What is the Prevalence of Undiagnosed Nevoid Basal Cell Carcinoma Syndrome in Children With an Odontogenic Keratocyst?

PURPOSE: Odontogenic keratocysts (OKCs) can occur in isolation or as part of nevoid basal cell carcinoma syndrome (NBCCS). Patients with NBCCS are younger at OKC diagnosis than those with nonsyndromic OKC (NS-OKC). The purpose of this study was to determine the prevalence of undiagnosed NBCCS in children who present with an OKC and to assess differences in demographic and presenting features between children with NBCCS and those with NS-OKC. MATERIALS AND METHODS: This study is a retrospective case series of children with an OKC presenting to Boston Children's Hospital (Boston, MA) from 2007 through 2018. To be included, patients had to be no older than 18 years and have a newly diagnosed OKC. Patients were excluded if they had previous or recurrent OKC or NBCCS diagnosis. Records were reviewed for age at presentation, gender, number and location of OKCs, treatment, recurrence, family history, and clinical features consistent with NBCCS. Descriptive data were summarized. RESULTS: The sample included 50 patients (27 boys) diagnosed with an OKC at a mean age of 11.7 years (range, 2 to 18 yr); 36% (n = 18) with NBCCS and 64% (n = 32) with NS-OKC. NBCCS diagnosis was made in 8 of 18 patients (44%) because of a family history at presentation, and in 10 patients (56%) the diagnosis was made by genetic testing or documentation of diagnostic criteria. Eight of 18 patients (44%) with undiagnosed NBCCS presented with a single OKC. Patients with NBCCS were significantly younger at presentation (NBCCS, 9.3 yr; NS-OKC, 13.0 yr), had more cysts at time of diagnosis (NBCCS, 1.7; NS-OKC, 1.0; P < .05), had more maxillary cysts (NBCCS, 13; NS-OKC, 11), and had a higher recurrence rate (P < .05). CONCLUSION: Given the prevalence of undiagnosed NBCCS in children with OKC, clinicians should have a low threshold for referral for complete examination or genetic testing in children with even a single OKC.

An Automated Machine Learning Classifier for Early Childhood Caries.

Purpose: The purpose of the study was to develop and evaluate an automated machine learning algorithm (AutoML) for children's classification according to early childhood caries (ECC) status. Methods: Clinical, demographic, behavioral, and parent-reported oral health status information for a sample of 6,404 three- to five-year-old children (mean age equals 54 months) participating in an epidemiologic study of early childhood oral health in North Carolina was used. ECC prevalence (decayed, missing, and filled primary teeth surfaces [dmfs] score greater than zero, using an International Caries Detection and Assessment System score greater than or equal to three caries lesion detection threshold) was 54 percent. Ten sets of ECC predictors were evaluated for ECC classification accuracy (i.e., area under the ROC curve [AUC], sensitivity [Se], and positive predictive value [PPV]) using an AutoML deployment on Google Cloud, followed by internal validation and external replication. Results: A parsimonious model including two terms (i.e., children's age and parent-reported child oral health status: excellent/very good/good/fair/poor) had the highest AUC (0.74), Se (0.67), and PPV (0.64) scores and similar performance using an external National Health and Nutrition Examination Survey (NHANES) dataset (AUC equals 0.80, Se equals 0.73, PPV equals 0.49). Contrarily, a comprehensive model with 12 variables covering demographics (e.g., race/ethnicity, parental education), oral health behaviors, fluoride exposure, and dental home had worse performance (AUC equals 0.66, Se equals 0.54, PPV equals 0.61). Conclusions: Parsimonious automated machine learning early childhood caries classifiers, including single-item self-reports, can be valuable for ECC screening. The classifier can accommodate biological information that can help improve its performance in the future.

Cohort Profile: ZOE 2.0-A Community-Based Genetic Epidemiologic Study of Early Childhood Oral Health.

Early childhood caries (ECC) is an aggressive form of dental caries occurring in the first five years of life. Despite its prevalence and consequences, little progress has been made in its prevention and even less is known about individuals' susceptibility or genomic risk factors. The genome-wide association study (GWAS) of ECC ("ZOE 2.0") is a community-based, multi-ethnic, cross-sectional, genetic epidemiologic study seeking to address this knowledge gap. This paper describes the study's design, the cohort's demographic profile, data domains, and key oral health outcomes. Between 2016 and 2019, the study enrolled 8059 3-5-year-old children attending public preschools in North Carolina, United States. Participants resided in 86 of the state's 100 counties and racial/ethnic minorities predominated-for example, 48% (n = 3872) were African American, 22% white, and 20% (n = 1611) were Hispanic/Latino. Seventy-nine percent (n = 6404) of participants underwent clinical dental examinations yielding ECC outcome measures-ECC (defined at the established caries lesion threshold) prevalence was 54% and the mean number of decayed, missing, filled surfaces due to caries was eight. Nearly all (98%) examined children provided sufficient DNA from saliva for genotyping. The cohort's community-based nature and rich data offer excellent opportunities for addressing important clinical, epidemiologic, and biological questions in early childhood.