Skip to main content

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