Comprehensive Intra-Institution stepping validation of knowledge-based models for automatic plan optimization.
PURPOSE
To develop and apply a stepping approach for the validation of Knowledge-based (KB) models for planning optimization: the method was applied to the case of concomitant irradiation of pelvic nodes and prostate + seminal-vesicles bed irradiation in post-prostatectomy patients.


METHODS
The clinical VMAT plans of 52 patients optimized by two reference planners were selected to generate a KB-model (RapidPlan, v.13.5 Varian). A stepping-validation approach was followed by comparing KB-generated plans (with and without planner-interaction, RP and only-RP respectively) against delivered clinical plans (RA). The validation followed three steps, gradually extending its generalization: 20 patients used to develop the model (closed-loop); 20 new patients, same planners (open-loop); 20 new patients, different planners (wide-loop). All plans were compared, in terms of relevant dose-volume parameters and generalized equivalent uniform dose (gEUD).


RESULTS
KB-plans were generally better than or equivalent to clinical plans. For RPvsRA, PTVs coverage was comparable, for OARs RP was always better. Comparing only-RPvsRA, PTVs coverage was always better; bowel\bladder V50Gy and D1%, bowel\bladder\rectum Dmean, femoral heads V40Gy and penile bulb V50Gy were significantly improved. For RPvsRA gEUD reduction >1 Gy was seen in 80% of plans for rectum, bladder and bowel; for only-RPvsRA, this was found in 50% for rectum/bladder and in 70% for bowel.


CONCLUSION
An extensive stepping validation approach of KB-model for planning optimization showed better or equal performances of automatically generated KB-plan compared to clinical plans. The interaction of a planner further improved planning performances.
['Castriconi|R|R|', 'Fiorino|C|C|', 'Broggi|S|S|', 'Cozzarini|C|C|', 'Di Muzio|N|N|', 'Calandrino|R|R|', 'Cattaneo|G M|GM|']
D001331:Automation D008962:Models, Theoretical D011880:Radiotherapy Planning, Computer-Assisted
Knowledge-based Automatic planning Machine learning Prostate radiotherapy