Table 4 A subgroup analysis was completed based on ER/PR+ and triple-negative tumours

From: Predicting breast cancer response to neoadjuvant chemotherapy using pretreatment diffuse optical spectroscopic texture analysis

Subgroup

Best feature

Model

%Sn

%Sp

AUC

ER/PR+

Hb-con

Logistic regression

76.2

66.7

0.746

 

HbO2-hom

Naive Bayes

93.3

90.1

0.883

 

HbO2-con

k-NN

85.8

82.5

0.851

Triple negative

Hb-hom

Logistic regression

100.0

33.3

0.917

 

Hb-ene

Naive Bayes

100.0

66.7

0.667

 

Hb-hom

k-NN

75.0

66.7

0.917

FEC-D

TOI-hom

Logistic regression

100.0

92.3

0.949

 

Hb-con

Naive Bayes

60.0

81.7

0.722

 

Hb-hom

k-NN

80.0

80.0

0.806

AC-T

HbO2-cor

Logistic regression

100.0

71.4

0.837

 

HbO2-hom

Naive Bayes

96.4

90.7

0.882

 

HbO2-hom

k-NN

83.6

85.0

0.896

  1. Abbreviations: AC-T=adriamycin, cyclophosphamide, taxol; AUC=area under curve; ER=oestrogen receptor; FEC-D=fluorouracil, epirubicin, cyclophosphamide, docetaxel; Hb=deoxy-haemoglobin; HbO2=oxy-haemoglobin; k-NN=k-nearest neighbour; PR=progesterone receptor; Sn=sensitivity; Sp=specificity; TOI=tissue optical index.
  2. Patients were also grouped according to chemotherapy type for analysis. Three classification models were used (logistic regression, naive Bayes, and k-NN) and the best predictive features are presented.
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