The radiobiological P(+) index for pretreatment plan assessment with emphasis on four-dimensional radiotherapy...

Radiation treatment modalities will continue to emerge that promise better clinical outcomes albeit technologically challenging to implement. An important question facing the radiotherapy community then is the need to justify the added technological effort for the clinical return. Mobile tumor radiotherapy is a typical example, where 4D tumor tracking radiotherapy (4DTRT) has been proposed over the simpler conventional modality for better results. The modality choice per patient can depend on a wide variety of factors. In this work, we studied the complication-free tumor control probability (P(+)) index, which combines the physical complexity of the treatment plan with the radiobiological characteristics of the clinical case at hand and therefore found to be useful in evaluating different treatment techniques and estimating the expected clinical effectiveness of different radiation modalities.

4DCT volumes of 18 previously treated lung cancer patients with tumor motion and size ranging from 2 mm to 15 mm and from 4 cc to 462 cc, respectively, were used. For each patient, 4D treatment plans were generated to extract the 4D dose distributions, which were subsequently used with clinically derived radiobiological parameters to compute the P(+) index per modality.

The authors observed, on average, a statistically significant increase in P(+) of 3.4% ± 3.8% (p < 0.003) in favor of 4DTRT. There was high variability among the patients with a <0.5% up to 13.4% improvement in P(+).

The observed variability in the improvement of the clinical effectiveness suggests that the relative benefit of tracking should be evaluated on a per patient basis. Most importantly, this variability could be effectively captured in the computed P(+). The index can thus be useful to discriminate and hence point out the need for a complex modality like 4DTRT over another. Besides tumor mobility, a wide range of other factors, e.g., size, location, fractionation, etc., can affect the relative benefits. Application of the P(+) objective is a simple and effective way to combine these factors in the evaluation of a treatment plan.