Cancers of unknown primary (CUP) , represent 1-3\% of all cancers and is an enigmatic disease where the primary site of origin cannot be determined. Pathologists routinely attempt to decipher the site of origin using extensive immunohistochemical staining, however, the rate of identification remains low. Modern cancer therapeutics including immune checkpoint inhibitor therapies are specific to the primary tumor hence broad-spectrum chemotherapy remains the standard-of-care for CUP patients\mbox{%DIFAUXCMD massard2011carcinomas . Recent work has focused on using genomics and transcriptomics for identification of tumors of unknown primary . However, genomic testing  lacks clinical penetration in low resource settings and may not be available at scale for every patient. Here we show that deep learning-based weakly supervised and multi-task computational pathology on H E slides can be used to determine the metastatic status and site of origin for tumors of unknown primary . We used 17,486 gigapixel whole slide images  spread over 18 URLans for training a multi-task deep model to simultaneously identify  primary or metastatic status and site of origin. We tested our model on an internal test set of 4,932 cases  and achieved a top-1 accuracy of 0.84, a top-3 accuracy of 0.94 and our external test set of 662 cases from 202 different hospitals  achieved a top-1 and top-3 accuracy of 0.79 and 0.93 respectively. We further curated a CUP dataset of 717 cases form 202 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 ( \kappa =0.4 when adjusted for agreement by chance) and a top-3 agreement of 75\%. Our proposed method can be used as a diagnostic mechanism in contrast with or in lieu of immunohistochemical staining and may also reduce the occurrence of CUP cases .
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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