Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary

• URL: http://arxiv.org/abs/2006.13932v2
• Date: Mon, 29 Jun 2020 02:38:40 GMT
• Title: Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary
• Authors: Ming Y. Lu, Melissa Zhao, Maha Shady, Jana Lipkova, Tiffany Y. Chen, Drew F. K. Williamson, Faisal Mahmood
• Abstract summary: Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined. Recent work has focused on using genomics and transcriptomics for identification of tumor origins. We present a deep learning-based computational pathology algorithm that can provide a differential diagnosis for CUP.
• Score: 2.645435564532842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined. This poses a significant challenge since modern therapeutics such as chemotherapy regimen and immune checkpoint inhibitors are specific to the primary tumor. Recent work has focused on using genomics and transcriptomics for identification of tumor origins. However, genomic testing is not conducted for every patient and lacks clinical penetration in low resource settings. Herein, to overcome these challenges, we present a deep learning-based computational pathology algorithm-TOAD-that can provide a differential diagnosis for CUP using routinely acquired histology slides. We used 17,486 gigapixel whole slide images with known primaries spread over 18 common origins to train a multi-task deep model to simultaneously identify the tumor as primary or metastatic and predict its site of origin. We tested our model on an internal test set of 4,932 cases with known primaries and achieved a top-1 accuracy of 0.84, a top-3 accuracy of 0.94 while on our external test set of 662 cases from 202 different hospitals, it achieved a top-1 and top-3 accuracy of 0.79 and 0.93 respectively. We further curated a dataset of 717 CUP cases from 151 different medical centers and identified a subset of 290 cases for which a differential diagnosis was assigned. Our model predictions resulted in concordance for 50% of cases (\k{appa}=0.4 when adjusted for agreement by chance) and a top-3 agreement of 75%. Our proposed method can be used as an assistive tool to assign differential diagnosis to complicated metastatic and CUP cases and could be used in conjunction with or in lieu of immunohistochemical analysis and extensive diagnostic work-ups to reduce the occurrence of CUP.

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