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Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO)

  • Radiotherapy
  • Published:
La radiologia medica Aims and scope Submit manuscript

Abstract

Introduction

The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT.

Methods

A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was “Radiomics” AND “Cone Beam Computed Tomography”; “Radiomics” AND “Magnetic Resonance guided Radiotherapy”; “Radiomics” AND “on board Magnetic Resonance Radiotherapy”; “Artificial Intelligence” AND “Cone Beam Computed Tomography”; “Artificial Intelligence” AND “Magnetic Resonance guided Radiotherapy”; “Artificial Intelligence” AND “on board Magnetic Resonance Radiotherapy” and only original articles up to 01.11.2022 were considered.

Results

A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques.

Discussion

AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.

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Acknowledgements

The Authors thank the Scientific Committee and Board of the AIRO for the critical revision and final approval of the manuscript (Nr. 7/2023).

Funding

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Authors and Affiliations

  1. UOC Radioterapia Oncologica, Fondazione Policlinico Universitario IRCCS “A. Gemelli”, Rome, Italy

    Luca Boldrini

  2. Università Cattolica del Sacro Cuore, Rome, Italy

    Luca Boldrini

  3. Radiation Oncology, Mater Olbia Hospital, Olbia, Sassari, Italy

    Andrea D’Aviero

  4. Radiation Oncology, Department of Radiological, Policlinico Umberto I, Rome, Italy

    Francesca De Felice

  5. Oncological and Pathological Sciences, “Sapienza” University of Rome, Rome, Italy

    Francesca De Felice

  6. Radiation Oncology Unit, Azienda Ospedaliero-Universitaria Careggi, Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy

    Isacco Desideri & Viola Salvestrini

  7. Department of Precision Medicine, University of Campania “L. Vanvitelli”, Naples, Italy

    Roberta Grassi & Valerio Nardone

  8. Department of Radiation Oncology, Università Campus Bio-Medico di Roma, Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy

    Carlo Greco

  9. Department of Oncology, Radiation Oncology, University of Turin, Turin, Italy

    Giuseppe Carlo Iorio

  10. UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy

    Antonio Piras

  11. Cyberknife Center, Istituto Fiorentino di Cura e Assistenza (IFCA), 50139, Florence, Italy

    Viola Salvestrini

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  1. Luca Boldrini
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  2. Andrea D’Aviero
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  3. Francesca De Felice
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  4. Isacco Desideri
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  8. Valerio Nardone
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  9. Antonio Piras
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  10. Viola Salvestrini
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Contributions

Conception and design: L.B., V.S., A.D’. Data Collection, analysis, and interpretation of data: A.D’., A.P. Analysis Validation: L.B., F.DeF., I.D., R.G., C.G., G.C.I., V.N., V.S. Manuscript Writing: A.D’., A.P., L.B. The Final Manuscript Approval: all authors.

Corresponding author

Correspondence to Antonio Piras.

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Boldrini, L., D’Aviero, A., De Felice, F. et al. Artificial intelligence applied to image-guided radiation therapy (IGRT): a systematic review by the Young Group of the Italian Association of Radiotherapy and Clinical Oncology (yAIRO). Radiol med 129, 133–151 (2024). https://doi.org/10.1007/s11547-023-01708-4

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