Knowledge-based multi-institution plan prediction of whole breast irradiation with tangential fields. (October 2022)

• Record Type: Journal Article
• Authors: Tudda, Alessia; Castriconi, Roberta; Benecchi, Giovanna; Cagni, Elisabetta; Cicchetti, Alessandro; Dusi, Francesca; Esposito, Pier Giorgio; Guernieri, Marika; Ianiro, Anna; Landoni, Valeria; Mazzilli, Aldo; Moretti, Eugenia; Oliviero, Caterina; Placidi, Lorenzo; Rambaldi Guidasci, Giulia; Rancati, Tiziana; Scaggion, Alessandro; Trojani, Valeria; Fiorino, Claudio
• Is Part Of: Radiotherapy and oncology. Volume 175(2022)
• Page Start: 10
• Page End: 16
• Publication Date: 2022-10
• Subjects: Radiotherapy planning -- Multi-institution plan prediction -- Automated planning -- Knowledge-based model -- Breast cancer; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2022.07.012
• Languages: English
• ISSNs: 0167-8140

Abstract

Highlights: 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. Abstract: Purpose: 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. Results: 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% (R 2 = 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. Conclusions: 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.