A multimodality test to guide the management of patients with a pancreatic cyst
Open accessAbstract
Pancreatic 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.
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A combined molecular and clinical marker panel was more accurate than current clinical features alone for classifying patients with pancreatic cysts who required surgery, routine monitoring, or no surveillance, and its use would have decreased unnecessary operations by 60% compared with conventional clinical and imaging criteria alone.
“A study of 862 individuals undergoing surgery for pancreatic cysts found that a combined molecular and clinical marker panel was more accurate than current clinical features alone for classifying patients who required surgery, or routine monitoring, or did not require surveillance; use of these combined markers would have decreased the number of unnecessary operations by 60% compared with the use”
From Early detection of pancreatic cancer (2020) · cites this paper as [41]
Almost two-thirds of intraductal papillary mucinous neoplasms and mucinous cystic neoplasms harbour a KRAS or GNAS mutation in the cyst fluid.
“Overall, almost two-thirds of intraductal papillary mucinous neoplasms and mucinous cystic neoplasms harbour a mutation in KRAS or GNAS in the cyst fluid.41”
From Early detection of pancreatic cancer (2020) · cites this paper as [41]
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- Authors
- Springer, S., Masica, D. L., Dal Molin, M., Douville, C., Thoburn, C. J., Afsari, B., Li, L., Cohen, J. D., Thompson, E., Allen, P. J., Klimstra, D. S., Schattner, M. A., Schmidt, C. M., Yip-Schneider, M., Simpson, R. E., Fernandez-Del Castillo, C., Mino-Kenudson, M., Brugge, W., Brand, R. E., Singhi, A. D., Scarpa, A., Lawlor, R., Salvia, R., Zamboni, G., Hong, S.-M., Hwang, D. W., Jang, J.-Y., Kwon, W., Swan, N., Geoghegan, J., Falconi, M., Crippa, S., Doglioni, C., Paulino, J., Schulick, R. D., Edil, B. H., Park, W., Yachida, S., Hijioka, S., van Hooft, J., He, J., Weiss, M. J., Burkhart, R., Makary, M., Canto, M. I., Goggins, M. G., Ptak, J., Dobbyn, L., Schaefer, J., Sillman, N., Popoli, M., Klein, A. P., Tomasetti, C., Karchin, R., Papadopoulos, N., Kinzler, K. W., Vogelstein, B., Wolfgang, C. L., Hruban, R. H., & Lennon, A. M.
- Affiliations
- Howard Hughes Medical Institute (US)
- Johns Hopkins University (US)
- Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21287, USA.
- Memorial Sloan Kettering Cancer Center (US)
- Indiana University School of Medicine (US)
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Harvard University (US)
- University of Pittsburgh (US)
- University of Verona (IT)
- Ospedale Sacro Cuore Don Calabria (IT)
- Asan Medical Center (KR)
- Seoul National University (KR)
- St. Vincent's University Hospital (IE)
- IRCCS Ospedale San Raffaele (IT)
- IRCCS San Raffaele Roma (IT)
- Hospital Curry Cabral (PT)
- University of Colorado Anschutz (US)
- Stanford Medicine (US)
- National Cancer Centre Japan (JP)
- Aichi Cancer Center (JP)
- Department of Gastroenterology and Hepatology, Amsterdam Medical Center, Amsterdam 1017 ZX, Netherlands.
- Ludwig Center and Howard Hughes Medical Institute at the Sidney Kimmel Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.
- Corresponding author
- Springer, Simeon
- Masica, David L.
- Tomasetti, Cristian
- Karchin, Rachel
- Vogelstein, Bert
- Wolfgang, Christopher L.
- Hruban, Ralph H.
- Lennon, Anne Marie
- ORCID
- Springer, Simeon
- Dal Molin, Marco
- Douville, Christopher
- Thoburn, Christopher J.
- Afsari, Bahman
- Li, Lu
- Cohen, Joshua D.
- Thompson, Elizabeth
- Klimstra, David S.
- Schattner, Mark A.
- Schmidt, C. Max
- Yip-Schneider, Michele
- Simpson, Rachel E.
- 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
- 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
- Venue
- Science Translational Medicine
- Published
- 2019-07-17
- Volume
- 11
- Issue
- 501
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- PMC
- PMC7859881
- OpenAlex
- W2962649098
- Cited by
- 217
- References
- 37
- Citations by year
- 2026: 24 · 2025: 34 · 2024: 26 · 2023: 42 · 2022: 18 · 2021: 39 · 2020: 26 · 2019: 7
- FWCI
- 9.39
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science Translational Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, cc-by)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
- Pure Amsterdam UMC (submittedVersion)
- IUScholarWorks (Indiana University) (submittedVersion)
- Funders
- National Institutes of Health (P50-CA062924, CA06973, CA176828, CA210170)
- Lustgarten Foundation
- The Sol Goldman Center for Pancreatic Cancer Research
- Susan Wojcicki and Dennis Troper
- The Virginia and D.K. Ludwig Fund
- The Michael Rolfe Pancreatic Cancer Research Foundation
- The John Templeton Foundation
- The Benjamin Baker Scholarship
- Topics
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Health Sciences › Medicine › Surgery › Pancreatitis Pathology and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- pancreatic cysts, machine learning classification, pancreatic cancer risk, cyst fluid biomarkers, clinical decision support
- MeSH terms
- Machine Learning, Aged, Algorithms, Female, Humans, Male, Middle Aged, Pancreatic Cyst
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