Tradeoffs for assuming rigid target motion in Mlc-based real time target tracking radiotherapy: a dosimetric and radiobiological analysis

Correlation of primary middle and distal esophageal cancers motion with surrounding tissues using four-dimensional computed tomography

Purpose

To investigate the correlation of gross tumor volume (GTV) motion with the structure of interest (SOI) motion and volume variation for middle and distal esophageal cancers using four-dimensional computed tomography (4DCT).

Patients and methods

Thirty-three patients with middle or distal esophageal carcinoma underwent 4DCT simulation scan during free breathing. All image sets were registered with 0% phase, and the GTV, apex of diaphragm, lung, and heart were delineated on each phase of the 4DCT data. The position of GTV and SOI was identified in all 4DCT phases, and the volume of lung and heart was also achieved. The phase relationship between the GTV and SOI was estimated through Pearson’s correlation test.

Results

The mean peak-to-peak displacement of all primary tumors in the lateral (LR), anteroposterior (AP), and superoinferior (SI) directions was 0.13 cm, 0.20 cm, and 0.30 cm, respectively. The SI peak-to-peak motion of the GTV was defined as the greatest magnitude of motion. The displacement of GTV correlated well with heart in three dimensions and significantly associated with bilateral lung in LR and SI directions. A significant correlation was found between the GTV and apex of the diaphragm in SI direction (r_left=0.918 and r_right=0.928). A significant inverse correlation was found between GTV motion and varying lung volume, but the correlation was not significant with heart (r_LR=−0.530, r_AP=−0.531, and r_SI=−0.588) during respiratory cycle.

Conclusion

For middle and distal esophageal cancers, GTV should expand asymmetric internal margins. The primary tumor motion has quite good correlation with diaphragm, heart, and lung.

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

PURPOSE

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.

METHODS

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.

RESULTS

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(+).

CONCLUSIONS

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.