Fig 1.
The figure shows the pipeline proposed for machine learning assisted tumor margin assessment during brain tumor surgery.
After the tumor removal, the surgeon resects samples from the excision cavity. Samples are analyzed via HRMAS NMR technique. Produced spectra are processed via a random forest classifier to label each region in the cavity (malignant/benign tumor vs healthy tissue). The feedback is sent to the surgeon for resecting more tissue for regions labeled positive for tumor tissue.
Fig 2.
The performance comparison of the benchmarked machine learning models with respect to the AUC and AUPR metrics.
Box plots represent the performance of the models obtained on the test folds, in an 8-fold cross validation setting which is repeated 3 times.
Fig 3.
The SHAP Values (y-axis) for each ppm in the spectrum (x-axis) is shown for each sample (dots).
Dot color purple indicates a high feature value, and blue indicates a low value. A positive SHAP value indicates that feature was important to classify that sample as (i) tumor as opposed to control in Panel A; and as (ii) malignant as opposed to benign in Panel B. Conversely, a negative SHAP value indicates that feature was important to classify that sample as (i) control as opposed to tumor in Panel A; and as (ii) benign as opposed to malignant in Panel B.