ESTRO 2023

Session Item

Automation

Session Code: 6028

Session Type: Poster (Digital)

Track: Physics

KB plan optimization models’ transferability: multi-institutional international consortia validation

Lorenzo Placidi, Italy

Presentation Number: PO-1646

Abstract

Abstract Title:

KB plan optimization models’ transferability: multi-institutional international consortia validation

Authors:

Lorenzo Placidi¹, Roberta Castriconi², Giovanna Benecchi³, Mark Burns⁴, Elisabetta Cagni⁵, Peter Griffin⁶, Cathy Markham⁷, Karen McGoldrick⁸, Valeria Landoni⁹, Eugenia Moretti¹⁰, Caterina Oliviero¹¹, Vanessa Panettieri¹², Giulia Rambaldi Guidasci¹³, Tiziana Rancati¹⁴, Alan Turner¹⁵, Alessandro Scaggion¹⁶, Alessia Tudda¹⁷, Claudio Fiorino¹⁷

¹Fondazione Policlinico Universitario A. Gemelli IRCCS, UOSD Medical Physics and Radioprotection, Roma, Italy; ²IRCCS San Raffaele Scientific Institute, Medical Physics Department , Milano, Italy; ³University Hospital of Parma AOUP, Medical Physics Department, Parma, Italy; ⁴Peter MacCallum Cancer Centre, Box Hill Campus, Melbourne, Australia; ⁵Azienda USL-IRCCS, Department of Advanced Technology, Reggio Emilia, Italy; ⁶The Alfred Hospital, Alfred Health Radiation Oncology, Melbourne, Italy; ⁷Peter MacCallum Cancer Centre, Parkville Campus, Australia, Australia; ⁸Peter MacCallum Cancer Centre, Parkville Campus, Melbourne, Australia; ⁹IRCSS Regina Elena National Cancer Institute, Department of Medical Physics, Rome, Italy; ¹⁰Udine University Hospital, Department of Medical Physics, Udine, Italy; ¹¹University Hospital, ‘‘Federico II”, Medical Physics, Napoli, Italy; ¹²The Alfred Hospital, Alfred Health Medical Physics, Melbourne, Australia; ¹³Fatebenefratelli Isola Tiberina – Gemelli Isola, UOC di Radioterapia Oncologica, Roma, Italy; ¹⁴Fondazione IRCCS Istituto Nazionale dei Tumori, Prosate Cancer Program, Milano, Italy; ¹⁵Peter MacCallum Cancer Centre, Bendigo Campus, Bendigo, Australia; ¹⁶Veneto Institute of Oncology IOV–IRCCS, Medical Physics Department, Padova, Italy; ¹⁷IRCCS San Raffaele Scientific Institute, Medical Physics Department, Milano, Italy

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Purpose or Objective

The Knowledge-based (KB) approach may efficiently translate planning experiences into models able to classify patients according to their individual anatomical features and possibly to automatically drive optimization on new patients. The reliability of model predictions between institutes may be due to poor overlap between the geometrical/anatomical features of Institutional training cohorts. The aim of current investigation was to assess models’ transferability between two multi-institutional international consortia (one in Europe and one outside Europe) in the case of tangential field (TF) irradiation of right whole breast (RWB).

Material and Methods

In the context of a national multi-institutional project (consortium A), ten institutes set their KB models using RapidPlan (Varian Inc.) for RWB irradiation using TF. Intra-consortium transferability was evaluated by the overlap of the geometric Principal Component (PC1) when applied to the test patients (two from each institution) of the other 9 institutes. This parameter was assessed as representative of the association between anatomy/geometry and DVH prediction of OARs, quantifying if a model can be reasonably applied to a patient owning to another Institute. With the same methodology, twenty patients belonging to another national multi-institutional consortium (consortium B) were used to perform cross inter-consortium validation tests. Each model of consortium A was evaluated on 20 patients provided by consortium B. The degree of transferability of all models was quantified by the number of cases in which the ipsilateral lung PC1 was within the 90th percentile of the training set.

Results

Figure 1 shows transferability values in terms of the percentage of patients with the ipsilateral lung PC1 within the 90th percentile of the training set for both consortia. For consortium A, test results already demonstrated a high inter-institute transferability, with a PC1 value inside the 90th in 9 out of 10 models for more than 90% of the patients.

When models from consortium A were applied to patients from consortium B, PC1 was inside the 90th percentile in 6 out of 10 models in 90% of the patients, highlighting a favourable transferability agreement. In 2 out of 10 models, all patients have a PC1 inside the 90th percentile against 7/10 of consortium A. PC1 for model 6 showed poorest transferability for both consortia.

Conclusion

Geometrical/anatomical quantitative parameter as PC1 confirms a reasonably good inter-consortium model’s transferability. Variation in models’ transferability will be better investigated looking at national contouring guideline differences, geometrical features, models’ dose prediction and the SD of several DVHs and dose/statistic parameters.

This study is supported by an AIRC grant (IG23150).