FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3048617165> ?p ?o ?g. }

• W3048617165 endingPage "9788" @default.
• W3048617165 startingPage "9779" @default.
• W3048617165 abstract "Rationale: Clinically relevant postoperative pancreatic fistula (CR-POPF) is among the most formidable complications after pancreatoduodenectomy (PD), heightening morbidity/mortality rates. Fistula Risk Score (FRS) is a well-developed predictor, but it is an intraoperative predictor and quantifies >50% patients as intermediate risk. Therefore, an accurate and easy-to-use preoperative index is desired. Herein, we test the hypothesis that quantitative analysis of contrast-enhanced computed tomography (CE-CT) with deep learning could predict CR-POPFs. Methods: A group of 513 patients underwent pancreatico-enteric anastomosis after PD at three institutions between 2006 and 2019 was retrospectively collected, and formed a training (70%) and a validation dataset (30%) randomly. A convolutional neural network was trained and generated a deep-learning score (DLS) to identify the patients with higher risk of CR-POPF preoperatively using CE-CT images, which was further externally tested in a prospective cohort collected from August 2018 to June 2019 at the fourth institution. The biological underpinnings of DLS were assessed using histomorphological data by multivariate linear regression analysis. Results: CR-POPFs developed in 95 patients (16.3%) in total. Compared to FRS, the DLS offered significantly greater predictability in training (AUC:0.85 [95% CI, 0.80-0.90] vs. 0.78 [95% CI, 0.72-0.84]; P = 0.03), validation (0.81 [95% CI, 0.72-0.89] vs. 0.76 [95% CI, 0.66-0.84], P = 0.05) and test (0.89 [95% CI, 0.79-0.96] vs. 0.73 [95% CI, 0.61-0.83], P < 0.001) cohorts. Especially in the challenging patients of intermediate risk (FRS: 3-6), the DLS showed significantly higher accuracy (training: 79.9% vs. 61.5% [P = 0.005]; validation: 70.3% vs. 56.3% [P = 0.04]; test: 92.1% vs. 65.8% [P = 0.013]). Additionally, DLS was independently associated with pancreatic fibrosis (coefficients: -0.167), main pancreatic duct (coefficients: -0.445) and remnant volume (coefficients: 0.138) in multivariate linear regression analysis (r2 = 0.512, P < 0.001). The user satisfaction score in the test cohort was 4 out of 5. Conclusions: Preoperative CT based deep-learning model provides a promising novel method for predicting CR-POPF occurrences after PD, especially at intermediate FRS risk level. This has a potential to be integrated into radiologic reporting system or incorporated into surgical planning software to accommodate the preferences of surgeons to optimize preoperative strategies, intraoperative decision-making, and even postoperative care." @default.
• W3048617165 created "2020-08-18" @default.
• W3048617165 creator A5006213688 @default.
• W3048617165 creator A5009043671 @default.
• W3048617165 creator A5015676600 @default.
• W3048617165 creator A5025298001 @default.
• W3048617165 creator A5026217652 @default.
• W3048617165 creator A5030403651 @default.
• W3048617165 creator A5030982858 @default.
• W3048617165 creator A5050613338 @default.
• W3048617165 creator A5072033890 @default.
• W3048617165 creator A5081917774 @default.
• W3048617165 creator A5082016315 @default.
• W3048617165 creator A5087758806 @default.
• W3048617165 date "2020-01-01" @default.
• W3048617165 modified "2023-10-18" @default.
• W3048617165 title "Prediction of clinically relevant Pancreatico-enteric Anastomotic Fistulas after Pancreatoduodenectomy using deep learning of Preoperative Computed Tomography" @default.
• W3048617165 cites W1965692622 @default.
• W3048617165 cites W1967615584 @default.
• W3048617165 cites W1970503091 @default.
• W3048617165 cites W1974515921 @default.
• W3048617165 cites W1989188741 @default.
• W3048617165 cites W1993062205 @default.
• W3048617165 cites W2028636108 @default.
• W3048617165 cites W2032763241 @default.
• W3048617165 cites W2033918728 @default.
• W3048617165 cites W2037912727 @default.
• W3048617165 cites W2059798467 @default.
• W3048617165 cites W2071401959 @default.
• W3048617165 cites W2076516312 @default.
• W3048617165 cites W2076864099 @default.
• W3048617165 cites W2080663125 @default.
• W3048617165 cites W2097105625 @default.
• W3048617165 cites W2098634324 @default.
• W3048617165 cites W2117853299 @default.
• W3048617165 cites W2127017048 @default.
• W3048617165 cites W2161793460 @default.
• W3048617165 cites W2162567352 @default.
• W3048617165 cites W2231223098 @default.
• W3048617165 cites W2328176404 @default.
• W3048617165 cites W2329341561 @default.
• W3048617165 cites W2395816473 @default.
• W3048617165 cites W2565595087 @default.
• W3048617165 cites W2599076725 @default.
• W3048617165 cites W2743061229 @default.
• W3048617165 cites W2754388541 @default.
• W3048617165 cites W2791094629 @default.
• W3048617165 cites W2801370787 @default.
• W3048617165 cites W2804407989 @default.
• W3048617165 cites W2804874444 @default.
• W3048617165 cites W2896760986 @default.
• W3048617165 cites W2903443299 @default.
• W3048617165 cites W2922268148 @default.
• W3048617165 cites W2944789887 @default.
• W3048617165 cites W2969293590 @default.
• W3048617165 cites W2982692821 @default.
• W3048617165 cites W2989361460 @default.
• W3048617165 cites W2992369104 @default.
• W3048617165 cites W2992477198 @default.
• W3048617165 doi "https://doi.org/10.7150/thno.49671" @default.
• W3048617165 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7449906" @default.
• W3048617165 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32863959" @default.
• W3048617165 hasPublicationYear "2020" @default.
• W3048617165 type Work @default.
• W3048617165 sameAs 3048617165 @default.
• W3048617165 citedByCount "14" @default.
• W3048617165 countsByYear W30486171652020 @default.
• W3048617165 countsByYear W30486171652021 @default.
• W3048617165 countsByYear W30486171652022 @default.
• W3048617165 countsByYear W30486171652023 @default.
• W3048617165 crossrefType "journal-article" @default.
• W3048617165 hasAuthorship W3048617165A5006213688 @default.
• W3048617165 hasAuthorship W3048617165A5009043671 @default.
• W3048617165 hasAuthorship W3048617165A5015676600 @default.
• W3048617165 hasAuthorship W3048617165A5025298001 @default.
• W3048617165 hasAuthorship W3048617165A5026217652 @default.
• W3048617165 hasAuthorship W3048617165A5030403651 @default.
• W3048617165 hasAuthorship W3048617165A5030982858 @default.
• W3048617165 hasAuthorship W3048617165A5050613338 @default.
• W3048617165 hasAuthorship W3048617165A5072033890 @default.
• W3048617165 hasAuthorship W3048617165A5081917774 @default.
• W3048617165 hasAuthorship W3048617165A5082016315 @default.
• W3048617165 hasAuthorship W3048617165A5087758806 @default.
• W3048617165 hasBestOaLocation W30486171651 @default.
• W3048617165 hasConcept C126838900 @default.
• W3048617165 hasConcept C141071460 @default.
• W3048617165 hasConcept C159110652 @default.
• W3048617165 hasConcept C2778975757 @default.
• W3048617165 hasConcept C544519230 @default.
• W3048617165 hasConcept C71924100 @default.
• W3048617165 hasConcept C8443397 @default.
• W3048617165 hasConceptScore W3048617165C126838900 @default.
• W3048617165 hasConceptScore W3048617165C141071460 @default.
• W3048617165 hasConceptScore W3048617165C159110652 @default.
• W3048617165 hasConceptScore W3048617165C2778975757 @default.
• W3048617165 hasConceptScore W3048617165C544519230 @default.
• W3048617165 hasConceptScore W3048617165C71924100 @default.
• W3048617165 hasConceptScore W3048617165C8443397 @default.

• next