Jonathan Loree

Assistant Professor

Research Interests

Carcinoid tumors

Relevant Thesis-Based Degree Programs


Graduate Student Supervision

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Deep learning framework for classification of neuroendocrine neoplasm whole slide images (2023)

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.

View record


If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.


Discover the amazing research that is being conducted at UBC!