Influence of image slice thickness on rectal dose-response relationships following radiotherapy of prostate cancer

图像切片厚度对前列腺癌放射治疗后直肠剂量反应关系的影响

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Abstract

When pooling retrospective data from different cohorts, slice thicknesses of acquired computed tomography (CT) images used for treatment planning may vary between cohorts. It is, however, not known if varying slice thickness influences derived dose-response relationships. We investigated this for rectal bleeding using dose-volume histograms (DVHs) of the rectum and rectal wall for dose distributions superimposed on images with varying CT slice thicknesses. We used dose and endpoint data from two prostate cancer cohorts treated with three-dimensional conformal radiotherapy to either 74 Gy (N = 159) or 78 Gy (N = 159) at 2 Gy per fraction. The rectum was defined as the whole organ with content, and the morbidity cut-off was Grade ≥2 late rectal bleeding. Rectal walls were defined as 3 mm inner margins added to the rectum. DVHs for simulated slice thicknesses from 3 to 13 mm were compared to DVHs for the originally acquired slice thicknesses at 3 and 5 mm. Volumes, mean, and maximum doses were assessed from the DVHs, and generalized equivalent uniform dose (gEUD) values were calculated. For each organ and each of the simulated slice thicknesses, we performed predictive modeling of late rectal bleeding using the Lyman-Kutcher-Burman (LKB) model. For the most coarse slice thickness, rectal volumes increased (≤18%), whereas maximum and mean doses decreased (≤0.8 and ≤4.2 Gy, respectively). For all a values, the gEUD for the simulated DVHs were ≤1.9 Gy different than the gEUD for the original DVHs. The best-fitting LKB model parameter values with 95% CIs were consistent between all DVHs. In conclusion, we found that the investigated slice thickness variations had minimal impact on rectal dose-response estimations. From the perspective of predictive modeling, our results suggest that variations within 10 mm in slice thickness between cohorts are unlikely to be a limiting factor when pooling multi-institutional rectal dose data that include slice thickness variations within this range.

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