Statistical classification strategy for proton magnetic resonance spectra of soft tissue sarcoma: an exploratory study with potential clinical utility.
OBJECTIVE
Histological grading is currently one of the best predictors of tumor behavior and outcome in soft tissue sarcoma. However, occasionally there is significant disagreement even among expert pathologists. An alternative method that gives more reliable and non-subjective diagnostic information is needed. The potential use of proton magnetic resonance spectroscopy in combination with an appropriate statistical classification strategy was tested here in differentiating normal mesenchymal tissue from soft tissue sarcoma.


METHODS
Fifty-four normal and soft tissue sarcoma specimens of various histological types were obtained from 15 patients. One-dimensional proton magnetic resonance spectra were acquired at 360 MHz. Spectral data were analyzed by using both the conventional peak area ratios and a specific statistical classification strategy.


RESULTS
The statistical classification strategy gave much better results than the conventional analysis. The overall classification accuracy (based on the histopathology of the MRS specimens) in differentiating normal mesenchymal from soft tissue sarcoma was 93%, with a sensitivity of 100% and specificity of 88%.The results in the test set were 83, 92 and 76%, respectively. Our optimal region selection algorithm identified six spectral regions with discriminating potential, including those assigned to choline, creatine, glutamine, glutamic acid and lipid.


CONCLUSIONS
Proton magnetic resonance spectroscopy combined with a statistical classification strategy gave good results in differentiating normal mesenchymal tissue from soft tissue sarcoma specimens ex vivo. Such an approach may also differentiate benign tumors from malignant ones and this will be explored in future studies.
Institute for Biodiagnostics National Research Council of Canada 435 Ellice Ave. Manitoba Winnipeg R3B 1Y6 Canada.
Tedros
T
Bezabeh
Samy
S
El-Sayed
Rakesh
R
Patel
Ray L
RL
Somorjai
Vivien
V
Bramwell
Rita
R
Kandel
Ian C P
IC
Smith