Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer

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Abstract

Background

Pancreatic cancer is a fatal tumor, and the status of perineural invasion (PNI) of pancreatic cancer was positively related to poor prognosis including overall survival and recurrence-free survival. This study aims to develop and validate a predictive model based on serum biomarkers to accurately predict the perineural invasion.

Materials and methods

The patients from No.924 Hospital of PLA Joint Logistic Support Force were included. The predictive model was developed in the training cohort using logistic regression analysis, and then tested in the validation cohort. The area under curve (AUC), calibration curves and decision curve analysis were used to validate the predictive accuracy and clinical benefits of nomogram.

Results

A nomogram was developed using preoperative total bilirubin, preoperative blood glucose, preoperative CA19-9. It achieved good AUC values of 0.753 and 0.737 in predicting PNI in training and validation cohorts, respectively. Calibration curves showed nomogram had good uniformity of the practical probability of PNI. Decision curve analyses revealed that the nomogram provided higher diagnostic accuracy and superior net benefit compared to single indicators.

Conclusion

The present study constructed and validate a novel nomogram predicted the PNI of resectable PHAC patients with high stability and accuracy. Besides, it could better screen high-risk probability of PNI in these patients, and optimize treatment decision-making.

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Most cited references 35

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pROC: an open-source package for R and S+ to analyze and compare ROC curves

Xavier Robin, Natacha Turck, Alexandre Hainard … (2013)

Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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Nomograms in oncology: more than meets the eye.

Vinod P Balachandran, Mithat Gonen, J Smith … (2015)

Nomograms are widely used as prognostic devices in oncology and medicine. With the ability to generate an individual probability of a clinical event by integrating diverse prognostic and determinant variables, nomograms meet our desire for biologically and clinically integrated models and fulfill our drive towards personalised medicine. Rapid computation through user-friendly digital interfaces, together with increased accuracy, and more easily understood prognoses compared with conventional staging, allow for seamless incorporation of nomogram-derived prognosis to aid clinical decision making. This has led to the appearance of many nomograms on the internet and in medical journals, and an increase in nomogram use by patients and physicians alike. However, the statistical foundations of nomogram construction, their precise interpretation, and evidence supporting their use are generally misunderstood. This issue is leading to an under-appreciation of the inherent uncertainties regarding nomogram use. We provide a systematic, practical approach to evaluating and comprehending nomogram-derived prognoses, with particular emphasis on clarifying common misconceptions and highlighting limitations.

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How to build and interpret a nomogram for cancer prognosis.

Alexia Iasonos, Deborah Schrag, Ganesh Raj … (2008)

Nomograms are widely used for cancer prognosis, primarily because of their ability to reduce statistical predictive models into a single numerical estimate of the probability of an event, such as death or recurrence, that is tailored to the profile of an individual patient. User-friendly graphical interfaces for generating these estimates facilitate the use of nomograms during clinical encounters to inform clinical decision making. However, the statistical underpinnings of these models require careful scrutiny, and the degree of uncertainty surrounding the point estimates requires attention. This guide provides a nonstatistical audience with a methodological approach for building, interpreting, and using nomograms to estimate cancer prognosis or other health outcomes.

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Author and article information

Contributors

Gao Huang: huanggao1981@163.com

Journal

Journal ID (nlm-ta): BMC Gastroenterol

Journal ID (iso-abbrev): BMC Gastroenterol

Title: BMC Gastroenterology

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-230X

Publication date (Electronic): 18 September 2023

Publication date PMC-release: 18 September 2023

Publication date Collection: 2023

Volume: 23

Electronic Location Identifier: 315

Affiliations

Department of General Surgery, No.924 Hospital of PLA Joint Logistic Support Force, Guilin, 541002 China

Article

Publisher ID: 2819

DOI: 10.1186/s12876-023-02819-y

PMC ID: 10508025

PubMed ID: 37723476

SO-VID: 9bb05701-4eaa-4330-9afc-a55656ad07af

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 15 February 2023

Date accepted : 15 May 2023

Custom metadata

ScienceOpen disciplines: Gastroenterology & Hepatology

Keywords: pancreatic head adenocarcinoma,surgery,perineural invasion,serum indicator,nomogram

Data availability:

ScienceOpen disciplines: Gastroenterology & Hepatology

Keywords: pancreatic head adenocarcinoma, surgery, perineural invasion, serum indicator, nomogram

Nomogram predicts risk of perineural invasion based on serum biomarkers for pancreatic cancer

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Abstract

Background

Materials and methods

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Conclusion

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Karger: Gastroenterology

Most cited references 35

pROC: an open-source package for R and S+ to analyze and compare ROC curves

Nomograms in oncology: more than meets the eye.

How to build and interpret a nomogram for cancer prognosis.

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