Diagnostics, Early Detection, and Imaging最新文献

{"title":"Abstract PO-010: Detection of early tissue changes on historical CT scans in the regions of the pancreas gland that subsequently develop adenocarcinoma using quantitative textural analysis and fat fraction analysis","authors":"R. Korn, D. V. Von Hoff, Andre Burkett, Dominic Zygadlo, Taylor Brodie, K. Pañak, Sweta Rajan, D. Cridebring, M. Demeure","doi":"10.1158/1538-7445.panca21-po-010","DOIUrl":"https://doi.org/10.1158/1538-7445.panca21-po-010","url":null,"abstract":"","PeriodicalId":315716,"journal":{"name":"Diagnostics, Early Detection, and Imaging","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127858373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract PO-007: Plasma-based detection of pancreatic cancer: A multiomics approach","authors":"Teng-Kuei Hsu, Tzu-Yu Liu, Billie A. Gould, Christine Decapite, A. Zureikat, A. Paniccia, E. Ariazi, Marvin Bertin, R. Bourgon, Kaitlyn E Coil, Hayley J. Donnella, Adam Drake, J. Granka, P. Kaur, M. Louie, Shivani Mahajan, A. Pasupathy, Ofer Shapira, Peter Ulz, Chun Yang, C. J. Lin, R. Brand","doi":"10.1158/1538-7445.panca21-po-007","DOIUrl":"https://doi.org/10.1158/1538-7445.panca21-po-007","url":null,"abstract":"","PeriodicalId":315716,"journal":{"name":"Diagnostics, Early Detection, and Imaging","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126589429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract PO-011: The spectrum of pathogenic germline variants in pancreatic cancer patients with multiple primary tumors","authors":"Valentyna Kryklyva, L. Brosens, M. Ligtenberg, I. Nagtegaal","doi":"10.1158/1538-7445.panca21-po-011","DOIUrl":"https://doi.org/10.1158/1538-7445.panca21-po-011","url":null,"abstract":"","PeriodicalId":315716,"journal":{"name":"Diagnostics, Early Detection, and Imaging","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131558525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract PO-013: Comparison of novel healthcare delivery models on the uptake of genetic education and testing in families with a history of pancreatic cancer: The GENetic Education, Risk Assessment and TEsting (GENERATE) study","authors":"N. Rodriguez, C. S. Furniss, M. Yurgelun, Chinedu Ukaegbu, P. Constantinou, Alison Schwartz, J. Stopfer, Meghan Underhill-Blazey, Barbara J. Kenner, Scott H. Nelson, Sydney Okumura, S. Law, A. Zhou, Tara Coffin, H. Uno, A. Ocean, F. McAllister, A. Lowy, S. Lippman, A. Klein, L. Madlensky, G. Petersen, J. Garber, M. Goggins, A. Maitra, S. Syngal","doi":"10.1158/1538-7445.panca21-po-013","DOIUrl":"https://doi.org/10.1158/1538-7445.panca21-po-013","url":null,"abstract":"","PeriodicalId":315716,"journal":{"name":"Diagnostics, Early Detection, and Imaging","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121392884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract PO-009: Multi-omic profiling of patient pancreatic cyst fluid for the identification of a novel biomarker panel of patient cancer risk","authors":"Laura E. Kane, G. Mellotte, S. Marcone, B. Ryan, S. Maher","doi":"10.1158/1538-7445.panca21-po-009","DOIUrl":"https://doi.org/10.1158/1538-7445.panca21-po-009","url":null,"abstract":"","PeriodicalId":315716,"journal":{"name":"Diagnostics, Early Detection, and Imaging","volume":"56 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114024155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract PO-008: Diagnostic accuracy of blood-based multi-omic biomarkers for pancreatic adenocarcinoma: A systematic review and meta-analysis","authors":"Laura E. Kane, G. Mellotte, E. Mylod, Rebecca M. O’Brien, F. O'Connell, Khanh Nguyen, C. E. Buckley, Jennifer Arlow, D. Mockler, A. Meade, B. Ryan, S. Maher","doi":"10.1158/1538-7445.panca21-po-008","DOIUrl":"https://doi.org/10.1158/1538-7445.panca21-po-008","url":null,"abstract":"","PeriodicalId":315716,"journal":{"name":"Diagnostics, Early Detection, and Imaging","volume":"149 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133527301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Abstract PO-012: The concept of artificial intelligence against pancreatic cancer","authors":"S. Kumar","doi":"10.1158/1538-7445.panca21-po-012","DOIUrl":"https://doi.org/10.1158/1538-7445.panca21-po-012","url":null,"abstract":"Pancreatic cancer (PC) remains the fourth leading cause of cancer-related death in both men and women in the United States. Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Pancreatic ductal adenocarcinoma (PDAC) is on track to become the number 2 cancer killer in the United States within the next decade unless there is a major improvement in outcomes. Surgical resection remains the only reasonable hope for a cure from PDAC. The potential for early detection of asymptomatic pancreatic neoplasms in high-risk individuals using an endoscopic approach, but this approach is operator dependent and at the same time, these existing techniques are favored once patients reach the age of 75 years. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. Machine learning refers to the study of algorithms that learn their behavior from data. To see why such algorithms are important, consider the following basic task, building a program to predict if an image contains a dog or a cat. Although it is exceedingly difficult for us to manually specify the exact rules to determine that a dog is a dog, it is comparatively straightforward to prepare a reference set of images and labels. This setting, where knowledge is more easily encoded in data rather than as a descriptive set of rules, is the focus of ML algorithms. One of the most promising areas of innovation in medical imaging in the past decade has been the application of deep learning. Deep learning has the potential to impact the entire medical imaging workflow from image acquisition, image registration, to interpretation. Traditional image processing is dominated by algorithms that are based on statistical models. These statistical model-based processing algorithms carry out inference based on a complete knowledge of the underlying statistical model relating the observations at hand and the desired information and do not require data to learn their mapping. In practice, accurate knowledge of the statistical model relating the observations and the desired information is typically unavailable. The past decade has witnessed a deep learning revolution. Deep learning methods have surpassed the state of the art for many problems in signal processing, imaging, and vision with unprecedented performance gains. Citation Format: Subash Kumar. The concept of artificial intelligence against pancreatic cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PO-012.","PeriodicalId":315716,"journal":{"name":"Diagnostics, Early Detection, and Imaging","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125640667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}