Knowledge-based multi-institution plan prediction of whole breast irradiation with tangential fields


      • Ten institutes set KB models for tangential fields of right-breast irradiation.
      • Inter-institute variability was quantified by SD of predicted DVHs and PCs.
      • The inter-institute variability of DVH ipsilateral lung prediction was around 2%.
      • High inter-institute interchangeability for 9 out of 10 models was found.
      • Results suggest the feasibility of multi-centric KB-model plan optimization.



      To quantify inter-institute variability of Knowledge-Based (KB) models for right breast cancer patients treated with tangential fields whole breast irradiation (WBI).

      Materials and methods

      Ten institutions set KB models by using RapidPlan (Varian Inc.), following previously shared methodologies. Models were tested on 20 new patients from the same institutes, exporting DVH predictions of heart, ipsilateral lung, contralateral lung, and contralateral breast. Inter-institute variability was quantified by the inter-institute SDint of predicted DVHs/Dmean. Association between lung sparing vs PTV coverage strategy was also investigated. The transferability of models was evaluated by the overlap of each model’s geometric Principal Component (PC1) when applied to the test patients of the other 9 institutes.


      The overall inter-institute variability of DVH/Dmean ipsilateral lung dose prediction, was less than 2% (20%-80% dose range) and 0.55 Gy respectively (1SD) for a 40 Gy in 15 fraction schedule; it was < 0.2 Gy for other OARs. Institute 6 showed the lowest mean dose prediction value and no overlap between PTV and ipsilateral lung. Once excluded, the predicted ipsilateral lung Dmean was correlated with median PTV D99% (R2 = 0.78). PC1 values were always within the range of applicability (90th percentile) for 7 models: for 2 models they were outside in 1/18 cases. For the model of institute 6, it failed in 7/18 cases. The impact of inter-institute variability of dose calculation was tested and found to be almost negligible.


      Results show limited inter-institute variability of plan prediction models translating in high inter-institute interchangeability, except for one of ten institutes. These results encourage future investigations in generating benchmarks for plan prediction incorporating inter-institute variability.


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      1. WHO Report on cancer, Annex 3: Cancer country profile 2020. WHO, Geneva; 2020.

        • Darby S.
        • McGale P.
        • Correa C.
        • Taylor C.
        • Arriagada R.
        • Clarke M.
        • et al.
        Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials.
        Lancet. 2011; 378: 1707-1716
        • Verhey L.J.
        Issues in optimization for planning of intensity-modulated radiation therapy.
        Seminars Radiat Oncol. 2002; 12: 210-218
        • Penninkhof J.
        • Spadola S.
        • Breedveld S.
        • Baaijens M.
        • Lanconelli N.
        • Heijmen B.
        Individualized selection of beam angles and treatment isocenter in tangential breast intensity modulated radiation therapy.
        Int J Radiat Oncol Biol Phys. 2017; 98: 447-453
        • Thompson R.F.
        • Valdes G.
        • Fuller C.D.
        • Carpenter C.M.
        • Morin O.
        • Aneja S.
        • et al.
        Artificial intelligence in radiation oncology: A specialty-wide disruptive transformation?.
        Radiother Oncol : J Eur Soc Therapeutic Radiol Oncol. 2018; 129: 421-426
        • Fogliata A.
        • Belosi F.
        • Clivio A.
        • Navarria P.
        • Nicolini G.
        • Scorsetti M.
        • 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 : J Eur Soc Therapeutic Radiol Oncol. 2014; 113: 385-391
        • Scaggion A.
        • Fusella M.
        • Roggio A.
        • Bacco S.
        • Pivato N.
        • Rossato M.A.
        • et al.
        Reducing inter- and intra-planner variability in radiotherapy plan output with a commercial knowledge-based planning solution.
        Phys Medica : PM : Int J Devoted Appl Phys Med Biol : Off J Italian Assoc Biomed Phys (AIFB). 2018; 53: 86-93
        • Yuan L.
        • Ge Y.
        • Lee W.R.
        • Yin F.F.
        • Kirkpatrick J.P.
        • Wu Q.J.
        Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans.
        Med Phys. 2012; 39: 6868-6878
        • Wu B.
        • Ricchetti F.
        • Sanguineti G.
        • Kazhdan M.
        • Simari P.
        • Chuang M.
        • et al.
        Patient geometry-driven information retrieval for IMRT treatment plan quality control.
        Med Phys. 2009; 36: 5497-5505
        • Ge Y.
        • Wu Q.J.
        Knowledge-based planning for intensity-modulated radiation therapy: A review of data-driven approaches.
        Med Phys. 2019; 46: 2760-2775
        • Sheng Y.
        • Zhang J.
        • Ge Y.
        • Li X.
        • Wang W.
        • Stephens H.
        • et al.
        Artificial intelligence applications in intensity modulated radiation treatment planning: an overview.
        Quant Imaging Med Surg. 2021; 11: 4859-4880
        • Moore K.L.
        Automated radiotherapy treatment planning.
        Semin Radiat Oncol. 2019; 29: 209-218
        • Wang Y.
        • Breedveld S.
        • Heijmen B.
        • Petit S.F.
        Evaluation of plan quality assurance models for prostate cancer patients based on fully automatically generated Pareto-optimal treatment plans.
        Phys Med Biol. 2016; 61: 4268-4282
        • Voet P.W.J.
        • Dirkx M.L.P.
        • Breedveld S.
        • Fransen D.
        • Levendag P.C.
        • Heijmen B.J.M.
        Toward fully automated multicriterial plan generation: a prospective clinical study.
        Int J Radiat Oncol Biol Phys. 2013; 85: 866-872
      2. La Radioterapia dei Tumori della Mammella: Indicazioni e Criteri Guida; 2013.

      3. Varian Medical System. Eclipse Photon and Electron reference guide v.13.7, Varian Medical System, June 2015 2015.

        • Castriconi R.
        • Esposito P.G.
        • Tudda A.
        • Mangili P.
        • Broggi S.
        • Fodor A.
        • et al.
        Replacing manual planning of whole breast irradiation with knowledge-based automatic optimization by virtual tangential-fields arc therapy.
        Front Oncol. 2021; 11
        • Cagni E.
        • Botti A.
        • Wang Y.
        • Iori M.
        • Petit S.F.
        • Heijmen B.J.M.
        Pareto-optimal plans as ground truth for validation of a commercial system for knowledge-based DVH-prediction.
        Phys Medica : PM : Int J Devoted Appl Phys Med Biol : Off J Italian Assoc Biomed Phys (AIFB). 2018; 55: 98-106
        • Valdes G.
        • Chan M.F.
        • Lim S.B.
        • Scheuermann R.
        • Deasy J.O.
        • Solberg T.D.
        <scp>IMRT QA</scp> using machine learning: A multi-institutional validation.
        J Appl Clin Medical Phys. 2017; 18: 279-284
        • Janssen T.M.
        • Kusters M.
        • Wang Y.
        • Wortel G.
        • Monshouwer R.
        • Damen E.
        • et al.
        Independent knowledge-based treatment planning QA to audit Pinnacle autoplanning.
        Radiother Oncol. 2019; 133: 198-204
        • Villaggi E.
        • Hernandez V.
        • Fusella M.
        • Moretti E.
        • Russo S.
        • Vaccara E.M.L.
        • et al.
        Plan quality improvement by DVH sharing and planner’s experience: Results of a SBRT multicentric planning study on prostate.
        Phys Medica. 2019; 62: 73-82
        • Habraken S.J.M.
        • Sharfo A.W.M.
        • Buijsen J.
        • Verbakel W.F.A.R.
        • Haasbeek C.J.A.
        • Öllers M.C.
        • et al.
        The TRENDY multi-center randomized trial on hepatocellular carcinoma - Trial QA including automated treatment planning and benchmark-case results.
        Radiother Oncol : J Eur Soc Therapeutic Radiol Oncol. 2017; 125: 507-513
        • Delaney A.R.
        • Dong L.
        • Mascia A.
        • Zou W.
        • Zhang Y.
        • Yin L.
        • et al.
        Automated knowledge-based intensity-modulated proton planning: an international multicenter benchmarking.
        Cancers (Basel). 2018; 10
        • Delaney A.R.
        • Tol J.P.
        • Dahele M.
        • Cuijpers J.
        • Slotman B.J.
        • Verbakel W.F.A.R.
        Effect of Dosimetric Outliers on the Performance of a Commercial Knowledge-Based Planning Solution.
        Int J Radiat Oncol Biol Phys. 2016; 94: 469-477
        • Berry S.L.
        • Ma R.
        • Boczkowski A.
        • Jackson A.
        • Zhang P.
        • Hunt M.
        Evaluating inter-campus plan consistency using a knowledge based planning model.
        Radiother Oncol. 2016; 120: 349-355
        • Schubert C.
        • Waletzko O.
        • Weiss C.
        • Voelzke D.
        • Toperim S.
        • Roeser A.
        • et al.
        Intercenter validation of a knowledge based model for automated planning of volumetric modulated arc therapy for prostate cancer. The experience of the German RapidPlan Consortium.
        PLOS ONE. 2017; 12: e0178034
        • Panettieri V.
        • Ball D.
        • Chapman A.
        • Cristofaro N.
        • Gawthrop J.
        • Griffin P.
        • et al.
        Development of a multicentre automated model to reduce planning variability in radiotherapy of prostate cancer.
        Phys Imaging Radiat Oncol. 2019; 11: 34-40
        • Good D.
        • Lo J.
        • Lee W.R.
        • Wu Q.J.
        • Yin F.-F.
        • Das S.K.
        A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning.
        Int J Radiat Oncol*Biol*Phys. 2013; 87: 176-181
        • Moore K.L.
        • Schmidt R.
        • Moiseenko V.
        • Olsen L.A.
        • Tan J.
        • Xiao Y.
        • et al.
        Quantifying Unnecessary Normal Tissue Complication Risks due to Suboptimal Planning: A Secondary Study of RTOG 0126.
        Int J Radiat Oncol*Biol*Phys. 2015; 92: 228-235
        • Li N.
        • Carmona R.
        • Sirak I.
        • Kasaova L.
        • Followill D.
        • Michalski J.
        • et al.
        Highly Efficient Training, Refinement, and Validation of a Knowledge-based Planning Quality-Control System for Radiation Therapy Clinical Trials.
        Int J Radiat Oncol*Biol*Phys. 2017; 97: 164-172
        • Kavanaugh J.A.
        • Holler S.
        • DeWees T.A.
        • Robinson C.G.
        • Bradley J.D.
        • Iyengar P.
        • et al.
        Multi-institutional validation of a knowledge-based planning model for patients enrolled in RTOG 0617: implications for plan quality controls in cooperative group trials.
        Practical Radiat Oncol. 2019; 9: e218-e227
        • Younge K.C.
        • Marsh R.B.
        • Owen D.
        • Geng H.
        • Xiao Y.
        • Spratt D.E.
        • et al.
        Improving quality and Consistency in NRG oncology radiation therapy oncology group 0631 for spine radiosurgery via knowledge-based planning.
        Int J Radiat Oncol*Biol*Phys. 2018; 100: 1067-1074
        • Ueda Y.
        • Fukunaga J.
        • Kamima T.
        • Adachi Y.
        • Nakamatsu K.
        • Monzen H.
        Evaluation of multiple institutions’ models for knowledge-based planning of volumetric modulated arc therapy (VMAT) for prostate cancer.
        Radiat Oncol. 2018; 13: 46
        • Kamima T.
        • Ueda Y.
        • Fukunaga J.-I.
        • Shimizu Y.
        • Tamura M.
        • Ishikawa K.
        • et al.
        Multi-institutional evaluation of knowledge-based planning performance of volumetric modulated arc therapy (VMAT) for head and neck cancer.
        Phys Medica. 2019; 64: 174-181
        • Esposito P.G.
        • Castriconi R.
        • Mangili P.
        • Fodor A.
        • Pasetti M.
        • Di Muzio N.G.
        • et al.
        Virtual Tangential-fields Arc Therapy (ViTAT) for whole breast irradiation: Technique optimization and validation.
        Phys Medica : PM : Int J Devoted Appl Phys Med Biol : Off J Italian Assoc Biomed Phys (AIFB). 2020; 77: 160-168