FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3037201900> ?p ?o ?g. }

• W3037201900 abstract "Purpose: Our paper contributes to the burgeoning field of surgical data science. Specifically, multimodal integration of relevant patient data is used to determine who should undergo a complex pancreatic resection. Intraductal papillary mucinous neoplasms (IPMNs) represent cystic precursor lesions of pancreatic cancer with varying risk for malignancy. We combine previously defined individual models of radiomic analysis of diagnostic computed tomography (CT) with protein markers extracted from the cyst fluid to create a unified prediction model to identify high-risk IPMNs. Patients with high-risk IPMN would be sent for resection, whereas patients with low-risk cystic lesions would be spared an invasive procedure. Approach: Retrospective analysis of prospectively acquired cyst fluid and CT scans was undertaken for this study. A predictive model combining clinical features with a cyst fluid inflammatory marker (CFIM) was applied to patient data. Quantitative imaging (QI) features describing radiomic patterns predictive of risk were extracted from scans. The CFIM model and QI model were combined into a single predictive model. An additional model was created with tumor-associated neutrophils (TANs) assessed by a pathologist at the time of resection. Results: Thirty-three patients were analyzed (7 high risk and 26 low risk). The CFIM model yielded an area under the curve (AUC) of 0.74. Adding the QI model improved performance with an AUC of 0.88. Combining the CFIM, QI, and TAN models further increased performance to an AUC of 0.98. Conclusions: Quantitative analysis of routinely acquired CT scans combined with CFIMs provides accurate prediction of risk of pancreatic cancer progression. Although a larger cohort is needed for validation, this model represents a promising tool for preoperative assessment of IPMN." @default.
• W3037201900 created "2020-07-02" @default.
• W3037201900 creator A5000336389 @default.
• W3037201900 creator A5009465403 @default.
• W3037201900 creator A5012791304 @default.
• W3037201900 creator A5028786576 @default.
• W3037201900 creator A5034568574 @default.
• W3037201900 creator A5034991877 @default.
• W3037201900 creator A5036762729 @default.
• W3037201900 creator A5040507192 @default.
• W3037201900 creator A5042440032 @default.
• W3037201900 creator A5046521042 @default.
• W3037201900 creator A5046852633 @default.
• W3037201900 creator A5051277222 @default.
• W3037201900 creator A5060380153 @default.
• W3037201900 creator A5061558169 @default.
• W3037201900 creator A5068108309 @default.
• W3037201900 creator A5070901426 @default.
• W3037201900 creator A5074457110 @default.
• W3037201900 creator A5075130652 @default.
• W3037201900 creator A5079031946 @default.
• W3037201900 creator A5085620004 @default.
• W3037201900 date "2020-06-25" @default.
• W3037201900 modified "2023-10-13" @default.
• W3037201900 title "Multimodal radiomics and cyst fluid inflammatory markers model to predict preoperative risk in intraductal papillary mucinous neoplasms" @default.
• W3037201900 cites W1963764467 @default.
• W3037201900 cites W1969977977 @default.
• W3037201900 cites W1970703737 @default.
• W3037201900 cites W1978866729 @default.
• W3037201900 cites W1982159979 @default.
• W3037201900 cites W1989466606 @default.
• W3037201900 cites W1993636613 @default.
• W3037201900 cites W2016691893 @default.
• W3037201900 cites W2024213140 @default.
• W3037201900 cites W2039051707 @default.
• W3037201900 cites W2045962387 @default.
• W3037201900 cites W2066510471 @default.
• W3037201900 cites W2079998750 @default.
• W3037201900 cites W2083670334 @default.
• W3037201900 cites W2100014692 @default.
• W3037201900 cites W2112083109 @default.
• W3037201900 cites W2129804864 @default.
• W3037201900 cites W2131537357 @default.
• W3037201900 cites W2134145309 @default.
• W3037201900 cites W2138857412 @default.
• W3037201900 cites W2148560417 @default.
• W3037201900 cites W2157716241 @default.
• W3037201900 cites W2280664730 @default.
• W3037201900 cites W2431396139 @default.
• W3037201900 cites W2510667250 @default.
• W3037201900 cites W2515797801 @default.
• W3037201900 cites W2522795941 @default.
• W3037201900 cites W2558318442 @default.
• W3037201900 cites W2585959450 @default.
• W3037201900 cites W2736069212 @default.
• W3037201900 cites W2736291532 @default.
• W3037201900 cites W2887163695 @default.
• W3037201900 cites W2892172066 @default.
• W3037201900 cites W2966562613 @default.
• W3037201900 cites W2971414690 @default.
• W3037201900 cites W2993163493 @default.
• W3037201900 cites W4225347221 @default.
• W3037201900 doi "https://doi.org/10.1117/1.jmi.7.3.031507" @default.
• W3037201900 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7315109" @default.
• W3037201900 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32613028" @default.
• W3037201900 hasPublicationYear "2020" @default.
• W3037201900 type Work @default.
• W3037201900 sameAs 3037201900 @default.
• W3037201900 citedByCount "6" @default.
• W3037201900 countsByYear W30372019002020 @default.
• W3037201900 countsByYear W30372019002021 @default.
• W3037201900 countsByYear W30372019002022 @default.
• W3037201900 crossrefType "journal-article" @default.
• W3037201900 hasAuthorship W3037201900A5000336389 @default.
• W3037201900 hasAuthorship W3037201900A5009465403 @default.
• W3037201900 hasAuthorship W3037201900A5012791304 @default.
• W3037201900 hasAuthorship W3037201900A5028786576 @default.
• W3037201900 hasAuthorship W3037201900A5034568574 @default.
• W3037201900 hasAuthorship W3037201900A5034991877 @default.
• W3037201900 hasAuthorship W3037201900A5036762729 @default.
• W3037201900 hasAuthorship W3037201900A5040507192 @default.
• W3037201900 hasAuthorship W3037201900A5042440032 @default.
• W3037201900 hasAuthorship W3037201900A5046521042 @default.
• W3037201900 hasAuthorship W3037201900A5046852633 @default.
• W3037201900 hasAuthorship W3037201900A5051277222 @default.
• W3037201900 hasAuthorship W3037201900A5060380153 @default.
• W3037201900 hasAuthorship W3037201900A5061558169 @default.
• W3037201900 hasAuthorship W3037201900A5068108309 @default.
• W3037201900 hasAuthorship W3037201900A5070901426 @default.
• W3037201900 hasAuthorship W3037201900A5074457110 @default.
• W3037201900 hasAuthorship W3037201900A5075130652 @default.
• W3037201900 hasAuthorship W3037201900A5079031946 @default.
• W3037201900 hasAuthorship W3037201900A5085620004 @default.
• W3037201900 hasBestOaLocation W30372019002 @default.
• W3037201900 hasConcept C121608353 @default.
• W3037201900 hasConcept C126322002 @default.
• W3037201900 hasConcept C126838900 @default.
• W3037201900 hasConcept C142724271 @default.
• W3037201900 hasConcept C2778559731 @default.
• W3037201900 hasConcept C2779399171 @default.

• next