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Fig 1.

Patient flow diagram.

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Fig 2.

Texture analysis.

(a) Manually outlining and filtering out the pixels with attenuation under -50 HU in locally advanced rectal cancer in 76-year-old man. (b) Corresponding images in the same patient applying LoG filters with fine, medium, and coarse filter values.

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Fig 2 Expand

Table 1.

Patient characteristics (n = 95).

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Table 2.

Results of texture feature analysis.

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Table 2 Expand

Table 3.

Texture features of non-responder versus responder group after CRT without filtration and for various filter scale values depicting fine, medium, and coarse textures.

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Table 3 Expand

Fig 3.

Kaplan–Meier curves according to texture features.

Kaplan-Meier curves without filtration showed a significant difference in DFS for (a) entropy, (b) uniformity, and (c) standard deviation.

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Table 4.

Spearman rank correlation for texture features without filtration.

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Table 5.

Multivariable Cox proportional hazards regression analysis of texture features with CT stage and age as dependent covariate.

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