Background/aims: Several prediction models for evaluating the prognosis of nonmetastatic resected pancreatic ductal adenocarcinoma (PDAC) have been developed, and their performances were reported to be superior to that of the 8th edition of the American Joint Committee on Cancer (AJCC) staging system. We developed a prediction model to evaluate the prognosis of resected PDAC and externally validated it with data from a nationwide Korean database.
Methods: Data from the Surveillance, Epidemiology and End Results (SEER) database were utilized for model development, and data from the Korea Tumor Registry System-Biliary Pancreas (KOTUS-BP) database were used for external validation. Potential candidate variables for model development were age, sex, histologic differentiation, tumor location, adjuvant chemotherapy, and the AJCC 8th staging system T and N stages. For external validation, the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) were evaluated.
Results: Between 2004 and 2016, data from 9,624 patients were utilized for model development, and data from 3,282 patients were used for external validation. In the multivariate Cox proportional hazard model, age, sex, tumor location, T and N stages, histologic differentiation, and adjuvant chemotherapy were independent prognostic factors for resected PDAC. After an exhaustive search and 10-fold cross validation, the best model was finally developed, which included all prognostic variables. The C-index, 1-year, 2-year, 3-year, and 5-year time-dependent AUCs were 0.628, 0.650, 0.665, 0.675, and 0.686, respectively.
Conclusions: The survival prediction model for resected PDAC could provide quantitative survival probabilities with reliable performance. External validation studies with other nationwide databases are needed to evaluate the performance of this model.
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http://dx.doi.org/10.5009/gnl20306 | DOI Listing |
Subst Abuse Treat Prev Policy
December 2024
Department of Preventive Medicine, Universidade Federal de São Paulo, Rua Botucatu, N 740 - Vila Clementino, São Paulo, SP, 04023-062, Brazil.
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January 2025
Department of Minimally Invasive Surgical and Medical Oncology, Fukushima Medical University, Koriyama, Japan.
Background/aim: Metastatic patterns are the most convenient and common prediction models for the prognosis of patients with stage IV colorectal cancer. However, current prediction models do not include the severity of metastases in organs and exclude certain types of metastatic patterns. The aim of this study was to develop a prediction model that included several metastatic organs as well as the severity of liver and lung metastases, based on the Japanese Classification of Colorectal, Appendiceal, and Anal Carcinoma: the 3 English Edition.
View Article and Find Full Text PDFJ Chromatogr A
December 2024
Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China. Electronic address:
Development of a next-generation chromatographic model, capable of simultaneously meeting academic demands for thermodynamic consistency and industrial requirements in everyday project work, has become a focal point of research. In this study, anti-Langmuirian to Langmuirian (AL-L) elution behavior was observed in cation-exchange chromatographic separation of charge variants of industrial Fc-fusion proteins. To characterize this behavior, the multi-protein Mollerup activity model was integrated into the steric mass action (SMA) model, resulting in a new model named the generalized ion-exchange (nGIEX) isotherm for multi-protein systems.
View Article and Find Full Text PDFComput Med Imaging Graph
November 2024
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Clinical Center, National Institutes of Health, United States of America. Electronic address:
Multiple intravenous contrast phases of CT scans are commonly used in clinical practice to facilitate disease diagnosis. However, contrast phase information is commonly missing or incorrect due to discrepancies in CT series descriptions and imaging practices. This work aims to develop a classification algorithm to automatically determine the contrast phase of a CT scan.
View Article and Find Full Text PDFGen Hosp Psychiatry
December 2024
National Sleep Foundation, Washington, DC 20036, USA.
Objective: The RU_SATED scale is increasingly used across the globe to measure sleep health. However, there is a lack of consensus around its psychometric and diagnostic performance. We conducted an empirical investigation into the psychometrics of the Chinese version of the RU_SATED (RU_SATED-C) scale, with a focus on structural validity and diagnostic performance.
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