Table 2. Results of univariate (A, B) and multivariate analysis (C) using three classification models: logistic regression analysis, naive Bayes classifier, and k-NN.

A
Significant univariate feature	Classifier/Model	%Sn	%Sp	AUC	P-value	Statistical power (n2)
	Logistic regression	60.0	60.0	0.726
Hb-homogeneity	Naive Bayes	82.0	82.0	0.799	0.030	71.8 (14)
	k-NN	61.5	67.5	0.577
	Logistic regression	70.0	70.0	0.756
HbO2-correlation	Naive Bayes	80.0	81.0	0.778	0.024	78.9 (11)
	k-NN	66.5	74.5	0.602
	Logistic regression	60.0	60.0	0.657
HbT-homogeneity	Naive Bayes	84.0	85.0	0.813	0.047	79.9 (11)
	k-NN	74.0	47.0	0.552
	Logistic regression	60.0	63.0	0.670
St-contrast	Naive Bayes	79.5	82.0	0.779	0.044	73.5 (13)
	k-NN	70.5	64.5	0.582
	Logistic regression	70.0	63.0	0.715
StO2-contrast	Naive Bayes	83.0	85.5	0.803	0.044	85.6 (enough)
	k-NN	70.0	66.5	0.610

B
Univariate features	Classifier/Model	%Sn	%Sp	%Acc
HbO2-correlation	Logistic regression	70.0	70.0	70.0
HbO2-homogeneity	Naive Bayes	86.5	89.0	87.8
HbO2-contrast	k-NN	81.0	73.0	77.0

C
Multivariate features	Classifier/Model	%Sn	%Sp	%Acc
HbO2-correlation+Hb-homogeneity	Logistic regression	80.0	78.0	79.5
Hb-contrast+HbO2-homogeneity	Naive Bayes	78.0	81.0	79.5
Hb-correlation+HbO2-contrast	k-NN	79.5	76.0	77.8

Abbreviations: %Acc=accuracy; AUC=area under curve; Hb=deoxy-haemoglobin; HbO₂=oxy-haemoglobin; HbT=total haemoglobin; k-NN=k-nearest neighbour; Sn=sensitivity; Sp=specificity; St=oxygen desaturation; StO₂=tumour oxygen saturation.

Bold values indicate best classifiers. The last column in Table 2A reports the percentage of the statistical power. The numbers inside parentheses in this column indicate the number of non-responders (n2) required in this study to achieve a statistical power of minimum 80% in case that the number of responders (n1) is fixed at 27.