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Title: WE-B-304-00: Point/Counterpoint: Biological Dose Optimization

Abstract

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning by the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades ofmore » experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.« less

Publication Date:
OSTI Identifier:
22570130
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 42; Journal Issue: 6; Other Information: (c) 2015 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ANIMAL TISSUES; BIOLOGICAL MODELS; ERRORS; LIMITING VALUES; NEOPLASMS; OPTIMIZATION; PATIENTS; PLANNING; RADIATION DOSE DISTRIBUTIONS; RADIATION DOSES; RADIOTHERAPY

Citation Formats

NONE. WE-B-304-00: Point/Counterpoint: Biological Dose Optimization. United States: N. p., 2015. Web. doi:10.1118/1.4925899.
NONE. WE-B-304-00: Point/Counterpoint: Biological Dose Optimization. United States. doi:10.1118/1.4925899.
NONE. 2015. "WE-B-304-00: Point/Counterpoint: Biological Dose Optimization". United States. doi:10.1118/1.4925899.
@article{osti_22570130,
title = {WE-B-304-00: Point/Counterpoint: Biological Dose Optimization},
author = {NONE},
abstractNote = {The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning by the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.},
doi = {10.1118/1.4925899},
journal = {Medical Physics},
number = 6,
volume = 42,
place = {United States},
year = 2015,
month = 6
}
  • No abstract prepared.
  • A variety of intensity modulated radiation therapy (IMRT) delivery techniques have been developed that have provided clinicians with the ability to deliver highly conformal dose distributions. The delivery techniques include compensators, step-and-shoot IMRT, sliding window IMRT, volumetric modulated arc therapy (VMAT), and tomotherapy. A key development in the field of IMRT was the introduction of new planning algorithms and delivery control systems in 2007 that made it possible to coordinate the gantry rotation speed, dose rate, and multileaf collimator leaf positions during the delivery of arc therapy. With these developments, VMAT became a routine clinical tool. The use of VMATmore » has continued to grow in recent years and some would argue that this will soon make conventional IMRT obsolete, and this is the premise of this debate. To introduce the debate, David Shepard, Ph.D. will provide an overview of IMRT delivery techniques including historical context and how they are being used today. The debate will follow with Richard Popple, Ph.D. arguing FOR the Proposition and Peter Balter, Ph.D. arguing AGAINST it. Learning Objectives: Understand the different delivery techniques for IMRT. Understand the potential benefits of conventional IMRT. Understand the potential benefits of arc-based IMRT delivery.« less
  • The standard computational method developed for internal radiation dosimetry is the MIRD (medical internal radiation dose) formalism, based on the assumption that tumor control is given by uniform dose and activity distributions. In modern systemic radiotherapy, however, the need for full 3D dose calculations that take into account the heterogeneous distribution of activity in the patient is now understood. When information on nonuniform distribution of activity becomes available from functional imaging, a more patient specific 3D dosimetry can be performed. Application of radiobiological models can be useful to correlate the calculated heterogeneous dose distributions to the current knowledge on tumormore » control probability of a homogeneous dose distribution. Our contribution to this field is the introduction of a parameter, the F factor, already used by our group in studying external beam radiotherapy treatments. This parameter allows one to write a simplified expression for tumor control probability (TCP) based on the standard linear quadratic (LQ) model and Poisson statistics. The LQ model was extended to include different treatment regimes involving source decay, incorporating the repair '{mu}' of sublethal radiation damage, the relative biological effectiveness and the effective 'waste' of dose delivered when repopulation occurs. The sensitivity of the F factor against radiobiological parameters ({alpha},{beta},{mu}) and the influence of the dose volume distribution was evaluated. Some test examples for {sup 131}I and {sup 90}Y labeled pharmaceuticals are described to further explain the properties of the F factor and its potential applications. To demonstrate dosimetric feasibility and advantages of the proposed F factor formalism in systemic radiotherapy, we have performed a retrospective planning study on selected patient case. F factor formalism helps to assess the total activity to be administered to the patient taking into account the heterogeneity in activity uptake and dose distribution, giving the same TCP of a homogeneous prescribed dose distribution. Animal studies and collection of standardized clinical data are needed to ascertain the effects of nonuniform dose distributions and to better assess the radiobiological input parameters of the model based on LQ model.« less
  • An inverse optimization package which is capable of generating nonuniform dose distribution with subregional dose escalation is developed to achieve maximum equivalent uniform dose (EUD) for target while keeping the critical structure doses as low as possible. Relative cerebral blood volume (rCBV) maps obtained with a dynamic susceptibility contrast-enhanced MRI technique were used to delineate spatial radiosensitivity distributions. The voxel rCBV was converted to voxel radiosensitivity parameters (e.g., {alpha} and {alpha}/{beta}) based on previously reported correlations between rCBV, tumor grade, and radiosensitivity. A software package, DOSEPAINT, developed using MATLAB, optimizes the beamlet weights to achieve maximum EUD for target whilemore » limiting doses to critical structures. Using DOSEPAINT, we have generated nonuniform 3D-dose distributions for selected patient cases. Depending on the variation of the pixel radiosensitivity, the subregional dose escalation can be as high as 35% of the uniform dose as planned conventionally. The target dose escalation comes from both the inhomogeneous radiosensitivities and the elimination of integral target dose constraint. The target EUDs are found to be higher than those for the uniform dose planned ignoring the spatial inhomogeneous radiosensitivity. The EUDs for organs at risk are found to be approximately equal to or lower than those for the uniform dose plans. In conclusion, we have developed a package that is capable of generating nonuniform dose distributions optimized for spatially inhomogeneous radiosensitivity. Subregional dose escalation may lead to increased treatment effectiveness as indicated by higher EUDs. The current development will impact biological image guided radiotherapy.« less
  • Purpose: Our aim is to demonstrate the feasibility of fast Monte Carlo (MC)–based inverse biological planning for the treatment of head and neck tumors in spot-scanning proton therapy. Methods and Materials: Recently, a fast and accurate graphics processor unit (GPU)–based MC simulation of proton transport was developed and used as the dose-calculation engine in a GPU-accelerated intensity modulated proton therapy (IMPT) optimizer. Besides dose, the MC can simultaneously score the dose-averaged linear energy transfer (LET{sub d}), which makes biological dose (BD) optimization possible. To convert from LET{sub d} to BD, a simple linear relation was assumed. By use of thismore » novel optimizer, inverse biological planning was applied to 4 patients, including 2 small and 1 large thyroid tumor targets, as well as 1 glioma case. To create these plans, constraints were placed to maintain the physical dose (PD) within 1.25 times the prescription while maximizing target BD. For comparison, conventional intensity modulated radiation therapy (IMRT) and IMPT plans were also created using Eclipse (Varian Medical Systems) in each case. The same critical-structure PD constraints were used for the IMRT, IMPT, and biologically optimized plans. The BD distributions for the IMPT plans were obtained through MC recalculations. Results: Compared with standard IMPT, the biologically optimal plans for patients with small tumor targets displayed a BD escalation that was around twice the PD increase. Dose sparing to critical structures was improved compared with both IMRT and IMPT. No significant BD increase could be achieved for the large thyroid tumor case and when the presence of critical structures mitigated the contribution of additional fields. The calculation of the biologically optimized plans can be completed in a clinically viable time (<30 minutes) on a small 24-GPU system. Conclusions: By exploiting GPU acceleration, MC-based, biologically optimized plans were created for small–tumor target patients. This optimizer will be used in an upcoming feasibility trial on LET{sub d} painting for radioresistant tumors.« less