Oral leiomyomatous hamartoma: presentation of 3 cases and review of the literature.

Oral leiomyomatous hamartoma (OLH) is a rare lesion, with only 40 cases reported in the literature. It typically presents early in life as a nodule on the anterior maxillary alveolar tissues or the tongue. Its growth potential is limited, with few cases reaching dimensions >2.0 cm, and its microscopic composition includes an intact surface mucosa with an underlying fibrovascular stroma possessing an unencapsulated proliferation of smooth muscle fascicles. Excision is considered the definitive treatment. Here we describe the clinical, microscopic, histochemical, and immunohistochemical features and management of 3 cases of OLH and review the literature. The findings we present here can assist in performing differential diagnosis, particularly in discriminating between OLH and similar yet non-hamartomatous processes and in selecting appropriate management.

Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions.

BACKGROUND: The effective detection and monitoring of potentially malignant oral lesions (PMOL) are critical to identifying early-stage cancer and improving outcomes. In the current study, the authors described cytopathology tools, including machine learning algorithms, clinical algorithms, and test reports developed to assist pathologists and clinicians with PMOL evaluation. METHODS: Data were acquired from a multisite clinical validation study of 999 subjects with PMOLs and oral squamous cell carcinoma (OSCC) using a cytology-on-a-chip approach. A machine learning model was trained to recognize and quantify the distributions of 4 cell phenotypes. A least absolute shrinkage and selection operator (lasso) logistic regression model was trained to distinguish PMOLs and cancer across a spectrum of histopathologic diagnoses ranging from benign, to increasing grades of oral epithelial dysplasia (OED), to OSCC using demographics, lesion characteristics, and cell phenotypes. Cytopathology software was developed to assist pathologists in reviewing brush cytology test results, including high-content cell analyses, data visualization tools, and results reporting. RESULTS: Cell phenotypes were determined accurately through an automated cytological assay and machine learning approach (99.3% accuracy). Significant differences in cell phenotype distributions across diagnostic categories were found in 3 phenotypes (type 1 ["mature squamous"], type 2 ["small round"], and type 3 ["leukocytes"]). The clinical algorithms resulted in acceptable performance characteristics (area under the curve of 0.81 for benign vs mild dysplasia and 0.95 for benign vs malignancy). CONCLUSIONS: These new cytopathology tools represent a practical solution for rapid PMOL assessment, with the potential to facilitate screening and longitudinal monitoring in primary, secondary, and tertiary clinical care settings.