Coen Hurkmans, Rob H. N. Tijssen, Pia A. W. Görts, Shyama U. Tetar, Davide Cusumano, Martijn PW Intven, Pim Borman, Marco Riboldi, Denis Dudáš, Hilary Byrne, Lorenzo Placidi, Marco Fusella, Michael Jameson, Miguel Palacios, Paul Cobussen, Tobias Finazzi, Cornelis J. A. Haasbeek, Paul Keall, Christopher Kurz, Guillaume Landry, Matteo Maspero Medical Physics, 2025
MOREOVER: multiomics MR-guided radiotherapy optimization in locally advanced rectal cancer Luca Boldrini, Giuditta Chiloiro, Silvia Di Franco, Angela Romano, Lana Smiljanic, Elena Huong Tran, Francesco Bono, Diepriye Charles Davies, Loris Lopetuso, Maria De Bonis, Angelo Minucci, Luciano Giacò, Davide Cusumano, Lorenzo Placidi, Diana Giannarelli, Evis Sala, Maria Antonietta Gambacorta Radiation Oncology, 2024
Multi-centre real-world validation of automated treatment planning for breast radiotherapy C. Fiandra, S. Zara, V. Richetto, L. Rossi, M. Leonardi, P. Ferrari, M. Marrocco, E. Gino, S. Cora, G. Loi, F. Rosica, S. Ren Kaiser, E. Verdolino, L. Strigari, N. Romeo, L. Placidi, S. Comi, G. De Otto, A. Roggio, A. Di Dio, L. Reversi, E. Pierpaoli, E. Infusino, E. Coeli, T. Licciardello, A. Ciarmatori, R. Caivano, A. Poggiu, N. Ciscognetti, U. Ricardi, B. Heijmen Physica Medica, 2024
Impact of data transfer between treatment planning systems on dosimetric parameters Guenda Meffe, Claudio Votta, Gabriele Turco, Elena Chillè, Matteo Nardini, Angela Romano, Giuditta Chiloiro, Giulia Panza, Matteo Galetto, Amedeo Capotosti, Roberto Moretti, Maria Antonietta Gambacorta, Luca Boldrini, Luca Indovina, Lorenzo Placidi Physica Medica, 2024
Multi-centre evaluation of variation in cumulative dose assessment in reirradiation scenarios Nicholas Hardcastle, Eliana Vasquez Osorio, Andrew Jackson, Charles Mayo, Anja Einebærholm Aarberg, Myriam Ayadi, Francesca Belosi, Cemile Ceylan, Angela Davey, Pauline Dupuis, Julia-Claire Handley, Theresa Hemminger, Lone Hoffmann, Colin Kelly, Chrysanthi Michailidou, Sarah Muscat, Donna H. Murrell, Jaime Pérez-Alija, Catherine Palmer, Lorenzo Placidi, Marija Popovic, Heidi S. Rønde, Adam Selby, Theodora Skopidou, Natasa Solomou, Joep Stroom, Christopher Thompson, Nicholas S West, Ali Zaila, Ane L Appelt Radiotherapy and Oncology, 2024
Inter-institutional variability of knowledge-based plan prediction of left whole breast irradiation Roberta Castriconi, Alessia Tudda, Lorenzo Placidi, Giovanna Benecchi, Elisabetta Cagni, Francesca Dusi, Anna Ianiro, Valeria Landoni, Tiziana Malatesta, Aldo Mazzilli, Guenda Meffe, Caterina Oliviero, Giulia Rambaldi Guidasci, Alessandro Scaggion, Valeria Trojani, Antonella del Vecchio, Claudio Fiorino Physica Medica, 2024
Stereotactic MR-guided on-table adaptive radiation therapy (SMART) for borderline resectable and locally advanced pancreatic cancer: A multi-center, open-label phase 2 study Michael D. Chuong, Percy Lee, Daniel A. Low, Joshua Kim, Kathryn E. Mittauer, Michael F. Bassetti, Carri K. Glide-Hurst, Ann C. Raldow, Yingli Yang, Lorraine Portelance, Kyle R. Padgett, Bassem Zaki, Rongxiao Zhang, Hyun Kim, Lauren E. Henke, Alex T. Price, Joseph D. Mancias, Christopher L. Williams, John Ng, Ryan Pennell, M. Raphael Pfeffer, Daphne Levin, Adam C. Mueller, Karen E. Mooney, Patrick Kelly, Amish P. Shah, Luca Boldrini, Lorenzo Placidi, Martin Fuss, Parag Jitendra Parikh Radiotherapy and Oncology, 2024
Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects Elia Lombardo, Jennifer Dhont, Denis Page, Cristina Garibaldi, Luise A. Künzel, Coen Hurkmans, Rob H.N. Tijssen, Chiara Paganelli, Paul Z.Y. Liu, Paul J. Keall, Marco Riboldi, Christopher Kurz, Guillaume Landry, Davide Cusumano, Marco Fusella, Lorenzo Placidi Radiotherapy and Oncology, 2024
Voxel-based analysis: Roadmap for clinical translation Alan McWilliam, Giuseppe Palma, Azadeh Abravan, Oscar Acosta, Ane Appelt, Marianne Aznar, Serena Monti, Eva Onjukka, Vanessa Panettieri, Lorenzo Placidi, Tiziana Rancati, Eliana Vasquez Osorio, Marnix Witte, Laura Cella Radiotherapy and Oncology, 2023
Why we should care about gas pockets in online adaptive MRgRT: a dosimetric evaluation Matteo Nardini, Guenda Meffe, Matteo Galetto, Luca Boldrini, Giuditta Chiloiro, Angela Romano, Giulia Panza, Andrea Bevacqua, Gabriele Turco, Claudio Votta, Amedeo Capotosti, Roberto Moretti, Maria Antonietta Gambacorta, Luca Indovina, Lorenzo Placidi Frontiers in Oncology, 2023
A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging Jessica Prunaretty, Gorkem Güngör, Thierry Gevaert, David Azria, Simon Valdenaire, Panagiotis Balermpas, Luca Boldrini, Michael David Chuong, Mark De Ridder, Leo Hardy, Sanmady Kandiban, Philippe Maingon, Kathryn Elizabeth Mittauer, Enis Ozyar, Thais Roque, Lorenzo Colombo, Nikos Paragios, Ryan Pennell, Lorenzo Placidi, Kumar Shreshtha, M. P. Speiser, Stephanie Tanadini-Lang, Vincenzo Valentini, Pascal Fenoglietto Frontiers in Oncology, 2023
Evaluation of a simplified optimizer for MR-guided adaptive RT in case of pancreatic cancer Davide Cusumano, Luca Boldrini, Sebastiano Menna, Stefania Teodoli, Elisa Placidi, Giuditta Chiloiro, Lorenzo Placidi, Francesca Greco, Gerardina Stimato, Francesco Cellini, Vincenzo Valentini, Luigi Azario, Marco De Spirito Journal of Applied Clinical Medical Physics, 2019