. Author manuscript; available in PMC: 2020 Oct 1.

Published in final edited form as: Int J Radiat Oncol Biol Phys. 2019 Jun 13;105(2):440–447. doi: 10.1016/j.ijrobp.2019.06.009

Table 2.

Comparison of xerostomia prediction with other studies

Method	Parameters	AUC (95% CI)
Logistic regression [4]	dose	0.68 (0.60–0.76)

Logistic regression [28]	dose and clinical parameters	0.69 (CI not available)

Logistic regression [6]	dose and clinical parameters	0.75 (0.69–0.81)

	dose, clinical parameters, and CT IBMs	0.77 (0.71–0.82)

Logistic regression [7]	dose and clinical parameters	0.69 (0.62–0.77)

	dose, clinical parameters, and CBCT IBMs	0.78 (0.64–0.91)

Logistic regression [24]	dose and clinical parameters	0.73 (0.65–0.81)

	dose and clinical parameters and PET IBMs	0.77 (0.69–0.84)

Logistic regression [25]	dose and clinical parameters	0.76 (0.67–0.86)

	dose, clinical parameters, and CT IBMs	0.82 (0.72–0.91)

Logistic regression [26]	dose and clinical parameters	0.65 (0.41–0.88)

	dose, clinical parameters, and MRI IBMs	0.83 (0.67–0.99)

Logistic regression [27]	dose and clinical parameters	0.77 (0.65–0.88)

	dose and clinical parameters and CT image texture	0.91 (0.75–0.98)

Our method (3D rCNN)	CT images, dose distributions, and contours of the glands	0.84 (0.74–0.91)

AUC: area under the curve; CI: confidence interval; CT: computed tomography; IBMs: image biomarkers; CBCT: cone-beam CT; PET: positron emission tomography; MRI: magnetic resonance imaging; 3D rCNN: three-dimensional residual convolutional neural network