1
|
Boldrini L, Chiloiro G, Cusumano D, Yadav P, Yu G, Romano A, Piras A, Votta C, Placidi L, Broggi S, Catucci F, Lenkowicz J, Indovina L, Bassetti MF, Yang Y, Fiorino C, Valentini V, Gambacorta MA. Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study. Radiol Med 2024; 129:615-622. [PMID: 38512616 DOI: 10.1007/s11547-024-01761-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/03/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERITCP) to evaluate treatment response in LARC patients treated with MRIgRT. METHODS Patients from three international sites were included and divided into training and validation sets. 0.35 T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes: gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERITCP with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves. RESULTS A total of 91 patients was enrolled: 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERITCP at BED = 26 Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40 Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERITCP alone (0.94 in training and 0.89 in validation set). CONCLUSION The integration of the radiomic analysis with ERITCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients.
Collapse
Affiliation(s)
- Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | | | - Poonam Yadav
- Northwestern Memorial Hospital, Northwestern University Feinberg, Chicago, IL, USA
| | - Gao Yu
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Angela Romano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy
| | - Claudio Votta
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | | | - Jacopo Lenkowicz
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Michael F Bassetti
- Department of Human Oncology, School of Medicine and Public Heath, University of Wisconsin - Madison, Madison, USA
| | - Yingli Yang
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | | |
Collapse
|
2
|
Lecoeur B, Barbone M, Gough J, Oelfke U, Luk W, Gaydadjiev G, Wetscherek A. Accelerating 4D image reconstruction for magnetic resonance-guided radiotherapy. Phys Imaging Radiat Oncol 2023; 27:100484. [PMID: 37664799 PMCID: PMC10474606 DOI: 10.1016/j.phro.2023.100484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/05/2023] Open
Abstract
Background and purpose Physiological motion impacts the dose delivered to tumours and vital organs in external beam radiotherapy and particularly in particle therapy. The excellent soft-tissue demarcation of 4D magnetic resonance imaging (4D-MRI) could inform on intra-fractional motion, but long image reconstruction times hinder its use in online treatment adaptation. Here we employ techniques from high-performance computing to reduce 4D-MRI reconstruction times below two minutes to facilitate their use in MR-guided radiotherapy. Material and methods Four patients with pancreatic adenocarcinoma were scanned with a radial stack-of-stars gradient echo sequence on a 1.5T MR-Linac. Fast parallelised open-source implementations of the extra-dimensional golden-angle radial sparse parallel algorithm were developed for central processing unit (CPU) and graphics processing unit (GPU) architectures. We assessed the impact of architecture, oversampling and respiratory binning strategy on 4D-MRI reconstruction time and compared images using the structural similarity (SSIM) index against a MATLAB reference implementation. Scaling and bottlenecks for the different architectures were studied using multi-GPU systems. Results All reconstructed 4D-MRI were identical to the reference implementation (SSIM > 0.99). Images reconstructed with overlapping respiratory bins were sharper at the cost of longer reconstruction times. The CPU + GPU implementation was over 17 times faster than the reference implementation, reconstructing images in 60 ± 1 s and hyper-scaled using multiple GPUs. Conclusion Respiratory-resolved 4D-MRI reconstruction times can be reduced using high-performance computing methods for online workflows in MR-guided radiotherapy with potential applications in particle therapy.
Collapse
Affiliation(s)
- Bastien Lecoeur
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, 15 Cotswold Rd, London SM2 5NG, United Kingdom
- Department of Computing, Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, United Kingdom
| | - Marco Barbone
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, 15 Cotswold Rd, London SM2 5NG, United Kingdom
- Department of Computing, Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, United Kingdom
| | - Jessica Gough
- Department of Radiotherapy at the Royal Marsden NHS Foundation Trust, Downs Rd, London SM2 5PT, United Kingdom
| | - Uwe Oelfke
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, 15 Cotswold Rd, London SM2 5NG, United Kingdom
| | - Wayne Luk
- Department of Computing, Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, United Kingdom
| | - Georgi Gaydadjiev
- Department of Computing, Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, United Kingdom
- Bernoulli Institute, University of Groningen, Nijenborgh 9, Groningen 9747 AG, The Netherlands
| | - Andreas Wetscherek
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, 15 Cotswold Rd, London SM2 5NG, United Kingdom
| |
Collapse
|
3
|
Regnery S, de Colle C, Eze C, Corradini S, Thieke C, Sedlaczek O, Schlemmer HP, Dinkel J, Seith F, Kopp-Schneider A, Gillmann C, Renkamp CK, Landry G, Thorwarth D, Zips D, Belka C, Jäkel O, Debus J, Hörner-Rieber J. Pulmonary magnetic resonance-guided online adaptive radiotherapy of locally advanced: the PUMA trial. Radiat Oncol 2023; 18:74. [PMID: 37143154 PMCID: PMC10161406 DOI: 10.1186/s13014-023-02258-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 04/03/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND Patients with locally-advanced non-small-cell lung cancer (LA-NSCLC) are often ineligible for surgery, so that definitive chemoradiotherapy (CRT) represents the treatment of choice. Nevertheless, long-term tumor control is often not achieved. Intensification of radiotherapy (RT) to improve locoregional tumor control is limited by the detrimental effect of higher radiation exposure of thoracic organs-at-risk (OAR). This narrow therapeutic ratio may be expanded by exploiting the advantages of magnetic resonance (MR) linear accelerators, mainly the online adaptation of the treatment plan to the current anatomy based on daily acquired MR images. However, MR-guidance is both labor-intensive and increases treatment times, which raises the question of its clinical feasibility to treat LA-NSCLC. Therefore, the PUMA trial was designed as a prospective, multicenter phase I trial to demonstrate the clinical feasibility of MR-guided online adaptive RT in LA-NSCLC. METHODS Thirty patients with LA-NSCLC in stage III A-C will be accrued at three German university hospitals to receive MR-guided online adaptive RT at two different MR-linac systems (MRIdian Linac®, View Ray Inc. and Elekta Unity®, Elekta AB) with concurrent chemotherapy. Conventionally fractioned RT with isotoxic dose escalation up to 70 Gy is applied. Online plan adaptation is performed once weekly or in case of major anatomical changes. Patients are followed-up by thoracic CT- and MR-imaging for 24 months after treatment. The primary endpoint is twofold: (1) successfully completed online adapted fractions, (2) on-table time. Main secondary endpoints include adaptation frequency, toxicity, local tumor control, progression-free and overall survival. DISCUSSION PUMA aims to demonstrate the clinical feasibility of MR-guided online adaptive RT of LA-NSCLC. If successful, PUMA will be followed by a clinical phase II trial that further investigates the clinical benefits of this approach. Moreover, PUMA is part of a large multidisciplinary project to develop MR-guidance techniques. TRIAL REGISTRATION ClinicalTrials.gov: NCT05237453 .
Collapse
Affiliation(s)
- Sebastian Regnery
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Chiara de Colle
- Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
| | - Chukwuka Eze
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Christian Thieke
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Oliver Sedlaczek
- Division of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Julien Dinkel
- Department of Radiology, LMU Munich, Munich, Germany
| | - Ferdinand Seith
- Department of Radiology, University Hospital Tübingen, Tübingen, Germany
| | | | - Clarissa Gillmann
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - C Katharina Renkamp
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
| | - Daniel Zips
- Department of Radiation Oncology, University Hospital Tübingen, Tübingen, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany
| | - Oliver Jäkel
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
- Division of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
- Department of Radiation Oncology, Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany
- National Center for Tumor diseases (NCT), Heidelberg, Germany
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Juliane Hörner-Rieber
- Department of Radiation Oncology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
- National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO), Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
- Department of Radiation Oncology, Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Heidelberg, Germany.
- National Center for Tumor diseases (NCT), Heidelberg, Germany.
- Clinical Cooperation Unit Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| |
Collapse
|
4
|
Lenkowicz J, Votta C, Nardini M, Quaranta F, Catucci F, Boldrini L, Vagni M, Menna S, Placidi L, Romano A, Chiloiro G, Gambacorta MA, Mattiucci GC, Indovina L, Valentini V, Cusumano D. A deep learning approach to generate synthetic CT in low field MR-guided radiotherapy for lung cases. Radiother Oncol 2022; 176:31-38. [PMID: 36063982 DOI: 10.1016/j.radonc.2022.08.028] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/23/2022] [Accepted: 08/24/2022] [Indexed: 12/14/2022]
Abstract
INTRODUCTION This study aims to apply a conditional Generative Adversarial Network (cGAN) to generate synthetic Computed Tomography (sCT) from 0.35 Tesla Magnetic Resonance (MR) images of the thorax. METHODS Sixty patients treated for lung lesions were enrolled and divided into training (32), validation (8), internal (10,TA) and external (10,TB) test set. Image accuracy of generated sCT was evaluated computing the mean absolute (MAE) and mean error (ME) with respect the original CT. Three treatment plans were calculated for each patient considering MRI as reference image: original CT, sCT (pure sCT) and sCT with GTV density override (hybrid sCT) were used as Electron Density (ED) map. Dose accuracy was evaluated comparing treatment plans in terms of gamma analysis and Dose Volume Histogram (DVH) parameters. RESULTS No significant difference was observed between the test sets for image and dose accuracy parameters. Considering the whole test cohort, a MAE of 54.9 ± 10.5 HU and a ME of 4.4 ± 7.4 HU was obtained. Mean gamma passing rates for 2%/2mm, and 3%/3mm tolerance criteria were 95.5 ± 5.9% and 98.2 ± 4.1% for pure sCT, 96.1 ± 5.1% and 98.5 ± 3.9% for hybrid sCT: the difference between the two approaches was significant (p = 0.01). As regards DVH analysis, differences in target parameters estimation were found to be within 5% using hybrid approach and 20% using pure sCT. CONCLUSION The DL algorithm here presented can generate sCT images in the thorax with good image and dose accuracy, especially when the hybrid approach is used. The algorithm does not suffer from inter-scanner variability, making feasible the implementation of MR-only workflows for palliative treatments.
Collapse
Affiliation(s)
- Jacopo Lenkowicz
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Claudio Votta
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy; Mater Olbia Hospital, Olbia (SS), Italy.
| | - Matteo Nardini
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | | | | | - Luca Boldrini
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Marica Vagni
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | | | - Gian Carlo Mattiucci
- Mater Olbia Hospital, Olbia (SS), Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario ''Agostino Gemelli'' IRCCS, Rome, Italy; Mater Olbia Hospital, Olbia (SS), Italy
| |
Collapse
|
5
|
Gupta A, Dunlop A, Mitchell A, McQuaid D, Nill S, Barnes H, Newbold K, Nutting C, Bhide S, Oelfke U, Harrington KJ, Wong KH. Online adaptive radiotherapy for head and neck cancers on the MR linear Accelerator: Introducing a novel modified Adapt-to-Shape approach. Clin Transl Radiat Oncol 2022; 32:48-51. [PMID: 34849412 PMCID: PMC8608651 DOI: 10.1016/j.ctro.2021.11.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 11/02/2021] [Accepted: 11/04/2021] [Indexed: 02/03/2023] Open
Abstract
INTRODUCTION The Elekta Unity MR-Linac (MRL) has enabled adaptive radiotherapy (ART) for patients with head and neck cancers (HNC). Adapt-To-Shape-Lite (ATS-Lite) is a novel Adapt-to-Shape strategy that provides ART without requiring daily clinician presence to perform online target and organ at risk (OAR) delineation. In this study we compared the performance of our clinically-delivered ATS-Lite strategy against three Adapt-To-Position (ATP) variants: Adapt Segments (ATP-AS), Optimise Weights (ATP-OW), and Optimise Shapes (ATP-OS). METHODS Two patients with HNC received radical-dose radiotherapy on the MRL. For each fraction, an ATS-Lite plan was generated online and delivered and additional plans were generated offline for each ATP variant. To assess the clinical acceptability of a plan for every fraction, twenty clinical goals for targets and OARs were assessed for all four plans. RESULTS 53 fractions were analysed. ATS-Lite passed 99.9% of mandatory dose constraints. ATP-AS and ATP-OW each failed 7.6% of mandatory dose constraints. The Planning Target Volumes for 54 Gy (D95% and D98%) were the most frequently failing dose constraint targets for ATP. ATS-Lite median fraction times for Patient 1 and 2 were 40 mins 9 s (range 28 mins 16 s - 47 mins 20 s) and 32 mins 14 s (range 25 mins 33 s - 44 mins 27 s), respectively. CONCLUSIONS Our early data show that the novel ATS-Lite strategy produced plans that fulfilled 99.9% of clinical dose constraints in a time frame that is tolerable for patients and comparable to ATP workflows. Therefore, ATS-Lite, which bridges the gap between ATP and full ATS, will be further utilised and developed within our institute and it is a workflow that should be considered for treating patients with HNC on the MRL.
Collapse
Affiliation(s)
- Amit Gupta
- The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, Head & Neck Unit, 15 Cotswold Road, Sutton, London SM2 5NG, United Kingdom
| | - Alex Dunlop
- The Joint Department of Physics, The Royal Marsden Hospital and the Institute of Cancer Research; Downs Road, Sutton SM2 5PT, United Kingdom
| | - Adam Mitchell
- The Joint Department of Physics, The Royal Marsden Hospital and the Institute of Cancer Research; Downs Road, Sutton SM2 5PT, United Kingdom
| | - Dualta McQuaid
- The Joint Department of Physics, The Royal Marsden Hospital and the Institute of Cancer Research; Downs Road, Sutton SM2 5PT, United Kingdom
| | - Simeon Nill
- The Joint Department of Physics, The Royal Marsden Hospital and the Institute of Cancer Research; Downs Road, Sutton SM2 5PT, United Kingdom
| | - Helen Barnes
- The Royal Marsden NHS Foundation Trust; Downs Road, Sutton SM2 5PT, United Kingdom
| | - Kate Newbold
- The Royal Marsden NHS Foundation Trust; Downs Road, Sutton SM2 5PT, United Kingdom
| | - Chris Nutting
- The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, Head & Neck Unit, 15 Cotswold Road, Sutton, London SM2 5NG, United Kingdom
| | - Shreerang Bhide
- The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, Head & Neck Unit, 15 Cotswold Road, Sutton, London SM2 5NG, United Kingdom
| | - Uwe Oelfke
- The Joint Department of Physics, The Royal Marsden Hospital and the Institute of Cancer Research; Downs Road, Sutton SM2 5PT, United Kingdom
| | - Kevin Joseph Harrington
- The Royal Marsden NHS Foundation Trust and the Institute of Cancer Research, Head & Neck Unit, 15 Cotswold Road, Sutton, London SM2 5NG, United Kingdom
| | - Kee Howe Wong
- The Royal Marsden NHS Foundation Trust; Downs Road, Sutton SM2 5PT, United Kingdom
| |
Collapse
|
6
|
Cusumano D, Boldrini L, Dhont J, Fiorino C, Green O, Güngör G, Jornet N, Klüter S, Landry G, Mattiucci GC, Placidi L, Reynaert N, Ruggieri R, Tanadini-Lang S, Thorwarth D, Yadav P, Yang Y, Valentini V, Verellen D, Indovina L. Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives. Phys Med 2021; 85:175-191. [PMID: 34022660 DOI: 10.1016/j.ejmp.2021.05.010] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 04/15/2021] [Accepted: 05/04/2021] [Indexed: 12/14/2022] Open
Abstract
Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.
Collapse
Affiliation(s)
- Davide Cusumano
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | | | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | - Olga Green
- Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Görkem Güngör
- Acıbadem MAA University, School of Medicine, Department of Radiation Oncology, Maslak Istanbul, Turkey
| | - Núria Jornet
- Servei de Radiofísica i Radioprotecció, Hospital de la Santa Creu i Sant Pau, Spain
| | - Sebastian Klüter
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Guillaume Landry
- Department of Radiation Oncology, LMU Munich, Munich, Germany; German Cancer Consortium (DKTK), Munich, Germany
| | | | - Lorenzo Placidi
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy.
| | - Nick Reynaert
- Department of Medical Physics, Institut Jules Bordet, Belgium
| | - Ruggero Ruggieri
- Dipartimento di Radioterapia Oncologica Avanzata, IRCCS "Sacro cuore - don Calabria", Negrar di Valpolicella (VR), Italy
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University Hospital Tüebingen, Tübingen, Germany
| | - Poonam Yadav
- Department of Human Oncology School of Medicine and Public Heath University of Wisconsin - Madison, USA
| | - Yingli Yang
- Department of Radiation Oncology, David Geffen School of Medicine, University of California Los Angeles, USA
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| | - Dirk Verellen
- Department of Medical Physics, Iridium Cancer Network, Belgium; Faculty of Medicine and Health Sciences, Antwerp University, Antwerp, Belgium
| | - Luca Indovina
- Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome, Italy
| |
Collapse
|
7
|
Placidi L, Nardini M, Cusumano D, Boldrini L, Chiloiro G, Romano A, Votta C, Antonelli MV, Valentini V, Indovina L. VMAT-like plans for magnetic resonance guided radiotherapy: Addressing unmet needs. Phys Med 2021; 85:72-78. [PMID: 33979726 DOI: 10.1016/j.ejmp.2021.05.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/29/2021] [Accepted: 05/03/2021] [Indexed: 12/12/2022] Open
Abstract
PURPOSE VMAT delivery technique is currently not applicable to Magnetic Resonance-guided radiotherapy (MRgRT) hybrid systems. Aim of this study is to evaluate an innovative VMAT-like (VML) delivery technique. MATERIAL AND METHODS First, planning and dosimetric evaluation of the MRgRT VML treatment have been performed on 10 different disease sites and the results have been compared with the corresponding IMRT plans. Then, in the second phase, 10 of the most dosimetrically challenging locally advanced pancreas treatment plans have been retrospectively re-planned using the VML approach to explore the potentiality of this new delivery technique. Finally, VML robustness was evaluated and compared with the IMRT plans, considering a lateral positioning error of ± 5 mm. RESULTS In phase one, all VML plans were within constraint for all OARs. When PTV coverage is considered, in the 50% of the cases VML PTV coverage is equal or higher than in IMRT plan. In the remaining 50%, the highest target under coverage difference in comparison with IMRT plan is -1.71%. The mean and maximum treatment time differences (VML-IMRT) is 0.2 min and 3.1 min respectively. In phase two, the treatment time variation (VML-IMRT), shows a mean, maximum and minimum variations of 1.3, 4.6 and -0.6 min respectively. All VML plans have a better target coverage if compared with IMRT plans, keeping in any case the OARs constraints within tolerance. VML doesn't increase plan robustness. CONCLUSION VMAT-like treatment approach appeared to be an efficient planning solution and it was decided to clinically implement it in daily practice, especially in the frame of hypo fractionated treatments.
Collapse
Affiliation(s)
- L Placidi
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - M Nardini
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - D Cusumano
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy.
| | - L Boldrini
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy
| | - G Chiloiro
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy
| | - A Romano
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy
| | - C Votta
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy
| | - M V Antonelli
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy
| | - V Valentini
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy; Università Cattolica del Sacro Cuore, Rome, Italy
| | - L Indovina
- Fondazione Policlinico Universitario ''A. Gemelli'' IRCCS, Roma, Italy
| |
Collapse
|
8
|
Cusumano D, Boldrini L, Menna S, Teodoli S, Placidi E, Chiloiro G, Placidi L, Greco F, Stimato G, Cellini F, Valentini V, Azario L, De Spirito M. Evaluation of a simplified optimizer for MR-guided adaptive RT in case of pancreatic cancer. J Appl Clin Med Phys 2019; 20:20-30. [PMID: 31444952 PMCID: PMC6753732 DOI: 10.1002/acm2.12697] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 07/19/2019] [Accepted: 07/22/2019] [Indexed: 12/14/2022] Open
Abstract
PURPOSE Magnetic resonance-guided adaptive radiotherapy (MRgART) is considered a promising resource for pancreatic cancer, as it allows to online modify the dose distribution according to daily anatomy. This study aims to compare the dosimetric performance of a simplified optimizer implemented on a MR-Linac treatment planning system (TPS) with those obtained using an advanced optimizer implemented on a conventional Linac. METHODS Twenty patients affected by locally advanced pancreatic cancer (LAPC) were considered. Gross tumor volume (GTV) and surrounding organ at risks (OARs) were contoured on the average 4DCT scan. Planning target volume was generated from GTV by adding an isotropic 3 mm margin and excluding overlap areas with OARs. Treatment plans were generated by using the simple optimizer for the MR-Linac in intensity-modulated radiation therapy (IMRT) and the advanced optimizer for conventional Linac in IMRT and volumetric modulated arc therapy (VMAT) technique. Prescription dose was 40 Gy in five fractions. The dosimetric comparison was performed on target coverage, dosimetric indicators, and low dose diffusion. RESULTS The simplified optimizer of MR-Linac generated clinically acceptable plans in 80% and optimal plans in 55% of cases. The number of clinically acceptable plans obtained using the advanced optimizer of the conventional Linac with IMRT was the same of MR-Linac, but the percentage of optimal plans was higher (65%). Using the VMAT technique, it is possible to obtain clinically acceptable plan in 95% and optimal plans in 90% of cases. The advanced optimizer combined with VMAT technique ensures higher target dose homogeneity and minor diffusion of low doses, but its actual optimization time is not suitable for MRgART. CONCLUSION Simplified optimization solutions implemented in the MR-Linac TPS allows to elaborate in most of cases treatment plans dosimetrically comparable with those obtained by using an advanced optimizer. A superior treatment plan quality is possible using the VMAT technique that could represent a breakthrough for the MRgART if the modern advancements will lead to shorter optimization times.
Collapse
Affiliation(s)
- Davide Cusumano
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Luca Boldrini
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Sebastiano Menna
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Stefania Teodoli
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Elisa Placidi
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Giuditta Chiloiro
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Lorenzo Placidi
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Francesca Greco
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Gerardina Stimato
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Francesco Cellini
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
| | - Vincenzo Valentini
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
- Istituto di RadiologiaUniversità Cattolica del Sacro CuoreRomaItaly
| | - Luigi Azario
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
- Istituto di FisicaUniversità Cattolica del Sacro CuoreRomaItaly
| | - Marco De Spirito
- Dipartimento di diagnostica per immagini, radioterapia oncologica ed ematologiaFondazione Policlinico Universitario “A. Gemelli” IRCCSRomaItaly
- Istituto di FisicaUniversità Cattolica del Sacro CuoreRomaItaly
| |
Collapse
|