33. Implementation of a statistical ideal DVH for the evaluation and optimization of treatment plans. (December 2018)

• Record Type: Journal Article
• Authors: Bresciani, S.; Cresto, F.; Di Dia, A.; Maggio, A.; Miranti, A.; Stasi, M.
• Is Part Of: Physica medica. Volume 56(2018)Supplement 2
• Page Start: 82
• Page End: 83
• Publication Date: 2018-12
• Subjects: Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2018.04.043
• Languages: English
• ISSNs: 1120-1797

Abstract

Abstract : Purpose: To improve quality and reduce variability of plans by developing a statistical ideal dose volume histogram (SI-DVH). From SI-DVH, a custom-built metric based on 21 functions was created to evaluate radiotherapic plans quantitatively. Methods: CT images and ROIs of 37 VMAT prostate patients were analysed using the software PlanIQ (SNC). PlanIQ defines regions of DVH that are difficult to reach, based on an ideal dose gradient. According to this dose distribution, the minimal dose to OARs is predominantly dictated by the geometric relationship between OARs and PTV and by the prescribed dose. An average SI-DVH for each OAR was created with feasibility equal to 0.1% (impossible) and 10% (difficult) (i.e. Fig. 1). Then we created a custom-built metric by applying a linearly dependent scoring system based on QUANTEC constraints and on the SI-DVH; we defined PQM (Plan Quality Metric), sum of all the scores on target and OARs. We evaluated the quality of retrospective plans by calculating PQM. Then we replanned ten prostate cases, applying the new SI-DVH in the optimizer as objective to achieve. We calculated ΔPQM, defined as the percentage difference between replanned PQM and PQM obtained previously. Results: Setting the maximum PQM to 200, ideally achievable, we obtained a median PQM equal to 133 (82 ÷ 177). After replanning we increased the value obtained a median replanned PQM equal to 161 and an average Δ PQM equal to 10% (5% ÷ 16%) and in terms of dose distribution we observed a decrease up to 10% of the dose to the OARs. Conclusions: SI-DVH is a promising method which allows for quantification and optimization of plans against historical experiences based on contouring, protocol preparation and dose scheduling. The hard score and an ideal DVH to reach could aid planners in generating plans that push the limits defined by QUANTEC that can easily achievable.