Literature DB >> 34022660

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives.

Davide Cusumano1, Luca Boldrini1, Jennifer Dhont2, Claudio Fiorino3, Olga Green4, Görkem Güngör5, Núria Jornet6, Sebastian Klüter7, Guillaume Landry8, Gian Carlo Mattiucci1, Lorenzo Placidi9, Nick Reynaert10, Ruggero Ruggieri11, Stephanie Tanadini-Lang12, Daniela Thorwarth13, Poonam Yadav14, Yingli Yang15, Vincenzo Valentini1, Dirk Verellen16, Luca Indovina1.   

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.
Copyright © 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Artificial Intelligence; Deep learning; MR-Linac; MR-guided Radiotherapy; Online Adaptive Radiotherapy

Year:  2021        PMID: 34022660     DOI: 10.1016/j.ejmp.2021.05.010

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  6 in total

Review 1.  Machine learning in neuro-oncology: toward novel development fields.

Authors:  Vincenzo Di Nunno; Mario Fordellone; Giuseppe Minniti; Sofia Asioli; Alfredo Conti; Diego Mazzatenta; Damiano Balestrini; Paolo Chiodini; Raffaele Agati; Caterina Tonon; Alicia Tosoni; Lidia Gatto; Stefania Bartolini; Raffaele Lodi; Enrico Franceschi
Journal:  J Neurooncol       Date:  2022-06-28       Impact factor: 4.506

2.  Effect of intrafraction adaptation on PTV margins for MRI guided online adaptive radiotherapy for rectal cancer.

Authors:  Chavelli M Kensen; Tomas M Janssen; Anja Betgen; Lisa Wiersema; Femke P Peters; Peter Remeijer; Corrie A M Marijnen; Uulke A van der Heide
Journal:  Radiat Oncol       Date:  2022-06-21       Impact factor: 4.309

3.  THUNDER 2: THeragnostic Utilities for Neoplastic DisEases of the Rectum by MRI guided radiotherapy.

Authors:  Giuditta Chiloiro; Davide Cusumano; Luca Boldrini; Angela Romano; Lorenzo Placidi; Matteo Nardini; Elisa Meldolesi; Brunella Barbaro; Claudio Coco; Antonio Crucitti; Roberto Persiani; Lucio Petruzziello; Riccardo Ricci; Lisa Salvatore; Luigi Sofo; Sergio Alfieri; Riccardo Manfredi; Vincenzo Valentini; Maria Antonietta Gambacorta
Journal:  BMC Cancer       Date:  2022-01-15       Impact factor: 4.430

4.  Dosimetric Impact of Inter-Fraction Variability in the Treatment of Breast Cancer: Towards New Criteria to Evaluate the Appropriateness of Online Adaptive Radiotherapy.

Authors:  Martina Iezzi; Davide Cusumano; Danila Piccari; Sebastiano Menna; Francesco Catucci; Andrea D'Aviero; Alessia Re; Carmela Di Dio; Flaviovincenzo Quaranta; Althea Boschetti; Marco Marras; Domenico Piro; Flavia Tomei; Claudio Votta; Vincenzo Valentini; Gian Carlo Mattiucci
Journal:  Front Oncol       Date:  2022-04-11       Impact factor: 5.738

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

Authors:  Andrea D'Aviero; Alessia Re; Francesco Catucci; Danila Piccari; Claudio Votta; Domenico Piro; Antonio Piras; Carmela Di Dio; Martina Iezzi; Francesco Preziosi; Sebastiano Menna; Flaviovincenzo Quaranta; Althea Boschetti; Marco Marras; Francesco Miccichè; Roberto Gallus; Luca Indovina; Francesco Bussu; Vincenzo Valentini; Davide Cusumano; Gian Carlo Mattiucci
Journal:  Int J Environ Res Public Health       Date:  2022-07-25       Impact factor: 4.614

6.  Synthetic CT generation for MRI-guided adaptive radiotherapy in prostate cancer.

Authors:  Shu-Hui Hsu; Zhaohui Han; Jonathan E Leeman; Yue-Houng Hu; Raymond H Mak; Atchar Sudhyadhom
Journal:  Front Oncol       Date:  2022-09-23       Impact factor: 5.738

  6 in total

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