2196 Isocentre Blurring: a Solution to the Limitations of the PTV Concept in IMRT

S.J. Thomas, S. Brooke

Medical Physics, Addenbrooke, Cambridge, United Kingdom

Purpose/Objective: ICRU report 50/62 defines the planning target volume (PTV) as a geometrical concept, used to selectappropriate beam sizes and beam arrangements, to ensure that the prescribed dose is actually delivered to the CTV. For a CTVnear the surface of the patient, margins can often result in a PTV that extends outside the patient outline. Since the purpose ofthe PTV is for the selection of beam sizes and arrangements, this is perfectly valid.

Problems arise when the PTV is used for dose reporting, when the dose distribution in the PTV is used to calculateradiobiological results, and when the dose distribution in the PTV is used to generate an objective function for inverse plannedIMRT. In all these cases the tendency of the PTV to extend towards and beyond the patient outline causes problems.

PTVs outside the contour present an obvious problem in IMRT. The dose in these regions is undefined, so cannot be used inan objective function; beams will be chosen that do not provide sufficient coverage for patient movement and set-up errors.

PTVs in build-up regions present a different problem. If an organ is always a certain distance below the skin surface, then nocancer cell is ever entering the region of low dose near the skin surface, regardless of variations in set-up. The part of the PTVnear the skin will be reported as receiving a low dose, which does not correspond to a low dose to any tumour cell. If the DVHfor the PTV is used to calculate a tumour control probability (TCP), it will considerably underestimate it. If the DVH is usedto calculate an objective function for IMRT planning, then solutions will be favoured that attempt to pile extra dose from otherbeams into the build-up region, giving excessive dose to normal tissue near the skin.

We propose a solution to these problems.

Materials/Methods: Random movements in the patient set-up relative to the isocentre were modelled as random changes inthe position of the isocentre relative to the patient. Each beam was calculated as the super-position of 49 beams, each with adifferent isocentre. The positions of the isocentres were chosen using the gaussian distribution. Dose distributions werecalculated using the SJTPLAN planning program. The beam was divided into 1cm wide pencils; delivery of IMRT wasmodelled by assigning weights to each pencil. Isocentre blurring could be applied to each beamlet before optimisation. Inverseplanning was done using simulated annealing. A quadratic objective function was used.

Results: Dose distrubutions have been calculated for a variety of situations.

For a conventional plan to a deep target, blurred dose distributions planned on the PTV show good coverage of the CTV, asexpected.

For an IMRT plan of a concave CTV and PTV, near an organ at risk, in the centre of a patient, “Standard IMRT” gives a goodcoverage of the PTV before blurring is applied, but less good coverage of the CTV when blurred. When blurring was appliedto the beamlets before optimisation, better coverage of the CTV was obtained. For a CTV near the surface, blurring beforeoptimisation avoids the overdosing of the build-up region that otherwise occurs. For a PTV extending beyond the skin surface,the method enables an IMRT plan to be calculated that gives acceptable coverage of the CTV.

Conclusions: Instead of using blurring to determine PTV margin, we are using blurring to eliminate the need for a PTV. Themethod gives better distributions than are obtained by planning on a PTV.

Since the dose distribution relates to a CTV (which is an oncologic target) rather than to a PTV (which is a geometric target),it is of greater value in calculating radiobiological parameters (TCP, NTCP, EUD).

A limitations of the method is that it only applies to random errors, not to systematic errors. To avoid this it is necessary eitherto do multiple planning scans, or use portal imaging to determine and remove systematic errors.

Blurring of distributions must not be done by applying a blurring kernel to a dose distribution, since this will give incorrectresults in build-up regions. An acceptable alternative to isocentre blurring is to apply a blurring function to a fluence map.

2197 Inverse Treatment Planning with Probabilistic Dose Prescription

J. Lian, C. Cotrutz, L. Xing

Department of Radiation Oncology, Stanford University, Stanford, CA

Purpose/Objective: In conventional inverse planning, one usually prescribes a rigid dose to the target and a tolerance to asensitive structure and obtains the optimal solution under the guidance of an objective function. The permissible dose variationrange is achieved by introducing DVH constraints. In reality, it is highly desirable to specify not only the permissible dosevariation range but also the acceptance level of a dose different from the most preferable dose and incorporate the informationinto IMRT inverse treatment planning to better direct the optimization process. This work is aimed at developing such aframework of incorporating prior knowledge of the system into IMRT inverse planning.

Materials/Methods: We assumed that the prescribed dose has a pre-designed “probability” distribution, f(Dp), where the valueof the “probability” represents the level of our acceptance of a prescription dose, Dp. We recast the conventional objectivefunction into the form of preference function, which quantify our preference level for a calculated dose Dc when the prescribeddose Dp is given. Based on the Bayesian theorem, the overall preference function can be obtained by summing a series of jointprobabilities, each is a product of two preference functions corresponding to a prescription dose Dp. The system was optimizedusing an iterative algorithm. The technique was applied to study a mathematical phantom with a concave target and a circularorgan at risk (OAR) and a few clinical IMRT cases.

Results: Clinically acceptable plans can be generated when the dose is prescribed with pre-designed preference levels.Comparing the proposed technique with the conventional methods based on either quadratic or DVH-based objective function,we found (1) the resulting treatment plan can be more easily predicted and controlled by the “probabilistic” prescription; (2)the sparing of sensitive structures can be improved because of expanded solution space. In Figure 1, we show an example ofa phantom case. The solid curves correspond to the conventional plan and the dotted lines represent the optimization result using

324 I. J. Radiation Oncology ● Biology ● Physics Volume 54, Number 2, Supplement, 2002

a probabilistic prescription. In obtaining this plan, we assumed that the target prescription could take two discrete values, 100Gywith 50% acceptance level and 120Gy with 50% acceptance level. As expected, the target dose tends to be shifted towardshigher doses. The dose to the critical structure is substantially lowered. Normal tissue DVH remains unchanged.

Conclusions: We have developed and implemented a new approach to include the planner’s prior knowledge into IMRT doseoptimization based on a statistical formalism. A salient feature of this technique is that the plan results become more predictable andcontrollable with the use of “probabilistic” prescription, which implies less trial-and-error during the planning process. In addition,the improved sparing of sensitive structure with a slight sacrifice of target dose inhomogeneity may have practical implications.

2198 A Biological-Response Dose-Volume Based Tool for Ranking IMRT Treatment Plans

M. Miften, S. Das, T. Shafman, L. Marks

Department of Radiation Oncology, Duke University Medical Center, Durham, NC

Purpose/Objective: The selection of an “optimal plan” among competing IMRT treatment plans is a daunting task. Whileplanners typically select plans based on visual inspection of the isodose distributions and dose-volume histograms (DVH), thisselection technique can be ambiguous, since in many cases one can generate multiple plans based on dosimetric and/orbiological constraints that may be deemed satisfactory. TCP and NTCP models have been suggested to extract single valuesof merit from competing DVHs to facilitate their comparison. The validity of these models however is questionable, as theyare not based on accurate biologic/physiologic criteria. Such accurate biologic/physiologic factors should be incorporated intothe plan selection process. An automated IMRT treatment plan ranking tool, based on accurate dosimetric and biological/physiological indices, is developed and herein presented.

Materials/Methods: In this work, three different figures of merit are extracted from the dose-volume data of each plan: i) theconformity volume index (CVI) which is the fraction of the planning target volume (PTV) enclosed by the prescription isodosesurface, ii) the toxicity volume index (TVI) which is the percent of an organ at risk (OAR) volume enclosed by a “clinical”significance dose, and iii) the uncomplicated equivalent uniform dose (EUD�), in which the target EUD and the biological-response of critical structures are calculated and combined. The treatment plan with the highest CVI for the target, the lowestTVI for the critical structures, and the highest EUD�, or combination thereof, is ranked at the top. The CVI and TVI valuesare scaled by penalty functions. The penalty functions specify sub-volume limits for the critical structures and target, which mayreceive dose above or below specified maximum and minimum dose limits, and the penalty values associated with theirviolation, thus decreasing the CVI and increasing the TVI values. A range of penalty values may be defined based on clinicalexperience. A model was constructed to assess the utility of this approach for lung cancer therapy. The EUD� incorporates ourrecent lung biological-response data relating the reduction of regional perfusion (RRP) to regional dose, and is calculated asEUDtarget(1–RRP). The optimum IMRT lung plan maximizes EUD�, which combines the competing objectives of target EUDmaximization and RRP minimization. Further, EUD� provides information on how to balance risks (i.e. target coverage andlungs sparing). For example, if the target volume and lungs receive equal dose, the EUD� value is dramatically reduced.

Results: A total number of four lung cancer patients are evaluated. For each patient, a 3DCRT plan and three competing IMRTplans are compared. The same beam energy, beam directions, and dose prescription are used for all plans. The ranking toolselected the IMRT plan with the best weighted combination of CVI, TVI, and EUD� values. A better critical structure sparingis observed in the IMRT plans at the expense of target coverage loss, compared to the 3DCRT plan. The CVI and TVI valuesagreed with the DVHs, thus providing single index measures of the plan’s dose conformity and dose toxicity. The TVI showsthat smaller volumes of critical structures are irradiated above a clinical significance dose in the IMRT plans than the 3DCRTplan. Further, the RRP of the IMRT plan improved by 30% relative to the 3DCRT plan. While the target EUD of the 3DCRTplan is higher by 4% than the IMRT plan, the IMRT plan EUD� is within 1% of the 3DCRT plan EUD�.

Conclusions: A treatment plan ranking tool based on accurate dosimetric and biological/physiological indices is developed. Ourstudy demonstrates that clinically relevant dose-volume- and biological-response-based indices, which summarize complexdose distributions through a single index in each anatomical structure, can be used to automatically select the optimal planamong competing plans and may be more appropriate than traditional methods for evaluating/ranking IMRT dose distributions.

This work is partially supported by NIH grant 69579 and Varian Medical Systems.

Fig. 1. Dose volume histograms (DVHs) for the rigid dose prescription (solid line) and the probabilistic dose prescription(dotted line). The rigid prescription: the target, normal tissue and OAR are prescribed with 100Gy, 0Gy and 0Gy respectively;the probabilistic prescription: the target is prescribed with two permissible values 100Gy with 50% probability and 120Gy with50% probability.

325Proceedings of the 44th Annual ASTRO Meeting