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For: Hussein M, Heijmen BJM, Verellen D, Nisbet A. Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations. Br J Radiol 2018;91:20180270. [PMID: 30074813 DOI: 10.1259/bjr.20180270] [Cited by in Crossref: 62] [Cited by in F6Publishing: 53] [Article Influence: 15.5] [Reference Citation Analysis]
Number Citing Articles
1 Wilkinson D, Mackie K, Novy D, Beaven F, Mcnamara J, Bailey R, Currie M, Nasser E. A comprehensive evaluation of the quality and complexity of prostate IMRT and VMAT plans generated by an automated inverse planning tool. J Radiother Pract. [DOI: 10.1017/s1460396921000327] [Reference Citation Analysis]
2 Hernandez V, Hansen CR, Widesott L, Bäck A, Canters R, Fusella M, Götstedt J, Jurado-Bruggeman D, Mukumoto N, Kaplan LP, Koniarová I, Piotrowski T, Placidi L, Vaniqui A, Jornet N. What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans. Radiother Oncol 2020;153:26-33. [PMID: 32987045 DOI: 10.1016/j.radonc.2020.09.038] [Cited by in Crossref: 10] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
3 Yu S, Xu H, Sinclair A, Zhang X, Langner U, Mak K. Dosimetric and planning efficiency comparison for lung SBRT: CyberKnife vs VMAT vs knowledge-based VMAT. Medical Dosimetry 2020;45:346-51. [DOI: 10.1016/j.meddos.2020.04.004] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
4 Sabater S, Rovirosa À, Arenas M. In response to Korreman s. et al. Radiation oncologists are, above all, medical doctors. Clin Transl Radiat Oncol 2021;28:116-7. [PMID: 33937531 DOI: 10.1016/j.ctro.2021.03.005] [Reference Citation Analysis]
5 Yang Y, Shao K, Zhang J, Chen M, Chen Y, Shan G. Automatic Planning for Nasopharyngeal Carcinoma Based on Progressive Optimization in RayStation Treatment Planning System. Technol Cancer Res Treat 2020;19:1533033820915710. [PMID: 32552600 DOI: 10.1177/1533033820915710] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
6 Cilla S, Romano C, Morabito VE, Macchia G, Buwenge M, Dinapoli N, Indovina L, Strigari L, Morganti AG, Valentini V, Deodato F. Personalized Treatment Planning Automation in Prostate Cancer Radiation Oncology: A Comprehensive Dosimetric Study. Front Oncol 2021;11:636529. [PMID: 34141608 DOI: 10.3389/fonc.2021.636529] [Reference Citation Analysis]
7 Cozzi L, Heijmen BJM, Muren LP. Advanced treatment planning strategies to enhance quality and efficiency of radiotherapy. Phys Imaging Radiat Oncol 2019;11:69-70. [PMID: 33458281 DOI: 10.1016/j.phro.2019.09.002] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
8 Hansen CR, Hussein M, Bernchou U, Zukauskaite R, Thwaites D. Plan quality in radiotherapy treatment planning - Review of the factors and challenges. J Med Imaging Radiat Oncol 2022;66:267-78. [PMID: 35243775 DOI: 10.1111/1754-9485.13374] [Reference Citation Analysis]
9 Barragán-montero AM, Thomas M, Defraene G, Michiels S, Haustermans K, Lee JA, Sterpin E. Deep learning dose prediction for IMRT of esophageal cancer: The effect of data quality and quantity on model performance. Physica Medica 2021;83:52-63. [DOI: 10.1016/j.ejmp.2021.02.026] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
10 Morén B, Larsson T, Tedgren ÅC. Optimization in treatment planning of high dose-rate brachytherapy - Review and analysis of mathematical models. Med Phys 2021;48:2057-82. [PMID: 33576027 DOI: 10.1002/mp.14762] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Wang M, Zhang Q, Lam S, Cai J, Yang R. A Review on Application of Deep Learning Algorithms in External Beam Radiotherapy Automated Treatment Planning. Front Oncol 2020;10:580919. [PMID: 33194711 DOI: 10.3389/fonc.2020.580919] [Cited by in Crossref: 9] [Cited by in F6Publishing: 6] [Article Influence: 4.5] [Reference Citation Analysis]
12 Cusumano D, Boldrini L, Dhont J, Fiorino C, Green O, Güngör G, Jornet N, Klüter S, Landry G, Mattiucci GC, Placidi L, Reynaert N, Ruggieri R, Tanadini-Lang S, Thorwarth D, Yadav P, Yang Y, Valentini V, Verellen D, Indovina L. Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives. Phys Med 2021;85:175-91. [PMID: 34022660 DOI: 10.1016/j.ejmp.2021.05.010] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
13 Petragallo R, Bardach N, Ramirez E, Lamb JM. Barriers and facilitators to clinical implementation of radiotherapy treatment planning automation: A survey study of medical dosimetrists. J Appl Clin Med Phys 2022;:e13568. [PMID: 35239234 DOI: 10.1002/acm2.13568] [Reference Citation Analysis]
14 Huang C, Yang Y, Xing L. Fully automated noncoplanar radiation therapy treatment planning. Med Phys 2021;48:7439-49. [PMID: 34519064 DOI: 10.1002/mp.15223] [Reference Citation Analysis]
15 Ng F, Jiang R, Chow JCL. Predicting radiation treatment planning evaluation parameter using artificial intelligence and machine learning. IOPSciNotes 2020;1:014003. [DOI: 10.1088/2633-1357/ab805d] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 3.5] [Reference Citation Analysis]
16 Huynh E, Hosny A, Guthier C, Bitterman DS, Petit SF, Haas-Kogan DA, Kann B, Aerts HJWL, Mak RH. Artificial intelligence in radiation oncology. Nat Rev Clin Oncol. 2020;17:771-781. [PMID: 32843739 DOI: 10.1038/s41571-020-0417-8] [Cited by in Crossref: 22] [Cited by in F6Publishing: 16] [Article Influence: 11.0] [Reference Citation Analysis]
17 Schipaanboord BWK, Giżyńska MK, Rossi L, de Vries KC, Heijmen BJM, Breedveld S. Fully automated treatment planning for MLC-based robotic radiotherapy. Med Phys 2021;48:4139-47. [PMID: 34037258 DOI: 10.1002/mp.14993] [Reference Citation Analysis]
18 Marrazzo L, Redapi L, Zani M, Calusi S, Meattini I, Arilli C, Casati M, Compagnucci A, Talamonti C, Raspanti D, Pertutti S, Di Cataldo V, Livi L, Pallotta S. A semi-automatic planning technique for whole breast irradiation with tangential IMRT fields. Physica Medica 2022;98:122-30. [DOI: 10.1016/j.ejmp.2022.05.001] [Reference Citation Analysis]
19 Breedveld S, Bennan ABA, Aluwini S, Schaart DR, Kolkman-deurloo IK, Heijmen BJM. Fast automated multi-criteria planning for HDR brachytherapy explored for prostate cancer. Phys Med Biol 2019;64:205002. [DOI: 10.1088/1361-6560/ab44ff] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
20 Nystrom H, Jensen MF, Nystrom PW. Treatment planning for proton therapy: what is needed in the next 10 years? Br J Radiol 2020;93:20190304. [PMID: 31356107 DOI: 10.1259/bjr.20190304] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
21 Momin S, Fu Y, Lei Y, Roper J, Bradley JD, Curran WJ, Liu T, Yang X. Knowledge-based radiation treatment planning: A data-driven method survey. J Appl Clin Med Phys 2021;22:16-44. [PMID: 34231970 DOI: 10.1002/acm2.13337] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
22 Jones B. Clinical Radiobiology of Fast Neutron Therapy: What Was Learnt? Front Oncol 2020;10:1537. [PMID: 33042798 DOI: 10.3389/fonc.2020.01537] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Kuznetsov M, Clairambault J, Volpert V. Improving cancer treatments via dynamical biophysical models. Phys Life Rev 2021;39:1-48. [PMID: 34688561 DOI: 10.1016/j.plrev.2021.10.001] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
24 Bijman R, Rossi L, Janssen T, de Ruiter P, Carbaat C, van Triest B, Breedveld S, Sonke JJ, Heijmen B. First system for fully-automated multi-criterial treatment planning for a high-magnetic field MR-Linac applied to rectal cancer. Acta Oncol 2020;59:926-32. [PMID: 32436450 DOI: 10.1080/0284186X.2020.1766697] [Cited by in Crossref: 8] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
25 Wang H, Wang R, Liu J, Zhang J, Yao K, Yue H, Zhang Y, You J, Wu H. Tree-based exploration of the optimization objectives for automatic cervical cancer IMRT treatment planning. Br J Radiol 2021;94:20210214. [PMID: 34111955 DOI: 10.1259/bjr.20210214] [Reference Citation Analysis]
26 Polizzi M, Watkins RW, Watkins WT. Data-Driven Dose-Volume Histogram Prediction. Advances in Radiation Oncology 2022;7:100841. [DOI: 10.1016/j.adro.2021.100841] [Reference Citation Analysis]
27 Li X, Zhang J, Sheng Y, Chang Y, Yin F, Ge Y, Wu QJ, Wang C. Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning. Phys Med Biol 2020;65:175014. [DOI: 10.1088/1361-6560/aba5eb] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
28 Bijman R, Rossi L, Janssen T, de Ruiter P, van Triest B, Breedveld S, Sonke JJ, Heijmen B. MR-Linac Radiotherapy - The Beam Angle Selection Problem. Front Oncol 2021;11:717681. [PMID: 34660281 DOI: 10.3389/fonc.2021.717681] [Reference Citation Analysis]
29 Zhong Y, Yu L, Zhao J, Fang Y, Yang Y, Wu Z, Wang J, Hu W. Clinical Implementation of Automated Treatment Planning for Rectum Intensity-Modulated Radiotherapy Using Voxel-Based Dose Prediction and Post-Optimization Strategies. Front Oncol 2021;11:697995. [PMID: 34249757 DOI: 10.3389/fonc.2021.697995] [Reference Citation Analysis]
30 Kyroudi A, Petersson K, Ozsahin E, Bourhis J, Bochud F, Moeckli R. Exploration of clinical preferences in treatment planning of radiotherapy for prostate cancer using Pareto fronts and clinical grading analysis. Phys Imaging Radiat Oncol 2020;14:82-6. [PMID: 33458319 DOI: 10.1016/j.phro.2020.05.008] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
31 Redapi L, Rossi L, Marrazzo L, Penninkhof JJ, Pallotta S, Heijmen B. Comparison of volumetric modulated arc therapy and intensity-modulated radiotherapy for left-sided whole-breast irradiation using automated planning. Strahlenther Onkol 2021. [PMID: 34351452 DOI: 10.1007/s00066-021-01817-x] [Reference Citation Analysis]
32 Oud M, Kolkman-deurloo I, Mens J, Lathouwers D, Perkó Z, Heijmen B, Breedveld S. Fast and fully-automated multi-criterial treatment planning for adaptive HDR brachytherapy for locally advanced cervical cancer. Radiotherapy and Oncology 2020;148:143-50. [DOI: 10.1016/j.radonc.2020.04.017] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
33 Huang C, Yang Y, Panjwani N, Boyd S, Xing L. Pareto Optimal Projection Search (POPS): Automated Radiation Therapy Treatment Planning by Direct Search of the Pareto Surface. IEEE Trans Biomed Eng 2021;68:2907-17. [PMID: 33523802 DOI: 10.1109/TBME.2021.3055822] [Reference Citation Analysis]
34 Ahmad I, Chufal KS, Bhatt CP, Miller AA, Bajpai R, Chhabra A, Chowdhary RL, Pahuja AK, Gairola M. Plan quality assessment of modern radiotherapy delivery techniques in left-sided breast cancer: an analysis stratified by target delineation guidelines. BJR Open 2020;2:20200007. [PMID: 33330831 DOI: 10.1259/bjro.20200007] [Reference Citation Analysis]
35 Kouwenberg J, Penninkhof J, Habraken S, Zindler J, Hoogeman M, Heijmen B. Model based patient pre-selection for intensity-modulated proton therapy (IMPT) using automated treatment planning and machine learning. Radiotherapy and Oncology 2021;158:224-9. [DOI: 10.1016/j.radonc.2021.02.034] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
36 Yu S, Xu H, Zhang Y, Zhang X, Dyer MA, Hirsch AE, Tam Truong M, Zhen H. Knowledge-based planning in robotic intracranial stereotactic radiosurgery treatments. J Appl Clin Med Phys 2021;22:48-54. [PMID: 33560592 DOI: 10.1002/acm2.13173] [Reference Citation Analysis]
37 Wortel G, Eekhout D, Lamers E, van der Bel R, Kiers K, Wiersma T, Janssen T, Damen E. Characterization of automatic treatment planning approaches in radiotherapy. Phys Imaging Radiat Oncol 2021;19:60-5. [PMID: 34307920 DOI: 10.1016/j.phro.2021.07.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Brown KH, Ghita M, Schettino G, Prise KM, Butterworth KT. Evaluation of a Novel Liquid Fiducial Marker, BioXmark®, for Small Animal Image-Guided Radiotherapy Applications. Cancers (Basel) 2020;12:E1276. [PMID: 32443537 DOI: 10.3390/cancers12051276] [Reference Citation Analysis]
39 Fiandra C, Rossi L, Alparone A, Zara S, Vecchi C, Sardo A, Bartoncini S, Loi G, Pisani C, Gino E, Ruo Redda MG, Marco Deotto G, Tini P, Comi S, Zerini D, Ametrano G, Borzillo V, Strigari L, Strolin S, Savini A, Romeo A, Reccanello S, Rumeileh IA, Ciscognetti N, Guerrisi F, Balestra G, Ricardi U, Heijmen B. Automatic genetic planning for volumetric modulated arc therapy: A large multi-centre validation for prostate cancer. Radiother Oncol 2020;148:126-32. [PMID: 32361572 DOI: 10.1016/j.radonc.2020.04.020] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
40 van den Ende RPJ, Peters FP, Harderwijk E, Rütten H, Bouwmans L, Berbee M, Canters RAM, Stoian G, Compagner K, Rozema T, de Smet M, Intven MPW, Tijssen RHN, Theuws J, van Haaren P, van Triest B, Eekhout D, Marijnen CAM, van der Heide UA, Kerkhof EM. Radiotherapy quality assurance for mesorectum treatment planning within the multi-center phase II STAR-TReC trial: Dutch results. Radiat Oncol 2020;15:41. [PMID: 32070386 DOI: 10.1186/s13014-020-01487-6] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
41 Hansen CR, Crijns W, Hussein M, Rossi L, Gallego P, Verbakel W, Unkelbach J, Thwaites D, Heijmen B. Radiotherapy Treatment plannINg study Guidelines (RATING): A framework for setting up and reporting on scientific treatment planning studies. Radiother Oncol 2020;153:67-78. [PMID: 32976873 DOI: 10.1016/j.radonc.2020.09.033] [Cited by in Crossref: 12] [Cited by in F6Publishing: 11] [Article Influence: 6.0] [Reference Citation Analysis]
42 Javor J, Robbins M, Rosewall T, Craig T, Villafuerte CJ, Cummings B, Dawson L. Can Conformity-Based Volumetric Modulated Arc Therapy Improve Dosimetry and Speed of Delivery in Radiation Therapy to Lumbosacral Spine Compared with Conventional Techniques? J Med Imaging Radiat Sci 2020;51:404-10. [PMID: 32439283 DOI: 10.1016/j.jmir.2020.04.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
43 Teruel JR, Malin M, Liu EK, McCarthy A, Hu K, Cooper BT, Sulman EP, Silverman JS, Barbee D. Full automation of spinal stereotactic radiosurgery and stereotactic body radiation therapy treatment planning using Varian Eclipse scripting. J Appl Clin Med Phys 2020;21:122-31. [PMID: 32965754 DOI: 10.1002/acm2.13017] [Reference Citation Analysis]
44 Naccarato S, Rigo M, Pellegrini R, Voet P, Akhiat H, Gurrera D, De Simone A, Sicignano G, Mazzola R, Figlia V, Ricchetti F, Nicosia L, Giaj-levra N, Cuccia F, Stavreva N, Pressyanov DS, Stavrev P, Alongi F, Ruggieri R. Automated Planning for Prostate Stereotactic Body Radiation Therapy on the 1.5 T MR-Linac. Advances in Radiation Oncology 2022;7:100865. [DOI: 10.1016/j.adro.2021.100865] [Reference Citation Analysis]
45 Li X, Wang C, Sheng Y, Zhang J, Wang W, Yin FF, Wu Q, Wu QJ, Ge Y. An artificial intelligence-driven agent for real-time head-and-neck IMRT plan generation using conditional generative adversarial network (cGAN). Med Phys 2021;48:2714-23. [PMID: 33577108 DOI: 10.1002/mp.14770] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
46 Fiorino C, Guckemberger M, Schwarz M, van der Heide UA, Heijmen B. Technology-driven research for radiotherapy innovation. Mol Oncol 2020;14:1500-13. [PMID: 32124546 DOI: 10.1002/1878-0261.12659] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 7.5] [Reference Citation Analysis]
47 Uehara T, Monzen H, Tamura M, Ishikawa K, Doi H, Nishimura Y. Dose-volume histogram analysis and clinical evaluation of knowledge-based plans with manual objective constraints for pharyngeal cancer. J Radiat Res 2020;61:499-505. [PMID: 32329509 DOI: 10.1093/jrr/rraa021] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
48 Duffy SR, Zheng Y, Muenkel J, Ellis RJ, Baig TN, Krancevic B, Langmack CB, Kelley KD, Choi S. Refining complex re-irradiation dosimetry through feasibility benchmarking and analysis for informed treatment planning. J Appl Clin Med Phys 2020;21:263-71. [PMID: 33270974 DOI: 10.1002/acm2.13102] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
49 van Haveren R, Heijmen BJM, Breedveld S. Automatically configuring the reference point method for automated multi-objective treatment planning. Phys Med Biol 2019;64:035002. [PMID: 30566906 DOI: 10.1088/1361-6560/aaf9fe] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
50 Bijman R, Rossi L, Sharfo AW, Heemsbergen W, Incrocci L, Breedveld S, Heijmen B. Automated Radiotherapy Planning for Patient-Specific Exploration of the Trade-Off Between Tumor Dose Coverage and Predicted Radiation-Induced Toxicity-A Proof of Principle Study for Prostate Cancer. Front Oncol 2020;10:943. [PMID: 32695670 DOI: 10.3389/fonc.2020.00943] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
51 van Haveren R, Heijmen BJM, Breedveld S. Automatic configuration of the reference point method for fully automated multi-objective treatment planning applied to oropharyngeal cancer. Med Phys 2020;47:1499-508. [PMID: 32017144 DOI: 10.1002/mp.14073] [Reference Citation Analysis]
52 Zhang T, Bokrantz R, Olsson J. Probabilistic feature extraction, dose statistic prediction and dose mimicking for automated radiation therapy treatment planning. Med Phys 2021. [PMID: 34265105 DOI: 10.1002/mp.15098] [Reference Citation Analysis]
53 Wang C, Zhu X, Hong JC, Zheng D. Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future. Technol Cancer Res Treat. 2019;18:1533033819873922. [PMID: 31495281 DOI: 10.1177/1533033819873922] [Cited by in Crossref: 34] [Cited by in F6Publishing: 28] [Article Influence: 17.0] [Reference Citation Analysis]
54 Meyer P, Biston MC, Khamphan C, Marghani T, Mazurier J, Bodez V, Fezzani L, Rigaud PA, Sidorski G, Simon L, Robert C. Automation in radiotherapy treatment planning: Examples of use in clinical practice and future trends for a complete automated workflow. Cancer Radiother 2021:S1278-3218(21)00103-7. [PMID: 34175222 DOI: 10.1016/j.canrad.2021.06.006] [Reference Citation Analysis]
55 van der Laan HP, van der Schaaf A, Van den Bosch L, Korevaar EW, Steenbakkers RJHM, Both S, Langendijk JA. Quality of life and toxicity guided treatment plan optimisation for head and neck cancer. Radiother Oncol 2021;162:85-90. [PMID: 34237344 DOI: 10.1016/j.radonc.2021.06.035] [Reference Citation Analysis]
56 Wheeler PA, Chu M, Holmes R, Woodley OW, Jones CS, Maggs R, Staffurth J, Palaniappan N, Spezi E, Lewis DG, Campbell S, Fitzgibbon J, Millin AE. Evaluating the application of Pareto navigation guided automated radiotherapy treatment planning to prostate cancer. Radiother Oncol 2019;141:220-6. [PMID: 31526670 DOI: 10.1016/j.radonc.2019.08.001] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 1.3] [Reference Citation Analysis]
57 Clark CH, Gagliardi G, Heijmen B, Malicki J, Thorwarth D, Verellen D, Muren LP. Adapting training for medical physicists to match future trends in radiation oncology. Phys Imaging Radiat Oncol 2019;11:71-5. [PMID: 33458282 DOI: 10.1016/j.phro.2019.09.003] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
58 Sonke J, Aznar M, Rasch C. Adaptive Radiotherapy for Anatomical Changes. Seminars in Radiation Oncology 2019;29:245-57. [DOI: 10.1016/j.semradonc.2019.02.007] [Cited by in Crossref: 45] [Cited by in F6Publishing: 37] [Article Influence: 15.0] [Reference Citation Analysis]
59 Fjellanger K, Hysing LB, Heijmen BJM, Pettersen HES, Sandvik IM, Sulen TH, Breedveld S, Rossi L. Enhancing Radiotherapy for Locally Advanced Non-Small Cell Lung Cancer Patients with iCE, a Novel System for Automated Multi-Criterial Treatment Planning Including Beam Angle Optimization. Cancers (Basel) 2021;13:5683. [PMID: 34830838 DOI: 10.3390/cancers13225683] [Reference Citation Analysis]
60 Vergalasova I, Li T, Cai J. Point/Counterpoint. Universal implementation of automated treatment planning software will be detrimental to future generations of trainees. Med Phys 2021;48:3409-12. [PMID: 33928653 DOI: 10.1002/mp.14900] [Reference Citation Analysis]
61 Shelley CE, Barraclough LH, Nelder CL, Otter SJ, Stewart AJ. Adaptive Radiotherapy in the Management of Cervical Cancer: Review of Strategies and Clinical Implementation. Clin Oncol (R Coll Radiol) 2021;33:579-90. [PMID: 34247890 DOI: 10.1016/j.clon.2021.06.007] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
62 Cagni E, Botti A, Rossi L, Iotti C, Iori M, Cozzi S, Galaverni M, Rosca A, Sghedoni R, Timon G, Spezi E, Heijmen B. Variations in Head and Neck Treatment Plan Quality Assessment Among Radiation Oncologists and Medical Physicists in a Single Radiotherapy Department. Front Oncol 2021;11:706034. [PMID: 34712606 DOI: 10.3389/fonc.2021.706034] [Reference Citation Analysis]
63 Bijman R, Sharfo AW, Rossi L, Breedveld S, Heijmen B. Pre-clinical validation of a novel system for fully-automated treatment planning. Radiotherapy and Oncology 2021;158:253-61. [DOI: 10.1016/j.radonc.2021.03.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
64 Biston MC, Costea M, Gassa F, Serre AA, Voet P, Larson R, Grégoire V. Evaluation of fully automated a priori MCO treatment planning in VMAT for head-and-neck cancer. Phys Med 2021;87:31-8. [PMID: 34116315 DOI: 10.1016/j.ejmp.2021.05.037] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
65 Alnowami M, Abolaban F, Hijazi H, Nisbet A. Regression Analysis of Rectal Cancer and Possible Application of Artificial Intelligence (AI) Utilization in Radiotherapy. Applied Sciences 2022;12:725. [DOI: 10.3390/app12020725] [Reference Citation Analysis]
66 Peng J, Chen Y, Zhao J, Wang J, Xia X, Cai B, Mazur TR, Zhu J, Zhang Z, Hu W. An atlas-guided automatic planning approach for rectal cancer intensity-modulated radiotherapy. Phys Med Biol 2021;66. [PMID: 34237715 DOI: 10.1088/1361-6560/ac127d] [Reference Citation Analysis]
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