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Article: The ‘Newcastle Nomogram’ – Statistical Modelling Predicts Malignant Transformation in Potentially Malignant Disorders

TitleThe ‘Newcastle Nomogram’ – Statistical Modelling Predicts Malignant Transformation in Potentially Malignant Disorders
Authors
Keywordsmalignant transformation
nomogram
potentially malignant disorders
prediction
Issue Date2019
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-0714
Citation
Journal of Oral Pathology & Medicine, 2019, v. 48 n. 8, p. 662-668 How to Cite?
AbstractBackground: Nomograms are graphical calculating devices used to predict risk of malignant transformation (MT) or response to treatment during cancer management. To date, a nomogram has not been used to predict clinical outcome during oral potentially malignant disorder (PMD) treatment. The aim of this study was to create a nomogram for use by clinicians to predict the probability of MT, thereby facilitating accurate assessment of risk and objective decision‐making during individual patient management. Methods: Clinico‐pathological data from a previously treated cohort of 590 newly presenting PMD patients were reviewed and clinical outcomes categorized as disease free, persistent PMD or MT. Multiple logistic regression was used to predict the probability of MT in the cohort using age, gender, lesion type, site and incision biopsy histopathological diagnoses. Internal validation and calibration of the model was performed using the bootstrap method (n = 1000), and bias‐corrected indices of model performance were computed. Results: Potentially malignant disorders were predominantly leukoplakias (79%), presenting most frequently at floor of mouth and lateral tongue sites (51%); 99 patients (17%) developed oral squamous cell carcinoma during the study period. The nomogram performed well when MT predictions were compared with patient outcome data, demonstrating good bias‐corrected discrimination and calibration (Dxy = 0.58; C = 0.790), with a sensitivity of 87% and specificity 63%, and a positive predictive value of 32% and negative predictive value 96%. Conclusion: The “Newcastle Nomogram” has been developed to predict the probability of MT in PMD, based on an internally validated statistical model. Based upon readily available and patient‐specific clinico‐pathological data, it provides clinicians with a pragmatic diagrammatic aid for clinical decision‐making during diagnosis and management of PMD.
Persistent Identifierhttp://hdl.handle.net/10722/271213
ISSN
2023 Impact Factor: 2.7
2023 SCImago Journal Rankings: 0.716
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorGoodson, ML-
dc.contributor.authorSmith, DR-
dc.contributor.authorThomson, PJ-
dc.date.accessioned2019-06-24T01:05:33Z-
dc.date.available2019-06-24T01:05:33Z-
dc.date.issued2019-
dc.identifier.citationJournal of Oral Pathology & Medicine, 2019, v. 48 n. 8, p. 662-668-
dc.identifier.issn0904-2512-
dc.identifier.urihttp://hdl.handle.net/10722/271213-
dc.description.abstractBackground: Nomograms are graphical calculating devices used to predict risk of malignant transformation (MT) or response to treatment during cancer management. To date, a nomogram has not been used to predict clinical outcome during oral potentially malignant disorder (PMD) treatment. The aim of this study was to create a nomogram for use by clinicians to predict the probability of MT, thereby facilitating accurate assessment of risk and objective decision‐making during individual patient management. Methods: Clinico‐pathological data from a previously treated cohort of 590 newly presenting PMD patients were reviewed and clinical outcomes categorized as disease free, persistent PMD or MT. Multiple logistic regression was used to predict the probability of MT in the cohort using age, gender, lesion type, site and incision biopsy histopathological diagnoses. Internal validation and calibration of the model was performed using the bootstrap method (n = 1000), and bias‐corrected indices of model performance were computed. Results: Potentially malignant disorders were predominantly leukoplakias (79%), presenting most frequently at floor of mouth and lateral tongue sites (51%); 99 patients (17%) developed oral squamous cell carcinoma during the study period. The nomogram performed well when MT predictions were compared with patient outcome data, demonstrating good bias‐corrected discrimination and calibration (Dxy = 0.58; C = 0.790), with a sensitivity of 87% and specificity 63%, and a positive predictive value of 32% and negative predictive value 96%. Conclusion: The “Newcastle Nomogram” has been developed to predict the probability of MT in PMD, based on an internally validated statistical model. Based upon readily available and patient‐specific clinico‐pathological data, it provides clinicians with a pragmatic diagrammatic aid for clinical decision‐making during diagnosis and management of PMD.-
dc.languageeng-
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-0714-
dc.relation.ispartofJournal of Oral Pathology & Medicine-
dc.rightsPostprint This is the peer reviewed version of the following article: [Journal of Oral Pathology & Medicine, 2019, v. 48 n. 8, p. 662-668], which has been published in final form at [http://dx.doi.org/10.1111/jop.12881]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.-
dc.subjectmalignant transformation-
dc.subjectnomogram-
dc.subjectpotentially malignant disorders-
dc.subjectprediction-
dc.titleThe ‘Newcastle Nomogram’ – Statistical Modelling Predicts Malignant Transformation in Potentially Malignant Disorders-
dc.typeArticle-
dc.identifier.emailThomson, PJ: thomsonp@hku.hk-
dc.identifier.authorityThomson, PJ=rp02327-
dc.description.naturepostprint-
dc.identifier.doi10.1111/jop.12881-
dc.identifier.pmid31125457-
dc.identifier.scopuseid_2-s2.0-85067411499-
dc.identifier.hkuros298054-
dc.identifier.volume48-
dc.identifier.issue8-
dc.identifier.spage662-
dc.identifier.epage668-
dc.identifier.isiWOS:000486086300003-
dc.publisher.placeDenmark-
dc.identifier.issnl0904-2512-

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