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Test of the Generalized Tumor Dose (gTD) Model with An Independent Lung Tumor Dataset

A Fontanella

A Fontanella¹*, C Robinson² , A Zuniga¹ , A Apte¹ , W Thorstad² , J Bradley² , J Deasy¹ , (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Washingon University School of Medicine, St. Louis, MO

Presentations

SU-E-T-312 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

Purpose:wWe previously developed a novel approach to tumor response modeling which was inspired by the gEUD model of tumor cell kill under inhomogeneous dose profiles. The model assumes that the cell-kill law is valid, but further adds a parameter analogous to the generalized equivalent uniform dose model to test the independence of response of tumor sub-volumes. The purpose of this study was to test the resulting generalized tumor dose (gTD) model on an independent lung cancer subset.

Methods:Niemierko’s gEUD model of cell kill introduces a generalization term describing interactions among tumor sub-volumes under non-uniform dose distributions. This parameter effectively applies weights to sub-volumes based off of tumor type-specific sensitivities to variance in dose. We combine the gEUD with the cell-kill based cEUD to form the “generalized tumor dose” (gTD) model, which is given by gTD=-α⁻¹*ln(Vt/Vref)-(α*a)⁻¹*ln(Σi=1:subvolsM#subvols⁻¹ *e (-α*a*di)). This model was previously applied to lung and H&N data. Here, we apply the model to an independent lung dataset (N= 36).

Results:We found that the model does not perform well for large tumors (volume > 70 cc.) However,. However, for tumors smaller than this volume, the model was well-correlated with local control, with a normalized log-likelihood value (log-likelihood divided by the number of cases) of -0.604.

Conclusion:The presented model continues to predict tumor response in this dataset, for tumors less than 70 cc. To date, no other TCP model has been validated on clinical datasets for tumors with varying volumes and dose distributions. More tests are needed to further refine and validate the model.


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