Reducing systematic errors due to deformation of organs at risk in radiotherapy
Journal article, Peer reviewed
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Original versionMedical Physics. 2021, 48 (11), 6578-6587. 10.1002/mp.15262
Purpose In radiotherapy (RT), the planning CT (pCT) is commonly used to plan the full RT-course. Due to organ deformation and motion, the organ shapes seen at the pCT will not be identical to their shapes during RT. Any difference between the pCT organ shape and the organ's mean shape during RT will cause systematic errors. We propose to use statistical shrinkage estimation to reduce this error using only the pCT and the population mean shape computed from training data. Methods The method was evaluated for the rectum in a cohort of 37 prostate cancer patients that had a pCT and 7–10 treatment CTs with rectum delineations. Deformable registration was performed both within-patient and between patients, resulting in point-to-point correspondence between all rectum shapes, which enabled us to compute a population mean rectum. Shrinkage estimates were found by combining the pCTs linearly with the population mean. The method was trained and evaluated using leave-one-out cross validation. The shrinkage estimates and the patient mean shapes were compared geometrically using the Dice similarity index (DSI), Hausdorff distance (HD), and bidirectional local distance. Clinical dose/volume histograms, equivalent uniform dose (EUD) and minimum dose to the hottest 5% volume (D5%) were compared for the shrinkage estimate and the pCT. Results The method resulted in moderate but statistically significant increase in similarity to the patient mean shape over the pCT. On average, the HD was reduced from 15.6 to 13.4 mm, while the DSI was increased from 0.74 to 0.78. Significant reduction in the bias of volume estimates was found in the DVH-range of 52.5–65 Gy, where the bias was reduced from −1.3 to −0.2 percentage points, but no significant improvement was found in EUD or D5%, Conclusions The results suggest that shrinkage estimation can reduce systematic errors due to organ deformations in RT. The method has potential to increase the accuracy in RT of deformable organs and can improve motion modeling.