Aaron Paul Osburg , Peter Lysakovski , Giuseppe Magro , Semi Harrabi , Thomas Haberer , Amir Abdollahi , Jürgen Debus , Thomas Tessonnier , Andrea Mairani
{"title":"Mixed- and multi-relative biological effectiveness model simultaneous optimization in carbon ion radiotherapy: A proof-of-concept","authors":"Aaron Paul Osburg , Peter Lysakovski , Giuseppe Magro , Semi Harrabi , Thomas Haberer , Amir Abdollahi , Jürgen Debus , Thomas Tessonnier , Andrea Mairani","doi":"10.1016/j.phro.2024.100679","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and purpose</h3><div>In carbon ion radiotherapy (CIRT), different relative biological effectiveness (RBE) models have been used for calculating RBE-weighted dose (D<sub>RBE</sub>). Conversion between current RBE predictions and introduction of novel approaches remains a challenging task. Our aim is to introduce a framework considering multiple RBE models simultaneously during CIRT plan optimization, easing the translation between D<sub>RBE</sub> prescriptions.</div></div><div><h3>Materials and methods</h3><div>An in-house developed Monte Carlo treatment planning system was extended to incorporate the local effect model version I (LEM-I), the modified microdosimetric kinetic model (mMKM) and the MKM-derived Japanese biological model (NIRS-MKM). Four clinical cases (two head-and-neck and two prostate patients), initially optimized with LEM-I for both targets and organs at risk (OARs), underwent two further optimizations: to fulfill mMKM/NIRS-MKM-based target prescriptions (mixed-RBE approach) or to simultaneously consider two biological models within the target regions (multi-RBE approach). Both approaches retained LEM-I-derived dose constraints for OARs.</div></div><div><h3>Results</h3><div>The developed optimization strategies have been successfully applied, fulfilling all the clinical criteria for the applied RBE models. One of the RBE models showed unfavorable dose distribution when not explicitly considered in the optimization, while multi-RBE model optimization allowed meeting dose objectives for the selected OARs for both models simultaneously.</div></div><div><h3>Conclusions</h3><div>The introduced optimization approaches allow for mixed- or multi-RBE optimization in CIRT through the selection of RBE models independently for each region of interest. This capability addresses challenges of adhering to multiple RBE frameworks and proposes an advanced solution for tailored patient treatment plans.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100679"},"PeriodicalIF":3.4000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics and Imaging in Radiation Oncology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2405631624001490","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background and purpose
In carbon ion radiotherapy (CIRT), different relative biological effectiveness (RBE) models have been used for calculating RBE-weighted dose (DRBE). Conversion between current RBE predictions and introduction of novel approaches remains a challenging task. Our aim is to introduce a framework considering multiple RBE models simultaneously during CIRT plan optimization, easing the translation between DRBE prescriptions.
Materials and methods
An in-house developed Monte Carlo treatment planning system was extended to incorporate the local effect model version I (LEM-I), the modified microdosimetric kinetic model (mMKM) and the MKM-derived Japanese biological model (NIRS-MKM). Four clinical cases (two head-and-neck and two prostate patients), initially optimized with LEM-I for both targets and organs at risk (OARs), underwent two further optimizations: to fulfill mMKM/NIRS-MKM-based target prescriptions (mixed-RBE approach) or to simultaneously consider two biological models within the target regions (multi-RBE approach). Both approaches retained LEM-I-derived dose constraints for OARs.
Results
The developed optimization strategies have been successfully applied, fulfilling all the clinical criteria for the applied RBE models. One of the RBE models showed unfavorable dose distribution when not explicitly considered in the optimization, while multi-RBE model optimization allowed meeting dose objectives for the selected OARs for both models simultaneously.
Conclusions
The introduced optimization approaches allow for mixed- or multi-RBE optimization in CIRT through the selection of RBE models independently for each region of interest. This capability addresses challenges of adhering to multiple RBE frameworks and proposes an advanced solution for tailored patient treatment plans.