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Browsing by Author "Wolfgang, Christopher L."
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Item Global Survey on Pancreatic Surgery During the COVID-19 Pandemic(Lippincott Williams & Wilkins, 2020-06-24) Oba, Atsushi; Stoop, Thomas F.; Löhr, Matthias; Hackert, Thilo; Zyromski, Nicholas; Nealon, William H.; Unno, Michiaki; Schulick, Richard D.; Al-Musawi, Mohammed H.; Wu, Wenming; Zhao, Yupei; Satoi, Sohei; Wolfgang, Christopher L.; Hilal, Mohammad Abu; Besselink, Marc G.; Del Chiaro, Marco; Surgery, School of MedicineThis global survey among members of seven international pancreatic associations and study groups elucidates the role of pancreatic surgery during the COVID-19 pandemic, regarding patient selection for the surgical and oncological treatment of pancreatic diseases to support clinical decision-making and creating a starting point for further discussion.Item Liquid Biopsy as Surrogate for Tissue for Molecular Profiling in Pancreatic Cancer: A Meta-Analysis Towards Precision Medicine(MDPI, 2019-08-10) Luchini, Claudio; Veronese, Nicola; Nottegar, Alessia; Cappelletti, Vera; Daidone, Maria G.; Smith, Lee; Parris, Christopher; Brosens, Lodewijk A. A.; Caruso, Maria G.; Cheng, Liang; Wolfgang, Christopher L.; Wood, Laura D.; Milella, Michele; Salvia, Roberto; Scarpa, Aldo; Pathology and Laboratory Medicine, School of MedicineLiquid biopsy (LB) is a non-invasive approach representing a promising tool for new precision medicine strategies for cancer treatment. However, a comprehensive analysis of its reliability for pancreatic cancer (PC) is lacking. To this aim, we performed the first meta-analysis on this topic. We calculated the pooled sensitivity, specificity, positive (LR+) and negative (LR-) likelihood ratio, and diagnostic odds ratio (DOR). A summary receiver operating characteristic curve (SROC) and area under curve (AUC) were used to evaluate the overall accuracy. We finally assessed the concordance rate of all mutations detected by multi-genes panels. Fourteen eligible studies involving 369 patients were included. The overall pooled sensitivity and specificity were 0.70 and 0.86, respectively. The LR+ was 3.85, the LR- was 0.34 and DOR was 15.84. The SROC curve with an AUC of 0.88 indicated a relatively high accuracy of LB for molecular characterization of PC. The concordance rate of all mutations detected by multi-genes panels was 31.9%. LB can serve as surrogate for tissue in the molecular profiling of PC, because of its relatively high sensitivity, specificity and accuracy. It represents a unique opportunity to be further explored towards its introduction in clinical practice and for developing new precision medicine approaches against PC.Item A multimodality test to guide the management of patients with a pancreatic cyst(American Association for the Advancement of Science, 2019-07-17) Springer, Simeon; Masica, David L.; Dal Molin, Marco; Douville, Christopher; Thoburn, Christopher J.; Afsari, Bahman; Li, Lu; Cohen, Joshua D.; Thompson, Elizabeth; Allen, Peter J.; Klimstra, David S.; Schattner, Mark A.; Schmidt, C. Max; Yip-Schneider, Michele; Simpson, Rachel E.; Castillo, Carlos Fernandez-Del; Mino-Kenudson, Mari; Brugge, William; Brand, Randall E.; Singhi, Aatur D.; Scarpa, Aldo; Lawlor, Rita; Salvia, Roberto; Zamboni, Giuseppe; Hong, Seung-Mo; Hwang, Dae Wook; Jang, Jin-Young; Kwon, Wooil; Swan, Niall; Geoghegan, Justin; Falconi, Massimo; Crippa, Stefano; Doglioni, Claudio; Paulino, Jorge; Schulick, Richard D.; Edil, Barish H.; Park, Walter; Yachida, Shinichi; Hijioka, Susumu; van Hooft, Jeanin; He, Jin; Weiss, Matthew J.; Burkhart, Richard; Makary, Martin; Canto, Marcia I.; Goggins, Michael G.; Ptak, Janine; Dobbyn, Lisa; Schaefer, Joy; Sillman, Natalie; Popoli, Maria; Klein, Alison P.; Tomasetti, Cristian; Karchin, Rachel; Papadopoulos, Nickolas; Kinzler, Kenneth W.; Vogelstein, Bert; Wolfgang, Christopher L.; Hruban, Ralph H.; Lennon, Anne Marie; Surgery, School of MedicinePancreatic cysts are common and often pose a management dilemma, because some cysts are precancerous, whereas others have little risk of developing into invasive cancers. We used supervised machine learning techniques to develop a comprehensive test, CompCyst, to guide the management of patients with pancreatic cysts. The test is based on selected clinical features, imaging characteristics, and cyst fluid genetic and biochemical markers. Using data from 436 patients with pancreatic cysts, we trained CompCyst to classify patients as those who required surgery, those who should be routinely monitored, and those who did not require further surveillance. We then tested CompCyst in an independent cohort of 426 patients, with histopathology used as the gold standard. We found that clinical management informed by the CompCyst test was more accurate than the management dictated by conventional clinical and imaging criteria alone. Application of the CompCyst test would have spared surgery in more than half of the patients who underwent unnecessary resection of their cysts. CompCyst therefore has the potential to reduce the patient morbidity and economic costs associated with current standard-of-care pancreatic cyst management practices.