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Table 3 Summary of the semi-quantitative methods investigated. Methods are principally grouped according to the particular technique for defining the SBR cut-off

From: Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Semi-quantification method

Comparison data

SBRs considered

Cut-offs defined by

SQ 1

Age-matched normals

Left and right putamen

Mean − 2SD

SQ 2

Age-matched normals

Left and right putamen and caudate

Mean − 2SD

SQ 3

Age-matched normals

Left and right putamen only

Mean − 1.5SD

SQ 4

Age-matched normals

Left and right putamen and caudate

Mean − 1.5SD

SQ 5

Age-matched normals

Left and right putamen

Mean − 1SD

SQ 6

Age-matched normals

Left and right putamen and caudate

Mean − 1SD

SQ 7

Age-matched normals

Left and right putamen

Minimum

SQ 8

Age-matched normals

Left and right putamen and caudate

Minimum

SQ 9

All normals

Left and right putamen

Linear regression − 2SE

SQ 10

All normals

Left and right putamen and caudate

Linear regression − 2SE

SQ 11

All normals

Left and right putamen

Linear regression − 1.5SE

SQ 12

All normals

Left and right putamen and caudate

Linear regression − 1.5SE

SQ 13

All normals

Left and right putamen

Linear regression − 1SE

SQ 14

All normals

Left and right putamen and caudate

Linear regression − 1SE

SQ 15

All normals and abnormals

Lowest putamen

Optimal point on ROC curve

SQ 16

All normals and abnormals

Lowest putamen and lowest caudate

Optimal point on ROC curve

SQ 17

Age-matched normals and abnormals

Lowest putamen

Optimal point on ROC curve

SQ 18

Age-matched normals and abnormals

Lowest putamen and lowest caudate

Optimal point on ROC curve