Intraoperative neuropathology of glioma recurrence: cell detection and classification

Intraoperative neuropathology of glioma recurrence represents significant visual challenges to pathologists as they carry significant clinical implications. For example, rendering a diagnosis of recurrent glioma can help the surgeon decide to perform more aggressive resection if surgically appropriate. In addition, the success of recent clinical trials for intraoperative administration of therapies, such as inoculation with oncolytic viruses, may suggest that refinement of the intraoperative diagnosis during neurosurgery is an emerging need for pathologists. Typically, these diagnoses require rapid/STAT processing lasting only 20-30 minutes after receipt from neurosurgery. In this relatively short time frame, only dyes, such as hematoxylin and eosin (H and E), can be implemented. The visual challenge lies in the fact that these patients have undergone chemotherapy and radiation, both of which induce cytological atypia in astrocytes, and pathologists are unable to implement helpful biomarkers in their diagnoses. Therefore, there is a need to help pathologists differentiate between astrocytes that are cytologically atypical due to treatment versus infiltrating, recurrent, neoplastic astrocytes. This study focuses on classification of neoplastic versus non-neoplastic astrocytes with the long term goal of providing a better neuropathological computer-aided consultation via classification of cells into reactive gliosis versus recurrent glioma. We present a method to detect cells in H and E stained digitized slides of intraoperative cytologic preparations. The method uses a combination of the ‘value’ component of the HSV color space and ‘b*’ component of the CIE L*a*b* color space to create an enhanced image that suppresses the background while revealing cells on an image. A composite image is formed based on the morphological closing of the hue-luminance combined image. Geometrical and textural features extracted from Discrete Wavelet Frames and combined to classify cells into neoplastic and non-neoplastic categories. Experimental results show that there is a strong consensus between the proposed method’s cell detection markings with those of the pathologist’s. Experiments on 48 images from six patients resulted in F1-score as high as 87.48%, 88.08% and 86.12% for Reader 1, Reader 2 and the reader consensus, respectively. Classification results showed that for both readers, binary classification tree and support vector machine performed the best with F1-scores ranging 0.92 to 0.94.