Can magnetic resonance spectroscopy differentiate malignant and benign causes of lymphadenopathy? An in-vitro approach

Lymphadenopathy continues to be a common problem to radiologists and treating physiciansbecause of the difficulty in confidently categorizing a node as being benign or malignantusing standard diagnostic techniques. The goal of our research was to assess whethermagnetic resonance (MR) spectroscopy contains the necessary information to allow differentiationof benign from malignant lymph nodes in an in-vitro approach using a modern patternrecognition method. Tissue samples from a tissue bank were analyzed on a nuclearmagnetic resonance (NMR) spectrometer. A total of 69 samples were studied. The samplesincluded a wide variety of malignant and benign etiologies. Using 45 samples, we initiallycreated a model which was able to predict if a certain spectrum originates from benign ormalignant lymph nodes using a pattern-recognition technique which takes into account theentire magnetic spectrum rather than single peaks alone. The remaining 24 samples wereblindly loaded in the model to assess its performance. We obtained an excellent accuracy indifferentiating benign and malignant lymphadenopathy using the model. It correctly differentiatedas malignant or benign, in a blinded fashion, all of the malignant samples (13 of 13)and 10 out of the 11 benign samples. We thus showed that magnetic spectroscopy is able todifferentiate benign from malignant causes of lymphadenopathy. [...

Correlation between ppm regions and the predictive component.

<p>The average of all of the studied spectra is represented along the ppm axis. This graph allows visualization of the correlation between each ppm region and the predictive t1 score. The orientation of the peak with regards to the axis indicates if they are benign predictors (above the x axis) or malignant predictors (below the x axis). Color coding corresponds to the relative weight of each peak in predicting benign vs. malignant classes. A red color indicates peaks that have a high predictive value while a blue color indicates one that has a lower predictive value. Note the peak with the highest predictive value is found at 3.8ppm.</p

Summary of the studied pathologies.

<p>Summary of the studied pathologies.</p

Blinded test set loaded in model predicting benign and malignant tissues.

<p>New benign and malignant samples not previously analyzed were blindly loaded into the OPLS-DA model from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182169#pone.0182169.g002" target="_blank">Fig 2</a>, and the categories were subsequently revealed. t1 and t1o are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182169#pone.0182169.g002" target="_blank">Fig 2</a>.</p

Score plot of training set differentiating benign and malignant causes of lymphadenopathy with cross-validation plot.

<p>A. t1 is the predictive component, it is used to achieve discrimination in between both groups. t1o is the orthogonal component, It is useful for understanding class variability. B. Cross-validation plot showing Tcv1 vs Tcv2 for the OPLS-DA model with three orthogonal components showing good separation of the benign and malignant groups (<i>R</i>2 = 0.96, <i>Q</i>2<i>Y</i> = 0.63, <i>n</i> = 45).</p