Advertisement

Implementation of volumetric-modulated arc therapy for locally advanced breast cancer patients: Dosimetric comparison with deliverability consideration of planning techniques and predictions of patient-specific QA results via supervised machine learning

Published:February 21, 2022DOI:https://doi.org/10.1016/j.ejmp.2022.02.015

      Highlights

      • Dosimetric comparison of VMAT techniques dedicated to advanced breast cancer.
      • Confrontation of dosimetric results with quality assurance outcomes of VMAT techniques.
      • Quality assurance outcome prediction via supervised machine learning with stacked models.

      Abstract

      Purpose

      The aim of this study was to implement a clinically deliverable VMAT planning technique dedicated to advanced breast cancer, and to predict failed QA using a machine learning (ML) model to optimize the QA workload.

      Methods

      For three planning methods (2A: 2-partial arc, 2AS: 2-partial arc with splitting, 4A: 4-partial arc), dosimetric results were compared with patient-specific QA performed with the electronic portal imaging device of the linac. A dataset was built with the pass/fail status of the plans and complexity metrics. It was divided into training and testing sets. An ML metamodel combining predictions from six base classifiers was trained on the training set to predict plans as ‘pass’ or ‘fail’. The predictive performances were evaluated using the unseen data of the testing set.

      Results

      The dosimetric comparison highlighted that 4A was the highest dosimetric performant method but also the poorest performant in the QA process. 2AS spared the best heart, but provided the highest dose to the contralateral breast and lowest node coverage. 2A provides a dosimetric compromise between organ at risk sparing and PTV coverage with satisfactory QA results. The metamodel had a median predictive sensitivity of 73% and a median specificity of 91%.

      Conclusions

      The 2A method was selected to calculate clinically deliverable VMAT plans; however, the 2AS method was maintained when the heart was of particular importance and breath-hold techniques were not applicable. The metamodel provides promising predictive performance, and it is intended to be improved as a larger dataset becomes available.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Physica Medica: European Journal of Medical Physics
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Popescu C.C.
        • Olivotto I.A.
        • Beckham W.A.
        • Ansbacher W.
        • Zavgorodni S.
        • Shaffer R.
        • et al.
        Volumetric modulated arc therapy improves dosimetry and reduces treatment time compared to conventional intensity-modulated radiotherapy for locoregional radiotherapy of left-sided breast cancer and internal mammary nodes.
        Int. J. Radiat Oncol Biol. Phys. 2010; 76: 287-295https://doi.org/10.1016/j.ijrobp.2009.05.038
        • Jo I.Y.
        • Kim E.S.
        • Kim W.C.
        • Min K.C.
        • Yeo S.G.
        Dosimetric comparison of incidental axillary irradiation between three-dimensional conformal and volumetric modulated arc techniques for breast cancer. Molecular and Clinical.
        Oncology. 2020; 12: 551-556
        • Johansen S.
        • Cozzi L.
        • Olsen D.R.
        A planning comparison of dose patterns in organs at risk and predicted risk for radiation induced malignancy in the contralateral breast following radiation therapy of primary breast using conventional IMRT and volumetric modulated arc treatment technique.
        Acta Oncol. 2009; 48: 495-503https://doi.org/10.1080/02841860802657227
        • Koivumäki T.
        • Fogliata A.
        • Zeverino M.
        • Boman E.
        • Sierpowska J.
        • Moeckli R.
        • et al.
        Dosimetric evaluation of modern radiation therapy techniques for left breast in deep-inspiration breath-hold.
        Physica Med. 2018; 45: 82-87https://doi.org/10.1016/j.ejmp.2017.12.009
        • Tyran M.
        • Mailleux H.
        • Tallet A.
        • Fau P.
        • Gonzague L.
        • Minsat M.
        • et al.
        Volumetric-modulated arc therapy for left-sided breast cancer and all regional nodes improves target volumes coverage and reduces treatment time and doses to the heart and left coronary artery, compared with a field-in-field technique.
        Journal of Radiation Research. 2015; 56: 927-937https://doi.org/10.1093/jrr/rrv052
        • Dumane V.A.
        • Bakst R.
        • Green S.
        Dose to organs in the supraclavicular region when covering the Internal Mammary Nodes (IMNs) in breast cancer patients: A comparison of Volumetric Modulated Arcs Therapy (VMAT) versus 3D and VMAT.
        PLoS ONE. 2018; 13https://doi.org/10.1371/journal.pone.0205770
        • Jensen C.A.
        • Acosta Roa A.M.
        • Johansen M.
        • Lund J.A.
        • Frengen J.
        Robustness of VMAT and 3DCRT plans toward setup errors in radiation therapy of locally advanced left-sided breast cancer with DIBH.
        Physica Med. 2018; 45: 12-18https://doi.org/10.1016/j.ejmp.2017.11.019
        • Osman S.O.S.
        • Hol S.
        • Poortmans P.M.
        • Essers M.
        Volumetric modulated arc therapy and breath-hold in image-guided locoregional left-sided breast irradiation.
        Radiother Oncol. 2014; 112: 17-22https://doi.org/10.1016/j.radonc.2014.04.004
        • Boman E.
        • Rossi M.
        • Haltamo M.
        • Skyttä T.
        • Kapanen M.
        A new split arc VMAT technique for lymph node positive breast cancer.
        Physica Med. 2016; 32: 1428-1436https://doi.org/10.1016/j.ejmp.2016.10.012
        • Fogliata A.
        • Seppälä J.
        • Reggiori G.
        • Lobefalo F.
        • Palumbo V.
        • De Rose F.
        • et al.
        Dosimetric trade-offs in breast treatment with VMAT technique.
        Br J Radiol. 2017; 90: 20160701https://doi.org/10.1259/bjr.20160701
        • Dunlop A.
        • Colgan R.
        • Kirby A.
        • Ranger A.
        • Blasiak-Wal I.
        Evaluation of organ motion-based robust optimisation for VMAT planning for breast and internal mammary chain radiotherapy.
        Clin Transl Radiat Oncol. 2019; 16: 60-66https://doi.org/10.1016/j.ctro.2019.04.004
        • Pham T.T.
        • Ward R.
        • Latty D.
        • Owen C.
        • Gebski V.
        • Chojnowski J.
        • et al.
        Left-sided breast cancer loco-regional radiotherapy with deep inspiration breath-hold: Does volumetric-modulated arc radiotherapy reduce heart dose further compared with tangential intensity-modulated radiotherapy ?.
        J Med Imag Radiat Oncol. 2016; 60: 545-553https://doi.org/10.1111/1754-9485.12459
        • Nicolini G.
        • Fogliata A.
        • Clivio A.
        • Vanetti E.
        • Cozzi L.
        Planning strategies in volumetric modulated arc therapy.
        Med Phys. 2011; 38: 4025-4030https://doi.org/10.1118/1.3598442
        • Tyran M.
        • Tallet A.
        • Resbeut M.
        • Ferre M.
        • Favrel V.
        • Fau P.
        • et al.
        Safety and benefit of using a virtual bolus during treatment planning for breast cancer treated with arc therapy.
        Radiat Oncol Phys. 2018; 19: 463-472https://doi.org/10.1002/acm2.12398
        • Liao X.
        • Wu F.
        • Wu J.
        • Peng Q.
        • Yao X.
        • Kang S.
        • et al.
        Impact of positioning errors in the dosimetry of VMAT left-sided post mastectomy irradiation.
        Radiat Oncol. 2020; 15https://doi.org/10.1186/s13014-020-01556-w
        • van der Veen G.J.
        • Janssen T.
        • Duijn A.
        • van Kranen S.
        • de Graaf R.J.
        • Wortel G.
        • et al.
        A robust volumetric arc therapy planning approach for breast cancer involving the axillary nodes.
        Med Dosim. 2019; 44: 183-189https://doi.org/10.1016/j.meddos.2018.06.001
        • Zhang R.
        • Heins D.
        • Sanders M.
        • Guo B.
        • Hogstrom K.
        Evaluation of a mixed beam therapy for postmastectomy breast cancer patients: Bolus electron conformal therapy combined with intensity modulated photon radiotherapy and volumetric modulated photon arc therapy.
        Med Phys. 2018; 45: 2912-2924https://doi.org/10.1002/mp.12958
        • Zhang W.
        • Ruisheng L.
        • You D.
        • Su Y.
        • Dong W.
        • Ma Z.
        Dosimetry and feasibility studies of volumetric modulated arc therapy with deep inspiration breath-hold using optical surface management system for left-sided breast cancer patients.
        Front Oncol. 2020; https://doi.org/10.3389/fonc.2020.01711
        • Kuo L.
        • Ballangrud A.M.
        • Ho A.Y.
        • Mechalakos J.G.
        • Li G.
        • Hong L.
        A VMAT planning technique for locally advanced breast cancer patients with expander or implant reconstructions requiring comprehensive postmastectomy radiation therapy.
        Med Dosim. 2019; 44: 150-154https://doi.org/10.1016/j.meddos.2018.04.006
        • Lang K.
        • Loritz B.
        • Schwartz A.
        • Hunzeker A.
        • Lenards N.
        • Culp L.
        • et al.
        Dosimetric comparison between volumetric-modulated arc therapy and a hybrid volumetric-modulated arc therapy and segmented field-in-field technique for postmastectomy chest wall and regional lymph node irradiation.
        Med Dosim. 2020; 45: 121-127https://doi.org/10.1016/j.meddos.2019.08.001
        • De Rose F.
        • Fogliata A.
        • Franceschini D.
        • Cozzi S.
        • Iftode C.
        • Stravato A.
        • et al.
        Postmastectomy radiation therapy using VMAT technique for breast cancer patients with expander reconstruction.
        Med Oncol. 2019; 36https://doi.org/10.1007/s12032-019-1275-z
        • Pasler M.
        • Georg D.
        • Bartelt S.
        • Lutterbach J.
        Node-positive left-sided breast cancer: does VMAT improve treatment plan quality with respect to IMRT ?Linksseitiges Mammakarzinom inklusive Lymphabfluss: Verbessert VMAT die Planqualität gegenüber IMRT?.
        Strahlenther Onkol. 2013; 189: 380-386
        • Zhao H.
        • He M.
        • Cheng G.
        • Han D.
        • Wu N.
        • Shi D.
        • et al.
        A comparative dosimetric study of left sided breast cancer after breast-conserving surgery treated with VMAT and IMRT.
        Radiation Oncology. 2015; 10https://doi.org/10.1186/s13014-015-0531-4
        • Moran J.M.
        • Dempsey M.
        • Eisbruch A.
        • Fraass B.A.
        • Galvin J.M.
        • Ibbott G.S.
        • et al.
        Safety considerations for IMRT: Executive summary.
        Pract Radiat Oncol. 2011; 1: 190-195https://doi.org/10.1016/j.prro.2011.04.008
        • Miften M.
        • Olch A.
        • Mihailidis D.
        • Moran J.
        • Pawlicki T.
        • Molineu A.
        • et al.
        Tolerance limits and methodologies for IMRT measurement-based verification QA: Recommendations of AAPM Task Group No. 218.
        Med Phys. 2018; 45: e53-e83
        • Chiavassa S.
        • Bessieres I.
        • Edouard M.
        • Mathot M.
        • Moignier A.
        Complexity metrics for IMRT and VMAT plans: a review of current literature and applications.
        Br J Radiol. 2019; 92: 20190270https://doi.org/10.1259/bjr.20190270
        • Antoine M.
        • Ralite F.
        • Soustiel C.
        • Marsac T.
        • Sargos P.
        • Cugny A.
        • et al.
        Use of metrics to quantify IMRT and VMAT treatment plan complexity: A systematic review and perspectives.
        Physica Med. 2019; 64: 98-108https://doi.org/10.1016/j.ejmp.2019.05.024
        • Wall P.D.H.
        • Fontenot J.D.
        Quality assurance-based optimization (QAO): Towards improving patient-specific quality assurance in volumetric modulated arc therapy plans using machine learning.
        Physica Med. 2021; 87: 136-143https://doi.org/10.1016/j.ejmp.2021.03.017
        • Chan M.F.
        • Witztum A.
        • Valder G.
        Integration of AI and Machine Learning in Radiotherapy QA.
        Front. Artif. Intell. 2020; 3577620https://doi.org/10.3389/frai.2020.577620
        • Li J.
        • Wang L.e.
        • Zhang X.
        • Liu L.u.
        • Li J.
        • Chan M.F.
        • et al.
        Machine learning for patient-specific quality assurance of VMAT: Prediction and classification accuracy.
        Int J Radiat Oncol Biol Phys. 2019; 105: 893-902https://doi.org/10.1016/j.ijrobp.2019.07.049
        • Lam D.
        • Zhang X.
        • Li H.
        • Deshan Y.
        • Schott B.
        • Zhao T.
        • et al.
        Predicting gamma passing rates for portal dosimetry-based IMRT QA using machine learning.
        Med Phys. 2019; 46: 4666-4675https://doi.org/10.1002/mp.13752
        • El Naqa I.
        • Irrer J.
        • Ritter T.A.
        • et al.
        Machine learning for automated quality assurance in radiotherapy: A proof of principle using EPID data description.
        Med Phys. 2019; 46: 1914-1921https://doi.org/10.1002/mp.13433
        • Valdes G.
        • Scheuermann R.
        • Hung C.Y.
        • Olszanski A.
        • Bellerive M.
        • Solberg T.D.
        A mathematical framework for virtual IMRT QA using machine learning.
        Med Phys. 2016; 43: 4323-4334https://doi.org/10.1118/1.4953835
        • Tomori S.
        • Kadoya N.
        • Takayama Y.
        • Kajikawa T.
        • Shima K.
        • Narazaki K.
        • et al.
        A deep learning-based prediction model for gamma evaluation in patient-specific quality assurance.
        Med Phys. 2018; 45: 4055-4065https://doi.org/10.1002/mp.13112
        • Ono T.
        • Hirashima H.
        • Iramina H.
        • Mukumoto N.
        • Miyabe Y.
        • Nakamura M.
        • et al.
        Prediction of dosimetric accuracy for VMAT plans using plan complexity parameters via machine learning.
        Med Phys. 2019; 46: 3823-3832https://doi.org/10.1002/mp.13669
        • Granville D.A.
        • Sutherland J.G.
        • Belec J.G.
        • La Russa D.J.
        Predicting VMAT patient-specific QA results using a support vector classifier trained on treatment plan characteristics and linac QC metrics.
        Phys Med Biol. 2019; 64: 095017https://doi.org/10.1088/1361-6560/ab142e
        • Yang R.
        • Yang X.
        • Wang L.e.
        • Li D.
        • Guo Y.
        • Li Y.
        • et al.
        Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario.
        Radiother Oncol. 2021; 161: 230-240https://doi.org/10.1016/j.radonc.2021.06.024
        • Lizondo M.
        • Latorre-Musoll A.
        • Ribas M.
        • Carrasco P.
        • Espinosa N.
        • Coral A.
        • et al.
        Pseudo skin flash on VMAT in breast radiotherapy: Optimization of virtual bolus thickness and HU values.
        Physica Med. 2019; 63: 56-62https://doi.org/10.1016/j.ejmp.2019.05.010
        • Rossi M.
        • Boman A.
        • Kapanen M.
        Optimal selection of optimization bolus thickness in planning of VMAT breast radiotherapy treatments.
        Med Dosim. 2019; 44: 266-273https://doi.org/10.1016/j.meddos.2018.10.001
        • Heilemann G.
        • Poppe B.
        • Laub W.
        On the sensitivity of common gamma-index evaluation methods to MLC misalignments in Rapidarc quality assurance.
        Med Phys. 2013; 40: 031702https://doi.org/10.1118/1.4789580
        • Crowe S.B.
        • Kairn T.
        • Kenny J.
        • Knight R.T.
        • Hill B.
        • Langton C.M.
        • et al.
        Treatment plan complexity metrics for predicting IMRT pre-treatment quality assurance results.
        Australas Phys Eng Sci Med. 2014; 37: 475-482https://doi.org/10.1007/s13246-014-0274-9
        • McNiven A.L.
        • Sharpe M.B.
        • Purdie T.G.
        A new metric for assessing IMRT modulation complexity and plan deliverability.
        Med Phys. 2010; 37: 505-515https://doi.org/10.1118/1.3276775
        • Younge K.C.
        • Matuszak M.M.
        • Moran J.M.
        • McShan D.L.
        • Fraass B.A.
        • Roberts D.A.
        Penalization of aperture complexity in inversely planned volumetric modulated arc therapy.
        Med Phys. 2012; 39: 7160-7170https://doi.org/10.1118/1.4762566
        • Park J.M.
        • Park S.-Y.
        • Kim H.
        • Kim J.H.
        • Carlson J.
        • Ye S.-J.
        Modulation indices for volumetric modulated arc therapy.
        Phys Med Biol. 2014; 59: 7315-7340https://doi.org/10.1088/0031-9155/59/23/7315
        • Du W.
        • Cho S.H.
        • Zhang X.
        • Hoffman K.E.
        • Kudchadker R.J.
        Quantification of beam complexity in intensity-modulated radiation therapy treatment plans.
        Med Phys. 2014; 41: 021716https://doi.org/10.1118/1.4861821
        • Li G.
        • Wu K.
        • Peng G.
        • Zhang Y.
        • Bai S.
        A retrospective analysis for patient-specific quality assurance of volumetric-modulated arctherapy plans.
        Med Dosim. 2014; 39: 309-313https://doi.org/10.1016/j.meddos.2014.05.003
        • Thorsen L.B.J.
        • Offersen B.V.
        • Danø H.
        • Berg M.
        • Jensen I.
        • Pedersen A.N.
        • et al.
        DBCG-IMN: A Population-Based Cohort Study on the Effect of Internal Mammary Node Irradiation in Early Node-Positive Breast Cancer.
        J Clin Oncol. 2016; 34: 314-320https://doi.org/10.1200/JCO.2015.63.6456
        • Poortmans P.M.
        • Weltens C.
        • Fortpied C.
        • Kirkove C.
        • Peignaux-Casasnovas K.
        • Budach V.
        • et al.
        Internal mammary and medial supraclavicular lymph node chain irradiation in stage I-III breast cancer (EORTC 22922/10925): 15-year results of a randomised, phase 3 trial.
        Lancet Oncol. 2020; 21: 1602-1610https://doi.org/10.1016/S1470-2045(20)30472-1
        • Borm K.J.
        • Simonetto C.
        • Kundrát P.
        • Eidemüller M.
        • Oechsner M.
        • Düsberg M.
        • et al.
        Toxicity of internal mammary irradiation in breast cancer. Are concerns still justified in times of modern treatment techniques?.
        Acta Oncol. 2020; 59: 1201-1209https://doi.org/10.1080/0284186X.2020.1787509
        • Ranger A.
        • Dunlop A.
        • Hutchinson K.
        • Convery H.
        • Maclennan M.K.
        • Chantler H.
        • et al.
        A Dosimetric Comparison of Breast Radiotherapy Techniques to Treat Locoregional Lymph Nodes Including the Internal Mammary Chain.
        Clinical Oncology. 2018; 30: 346-353https://doi.org/10.1016/j.clon.2018.01.017
      1. Zhang Q, Yu XL, Hu WG, et al. Dosimetric comparison for volumetric modulated arc therapy and intensity-modulated radiotherapy on the left-sided chest wall and internal mammary nodes irradiation in treating post-mastectomy breast cancer. 2015;49(1):91-98. 10.2478/raon-2014-0033.

        • Darby S.C.
        • Ewertz M.
        • McGale P.
        • Bennet A.M.
        • Blom-Goldman U.
        • Brønnum D.
        • et al.
        Risk of Ischemic Heart Disease in Women after Radiotherapy for Breast Cancer.
        The New England Journal of Medicine. 2013; 368: 987-998https://doi.org/10.1056/NEJMoa1209825
        • Schneider U.
        • Ernst M.
        • Hartmann M.
        The dose-response relationship for cardiovascular disease is not necessarily linear.
        Radiation Oncology. 2017; 12https://doi.org/10.1186/s13014-017-0811-2
        • van den Bogaard V.A.B.
        • van Luijk P.
        • Hummel Y.M.
        • et al.
        Cardiac function after radiotherapy for breast cancer.
        Int J Radiation Oncol Biol Phys. 2019; 104: 392-400https://doi.org/10.1016/j.ijrobp.2019.02.003
        • Skytta T.
        • Tuohinen S.
        • Luukkaala T.
        • Virtanen V.
        • Raatikainen P.
        • Kellokumpu-Lehtinen P.L.
        Adjuvant radiotherapy-induced cardiac changes among patients with early breast cancer: a three-year follow-up study.
        Acta Oncol. 2019; 58: 1250-1258https://doi.org/10.1080/0284186X.2019.1630751
        • Goldman U.B.
        • Svane G.
        • Anderson M.
        • Wennberg B.
        • Lind P.
        Long-term functional and radiological pulmonary changes after radiation therapy for breast cancer.
        Acta Oncol. 2014; 53: 1373-1379https://doi.org/10.3109/0284186X.2014.934967
        • Erven K.
        • Weltens C.
        • Nackaerts K.
        • Fieuws S.
        • Decramer M.
        • Lievens Y.
        Changes in pulmonary function up to 10 years after locoregional breast irradiation.
        Int J Radiat Oncol Biol Phys. 2012; 82: 701-707https://doi.org/10.1016/j.ijrobp.2010.12.058
        • Seppälä J.
        • Vuolukka K.
        • Virén T.
        • Heikkilä J.
        • Honkanen J.T.J.
        • Pandey A.
        • et al.
        Breast deformation during the course of radiotherapy: The need for an additional outer margin.
        Physica Med. 2019; 65: 1-5https://doi.org/10.1016/j.ejmp.2019.07.021
        • Bosmans H.
        • Zanca F.
        • Gelaude F.
        Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice.
        Physica Med. 2021; 83: 257-263https://doi.org/10.1016/j.ejmp.2021.04.006
        • Harrer C.
        • Ullrich W.
        • Wilkens J.J.
        Prediction of multi-criteria optimization (MCO) parameter efficiency in volumetric modulated arc therapy (VMAT) treatment planning using machine learning (ML).
        Physica Med. 2021; 81: 102-113https://doi.org/10.1016/j.ejmp.2020.12.004
        • Olaciregui-Ruiz I.
        • Torres-Xirau I.
        • Teuwen J.
        • van der Heide U.A.
        • Mans A.
        A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system.
        Physica Med. 2020; 71: 124-131https://doi.org/10.1016/j.ejmp.2020.02.020
        • Interian Y.
        • Rideout V.
        • Kearney V.P.
        • Gennatas E.
        • Morin O.
        • Cheung J.
        • et al.
        Deep Nets vs Expert Designed Features in Medical Physics: An IMRT QA case study.
        Med Phys. 2018; 45: 2672-2680https://doi.org/10.1002/mp.12890
        • Lizar J.C.
        • Yaly C.C.
        • Bruno A.C.
        • Viani G.A.
        • Pavoni J.F.
        Patient-specific IMRT QA verification using machine learning and gamma radiomics.
        Physica Med. 2021; 82: 100-108https://doi.org/10.1016/j.ejmp.2021.01.071
        • Wootton L.S.
        • Nyflot M.J.
        • Chaovalitwongse W.A.
        • Ford E.
        Error Detection in Intensity-Modulated Radiation Therapy Quality Assurance Using Radiomic Analysis of Gamma Distributions.
        Radiation Oncology Biology Physics. 2018; 102: 219-228https://doi.org/10.1016/j.ijrobp.2018.05.033