Table 1

DSS 3.0 Datasets (number of cases) and classifiers they served to train.

Superclasses used for training the classifiers

Dataset

Spectra available

Classification

Meningioma

Aggressive

Low-grade glial

Subtotal

Cases from other classes

Total

problem

Method(s)

INTERPRET

Short TE (20-32 ms)

MCTT

LDA

58

124

35

217

87

304

Long TE (135-144 ms)

MCTT

LDA

55

109

31

195

71

266

Short + Long TE

MCTT

LDA

55

109

31

195

71

266

Pseudotumoural

Tumoural

Normal

brain

Subtotal

Cases from other classes

Total

IDI-Bellvitge

Short TE (30 ms)

T vs.PS

LDA

19

46

5

70

0

70

Long TE (135 ms)

T vs.PS

LDA

19

46

5

70

0

70

Short + Long TE

T vs.PS

LDA and Ratios[14]

19

46

5

70

0

70

Specifications of the two main datasets included in the system, the INTERPRET and the IDI-Bellvitge dataset. Each dataset has short and long TE spectra and both short and long TE spectra concatenated. Different classification problems have been analysed with these datasets. Furthermore, in the IDI-Bellvitge dataset, the same classification problem has been solved in two different ways, either by an LDA classification or by a peak height ratio-based classifier [14]. The INTERPRET dataset contains cases used for training the classifiers as well as from other less common types of tumours. Note that for INTERPRET the number of cases available at short and long TE is different. MCTT: Most common tumour types; T vs.PS: Tumour vs. pseudotumoural disease. See [4,13] for further details on superclass definition.

Pérez-Ruiz et al. BMC Bioinformatics 2010 11:581   doi:10.1186/1471-2105-11-581