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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. |
