Frontiers in bioscience (Elite edition) · 2025
Artificial Intelligence-Assisted Automated DNA Ploidy Analysis of Oral Lesions From Fanconi Anemia Patients With DNA Karyometry.
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Silva de Araujo Bruno Eduardo, de Santana Almeida Araujo Isabela Karoline, Velleuer Eunike, Dietrich Ralf, Hirner Leonie, Pomjanski Natalia, Schramm Martin
Аннотация
BACKGROUND: Fanconi anemia (FA) is an inherited genetic instability syndrome that increases the risk of developing head and neck squamous cell carcinoma, particularly in the oral cavity. These epithelial cancers often arise from visible oral and potentially malignant disorders (OPMD). Research has shown that oral brush biopsies combined with cytology, such as manual DNA cytometry, can facilitate the early detection of OPMDs that require treatment. Thus, this study aimed to evaluate the diagnostic accuracy of a DNA karyometry (DNA-KM) system in the brush biopsy-based diagnostic workup for OPMDs with FA.
METHODS: Feulgen-stained liquid-based oral smears were included from 327 independent OPMD cases, which had available cytological diagnoses and clinicopathological reference standards. These samples were automatically analyzed using a DNA-KM system (MotiCyte-auto), which employs digital nuclear classifiers based on expert classification of nuclear images and machine learning algorithms.
RESULTS: The detection of (suspected) DNA stemline aneuploidy or single-cell aneuploidy with DNA-KM demonstrated a sensitivity of 69% and a specificity of 96%. In our analysis, when DNA-KM was combined with cytology, we observed a sensitivity of 75% and a specificity of 96%. Meanwhile, additional research using the variation coefficient of a "broad-based" peritetraploid stemline (BPS) as an alternative algorithm further increased the sensitivity to 84%. However, employing this algorithm slightly decreased specificity to 92% at a cut-off of 5.83.
CONCLUSIONS: Artificial intelligence (AI)-assisted DNA-KM, with automated slide-scanning and digital classification of nuclei, can serve as a valuable additional method in the brush biopsy-based cytological diagnosis of OPMD in FA. This approach can help identify lesions that require clinical intervention.
METHODS: Feulgen-stained liquid-based oral smears were included from 327 independent OPMD cases, which had available cytological diagnoses and clinicopathological reference standards. These samples were automatically analyzed using a DNA-KM system (MotiCyte-auto), which employs digital nuclear classifiers based on expert classification of nuclear images and machine learning algorithms.
RESULTS: The detection of (suspected) DNA stemline aneuploidy or single-cell aneuploidy with DNA-KM demonstrated a sensitivity of 69% and a specificity of 96%. In our analysis, when DNA-KM was combined with cytology, we observed a sensitivity of 75% and a specificity of 96%. Meanwhile, additional research using the variation coefficient of a "broad-based" peritetraploid stemline (BPS) as an alternative algorithm further increased the sensitivity to 84%. However, employing this algorithm slightly decreased specificity to 92% at a cut-off of 5.83.
CONCLUSIONS: Artificial intelligence (AI)-assisted DNA-KM, with automated slide-scanning and digital classification of nuclei, can serve as a valuable additional method in the brush biopsy-based cytological diagnosis of OPMD in FA. This approach can help identify lesions that require clinical intervention.