Script-based automatic radiotherapy planning for cervical cancer

References

• Yusufaly TI, Meyers SM, Mell LK, et al. Knowledge-based planning for intact cervical cancer. In: Seminars in radiation oncology. Vol. 30; Elsevier; 2020. p. 328–339. doi: 10.1016/j.semradonc.2020.05.009.
   Google Scholar
• Jensen NBK, Pötter R, Kirchheiner K, et al. Bowel morbidity following radiochemotherapy and image-guided adaptive brachytherapy for cervical cancer: physician-and patient reported outcome from the EMBRACE study. Radiother Oncol. 2018;127(3):431–439. doi: 10.1016/j.radonc.2018.05.016.
   PubMed Web of Science ®Google Scholar
• Spampinato S, Fokdal LU, Pötter R, et al. Risk factors and dose-effects for bladder fistula, bleeding, and cystitis after radiotherapy with imaged-guided adaptive brachytherapy for cervical cancer: an EMBRACE analysis. Radiother Oncol. 2021;158:312–320. doi: 10.1016/j.radonc.2021.01.019.
   PubMedGoogle Scholar
• Westerveld H, Kirchheiner K, Nout RA, et al. Dose-effect relationship between vaginal dose points and vaginal stenosis in cervical cancer: an EMBRACE-I sub-study. Radiother Oncol. 2022;168:8–15. doi: 10.1016/j.radonc.2021.12.034.
   PubMed Web of Science ®Google Scholar
• Berger T, Seppenwoolde Y, Pötter R, et al. Importance of technique, target selection, contouring, dose prescription, and dose-planning in external beam radiation therapy for cervical cancer: evolution of practice from EMBRACE-I to II. Int J Radiat Oncol Biol Phys. 2019;104(4):885–894. doi: 10.1016/j.ijrobp.2019.03.020.
   PubMed Web of Science ®Google Scholar
• Gandhi AK, Sharma DN, Rath GK, et al. Early clinical outcomes and toxicity of intensity modulated versus conventional pelvic radiation therapy for locally advanced cervix carcinoma: a prospective randomized study. Int J Radiat Oncol Biol Phys. 2013;87(3):542–548. doi: 10.1016/j.ijrobp.2013.06.2059.
   PubMed Web of Science ®Google Scholar
• Klopp A, Yeung A, Deshmukh S, et al. A phase III randomized trial comparing patient-reported toxicity and quality of life (QOL) during pelvic intensity modulated radiation therapy as compared to conventional radiation therapy. Int J Radiat Oncol Biol Phys. 2016;96(2):S3. doi: 10.1016/j.ijrobp.2016.06.024.
   Web of Science ®Google Scholar
• Ray A, Sarkar B. Small bowel toxicity in pelvic radiotherapy for postoperative gynecological cancer: comparison between conformal radiotherapy and intensity modulated radiotherapy. Asia Pac J Clin Oncol. 2013;9(3):280–284. doi: 10.1111/ajco.12049.
   PubMed Web of Science ®Google Scholar
• Shepherd M, Bromley R, Stevens M, et al. Developing knowledge-based planning for gynecological and rectal cancers: clinical validation of RapidPlan™. J Med Radiat Sci. 2020;67(3):217–224. doi: 10.1002/jmrs.396.
   PubMed Web of Science ®Google Scholar
• Wang H, Wang R, Liu J, et al. Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning. Br J Radiol. 2021;94(1123):20210214. doi: 10.1259/bjr.20210214.
   PubMed Web of Science ®Google Scholar
• Jihong C, Penggang B, Xiuchun Z, et al. Automated intensity modulated radiation therapy treatment planning for cervical cancer based on convolution neural network. Technol Cancer Res Treat. 2020;19:1533033820957002. doi: 10.1177/1533033820957002.
   Web of Science ®Google Scholar
• Hansen CR, Bertelsen A, Hazell I, et al. Automatic treatment planning improves the clinical quality of head and neck cancer treatment plans. Clin Transl Radiat Oncol. 2016;1:2–8. doi: 10.1016/j.ctro.2016.08.001.
   PubMedGoogle Scholar
• Hussein M, South CP, Barry MA, et al. Clinical validation and benchmarking of knowledge-based IMRT and VMAT treatment planning in pelvic anatomy. Radiother Oncol. 2016;120(3):473–479. doi: 10.1016/j.radonc.2016.06.022.
   PubMed Web of Science ®Google Scholar
• Sharfo AWM, Breedveld S, Voet PW, et al. Validation of fully automated VMAT plan generation for library-based plan-of-the-day cervical cancer radiotherapy. PLOS One. 2016;11(12):e0169202. doi: 10.1371/journal.pone.0169202.
   PubMed Web of Science ®Google Scholar
• Hazell I, Bzdusek K, Kumar P, et al. Automatic planning of head and neck treatment plans. J Appl Clin Med Phys. 2016;17(1):272–282. doi: 10.1120/jacmp.v17i1.5901.
   PubMed Web of Science ®Google Scholar
• Gallio E, Giglioli FR, Girardi A, et al. Evaluation of a commercial automatic treatment planning system for liver stereotactic body radiation therapy treatments. Phys Med. 2018;46:153–159. doi: 10.1016/j.ejmp.2018.01.016.
   PubMed Web of Science ®Google Scholar
• Breedveld S, Storchi PR, Voet PW, et al. iCycle: integrated, multicriterial beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans. Med Phys. 2012;39(2):951–963. doi: 10.1118/1.3676689.
   PubMed Web of Science ®Google Scholar
• Boylan C, Rowbottom C. A bias-free, automated planning tool for technique comparison in radiotherapy-application to nasopharyngeal carcinoma treatments. J Appl Clin Med Phys. 2014;15(1):4530–4225. doi: 10.1120/jacmp.v15i1.4530.
   PubMed Web of Science ®Google Scholar
• Xhaferllari I, Wong E, Bzdusek K, et al. Automated IMRT planning with regional optimization using planning scripts. J Appl Clin Med Phys. 2013;14(1):4052–4191. doi: 10.1120/jacmp.v14i1.4052.
   PubMed Web of Science ®Google Scholar
• Rigaud B, Anderson BM, Zhiqian HY, 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. 2021;109(4):1096–1110. doi: 10.1016/j.ijrobp.2020.10.038.
   PubMed Web of Science ®Google Scholar
• McIntosh C, Welch M, McNiven A, et al. Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method. Phys Med Biol. 2017;62(15):5926–5944. doi: 10.1088/1361-6560/aa71f8.
   PubMed Web of Science ®Google Scholar
• Ayala R, Ruiz G, Valdivielso T. Automatizing a nonscripting TPS for optimizing clinical workflow and reoptimizing IMRT/VMAT plans. Med Dosim. 2019;44(4):409–414. doi: 10.1016/j.meddos.2019.02.006.
   PubMed Web of Science ®Google Scholar
• Rhee DJ, Jhingran A, Kisling K, et al. Automated radiation treatment planning for cervical cancer. In: seminars in radiation oncology. Vol. 30; Elsevier; 2020. p. 340–347. doi: 10.1016/j.semradonc.2020.05.006.
   Google Scholar
• Fogliata A, Belosi F, Clivio A, et al. On the pre-clinical validation of a commercial model-based optimisation engine: application to volumetric modulated arc therapy for patients with lung or prostate cancer. Radiother Oncol. 2014;113(3):385–391. doi: 10.1016/j.radonc.2014.11.009.
   PubMed Web of Science ®Google Scholar
• Bai P, Weng X, Quan K, et al. A knowledge-based intensity-modulated radiation therapy treatment planning technique for locally advanced nasopharyngeal carcinoma radiotherapy. Radiat Oncol. 2020;15(1):1–10. doi: 10.1186/s13014-020-01626-z.
   Web of Science ®Google Scholar
• Ling C, Han X, Zhai P, et al. A hybrid automated treatment planning solution for esophageal cancer. Radiat Oncol. 2019;14(1):1–7. doi: 10.1186/s13014-019-1443-5.
   PubMed Web of Science ®Google Scholar
• Lou Z, Cheng C, Mao R, et al. A novel automated planning approach for multi-anatomical sites cancer in raystation treatment planning system. Phys Med. 2023;109:102586. doi: 10.1016/j.ejmp.2023.102586.
   PubMed Web of Science ®Google Scholar
• Ma C, Huang F. Assessment of a knowledge-based RapidPlan model for patients with postoperative cervical cancer. Prec. Radiat. Oncol. 2017;1(3):102–107. doi: 10.1002/pro6.23.
   Google Scholar
• Zhang D, Yuan Z, Hu P, et al. Automatic treatment planning for cervical cancer radiation therapy using direct three-dimensional patient anatomy match. J Appl Clin Med Phys. 2022;23(8):e13649. doi: 10.1002/acm2.13649.
   PubMed Web of Science ®Google Scholar
• Bijman R, Sharfo AW, Rossi L, et al. Pre-clinical validation of a novel system for fully-automated treatment planning. Radiother Oncol. 2021;158:253–261. doi: 10.1016/j.radonc.2021.03.003.
   PubMed Web of Science ®Google Scholar
• Trivellato S, Caricato P, Pellegrini R, et al. Comprehensive dosimetric and clinical evaluation of lexicographic optimization-based planning for cervical cancer. Front Oncol. 2022;12:1041839. doi: 10.3389/fonc.2022.1041839.
   PubMed Web of Science ®Google Scholar
• Norwegian Directorate of Health. Nasjonalt handlingsprogram med retningslinjer for diagnostikk, behandling og oppfølging av gynekologisk kreft [nettdokument], faglig oppdatert 11. 2023, https://www.helsedirektoratet.no/retningslinjer/gynekologisk-kreft-handlingsprogram.]; 2016.
   Google Scholar
• Tanderup K, Pötter R, Lindegaard J, et al. EMBRACE II study protocol v.1.0, Image guided intensity modulated External beam radiochemotherapy and MRI based adaptive BRAchytherapy in locally advanced CErvical cancer EMBRACE-II (retrieved 28.04.2023) [https://www.embracestudy.dk/UserUpload/PublicDocuments/EMBRACE\%20II\%20Protocol.pdf]; 2015.
   Google Scholar
• Pötter R, Tanderup K, Kirisits C, et al. The EMBRACE II study: the outcome and prospect of two decades of evolution within the GEC-ESTRO GYN working group and the EMBRACE studies. Clin Transl Radiat Oncol. 2018;9:48–60. doi: 10.1016/j.ctro.2018.01.001.
   PubMed Web of Science ®Google Scholar
• Ogrinc G, Davies L, Goodman D, et al. Squire 2.0 (standards for quality improvement reporting excellence): revised publication guidelines from a detailed consensus process [https://www.equator-network.org/reporting-guidelines/squire/.]; 2015.
   Google Scholar
• Dawson LA, Kavanagh BD, Paulino AC, et al. Radiation-associated kidney injury. Int J Radiat Oncol Biol Phys. 2010;76(3 Suppl):S108–S115. doi: 10.1016/j.ijrobp.2009.02.089.
   PubMed Web of Science ®Google Scholar
• Wortel G, Eekhout D, Lamers E, et al. Characterization of automatic treatment planning approaches in radiotherapy. Phys Imaging Radiat Oncol. 2021;19:60–65. doi: 10.1016/j.phro.2021.07.003.
   PubMedGoogle Scholar
• Frengen J, Vikström J, Mjaaland I, et al. Locoregional breast radiotherapy including IMN: optimizing the dose distribution using an automated non-coplanar VMAT-technique. Acta Oncol. 2023. doi: 10.1080/0284186X.2023.2264488.
   Google Scholar