Artificial intelligence technology for radiation oncology understaff mitigation and cost-effective treatment planning

Francisco Roberto Cassetta Júnior; Felipe Orsolin  Teixeira

doi:10.23925/1984-4840.2022v24i1/4a7

Autores

Francisco Roberto Cassetta Júnior Proton Solutions, Varian, a Siemiens Healthineers Company https://orcid.org/0000-0002-3073-4131
Felipe Orsolin Teixeira Universidade Estadual do Centro-Oeste https://orcid.org/0000-0002-6686-0164

DOI:

https://doi.org/10.23925/1984-4840.2022v24i1/4a7

Palavras-chave:

inteligência artificial, planejamento da radioterapia assistida por computador, custos de cuidados de saúde, investimentos em saúde, neoplasias

Resumo

O tratamento com radioterapia pode ser relativamente barato e altamente eficaz, reduzindo o custo geral dos cuidados de saúde, bem como salvar vidas de pacientes com câncer. Para enfrentar os desafios impostos por tarefas laboriosas e falta de mão-de-obra na radioterapia, o uso de modelos baseados em inteligência artificial, para reduzir os tempos de planejamento de tratamento em até 95%, pode ser uma estratégia promissora. Um exemplo de tal ferramenta, denominada RapidPlan (Varian Medical Systems, Palo Alto-CA), pode ser adquirida com o investimento de uma fração do custo do sistema de planejamento de tratamento. O suporte do RapidPlan durante o planejamento do tratamento pode resultar em um aumento considerável na qualidade do plano, reduzindo a variabilidade e o tempo de planejamento. O objetivo desta dissertação foi estimar o ponto de equilíbrio a partir do qual o tempo economizado durante o tempo de tratamento pagaria o investimento inicial no RapidPlan. Pela avaliação dos dados publicados, pode-se concluir que o RapidPlan pode beneficiar amplamente as instituições de radioterapia, agilizando o processo de planejamento do tratamento e o ponto de equilíbrio começou a ser alcançado após o tratamento de 112 a 2688 pacientes, dependendo dos tipos de câncer tratados para cada grupo. Portanto, é possível prever um retorno do investimento em um tempo razoável e, ao mesmo tempo que se usufrui de ganhos em eficiência e potencial mitigação da falta de pessoal e experiência em planejamento de tratamento.

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Referências

Price P, Fleurent B, Barney SE. The role of the global coalition for radiotherapy in political advocacy for radiation therapy as a cost-effective and underfunded modality around the world. Int J Radiat Oncol Biol Phys. 2021;111(1):23–6. doi: 10.1016/j.ijrobp.2021.04.010.

Price P, Barney SE. Initiation of the global coalition for radiotherapy during the COVID-19 pandemic. Lancet Oncol. 2020;21:752–3. doi: 10.1016/S1470-2045(20)30281-3.

Hanna TP, Evans GA, Booth CM. Cancer, COVID-19 and the precautionary principle: prioritizing treatment during a global pandemic. Nature Rev Clin Oncol. 2020;17:268–70. doi: 10.1038/s41571-020-0362-6.

Lievens Y, Defourny N, Corral J, Gasparotto C, Grau C, Borras JM, et al. How public health services pay for radiotherapy in Europe: an ESTRO–HERO analysis of reimbursement. Lancet Oncol. 2020;21:e42–54. doi: 10.1016/S1470-2045(19)30794-6.

Atun R, Jaffray DA, Barton MB, Bray F, Baumann M, Vikram B, et al. Expanding global access to radiotherapy. Lancet Oncol. 2015;16:1153–86. doi: 10.1016/S1470-2045(15)00222-3.

O’Connor SD, Bhalla M. Should artificial intelligence tell radiologists which study to read next? Radiol Artif Intell. 2021;3(2):e210009. doi: 10.1148/ryai.2021210009.

O’Neill TJ, Xi Y, Stehel E, Browning T, Ng YS, Baker C, et al. Active reprioritization of the reading worklist using artificial intelligence has a beneficial effect on the turnaround time for interpretation of head ct with intracranial hemorrhage. Radiol Artif Intell. 2021;3(2):e200024. doi: 10.1148/ryai.2020200024.

The Topol Review. Preparing the healthcare workforce to deliver the digital future: an independent report on behalf of the Secretary of State for Health and Social Care [Internet]. England: The Topol Review, Health Education England, NHS; 2019 [acesso em 9 abr. 2022]. p. 102. Disponível em: https://topol.hee.nhs.uk/wp-content/uploads/HEE-Topol-Review-2019.pdf.

Chen W, Wang C, Zhan W, Jia Y, Ruan F, Qiu L, et al. A comparative study of auto-contouring softwares in delineation of organs at risk in lung cancer and rectal cancer. Sci Rep. 2021;11(1):1–8. doi: 10.1038/s41598-021-02330-y.

Downing M. Barts Health using AI to prioritise care for colon cancer patients. Our news - Barts Health NHS Trust [Internet]. 2020 [acesso em 9 abr. 2022]. p. 1. Disponível em: https://www.bartshealth.nhs.uk/news/barts-health-using-ai-to-prioritise-care-for-high-risk-colon-cancer-patients-8867.

van Duren-Koopman MJ, Tol JP, Dahele M, Bucko E, Meijnen P, Slotman BJ, et al. Personalized automated treatment planning for breast plus locoregional lymph nodes using Hybrid RapidArc. Pract Radiat Oncol. 2018;8(5):332–41. doi: 10.1016/j.prro.2018.03.008.

Lindberg J, Holmström P, Hallberg S, Björk-Eriksson T, Olsson CE. A national perspective about the current work situation at modern radiotherapy departments. Clin Transl Radiat Oncol. 2020;24:127–34. doi: 10.1016/j.ctro.2020.08.001.

Souza JAM, Rocha HA, Santos MAC, Cherchiglia ML. Factors associated with time to initiate lung cancer treatment in Minas Gerais, Brazil. Ciênc Saude Coletiva. 2022;27(3):1133–46. doi: 10.1590/1413-81232022273.02992021.

Nascimento MI, Azevedo e Silva G. Efeito do tempo de espera para radioterapia na sobrevida geral em cinco anos de mulheres com câncer do colo do útero, 1995-2010. Cad Saúde Pública. 2015;31(11):2437–48. doi: 10.1590/0102-311X00004015.

Mendez LC, Moraes FY, Fernandes GS, Weltman E. Cancer deaths due to lack of universal access to radiotherapy in the Brazilian public health system. Clin Oncol. 2018;30(1):e29–36. doi: 10.1016/j.clon.2017.09.003.

Mendez LC, Moraes FY, Castilho MS, Louie AV, Qu XM. Lives and economic loss in Brazil due to lack of radiotherapy access in cervical cancer: a cost-effectiveness analysis. Clin Oncol. 2019;31(9):e143–8. doi: 10.1016/j.clon.2019.05.004.

Zemplényi AT, Kaló Z, Kovács G, Farkas R, Beöthe T, Bányai D, et al. Cost-effectiveness analysis of intensity-modulated radiation therapy with normal and hypofractionated schemes for the treatment of localised prostate cancer. Eur J Cancer Care (Engl). 2018;27(1):e12430. doi: 10.1111/ecc.12430.

Hospodková P, Husár T, Klíčová B, Severová L, Šrédl K, Svoboda R. Cost analysis of selected radiotherapeutic modalities for prostate cancer treatment: Czech Republic case study for the purposes of Hospital Based HTA. Healthcare. 2021;9(1):98. doi: 10.3390/healthcare9010098.

van Gysen K, O’Toole J, Le A, Wu K, Schuler T, Porter B, et al. Rolling out RapidPlan: what we’ve learnt. J Med Radiat Sci. 2020;67(4):310–7. doi: 10.1002/jmrs.420.

Scaggion A, Fusella M, Roggio A, Bacco S, Pivato N, Rossato MA, et al. Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution. Phys Med. 2018;53:86–93. doi: 10.1016/j.ejmp.2018.08.016.

Van Dyk J, Zubizarreta E, Lievens Y. Cost evaluation to optimise radiation therapy implementation in different income settings: a time-driven activity-based analysis. Radiother Oncol. 2017;125(2):178–85. doi: 10.1016/j.radonc.2017.08.021.

Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30. doi: 10.3322/caac.21590.

Chang ATY, Hung AWM, Cheung FWK, Lee MCH, Chan OSH, Philips H, et al. Comparison of planning quality and efficiency between conventional and knowledge-based algorithms in nasopharyngeal cancer patients using intensity modulated radiation therapy. Int J Radiat Oncol Biol Phys. 2016;95(3):981–90. doi: 10.1016/j.ijrobp.2016.02.017.

Rusu I, Roeske J, Solanki A, Kang H. Fully automated planning and delivery of hippocampal-sparing whole brain irradiation. Med Dosim. 2022;47(1):8–13. doi: 10.1016/j.meddos.2021.06.004.

Varian Medical Systems. HyperArc [Internet]. 2022 [acesso em 6 out. 2022]. Disponível em: https://www.varian.com/en-ch/products/radiotherapy/treatment-planning/hyperarc.

Kubo K, Monzen H, Ishii K, Tamura M, Kawamorita R, Sumida I, et al. Dosimetric comparison of RapidPlan and manually optimized plans in volumetric modulated arc therapy for prostate cancer. Phys Med. 2017;44:199–204. doi: 10.1016/j.ejmp.2017.06.026.

Mayo Clinic. Prostate cancer care at Mayo Clinic [Internet]. 2022 [acesso em 24 jun. 2022]. Disponivel em: https://www.mayoclinic.org/diseases-conditions/prostate-cancer/care-at-mayo-clinic/mac-20353097.

Dumane VA, Tam J, Lo YC, Rosenzweig KE. RapidPlan for knowledge-based planning of malignant pleural mesothelioma. Pract Radiat Oncol. 2021;11(2):e219–28. doi: 10.1016/j.prro.2020.06.003.

Fogliata A, Cozzi L, Reggiori G, Stravato A, Lobefalo F, Franzese C, et al. RapidPlan knowledge based planning: Iterative learning process and model ability to steer planning strategies. Radiat Oncol. 2019;14(1):1–12. doi: 10.1186/s13014-019-1403-0.

Hansen CR, Bertelsen A, Hazell I, Zukauskaite R, Gyldenkerne N, Johansen J, 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.

Hazell I, Bzdusek K, Kumar P, Hansen CR, Bertelsen A, Eriksen JG, et al. Automatic planning of head and neck treatment plans. J Appl Clin Med Phys. 2016;17(1):272–82. doi: 10.1120/jacmp.v17i1.5901.

Yang Y, Ford EC, Wu B, Pinkawa M, Van Triest B, Campbell P, et al. An overlap-volume-histogram based method for rectal dose prediction and automated treatment planning in the external beam prostate radiotherapy following hydrogel injection. Med Phys. 2013;40(1):011709. doi: 10.1118/1.4769424.

Shiraishi S, Moore KL. Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy. Med Phys. 2016;43(1):378–87. doi: 10.1118/1.4938583.

Lahanas M, Schreibmann E, Baltas D. Multiobjective inverse planning for intensity modulated radiotherapy with constraint-free gradient-based optimization algorithms. Phys Med Biol. 2003;48(17):2843–71. doi: 10.1088/0031-9155/48/17/308.

Monz M, Küfer KH, Bortfeld TR, Thieke C. Pareto navigation: algorithmic foundation of interactive multi-criteria IMRT planning. Phys Med Biol. 2008;53(4):985–98. doi: 10.1088/0031-9155/53/4/011.

Vaccarella S, Laversanne M, Ferlay J, Bray F. Cervical cancer in Africa, Latin America and the Caribbean and Asia: regional inequalities and changing trends. Int J Cancer. 2017;141(10):1997–2001. doi: 10.1002/ijc.30901.

Tinoco M, Waga E, Tran K, Vo H, Baker J, Hunter R, et al. RapidPlan development of VMAT plans for cervical cancer patients in low- and middle-income countries. Med Dosim. 2020;45(2):172–8. doi: 10.1016/j.meddos.2019.10.002.