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Editorial
. 2022 Nov 3;12(11):1834.
doi: 10.3390/jpm12111834.

What We Talk about When We Talk about Artificial Intelligence in Radiation Oncology

Francesco Cuccia  1 , Giuseppe Carruba  2 , Guseppe Ferrera  1
Affiliations
Editorial

What We Talk about When We Talk about Artificial Intelligence in Radiation Oncology

Francesco Cuccia et al. J Pers Med. .

Abstract

The constant evolution of technology has dramatically changed the history of radiation oncology, allowing clinicians to deliver increasingly accurate and precise treatments, moving from 2D radiotherapy to 3D conformal radiotherapy, leading to intensity-modulated image-guided (IMRT-IGRT) and stereotactic body radiotherapy treatments [...].

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Conflict of interest statement

The authors declare no conflict of interest.

References

    1. Siddique S., Chow J.C. Artificial intelligence in radiotherapy. Rep. Pract. Oncol. Radiother. 2020;25:656–666. doi: 10.1016/j.rpor.2020.03.015. - DOI - PMC - PubMed
    1. Zhao W., Shen L., Islam T., Qin W., Zhang Z., Liang X., Zhang G., Xu S., Li X. Artificial intelligence in image-guided radiotherapy: A review of treatment target localization. Quant. Imaging Med. Surg. 2021;11:4881–4894. doi: 10.21037/qims-21-199. - DOI - PMC - PubMed
    1. Cusumano D., Dinapoli N., Boldrini L., Chiloiro G., Gatta R., Masciocchi C., Lenkowicz J., Casà C., Damiani A., Azario L., et al. Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer. Radiol. Med. 2018;123:286–295. doi: 10.1007/s11547-017-0838-3. - DOI - PubMed
    1. Chiloiro G., Boldrini L., Preziosi F., Cusumano D., Yadav P., Romano A., Placidi L., Lenkowicz J., Dinapoli N., Bassetti M.F., et al. A Predictive Model of 2yDFS During MR-Guided RT Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Patients. Front. Oncol. 2022;12:831712. doi: 10.3389/fonc.2022.831712. - DOI - PMC - PubMed
    1. Kocher M., Ruge M.I., Galldiks N., Lohmann P. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther. Onkol. 2020;196:856–867. doi: 10.1007/s00066-020-01626-8. - DOI - PMC - PubMed

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