Statistical Classification Strategy for Proton Magnetic ResonanceSpectra of Soft Tissue Sarcoma: An Exploratory Study withPotential Clinical Utility

[摘要] Purpose: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 givesmore reliable and non-subjective diagnostic information is needed. The potential use of proton magnetic resonance spectroscopyin combination with an appropriate statistical classification strategy was tested here in differentiating normalmesenchymal 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 boththe 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 classificationaccuracy (based on the histopathology of the MRS specimens) in differentiating normal mesenchymal from soft tissuesarcoma 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 thoseassigned to choline, creatine, glutamine, glutamic acid and lipid.Conclusion:Proton magnetic resonance spectroscopy combined with a statistical classification strategy gave good results indifferentiating normal mesenchymal tissue from soft tissue sarcoma specimensex vivo. Such an approach may also differentiatebenign tumors from malignant ones and this will be explored in future studies.

[学科分类] 肿瘤学