(Tuesday, 7/16/2019) 9:30 AM - 10:00 AM
Room: Exhibit Hall | Forum 7
Purpose: Biological optimization models are available in some commercial treatment planning systems (TPS), however, optimization parameters were not well understood (from the physician perspective), do not account for tumor size, and are very expensive. Tumor size is an important prognostic factor for lung SBRT. This report incorporates a size-adjusted biologically effective dose (s-BED) model in to the TPS and uses physical dose parameters based off individual DVH’s of lung SBRT patients.
Methods: Utilizing standard linear-quadratic (LQ) model and assuming BED decreases nearly linearly with increasing tumor-diameter; s-BED=BED10-c×d was defined for tumor-diameter, d(cm) and α/β=10Gy, where c is 10Gy/cm(Ohri et al.,IJROBP). Crude local-control rates ≥ 2-years, as a function of s-BED was computed with:TCP=(EXP[s-BED-TCD50]/k)÷(1.0+EXP[s-BED-TCD50]/k), where parameters TCD50 and k defined the shape of the sigmoid curve. This model was adopted/incorporated into Eclipse TPS using physical dose parameters from the individual tumor’s DVH and tumor-size. This script was tested using 10/10 early stage lung cancer patients who received 50/54Gy in 5/3 fractions of lung SBRT.
Results: Average tumor diameter was 3.7/3.9cm for 50/54Gy regimens. For 50Gy scheme, using PTVD95, our predicted TCP indicates that an s-BED value of 63.3Gy provided 88.2%, on average. For 54Gy dose, s-BED value of 113.9Gy provided 97.5%, on average, chances of tumor-control at 2-years. These predicted TCP values agreed with independent calculation using the same model parameters and are consistent with Ohri’s findings.
Conclusion: The s-BED model that accounts for tumor-size and more realistic physical dose parameters (tied with historically outcome) from individual DVH’s was incorporated into Eclipse TPS. This tool potentially guides treatment decisions by suggesting escalation or de-escalation of dose based on tumor-size and DVH parameters. Additional research is needed to further validate the s-BED model using clinical results to correlate which DVH parameter match clinical outcomes and incorporating NTCP model for lung-toxicity alongside TCP.
Keywords
Lung, Radiobiology, Stereotactic Radiosurgery
Taxonomy
TH- Radiobiology(RBio)/Biology(Bio): RBio- LQ/TCP/NTCP/outcome modeling
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