Neuroendocrine neoplasms (NENs) are uncommon neoplasms, which can arise in cells across the body. The last few decades have seen NEN incidence increase by 7-fold. NEN grades are morphologically indistinguishable for well-differentiated tumours and are determined from the tumour proliferation. Cell proliferation is defined by the number of mitotic figures in a 2mm² area for H&E slides and by the percent of Ki-67 positive cells in Ki-67 slides. Unfortunately, these measures suffer inter- and intra-observer variability, and are cumbersome to quantify. We developed a novel machine learning framework to identify candidate mitotic figures in H&E images, calculate the Ki-67 index in Ki-67 images, and aggregate proliferating features to grade NENs. Our work included 186 gastroenteropancreatic NENs with 385 samples from across British Columbia with two different stains (247 H&E and 138 Ki-67 images) and patient-level labels for grade. The H&E portion of the framework achieved a balanced accuracy of 72.1% across 6-folds for three-class classification (G1, G2, G3). We demonstrated that the survival outcomes for computer assigned grades are comparable to those based on pathologist assessment, with c-index values of 0.63 and 0.64, respectively. Our Ki-67 algorithm achieves a balanced accuracy of 83.9% to predict the grade of Ki-67 slides. With Ki-67 added to our pipeline, grading improves the balanced accuracy to 78.5%. Analysis of the survival outcome for pathologist assessed G1s demonstrated significant (p-value=0.01) separation amongst samples the algorithm assigned to a higher grade (n=27; median survival 4.63 years) compared to concordant G1 samples (n=55; median survival 10.13 years). By identifying both mitotic figures and Ki-67 positive cells, our method may provide a tool to verify hot-spots in both H&E and Ki-67 slides for further analysis of grades. Misclassified G1 patients have lower median survival, and further analysis is needed to determine if this group should be considered a different clinical entity and if our machine learning approach has identified two distinct populations within the grade 1 group.
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