A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation

Guidi, G.; Maffei, N.; Meduri, Bruno; D’Angelo, Elisa; Mistretta, G.M.; Ceroni, P.; Ciarmatori, A.; Bernabei, Annalisa; Maggi, Stefania; Cardinali, M.; Morabito, V.E.; Rosica, F.; Malara, S.; Savini, A.; Orlandi, Giovanni; D'Ugo, Carlo; Bunkheila, Feisal; Bono, Maria Grazia Di; Lappi, S.; Blasi, Claudio; Lohr, Frank; Costi, T.

doi:10.1016/j.ejmp.2016.10.005

Cited by 50 publications

(21 citation statements)

References 22 publications

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“…ML can also be potentially used to predict the need of adaptive radiotherapy due to changes in anatomy though treatment. Early retrospective work has been explored in head and neck cancer to predict the need to re-plan radiotherapy plan due to changes of radiotherapy doses to parotid glands through treatment [55].…”

Section: Opportunity and Challenges To Application Of Machine Learmentioning

confidence: 99%

Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation

2018

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The fields of radiotherapy and clinical oncology have been rapidly changed by the advances of technology. Improvement in computer processing power and imaging quality heralded precision radiotherapy allowing radiotherapy to be delivered efficiently, safely and effectively for patient benefit. Artificial intelligence (AI) is an emerging field of computer science which uses computer models and algorithms to replicate human-like intelligence and perform specific tasks which offers a huge potential to healthcare. We reviewed and presented the history, evolution and advancement in the fields of radiotherapy, clinical oncology and machine learning. Radiotherapy target delineation is a complex task of outlining tumour and organ at risks volumes to allow accurate delivery of radiotherapy. We discussed the radiotherapy planning, treatment delivery and reviewed how technology can help with this challenging process. We explored the evidence and clinical application of machine learning to radiotherapy. We concluded on the challenges, possible future directions and potential collaborations to achieve better outcome for cancer patients.

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Section: Opportunity and Challenges To Application Of Machine Learmentioning

confidence: 99%

Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation

2018

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“…Thus, the main obstacle to test ART on a higher number of patients is the supplementary workload even with hybrid deformable image registration (HDIR) (35). Guidi et al created (41) and tested (42) a support vector machine tool for adaptive tomotherapy treatment in head and neck cancer to completely automate this process in order to raise an alert when the patient would need a new dosimetric scan to adapt the volumes. Between 2013 and 2014, 40 patients diagnosed with a head and neck cancer treated by radiation therapy with 66 Gy on high-risk volume and 54 Gy on low-risk volume were included.…”

Section: Radiation Oncology Treatment Planningmentioning

confidence: 99%

Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers

Giraud

Gasnier

et al. 2019

Front. Oncol.

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Introduction: An increasing number of parameters can be considered when making decisions in oncology. Tumor characteristics can also be extracted from imaging through the use of radiomics and add to this wealth of clinical data. Machine learning can encompass these parameters and thus enhance clinical decision as well as radiotherapy workflow. Methods: We performed a description of machine learning applications at each step of treatment by radiotherapy in head and neck cancers. We then performed a systematic review on radiomics and machine learning outcome prediction models in head and neck cancers. Results: Machine Learning has several promising applications in treatment planning with automatic organ at risk delineation improvements and adaptative radiotherapy workflow automation. It may also provide new approaches for Normal Tissue Complication Probability models. Radiomics may provide additional data on tumors for improved machine learning powered predictive models, not only on survival, but also on risk of distant metastasis, in field recurrence, HPV status and extra nodal spread. However, most studies provide preliminary data requiring further validation. Conclusion: Promising perspectives arise from machine learning applications and radiomics based models, yet further data are necessary for their implementation in daily care.

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“…Other important applications of ML include predicting planning deviations from the initial intentions and predicting the need for re-planning. Guidi et al developed a ML-based tool to predict when head and neck patients treated with photons need re-planning ( 58 ). In a similar fashion, Tseng et al used three deep neural networks to predict the need for treatment adaptation for lung patients ( 59 , 60 ).…”

Section: Qa and Treatment Deliverymentioning

confidence: 99%

Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

et al. 2018

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Machine learning (ML) has the potential to revolutionize the field of radiation oncology, but there is much work to be done. In this article, we approach the radiotherapy process from a workflow perspective, identifying specific areas where a data-centric approach using ML could improve the quality and efficiency of patient care. We highlight areas where ML has already been used, and identify areas where we should invest additional resources. We believe that this article can serve as a guide for both clinicians and researchers to start discussing issues that must be addressed in a timely manner.

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A machine learning tool for re-planning and adaptive RT: A multicenter cohort investigation

Cited by 50 publications

References 22 publications

Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation

Assessing the Role of Artificial Intelligence (AI) in Clinical Oncology: Utility of Machine Learning in Radiotherapy Target Volume Delineation

Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers

Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs

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