Literature DB >> 19928099

A role for biological optimization within the current treatment planning paradigm.

Shiva Das1.   

Abstract

PURPOSE: Biological optimization using complication probability models in intensity modulated radiotherapy (IMRT) planning has tremendous potential for reducing radiation-induced toxicity. Nevertheless, biological optimization is almost never clinically utilized, probably because of clinician confidence in, and familiarity with, physical dose-volume constraints. The method proposed here incorporates biological optimization after dose-volume constrained optimization so as to improve the dose distribution without detrimentally affecting the important reductions achieved by dose-volume optimization (DVO).
METHODS: Following DVO, the clinician/planner first identifies "fixed points" on the target and organ-at-risk (OAR) dose-volume histograms. These points represent important DVO plan qualities that are not to be violated within a specified tolerance. Biological optimization then maximally reduces a biological metric (illustrated with equivalent uniform dose (EUD) in this work) while keeping the fixed dose-volume points within tolerance limits, as follows. Incremental fluence adjustments are computed and applied to incrementally reduce the OAR EUDs while approximately maintaining the fixed points. This process of incremental fluence adjustment is iterated until the fixed points exceed tolerance. At this juncture, remedial fluence adjustments are computed and iteratively applied to bring the fixed points back within tolerance, without increasing OAR EUDs. This process of EUD reduction followed by fixed-point correction is repeated until no further EUD reduction is possible. The method is demonstrated in the context of a prostate cancer case and olfactory neuroblastoma case. The efficacy of EUD reduction after DVO is evaluated by comparison to an optimizer with purely biological (EUD) OAR objectives.
RESULTS: For both cases, EUD reduction after DVO additionally reduced doses, especially high doses, to normal organs. For the prostate case, bladder/rectum EUDs were reduced (after DVO) by 5.0%/3.9%, and highest doses were reduced by 4.6%/7.8%. The optimization with purely biological OAR objectives achieved bladder/rectal EUDs that were 7.4%/3.1% lower than from DVO, but only reduced highest doses by 1.4%/0.7%. In the olfactory neuroblastoma case, the target was closely surrounded by the eyes, optic nerves, chiasm, and brainstem. In one of the scenarios studied, the eyes, optic nerves, and chiasm were targeted for EUD reduction after DVO. EUD to the left eye, right eye, left optic nerve, right optic nerve, and chiasm were reduced by 7.0%, 5.7%, 4.7%, 4.1%, and 0.6%, respectively, and highest doses were reduced by 16.5%, 11.0%, 5.1%, 3.8%, and 1.5%, respectively. The optimization with purely biological OAR objectives was less effective for the eyes and optics nerves. EUDs for the left eye/right eye/left optic nerve/right optic nerve/chiasm were lower than that from DVO by 0.4%/2.7%/4.0%/2.8%/15.6% and highest doses were lower by 4.6%/1.4%/2.4%/6.4%/7.1% (but purely biological optimization was better overall for the OARs not targeted for EUD reduction).
CONCLUSIONS: Incorporating biological optimization after dose-volume constrained optimization can further reduce biological metrics, while preserving the important dose reductions achieved by dose-volume constrained optimization. Thus, biological optimization may be accommodated within the framework of current IMRT planning clinical expectations.

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Year:  2009        PMID: 19928099     DOI: 10.1118/1.3220211

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  10 in total

Review 1.  Mathematical Formulation of DMH-Based Inverse Optimization.

Authors:  Ivaylo B Mihaylov; Eduardo G Moros
Journal:  Front Oncol       Date:  2014-11-17       Impact factor: 6.244

2.  The use of biologically related model (Eclipse) for the intensity-modulated radiation therapy planning of nasopharyngeal carcinomas.

Authors:  Monica W K Kan; Lucullus H T Leung; Peter K N Yu
Journal:  PLoS One       Date:  2014-11-05       Impact factor: 3.240

Review 3.  Modeling Radiotherapy Induced Normal Tissue Complications: An Overview beyond Phenomenological Models.

Authors:  Marco D'Andrea; Marcello Benassi; Lidia Strigari
Journal:  Comput Math Methods Med       Date:  2016-12-01       Impact factor: 2.238

Review 4.  Recent advances in radiation oncology.

Authors:  Cristina Garibaldi; Barbara Alicja Jereczek-Fossa; Giulia Marvaso; Samantha Dicuonzo; Damaris Patricia Rojas; Federica Cattani; Anna Starzyńska; Delia Ciardo; Alessia Surgo; Maria Cristina Leonardi; Rosalinda Ricotti
Journal:  Ecancermedicalscience       Date:  2017-11-30

5.  An integrated strategy of biological and physical constraints in biological optimization for cervical carcinoma.

Authors:  Ziwei Feng; Cheng Tao; Jian Zhu; Jinhu Chen; Gang Yu; Shaohua Qin; Yong Yin; Dengwang Li
Journal:  Radiat Oncol       Date:  2017-04-04       Impact factor: 3.481

6.  Integral Dose-Based Inverse Optimization May Reduce Side Effects in Radiotherapy of Prostate Carcinoma.

Authors:  Ivaylo B Mihaylov
Journal:  Front Oncol       Date:  2017-03-01       Impact factor: 6.244

Review 7.  Mathematical formulation of energy minimization - based inverse optimization.

Authors:  Ivaylo B Mihaylov
Journal:  Front Oncol       Date:  2014-07-18       Impact factor: 6.244

8.  Radiobiological impact of dose calculation algorithms on biologically optimized IMRT lung stereotactic body radiation therapy plans.

Authors:  X Liang; J Penagaricano; D Zheng; S Morrill; X Zhang; P Corry; R J Griffin; E Y Han; M Hardee; V Ratanatharathom
Journal:  Radiat Oncol       Date:  2016-01-22       Impact factor: 3.481

9.  Prescription Value-Based Automatic Optimization of Importance Factors in Inverse Planning.

Authors:  Caiping Guo; Pengcheng Zhang; Zhiguo Gui; Huazhong Shu; Lihong Zhai; Jinrong Xu
Journal:  Technol Cancer Res Treat       Date:  2019 Jan-Dec

10.  Correlation of clinical outcome, radiobiological modeling of tumor control, normal tissue complication probability in lung cancer patients treated with SBRT using Monte Carlo calculation algorithm.

Authors:  Sumit S Sood; Damodar Pokhrel; Rajeev Badkul; Mindi TenNapel; Christopher McClinton; Bruce Kimler; Fen Wang
Journal:  J Appl Clin Med Phys       Date:  2020-08-14       Impact factor: 2.102

  10 in total

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