Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy
In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-06-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fonc.2020.00790/full |
| _version_ | 1818144514113536000 |
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| author | Lars J. Isaksson Matteo Pepa Mattia Zaffaroni Giulia Marvaso Giulia Marvaso Daniela Alterio Stefania Volpe Giulia Corrao Giulia Corrao Matteo Augugliaro Anna Starzyńska Maria C. Leonardi Roberto Orecchia Barbara A. Jereczek-Fossa Barbara A. Jereczek-Fossa |
| author_facet | Lars J. Isaksson Matteo Pepa Mattia Zaffaroni Giulia Marvaso Giulia Marvaso Daniela Alterio Stefania Volpe Giulia Corrao Giulia Corrao Matteo Augugliaro Anna Starzyńska Maria C. Leonardi Roberto Orecchia Barbara A. Jereczek-Fossa Barbara A. Jereczek-Fossa |
| author_sort | Lars J. Isaksson |
| collection | DOAJ |
| description | In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians. |
| first_indexed | 2024-12-11T11:48:46Z |
| format | Article |
| id | doaj.art-75576e930e4a4607b6f401cea6ce7a4c |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-11T11:48:46Z |
| publishDate | 2020-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-75576e930e4a4607b6f401cea6ce7a4c2022-12-22T01:08:24ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2020-06-011010.3389/fonc.2020.00790530977Machine Learning-Based Models for Prediction of Toxicity Outcomes in RadiotherapyLars J. Isaksson0Matteo Pepa1Mattia Zaffaroni2Giulia Marvaso3Giulia Marvaso4Daniela Alterio5Stefania Volpe6Giulia Corrao7Giulia Corrao8Matteo Augugliaro9Anna Starzyńska10Maria C. Leonardi11Roberto Orecchia12Barbara A. Jereczek-Fossa13Barbara A. Jereczek-Fossa14Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyDivision of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyDivision of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyDivision of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyDepartment of Oncology and Hemato-Oncology, University of Milan, Milan, ItalyDivision of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyDivision of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyDivision of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyDepartment of Oncology and Hemato-Oncology, University of Milan, Milan, ItalyDivision of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyDepartment of Oral Surgery, Medical University of Gdańsk, Gdańsk, PolandDivision of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyScientific Directorate, IEO European Institute of Oncology IRCCS, Milan, ItalyDivision of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, ItalyDepartment of Oncology and Hemato-Oncology, University of Milan, Milan, ItalyIn order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.https://www.frontiersin.org/article/10.3389/fonc.2020.00790/fullradiotherapytoxicitypredictive modelsmachine-learningradiomics |
| spellingShingle | Lars J. Isaksson Matteo Pepa Mattia Zaffaroni Giulia Marvaso Giulia Marvaso Daniela Alterio Stefania Volpe Giulia Corrao Giulia Corrao Matteo Augugliaro Anna Starzyńska Maria C. Leonardi Roberto Orecchia Barbara A. Jereczek-Fossa Barbara A. Jereczek-Fossa Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy Frontiers in Oncology radiotherapy toxicity predictive models machine-learning radiomics |
| title | Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy |
| title_full | Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy |
| title_fullStr | Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy |
| title_full_unstemmed | Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy |
| title_short | Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy |
| title_sort | machine learning based models for prediction of toxicity outcomes in radiotherapy |
| topic | radiotherapy toxicity predictive models machine-learning radiomics |
| url | https://www.frontiersin.org/article/10.3389/fonc.2020.00790/full |
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