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UBC Theses and Dissertations

A deep learning approach for classification of pancreatic adenocarcinoma whole-slide pathology images Ahmadvand, Pouya


Pancreatic ductal adenocarcinoma (PDAC) mortality rates are projected to rise by 2030 due to factors such as delayed diagnosis and resistance to chemotherapy and radiation therapy. A key challenge in treating PDAC is the lack of biomarkers for predicting treatment effectiveness and chemotherapy resistance. Researchers suggest a binary subtype system, basal-like and classical, can predict treatment selection and response, but identifying these subtypes requires costly and time-consuming RNA profiling. Histopathology, which provides an inexpensive and convenient visual readout of disease biology, has been essential in cancer diagnosis and prognosis for over a century. Artificial intelligence (AI) has recently been successfully applied to histopathology data, with AI-based models potentially outperforming traditional pathology assessments. However, an AI expert is needed to utilize and interpret these techniques. This research aimed to: 1) develop an AI-based pipeline to identify and detect histological features for classifying PDAC molecular subtypes, and 2) generalize the pipeline using a “Machine Learning Workflow Engine” and a “Web-based Slide Manager and Annotator” for processing and interpreting histopathology data. The researchers used the developed infrastructures to train and evaluate a deep-learning model for classifying PDAC patients into prognostic subgroups. They used 130 histological slides from the TCGA-PAAD dataset for training and 81 slides from 19 patients from an in-house dataset as the external test dataset. A two-step machine learning model was trained: 1) a classifier distinguishing tumor patches from stroma patches, and 2) a classifier predicting the molecular subtype of a slide based on tumor patches. The tumor/stroma classifier showed excellent performance with an AUC of 96.18% ± 1.84%, while the subtype classifier achieved a balanced accuracy of 96.19% ± 2.45% at the slide level. The model correctly classified 83.03% ± 6.35 of the patients' tumor molecular subtypes in the validation cohort. This classifier is the first to categorize PDAC patients based on biopsy samples.

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