Quaresma, M., Coleman, M. P. & Rachet, B. 40-year trends in an index of survival for all cancers combined and survival adjusted for age and sex for each cancer in England and Wales, 1971–2011: a population-based study. Lancet 385, 1206–1218 (2015).

Article  PubMed  Google Scholar 

Atun, R. et al. Expanding global access to radiotherapy. Lancet Oncol. 16, 1153–1186 (2015).

Article  PubMed  Google Scholar 

Dzau, V. J., Balatbat, C. A. & Ellaissi, W. F. Revisiting academic health sciences systems a decade later: discovery to health to population to society. Lancet 398, 2300–2304 (2021).

Article  PubMed  Google Scholar 

Baumann, M. et al. Radiation oncology in the era of precision medicine. Nat. Rev. Cancer 16, 234–249 (2016).

Article  CAS  PubMed  Google Scholar 

Mackie, T. R. et al. Tomotherapy. Semin. Radiat. Oncol. 9, 108–117 (1999).

Article  CAS  PubMed  Google Scholar 

Brahme, A. Optimization of stationary and moving beam radiation therapy techniques. Radiother. Oncol. 12, 129–140 (1988).

Article  CAS  PubMed  Google Scholar 

Jaffray, D. A. et al. How advances in imaging will affect precision radiation oncology. Int. J. Radiat. Oncol. Biol. Phys. 101, 292–298 (2018).

Article  PubMed  Google Scholar 

Brock, K. K. et al. Feasibility of a novel deformable image registration technique to facilitate classification, targeting, and monitoring of tumor and normal tissue. Int. J. Radiat. Oncol. Biol. Phys. 64, 1245–1254 (2006).

Article  PubMed  Google Scholar 

Raaymakers, B. W. et al. Integrating a 1.5 T MRI scanner with a 6 MV accelerator: proof of concept. Phys. Med. Biol. 54, N229–N237 (2009).

Article  CAS  PubMed  Google Scholar 

Thwaites, D. Accuracy required and achievable in radiotherapy dosimetry: have modern technology and techniques changed our views? J. Phys. Conf. Ser. 444, 012006 (2013).

Article  Google Scholar 

Jaffray, D. A. Image-guided radiotherapy: from current concept to future perspectives. Nat. Rev. Clin. Oncol. 9, 688–699 (2012).

Article  CAS  PubMed  Google Scholar 

Otazo, R. et al. MRI-guided radiation therapy: an emerging paradigm in adaptive radiation oncology. Radiology 298, 248–260 (2021).

Article  PubMed  Google Scholar 

Gooding, M. J. et al. Comparative evaluation of autocontouring in clinical practice: a practical method using the Turing test. Med. Phys. 45, 5105–5115 (2018).

Article  PubMed  Google Scholar 

McIntosh, C. et al. Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method. Phys. Med. Biol. 62, 5926–5944 (2017).

Article  PubMed  Google Scholar 

Rigaud, B. et al. Automatic segmentation using deep learning to enable online dose optimization during adaptive radiation therapy of cervical cancer. Int. J. Radiat. Oncol. Biol. Phys. 109, 1096–1110 (2021).

Lim, K. et al. Dosimetrically triggered adaptive intensity modulated radiation therapy for cervical cancer. Int. J. Radiat. Oncol. Biol. Phys. 90, 147–154 (2014).

Article  PubMed  Google Scholar 

van Elmpt, W. et al. Response assessment using 18F-FDG PET early in the course of radiotherapy correlates with survival in advanced-stage non-small cell lung cancer. J. Nucl. Med. 53, 1514–1520 (2012).

Article  PubMed  Google Scholar 

Butner, J. D. et al. A mathematical model for the quantification of a patient’s sensitivity to checkpoint inhibitors and long-term tumour burden. Nat. Biomed. Eng. 5, 297–308 (2021).

Hormuth, D. A. et al. Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation. Sci. Rep. 11, 8520 (2021).

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wu, C. et al. Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology. Biophys. Rev. 3, 021304 (2022).

Article  Google Scholar 

Di Franco, R. et al. COVID-19 and radiotherapy: potential new strategies for patients management with hypofractionation and telemedicine. Eur. Rev. Med. Pharmacol. Sci. 24, 12480–12489 (2020).

PubMed  Google Scholar 

Aznar, M. C. et al. Radiation oncology in the new virtual and digital era. Radiother. Oncol. 154, A1–A4 (2021).

Article  CAS  PubMed  Google Scholar 

McIntosh, C. et al. Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nat. Med. 27, 999–1005 (2021).

Article  CAS  PubMed  Google Scholar 

Kisling, K. et al. Fully automatic treatment planning for external-beam radiation therapy of locally advanced cervical cancer: a tool for low-resource clinics. J. Glob. Oncol. 5, 1–9 (2019).

PubMed  Google Scholar 

Ngwa, W. et al. Potential for information and communication technologies to catalyze global collaborations in radiation oncology. Int. J. Radiat. Oncol. Biol. Phys. 91, 444–447 (2015).

Article  PubMed  PubMed Central  Google Scholar 

Segan, S. Tested: SpaceX’s Starlink satellite internet service is fast, but it'll cost you. PCMag https://www.pcmag.com/news/tested-spacexs-starlink-satellite-internet-service-is-fast-but-itll-cost (29 October 2020).

Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).

Article  CAS  PubMed  Google Scholar 

Kung, T. H. et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLoS Digit. Health 2, e0000198 (2023).

Article  PubMed  PubMed Central  Google Scholar 

Jaffray, D. A. et al. Quantitative imaging in radiation oncology: an emerging science and clinical service. Semin. Radiat. Oncol. 25, 292–304 (2015).

Article  PubMed  Google Scholar 

Clarke, L. P. et al. The Quantitative Imaging Network: NCI’s historical perspective and planned goals. Transl. Oncol. 7, 1–4 (2014).

Article  PubMed  PubMed Central  Google Scholar 

Shukla-Dave, A. et al. Quantitative Imaging Biomarkers Alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J. Magn. Reson. Imaging 49, e101–e121 (2019).

Article  PubMed  Google Scholar 

Press, R. H. et al. The use of quantitative imaging in radiation oncology: a Quantitative Imaging Network (QIN) perspective. Int. J. Radiat. Oncol. Biol. Phys. 102, 1219–1235 (2018).

Article  PubMed  PubMed Central  Google Scholar 

Maspero, M. et al. Dose evaluation of fast synthetic-CT generation using a generative adversarial network for general pelvis MR-only radiotherapy. Phys. Med. Biol. 63, 185001 (2018).

Article  PubMed  Google Scholar 

Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).

Article  PubMed  PubMed Central  Google Scholar 

Aerts, H. J. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).

Article  CAS  PubMed  Google Scholar 

Islam, M. K. et al. An integral quality monitoring system for real-time verification of intensity modulated radiation therapy. Med. Phys. 36, 5420–5428 (2009).

Article  CAS  PubMed  Google Scholar 

Teke, T. et al. Monte Carlo based, patient-specific RapidArc QA using Linac log files. Med. Phys. 37, 116–123 (2010).

Article  PubMed  Google Scholar 

Meidan, Y. et al. Detection of unauthorized IoT devices using machine learning techniques. Preprint at https://doi.org/10.48550/arXiv.1709.04647 (2017).

Zhao, Y. et al. Clinical applications of 3-dimensional printing in radiation therapy. Med. Dosim. 42, 150–155 (2017).

Article  PubMed  Google Scholar 

Sarracanie, M. & Salameh, N. Low-field MRI: how low can we go? A fresh view on an old debate. Front. Phys. https://doi.org/10.3389/fphy.2020.00172 (2020).

Fazio, M. Basic research needs workshop on compact accelerators for security and medicine: tools for the 21st century, May 6-8, 2019 OSTI.gov https://www.osti.gov/biblio/1631121 (2019).

Bottura, L. et al. GaToroid: a novel toroidal gantry for hadron therapy. Nucl. Instrum. Methods Phys. Res. A Accel. Spectrom. Detect. Assoc. Equip. 983, 164588 (2020).

Maxim, P. G., Tantawi, S. G. & Loo, B. W. Jr. PHASER: a platform for clinical translation of FLASH cancer radiotherapy. Radiother. Oncol. 139, 28–33 (2019).

Article  PubMed  Google Scholar 

Shirvani, S. M. et al. Biology-guided radiotherapy: redefining the role of radiotherapy in metastatic cancer. Br. J. Radiol. 94, 20200873 (2021).

Article  PubMed  Google Scholar 

Vozenin, M. C., Hendry, J. H. & Limoli, C. L. Biological benefits of ultra-high dose rate FLASH radiotherapy: sleeping beauty awoken. Clin. Oncol. 31, 407–415 (2019).

Article  Google Scholar 

Favaudon, V. et al. Ultrahigh dose-rate FLASH irradiation increases the differential response between normal and tumor tissue in mice. Sci. Transl. Med. 6, 245ra93 (2014).

Article  PubMed  Google Scholar 

Oraiqat, I. et al. An ionizing radiation acoustic imaging (iRAI) technique for real-time dosimetric measurements for FLASH radiotherapy. Med. Phys. 47, 5090–5101 (2020).

Article