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MANAGEMENT OF GEOMETRICAL UNCERTAINTIES IN RADIOTHERAPY: MARGINS AND CORRECTION STRATEGIES
S 124
mm) and 194 cases (13%) had DCIS (median size =8 mm). Median follow-up was 50 months for all surviving patients. Results: 38 cases (2.6%) developed an ipsilateral breast tumor recurrence (IBTR). The 5-year actuarial rate of IBTR was 3.84%. A subset analysis of the first 400 consecutive cases enrolled was performed (352 with IBC, 48 DCIS) and constitute the study population With a median follow-up of 58 months, the 5-year actuarial rate of IBTR was 3.26%.(3.69% for IBC and 0.0% for DCIS). 5 IBTRs (1.3% crude rate and 1.20% 5-yr actuarial) were considered TR/MM failures and 6 (1.5% crude rate and 2.09% 5-yr actuarial) elsewhere. No variables were associated with IBTR including patient age < 50 (p=0.6305), margins (p=0.9997), tumor size (p=0.9997) or positive nodes (p=0.8251). 9 patients (0.6%) developed an axillary failure. The percentage of breasts with good/excellent cosmetic results at 36 (n=753), 48 (n=608), and 60 months (n=264 cases) was: 93.2%, 90.6%, and 87.9%, respectively. The development of an infection (p=0.0104), and skin spacing (p=0.0252) were associated with cosmetic results. Seromas have been reported in _27.6% of patients overall, 13.0% of which were symptomatic. Infections have been reported in 9.5% of patients overall. Conclusions: Five-year treatment efficacy and cosmesis after treatment with R APBI using the MammoSite device are good and similar to those reported with other forms of APBI or whole breast irradiation with similar follow-up and risk factors for IBTR.
T HURSDAY, S EPTEMBER 3, 2009
Thursday, September 3, 2009 Teaching lecture 335 speaker BIOLOGICAL AND GENETIC PREDICTIVE ASSAYS: A REVIEW FOR THE PHYSICIST D. Zips1 1
U NIVERSITY H OSPITAL AND M EDICAL FACULTY, T ECHNISCHE U NIVERSITÄT D RESDEN, Radiation Oncology, Dresden, Germany
Individualization of radiotherapy on the basis of tumour and patient characteristics is a field of major interest in radiation oncology. Clinical characteristics such as tumour volume, stage, histology etc. are established predictive parameters for tailored therapy and specific interventions. Recent developments in molecular profiling and imaging aimed to predict outcome after radiotherapy will be reviewed. Implications for experimental and clinical applications will be discussed. 336 speaker MANAGEMENT OF GEOMETRICAL UNCERTAINTIES IN RADIOTHERAPY: MARGINS AND CORRECTION STRATEGIES P. Remeijer1 1 N ETHERLANDS C ANCER I NSITUTE, Radiation Oncology, Amsterdam, Netherlands
Introduction: Geometrical uncertainties are unavoidable in any radiotherapy practice. Lasers can be misaligned, patients are mobile and the definition of the target volume is not always very easy. To deal with these uncertainties a safety margin is applied, i.e. a larger volume than the target itself is treated. Since there is a direct relation between the size of the margin and the uncertainties, reducing the latter will shrink the margins as well. In this paper we will discuss a number of strategies that can be used to deal with uncertainties. Margins: Currently, the axiom of geometric margins in radiotherapy is that group statistics are used to predict what errors one can encounter in patients. Subsequently, the margin is chosen in such a way that most errors are covered (e.g. 90%). This methodology has the advantage that a margin can be determined without the need to measure patient specific errors.Random or systematic errors require different margins, however. Random errors, generally quantified by a standard deviation σ , cause dose blurring and will require a relatively small margin. Systematic errors, quantified by Σ, shift the dose distribution and require a much larger margin. From a simple mathematical model one can deduce that the geometrical margin should be 2.5Σ + 0.7σ , assuming a spherical CTV and a large number of fractions. The problem of computing a margin for more complicated case, like in the presence of shape changes, still remains unsolved. Correction strategies: Because the multiplication factor that precedes Σ in the margin formula is the largest, systematic errors have a larger effect on the margin than random errors. Therefore, the most efficient way to reduce margins is to focus on systematic errors. Delineation variability is purely systematic and can be reduced by adopting strict protocols and including multiple modalities. Setup errors can be measured by portal imaging or cone-beam CT. The data can be used to drive average corrections aimed at reducing the systematic error (e.g. No Action Level or NAL protocol) or online corrections which reduce both systematic and random errors. To reduce workload, some correction protocols include a threshold below which no corrections are being made (e.g. Shrinking Action Level or SAL protocol).Changes in the position of the tumor can be monitored by either fiducials or imaging modalities capable of visualizing soft-tissue structures (e.g. cone-beam CT or ultrasound). Corrections can again be performed through an offline strategy, e.g. by introducing an adapted plan that is deployed from the second week onward. To reduce the margin even further, random errors can be corrected as well through an online strategy. Because this requires daily measurements and corrections, it is not always feasible from a workload point of view. Furthermore, online adaptation of the treatment beyond translations and rotations is not widely available at present. Conclusion: A margin reduction can be achieved most effectively by addressing systematic errors. Because this usually only involves measuring the errors over a couple of fractions and offline analysis, the amount of additional treatment machine time required for such a procedure is very limited. Although organ motion will mostly be the predominant cause for geometrical uncertainties, the effect of delineation uncertainties should not be underestimated, especially because these are purely systematic errors.
mm) and 194 cases (13%) had DCIS (median size =8 mm). Median follow-up was 50 months for all surviving patients. Results: 38 cases (2.6%) developed an ipsilateral breast tumor recurrence (IBTR). The 5-year actuarial rate of IBTR was 3.84%. A subset analysis of the first 400 consecutive cases enrolled was performed (352 with IBC, 48 DCIS) and constitute the study population With a median follow-up of 58 months, the 5-year actuarial rate of IBTR was 3.26%.(3.69% for IBC and 0.0% for DCIS). 5 IBTRs (1.3% crude rate and 1.20% 5-yr actuarial) were considered TR/MM failures and 6 (1.5% crude rate and 2.09% 5-yr actuarial) elsewhere. No variables were associated with IBTR including patient age < 50 (p=0.6305), margins (p=0.9997), tumor size (p=0.9997) or positive nodes (p=0.8251). 9 patients (0.6%) developed an axillary failure. The percentage of breasts with good/excellent cosmetic results at 36 (n=753), 48 (n=608), and 60 months (n=264 cases) was: 93.2%, 90.6%, and 87.9%, respectively. The development of an infection (p=0.0104), and skin spacing (p=0.0252) were associated with cosmetic results. Seromas have been reported in _27.6% of patients overall, 13.0% of which were symptomatic. Infections have been reported in 9.5% of patients overall. Conclusions: Five-year treatment efficacy and cosmesis after treatment with R APBI using the MammoSite device are good and similar to those reported with other forms of APBI or whole breast irradiation with similar follow-up and risk factors for IBTR.
T HURSDAY, S EPTEMBER 3, 2009
Thursday, September 3, 2009 Teaching lecture 335 speaker BIOLOGICAL AND GENETIC PREDICTIVE ASSAYS: A REVIEW FOR THE PHYSICIST D. Zips1 1
U NIVERSITY H OSPITAL AND M EDICAL FACULTY, T ECHNISCHE U NIVERSITÄT D RESDEN, Radiation Oncology, Dresden, Germany
Individualization of radiotherapy on the basis of tumour and patient characteristics is a field of major interest in radiation oncology. Clinical characteristics such as tumour volume, stage, histology etc. are established predictive parameters for tailored therapy and specific interventions. Recent developments in molecular profiling and imaging aimed to predict outcome after radiotherapy will be reviewed. Implications for experimental and clinical applications will be discussed. 336 speaker MANAGEMENT OF GEOMETRICAL UNCERTAINTIES IN RADIOTHERAPY: MARGINS AND CORRECTION STRATEGIES P. Remeijer1 1 N ETHERLANDS C ANCER I NSITUTE, Radiation Oncology, Amsterdam, Netherlands
Introduction: Geometrical uncertainties are unavoidable in any radiotherapy practice. Lasers can be misaligned, patients are mobile and the definition of the target volume is not always very easy. To deal with these uncertainties a safety margin is applied, i.e. a larger volume than the target itself is treated. Since there is a direct relation between the size of the margin and the uncertainties, reducing the latter will shrink the margins as well. In this paper we will discuss a number of strategies that can be used to deal with uncertainties. Margins: Currently, the axiom of geometric margins in radiotherapy is that group statistics are used to predict what errors one can encounter in patients. Subsequently, the margin is chosen in such a way that most errors are covered (e.g. 90%). This methodology has the advantage that a margin can be determined without the need to measure patient specific errors.Random or systematic errors require different margins, however. Random errors, generally quantified by a standard deviation σ , cause dose blurring and will require a relatively small margin. Systematic errors, quantified by Σ, shift the dose distribution and require a much larger margin. From a simple mathematical model one can deduce that the geometrical margin should be 2.5Σ + 0.7σ , assuming a spherical CTV and a large number of fractions. The problem of computing a margin for more complicated case, like in the presence of shape changes, still remains unsolved. Correction strategies: Because the multiplication factor that precedes Σ in the margin formula is the largest, systematic errors have a larger effect on the margin than random errors. Therefore, the most efficient way to reduce margins is to focus on systematic errors. Delineation variability is purely systematic and can be reduced by adopting strict protocols and including multiple modalities. Setup errors can be measured by portal imaging or cone-beam CT. The data can be used to drive average corrections aimed at reducing the systematic error (e.g. No Action Level or NAL protocol) or online corrections which reduce both systematic and random errors. To reduce workload, some correction protocols include a threshold below which no corrections are being made (e.g. Shrinking Action Level or SAL protocol).Changes in the position of the tumor can be monitored by either fiducials or imaging modalities capable of visualizing soft-tissue structures (e.g. cone-beam CT or ultrasound). Corrections can again be performed through an offline strategy, e.g. by introducing an adapted plan that is deployed from the second week onward. To reduce the margin even further, random errors can be corrected as well through an online strategy. Because this requires daily measurements and corrections, it is not always feasible from a workload point of view. Furthermore, online adaptation of the treatment beyond translations and rotations is not widely available at present. Conclusion: A margin reduction can be achieved most effectively by addressing systematic errors. Because this usually only involves measuring the errors over a couple of fractions and offline analysis, the amount of additional treatment machine time required for such a procedure is very limited. Although organ motion will mostly be the predominant cause for geometrical uncertainties, the effect of delineation uncertainties should not be underestimated, especially because these are purely systematic errors.