Development of a decision tree model for predicting the malignancy of localized gingival enlargements based on clinical characteristics
Issued Date
2024-09-27
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-85205275868
Pubmed ID
39333317
Journal Title
Scientific reports
Volume
14
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific reports Vol.14 No.1 (2024) , 22185
Suggested Citation
Sripodok P., Lapthanasupkul P., Arayapisit T., Kitkumthorn N., Srimaneekarn N., Neeranadpuree V., Amornwatcharapong W., Hempornwisarn S., Amornwikaikul S., Rungraungrayabkul D. Development of a decision tree model for predicting the malignancy of localized gingival enlargements based on clinical characteristics. Scientific reports Vol.14 No.1 (2024) , 22185. doi:10.1038/s41598-024-73013-7 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/101495
Title
Development of a decision tree model for predicting the malignancy of localized gingival enlargements based on clinical characteristics
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Abstract
The present study aimed to determine the prevalence of localized gingival enlargements (LGEs) and their clinical characteristics in a group of Thai patients, as well as utilize this information to develop a clinical diagnostic guide for predicting malignant LGEs. All LGE cases were retrospectively reviewed during a 20-year period. Clinical diagnoses, pathological diagnoses, patient demographic data, and clinical information were analyzed. The prevalence of LGEs was determined and categorized based on their nature, and concordance rates between clinical and pathological diagnoses among the groups were evaluated. Finally, a diagnostic guide was developed using clinical information through a decision tree model. Of 14,487 biopsied cases, 946 cases (6.53%) were identified as LGEs. The majority of LGEs were reactive lesions (72.62%), while a small subset was malignant tumors (7.51%). Diagnostic concordance rates were lower in malignant LGEs (54.93%) compared to non-malignant LGEs (80.69%). Size, consistency, color, duration, and patient age were identified as pivotal factors to formulate a clinical diagnostic guide for distinguishing between malignant and non-malignant LGEs. Using a decision tree model, we propose a novel diagnostic guide to assist clinicians in enhancing the accuracy of clinical differentiation between malignant and non-malignant LGEs.