Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation

Rahib, 2014, Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States, Cancer Res, 74, 2913, 10.1158/0008-5472.CAN-14-0155 Siegel, 2019, Cancer statistics, 2019, CA Cancer J Clin, 69, 7, 10.3322/caac.21551 Ryan, 2014, Pancreatic adenocarcinoma, N Engl J Med, 371, 1039, 10.1056/NEJMra1404198 Al-Hawary, 2014, Pancreatic ductal adenocarcinoma radiology reporting template: consensus statement of the Society of Abdominal Radiology and the American Pancreatic Association, Radiology, 270, 248, 10.1148/radiol.13131184 Dewitt, 2006, Comparison of endoscopic ultrasound and computed tomography for the preoperative evaluation of pancreatic cancer: a systematic review, Clin Gastroenterol Hepatol, 4, 717, 10.1016/j.cgh.2006.02.020 Yasaka, 2018, Deep learning and artificial intelligence in radiology: current applications and future directions, PLoS Med, 15, 10.1371/journal.pmed.1002707 Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056 Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 2402, 10.1001/jama.2016.17216 Yasaka, 2018, Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study, Radiology, 286, 887, 10.1148/radiol.2017170706 Frampas, 2016, Pancreatic carcinoma: key-points from diagnosis to treatment, Diagn Interv Imaging, 97, 1207, 10.1016/j.diii.2016.07.008 DeWitt, 2004, Comparison of endoscopic ultrasonography and multidetector computed tomography for detecting and staging pancreatic cancer, Ann Intern Med, 141, 753, 10.7326/0003-4819-141-10-200411160-00006 Simonyan, 2014, Very deep convolutional networks for large-scale image recognition, arXiv Panchapagesan, 2016 Chollet Dillon, 2017, TensorFlow distributions, arXiv Simpson, 2019, A large annotated medical image dataset for the development and evaluation of segmentation algorithms, arXiv Clark, 2013, The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository, J Digit Imaging, 26, 1045, 10.1007/s10278-013-9622-7 Berryman Roth, 2015, DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation, vol 9349, 556 Chen, 2018, Classification of pancreatic cystic neoplasms based on multimodality images, 161 Zhou, 2017, Deep supervision for pancreatic cyst segmentation in abdominal CT scans, 222 Li, 2018, Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: using whole-tumor CT texture analysis as quantitative biomarkers, Cancer Med, 7, 4924, 10.1002/cam4.1746 Fu, 2018, Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images, BMC Syst Biol, 12, 56, 10.1186/s12918-018-0572-z Gibson, 2018, Automatic multi-organ segmentation on abdominal CT with dense V-networks, IEEE Trans Med Imaging, 37, 1822, 10.1109/TMI.2018.2806309 Roth, 2018, Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation, Med Image Anal, 45, 94, 10.1016/j.media.2018.01.006 Yu, 2018, Recurrent saliency transformation network: incorporating multi-stage visual cues for small organ segmentation Chu, 2019, Application of deep learning to pancreatic cancer detection: lessons learned from our initial experience, J Am Coll Radiol, 16, 1338, 10.1016/j.jacr.2019.05.034 Liu, 2015, SSD: single shot multibox detector, arXiv Noh, 2017, Learning deconvolution network for semantic segmentation Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weaklysupervised classification and localization of common thorax diseases. IEEE conference on computer vision and pattern recognition; Honolulu, HI, USA; July 21–26. Wang, 2016, Deep learning for identifying metastatic breast cancer, arXiv Maninis, 2016, Deep retinal image understanding, 140 Agarwal, 2008, Survival in pancreatic carcinoma based on tumor size, Pancreas, 36, e15, 10.1097/mpa.0b013e31814de421 Wu, 2018, Rising trends in pancreatic cancer incidence and mortality in 2000–2014, Clin Epidemiol, 10, 789, 10.2147/CLEP.S160018 Chari, 2007, Detecting early pancreatic cancer: problems and prospects, Semin Oncol, 34, 284, 10.1053/j.seminoncol.2007.05.005 Pannala, 2009, New-onset diabetes: a potential clue to the early diagnosis of pancreatic cancer, Lancet Oncol, 10, 88, 10.1016/S1470-2045(08)70337-1 Szczepaniak, 2012, Pancreatic steatosis and its relationship to β-cell dysfunction in humans: racial and ethnic variations, Diabetes Care, 35, 2377, 10.2337/dc12-0701 Lê, 2011, Ethnic differences in pancreatic fat accumulation and its relationship with other fat depots and inflammatory markers, Diabetes Care, 34, 485, 10.2337/dc10-0760 Tunaci, 2004, Multidetector row CT of the pancreas, Eur J Radiol, 52, 18, 10.1016/j.ejrad.2004.03.030