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 [,].
In recent years, we have witnessed an ever-growing attention in the scientific community towards the application of artificial intelligence (AI), with initial experiences that highlight the potential benefits of this technological ally [,,].
Within the definition of AI, there is a wider concept that encompasses all the potential declinations of the aptitude of a machine to mimic human intelligence [].
In recent decades, the scientific community has been overwhelmed by a dramatically increasing amount of data and information, from both a technological (imaging data) and a biological perspective (DNA and RNA analysis and mutational signatures) [].
In this scenario, devices endowed with the capacity of speeding up the acquisition and analysis of data are attractive and promising tools for clinicians to provide more tailored and personalized approaches.
Among all medical disciplines, radiation oncology is probably one of the most strictly connected to technological advances. Thus, the potential applications of AI may be implemented across all phases of radiotherapy treatment: from the analysis of diagnostic imaging to identifying radiological features predictive of clinical outcomes, to target auto-contouring or treatment planning automation, eventually leading to on-board imaging analysis to identify potential features predictive of higher toxicity or lower local control rates [,,,].
With this aim, machines trained to translate radiological features into clinical data may identify predictive and prognostic information that is useful to deeply customize treatment, as exemplified in a study by Cunliffe et al. that investigated radiomics features as potential predictors of radiation pneumonitis after lung radiotherapy, or in the study by Matrone et al., published in this issue, reporting the potential identification of local failure in patients who received partial prostate re-irradiation [,].
This Special Issue, entitled “Artificial Intelligence in Radiation Oncology”, aims to collect preliminary experiences of AI-based radiotherapy and present challenges, hopes, and food for thought for future studies.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Siddique, S.; Chow, J.C. Artificial intelligence in radiotherapy. Rep. Pract. Oncol. Radiother. 2020, 25, 656–666. [Google Scholar] [CrossRef] [PubMed]
- 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. [Google Scholar] [CrossRef] [PubMed]
- 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. [Google Scholar] [CrossRef] [PubMed]
- 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. [Google Scholar] [CrossRef] [PubMed]
- 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. [Google Scholar] [CrossRef] [PubMed]
- Mintz, Y.; Brodie, R. Introduction to artificial intelligence in medicine. Minim. Invasive Ther. Allied Technol. 2019, 28, 73–81. [Google Scholar] [CrossRef] [PubMed]
- Qiu, Q.; Duan, J.; Yin, Y. Radiomics in radiotherapy: Applications and future challenges. Precis. Radiat. Oncol. 2020, 4, 29–33. [Google Scholar] [CrossRef]
- Boldrini, L.; Cusumano, D.; Chiloiro, G.; Casà, C.; Masciocchi, C.; Lenkowicz, J.; Cellini, F.; Dinapoli, N.; Azario, L.; Teodoli, S.; et al. Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): A hypothesis-generating study for an innovative personalized medicine approach. Radiol. Med. 2019, 124, 145–153. [Google Scholar] [CrossRef] [PubMed]
- Figlia, V.; Mazzola, R.; Cuccia, F.; Alongi, F.; Mortellaro, G.; Cespuglio, D.; Cucchiara, T.; Iacoviello, G.; Valenti, V.; Molino, M.; et al. Hypo-fractionated stereotactic radiation therapy for lung malignancies by means of helical tomotherapy: Report of feasibility by a single-center experience. Radiol. Med. 2018, 123, 406–414. [Google Scholar] [CrossRef] [PubMed]
- Giaj-Levra, N.; Figlia, V.; Cuccia, F.; Mazzola, R.; Nicosia, L.; Ricchetti, F.; Rigo, M.; Attinà, G.; Vitale, C.; Sicignano, G.; et al. Reduction of inter-observer differences in the delineation of the target in spinal metastases SBRT using an automatic contouring dedicated system. Radiat. Oncol. 2021, 16, 197. [Google Scholar] [CrossRef] [PubMed]
- Ubaldi, L.; Valenti, V.; Borgese, R.; Collura, G.; Fantacci, M.; Ferrera, G.; Iacoviello, G.; Abbate, B.; Laruina, F.; Tripoli, A.; et al. Strategies to develop radiomics and machine learning models for lung cancer stage and histology prediction using small data samples. Phys. Med. 2021, 90, 13–22. [Google Scholar] [CrossRef] [PubMed]
- Cunliffe, A.; Armato, S.G.; Castillo, R.; Pham, N.; Guerrero, T.; Al-Hallaq, H.A. Lung Texture in Serial Thoracic Computed Tomography Scans: Correlation of Radiomics-based Features with Radiation Therapy Dose and Radiation Pneumonitis Development. Int. J. Radiat. Oncol. 2015, 91, 1048–1056. [Google Scholar] [CrossRef] [PubMed]
- Pirrone, G.; Matrone, F.; Chiovati, P.; Manente, S.; Drigo, A.; Donofrio, A.; Cappelletto, C.; Borsatti, E.; Dassie, A.; Bortolus, R.; et al. Predicting Local Failure after Partial Prostate Re-Irradiation Using a Dosiomic-Based Machine Learning Model. J. Pers. Med. 2022, 12, 1491. [Google Scholar] [CrossRef] [PubMed]
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