This study showed that combining different PET segmentation methods by the use of a consensus algorithm offers robustness against the variable performance of individual segmentation methods and this approach would therefore be useful in radiation oncology. It might also be relevant for other scenarios such as the merging of expert recommendations in clinical routine and trials or the multiobserver generation of contours for standardization of automatic contouring.

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PET/CT plays an important role in radiotherapy planning for lung tumors. Several segmentation algorithms have been proposed for PET tumor segmentation. However, most of them do not take into account respiratory motion and are not well validated. The aim of this work was to evaluate a semi-automated contrast-oriented algorithm (COA) for PET tumor segmentation adapted to retrospectively gated (4D) images. The evaluation involved a wide set of 4D-PET/CT acquisitions of dynamic experimental phantoms and lung cancer patients. In addition, segmentation accuracy of 4D-COA was compared with four other state-of-the-art algorithms. In phantom evaluation, the physical properties of the objects defined the gold standard. In clinical evaluation, the ground truth was estimated by the STAPLE (Simultaneous Truth and Performance Level Estimation) consensus of three manual PET contours by experts. Algorithm evaluation with phantoms resulted in: (i) no statistically significant diameter differences for different targets and movements (Δφ = 0.3 ± 1.6 mm); (ii) reproducibility for heterogeneous and irregular targets independent of user initial interaction and (iii) good segmentation agreement for irregular targets compared to manual CT delineation in terms of Dice Similarity Coefficient (DSC = 0.66 ± 0.04), Positive Predictive Value (PPV  = 0.81 ± 0.06) and Sensitivity (Sen. = 0.49 ± 0.05). In clinical evaluation, the segmented volume was in reasonable agreement with the consensus volume (difference in volume (%Vol) = 40 ± 30, DSC = 0.71 ± 0.07 and PPV = 0.90 ± 0.13). High accuracy in target tracking position (ΔME) was obtained for experimental and clinical data (ΔME(exp) = 0 ± 3 mm; ΔME(clin) 0.3 ± 1.4 mm). In the comparison with other lung segmentation methods, 4D-COA has shown the highest volume accuracy in both experimental and clinical data. In conclusion, the accuracy in volume delineation, position tracking and its robustness on highly irregular target movements, make this algorithm a useful tool for 4D-PET based volume definition for radiotherapy planning of lung cancer and may help to improve the reproducibility in PET quantification for therapy response assessment and prognosis.

The radiation dose to the kidneys should be monitored in prostate cancer patients treated with radioligand therapy (RLT) targeting the prostate-specific membrane antigen (PSMA). We analyzed whether pretherapeutic kidney function is predictive of subsequent kidney dose and to what extend the cumulative kidney dose after multiple therapy cycles at the end of treatment can be predicted from a dosimetry based on the first cycle. Methods: Data of 59 patients treated with at least 2 cycles of 177 Lu-PSMA-617 (PSMA-RLT) were analyzed. Treatment (median: 6 GBq/cycle) was performed at 6-8 week intervals, accompanied by voxel-based 3D-dosimetry (measured kidney dose) with SPECT/CT on each of days 0-3 and once during days 6-9. Pretherapeutic kidney function (eGFR, MAG3-clearance) was correlated to the kidney doses.Cumulative kidney doses at the end of treatment were compared to a dose estimation based on the population-based mean kidney dose, individual first cycle kidney dose and mean kidney doses of cycles 1, 3 and 5 per administered activity. Results: A total of 176 PSMA-RLT cycles were performed with a median of 3 cycles per patient. The average kidney dose per administered activity of all 176 cycles was 0.67 ± 0.24 Gy/GBq (range 0.21 -1.60). MAG3-clearance and eGFR were no reliable predictors of subsequent absorbed kidney dose and showed only small effect sizes (R 2 = 0.080 and 0.014, p = 0.039 and 0.375). All simplified estimations of cumulative kidney dose correlated significantly (p < 0.001) with measured kidney doses: Estimations based on the individual first-cycle dose were more accurate than the use of the population-based average kidney dose (R 2 = 0.853 vs. R 2 = 0.560). Dose estimation was best when the doses of cycles 3 and 5 were included as well (R 2 = 0.960). Conclusion: Pretherapeutic renal function was not predictive for subsequent kidney dose during therapy. Extrapolation of individual data from dosimetry of the first cycle was highly predictive for the cumulative kidney dose at the end of treatment. This is further improved by the integration of dose information from every other cycle. In any case, because of a high interindividual variance, an individual dosimetry is advisable.