Table 2.

AUC values and 95% CIs for ΔFV with and without SUV_SD in three dose ranges to assess feature performance in determining RP status

	Low dose (0–10 Gy)		Medium dose (10–30 Gy)		High dose (> 30 Gy)
	Feature alone	Feature + SUVSD	Feature alone	Feature + SUVSD	Feature alone	Feature + SUVSD
First‐order features
70% quantilea	0.71b [0.58, 0.85]	0.79c [0.67, 0.90]	0.78b [0.66, 0.91]	0.82 [0.72, 0.93]	0.80b [0.68, 0.92]	0.83 [0.72, 0.94]
Mediana	0.70b [0.56, 0.84]	0.78c [0.66, 0.90]	0.75b [0.61, 0.89]	0.80c [0.69, 0.92]	0.78b [0.65, 0.91]	0.83 [0.72, 0.93]
Meana	0.68b [0.54, 0.83]	0.77c [0.63, 0.89]	0.76b [0.62, 0.89]	0.81c [0.70, 0.92]	0.79b [0.67, 0.90]	0.83c [0.72, 0.94]
Binned entropya	0.66b [0.51, 0.80]	0.74c [0.61, 0.85]	0.74b [0.61, 0.87]	0.78 [0.66, 0.90]	0.77b [0.64, 0.90]	0.80 [0.68, 0.91]
30% quantilea	0.68b [0.53, 0.83]	0.76c [0.64, 0.89]	0.72b [0.57, 0.87]	0.78c [0.66, 0.90]	0.76b [0.62, 0.90]	0.82c [0.71, 0.94]
Unbinned entropy	0.64b [0.50, 0.78]	0.70c [0.56, 0.83]	0.68b [0.54, 0.82]	0.73c [0.61, 0.85]	0.73b [0.60, 0.85]	0.77c [0.67, 0.88]
5% quantile	0.63 [0.48, 0.79]	0.71c [0.58, 0.83]	0.67b [0.52, 0.82]	0.73c [0.60, 0.85]	0.69b [0.55, 0.84]	0.76c [0.63, 0.89]
Minimum	0.61 [0.45, 0.77]	0.68c [0.53, 0.80]	0.60 [0.44, 0.76]	0.68c [0.55, 0.81]	0.70b [0.55, 0.84]	0.70c [0.56, 0.83]
Fractal features
Brownian dimensiona	0.67b [0.51, 0.83]	0.71 [0.57, 0.85]	0.74b [0.61, 0.87]	0.79 [0.68, 0.89]	0.81b [0.70, 0.92]	0.79c [0.67, 0.91]
Box‐counting dimension	0.53 [0.36, 0.69]	0.65c [0.51, 0.78]	0.55 [0.39, 0.71]	0.66c [0.52, 0.79]	0.64 [0.48, 0.80]	0.72c [0.58, 0.84]
Fine box‐counting dimension	0.55 [0.39, 0.71]	0.65c [0.51, 0.77]	0.54 [0.37, 0.70]	0.65c [0.51, 0.77]	0.58 [0.41, 0.75]	0.67c [0.53, 0.80]
Laws' filter features
E5L5 entropya	0.62 [0.48, 0.76]	0.72c [0.56, 0.84]	0.74b [0.62, 0.86]	0.80c [0.70, 0.90]	0.80b [0.70, 0.90]	0.83c [0.73, 0.92]
R5L5 entropya	0.63 [0.49, 0.77]	0.73 [0.60, 0.86]	0.74b [0.61, 0.86]	0.78 [0.68, 0.89]	0.80b [0.68, 0.91]	0.81 [0.70, 0.91]
S5L5 entropya	0.62 [0.49, 0.76]	0.71c [0.56, 0.83]	0.73b [0.61, 0.86]	0.79c [0.68, 0.89]	0.79b [0.69, 0.90]	0.82 [0.72, 0.91]
W5L5 entropya	0.63b [0.50, 0.76]	0.73c [0.61, 0.83]	0.74b [0.61, 0.86]	0.79c [0.70, 0.89]	0.79b [0.68, 0.90]	0.81 [0.71, 0.91]
GLCM features
Sum averagea	0.68b [0.54, 0.83]	0.77c [0.65, 0.88]	0.75b [0.62, 0.88]	0.81 [0.70, 0.92]	0.79b [0.67, 0.90]	0.83 [0.73, 0.93]
Sum of squares variancea	0.69b [0.55, 0.83]	0.77c [0.65, 0.88]	0.76b [0.63, 0.89]	0.81 [0.70, 0.92]	0.79b [0.68, 0.90]	0.83 [0.73, 0.94]
Sum entropy	0.66b [0.52, 0.80]	0.71c [0.58, 0.83]	0.70b [0.56, 0.83]	0.75c [0.62, 0.87]	0.75b [0.63, 0.86]	0.78 [0.68, 0.89]
Difference entropy	0.60 [0.45, 0.75]	0.67c [0.53, 0.80]	0.64 [0.49, 0.79]	0.70c [0.57, 0.82]	0.68b [0.54, 0.82]	0.73c [0.61, 0.85]
Entropy	0.57 [0.42, 0.72]	0.67c [0.48, 0.81]	0.59 [0.44, 0.75]	0.68c [0.54, 0.80]	0.61 [0.46, 0.77]	0.68c [0.53, 0.80]
Average AUC	0.64	0.72	0.70	0.76	0.74	0.78

^aSignificant relationship between change in feature value (ΔFV) and grade ≥ 2 RP (P < 0.0025).

^b95% CI of AUC shown in brackets does not overlap 0.5.

^cSignificant improvement of logistic regression model fit with addition of SUV_SD using ANOVA (P < 0.0025).