Abstract PO-007: Deep learning-based computational pathology predicts origins for cancers of unknown primary

摘要: Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin is undetermined. This poses a significant challenge to effective patient care as modern therapeutics are often specific to the primary tumor. Patients with a CUP diagnosis routinely undergo an extensive diagnostic work-up to determine the primary origin, which is resource-intensive, might significantly delay the administration of suitable treatment, and is not always successful. While previous works have demonstrated the feasibility of predicting the primary origin based on molecular information, we present a deep learning-based algorithm that can provide a differential diagnosis for CUP using routine histology slides. We used 22,833 gigapixel whole slide images with known primaries spread over 18 common origins to train a multi-task deep model to identify the tumor as primary or metastatic and …