Information theory approaches to improve glioma diagnostic workflows in surgical neuropathology

Abstract

Aims Resource‐strained healthcare ecosystems often struggle with the adoption of the World Health Organization (WHO) recommendations for the classification of central nervous system (CNS) tumors. The generation of robust clinical diagnostic aids and the advancement of simple solutions to inform investment strategies in surgical neuropathology would improve patient care in these settings. Methods We used simple information theory calculations on a brain cancer simulation model and real‐world data sets to compare contributions of clinical, histologic, immunohistochemical, and molecular information. An image noise assay was generated to compare the efficiencies of different image segmentation methods in H&E and Olig2 stained images obtained from digital slides. An auto‐adjustable image analysis workflow was generated and compared with neuropathologists for p53 positivity quantification. Finally, the density of extracted features of the nuclei, p53 positivity quantification, and combined ATRX/age feature was used to generate a predictive model for 1p/19q codeletion in IDH ‐mutant tumors. Results Information theory calculations can be performed on open access platforms and provide significant insight into linear and nonlinear associations between diagnostic biomarkers. Age, p53 , and ATRX status have significant information for the diagnosis of IDH ‐mutant tumors. The predictive models may facilitate the reduction of false‐positive 1p/19q codeletion by fluorescence in situ hybridization (FISH) testing. Conclusions We posit that this approach provides an improvement on the cIMPACT‐NOW workflow recommendations for IDH ‐mutant tumors and a framework for future resource and testing allocation.

Glioblastoma pseudoprogression and true progression reveal spatially variable transcriptional differences

“…While image grayscaling and thresholding were processed using EBimage in R [ 36 ]. In contrast, CD163 was segmented using a UNET convolutional neural network (CNN) in python using tensorflow due to high DAB background staining [ 37 – 39 ].…”

Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

“…Furthermore, the logistics of e ciently scheduling patients between visits with neuro-oncology, neurosurgery, neuroradiology, and radiation oncology would signi cantly bene t from improved triaging methods that prioritize the most sensitive patients. Unfortunately, the accessibility of molecular markers is not equal across healthcare ecosystems (9)(10). Current discussions in the eld of neuro-oncology have pointed to major accessibility barriers and bioethical implications of pure reliance of molecular biomarkers of disease to understand cancer (10).…”

“…Molecular pathology testing requires batching to reduce patient costs, a need that results in signi cant costs in turn-around-time for molecular assays (11). In consequence, groups have sought ways to evaluate outcomes of glioma patients using surrogate measures such as neuro-cognitive testing, psychiatric examination, or image analysis of histology to predict molecular phenotypes as these turn-around-times are superior to those of molecular pathology (9,(12)(13). While promising however, integration of these approaches will take time whereas routinely collected data is readily available.…”

Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation

“…Unfortunately, molecular marker accessibility is not equal across healthcare ecosystems. 9 , 10 Current discussions in the field of neuro-oncology have pointed to major accessibility barriers and bioethical implications of pure reliance on molecular biomarkers of disease to understand cancer. 10 Molecular pathology testing requires batching to reduce patient costs, resulting in significant turn-around time delays for molecular assays.…”

“… 11 In consequence, groups have sought ways to evaluate outcomes of glioma patients using surrogate measures such as neuro-cognitive testing, psychiatric examination, or image analysis of histology to predict molecular phenotypes as these turn-around-times are superior to those of molecular pathology. 9 , 12 , 13 Although promising, however, integration of these approaches will take time whereas routinely collected data is readily available.…”