Oral Surgery Oral Medicine Oral Pathology Oral Radiology最新文献

Objective: This study aimed to identify the biochemical signatures that distinguish pleomorphic adenoma (PA) and mucoepidermoid carcinoma (MEC) from normal parotid tissue (NP), while also exploring molecular differences relevant to benign-malignant tumor differentiation using Attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy combined with machine-learning (ML) approaches.

Study design: A total of 48 formalin-fixed, paraffin-embedded (FFPE) salivary gland tissue sections (5 µm thickness) were analyzed, including 8 NP, 24 PA, and 16 MEC specimens. ATR-FTIR spectra were acquired directly from routine histopathological slides over the 4000-400 per cm range without chemical deparaffinization. The spectral data were then processed using multivariate analysis (PCA and OPLS-DA) to reveal biochemical variations among the tissue groups. Supervised classification was then performed using Support Vector Machine (SVM) and k-Nearest Neighbours (kNN) algorithms with 6-fold cross-validation to evaluate classification performance.

Results: Distinct spectral differences were detected among NP, PA, and MEC tissues, particularly in protein (amide I-II), lipid (CH-stretching), and nucleic acid (phosphate) regions. OPLS-DA demonstrated clear class discrimination (3D: J₁ = 4.1250, J₂ = 2.2121). The SVM classifier achieved the highest diagnostic accuracy (F1-scores: NP = 1.00, PA = 0.96, MEC = 0.94; overall accuracy = 95.83%), outperforming the kNN model (accuracy = 93.75%).

Conclusions: Combining ATR-FTIR spectroscopy with ML-based multivariate analysis provides a highly accurate method of differentiating NP, PA and MEC salivary gland tissues based on their biochemical fingerprints. This integrative approach establishes the basis for a rapid, non-invasive, label-free diagnostic tool that is compatible with clinical practice and applicable to routine histopathological samples.

Objective: This study aims to evaluate the clinical outcomes of a graftless sinus lift technique performed simultaneously with dental implant placement in the posterior maxilla, using a prospective cohort design. The primary objectives are to assess implant survival, bone gain, and complication rates over a minimum 60-month follow-up period.

Study design: A prospective cohort study was conducted over an 8-year period. Patients were divided into 2 groups: the test group, consisting of 74 patients who underwent sinus membrane elevation without the use of any grafting material (graftless), and the control group, consisting of 15 patients who received sinus augmentation with bone grafts.

Results: Our results clearly showed a high implant success rate of 97.76% in the graftless group, outperforming the 86.6% success rate observed in the grafted control group. Further analysis revealed that residual bone height was a statistically significant factor influencing implant success across both groups.

Conclusions: The findings of this study demonstrate that graftless sinus floor elevation performed simultaneously with dental implant placement in the posterior maxilla is a safe and reliable procedure. This study benefits from several strengths, including its prospective design, relatively large patient cohort, and long-term follow-up period of over 5 years. (Oral Surg Oral Med Oral Pathol Oral Radiol 2026;XXX:XXX-XXX).

Objective: We aimed to evaluate the clinical outcomes of a minimally invasive transcrestal sinus floor elevation (MI-TSFE) technique in patients with ≤4 mm residual bone height.

Materials and methods: A retrospective cohort study included 55 MI-TSFE procedures performed between 2016 and 2025. Inclusion criteria were residual bone height ≤4 mm, bone width ≥4 mm, and absence of sinus pathosis. Outcomes assessed were intraoperative and postoperative surgical height, lateromedial length, anteroposterior width, implant survival, and complications. Categorical variables were compared using chi-square tests, quantitative variables with Student's t or Wilcoxon signed-rank test, and comparisons among three or more groups were performed using ANOVA. Correlations between quantitative variables were evaluated with Pearson or Spearman coefficients.

Results: Mean residual bone height was 2.5 mm, increasing to 12.2 mm intraoperatively and 10.1 mm at late follow-up (mean 17 months), corresponding to a mean gain of 7.5 mm (P < .05). Multiple MI-TSFE procedures and simultaneous implant placement (83.6% of cases) were associated with improved postoperative height stability. Complications occurred in 14.5% of cases, mainly partial or total graft loss. Implant survival was 91.3%.

Conclusion: MI-TSFE showed favorable performance, achieving bone gains comparable to lateral approaches. This technique may broaden the indications for transcrestal sinus floor elevation in severely atrophic maxillae.

Objectives: To describe the clinicopathological and immunophenotypic features of extranodal idiopathic lymphoid hyperplasia/follicular lymphoid hyperplasia (ILH/FLH) of the oral cavity.

Study design: A case series conducted across nine Brazilian oral and maxillofacial pathology centers. Cases were morphologically evaluated using hematoxylin and eosin staining and a panel of immunohistochemical markers. Data were analyzed descriptively.

Results: Of the 97 cases initially screened, 15 fulfilled the diagnostic criteria. Most patients were female (73.3%), aged between 32 and 88 years. The palate was the most frequently affected site (40%). Lesions had a mean size of 15.7 mm and a mean duration of 12.9 months, typically presenting as painless nodules (73.3%) with a smooth surface (53.3%). Histopathological examination revealed dense lymphoid infiltrates with follicular architecture and well-developed germinal centers. Immunohistochemically, CD20 was positive in germinal centers, CD3 in interfollicular zones, Bcl-2 was negative in germinal centers, and Ki-67 exhibited focal positivity.

Conclusion: extranodal ILH/FLH of the oral cavity is a benign, indolent lymphoproliferative disorder that predominantly affects women beyond the third decade of life. CD20 and Bcl-2 are key markers for accurate differential diagnosis and microscopic confirmation.

Objectives: This pilot study aims to analyze how the site and size of minimum cross-sectional area (CSAmin) affect the airflow characteristics in the upper airway models.

Methods: Eleven upper airway models were created, including three models with CSAmin at different sites and eight models with varying CSAmin sizes at the same site, then computational fluid dynamics (CFD) simulations were conducted.

Results: The CFD results indicated that the maximum airflow velocity, minimum pressure, and maximum pressure difference consistently occurred at the CSAmin. The smaller the CSAmin, the greater the airflow velocity and pressure drop, the lower the minimum static pressure, and the higher the overall airway resistance.

Conclusion: The site and size of the CSAmin are critical to the airflow characteristics in the upper airway models. The location of the CSAmin may be the most likely site for airway collapse. Patients with a smaller CSAmin (<79mm2) may have a higher risk of obstructive sleep apnea.

Objectives: This study aimed to integrate soft tissue calcifications and ossifications (STCO) detected on cone beam computed tomography (CBCT) into an artificial intelligence (AI) system and assess its diagnostic accuracy in both single-class and multi-class classification.

Study design: CBCT images from 287 patients were retrospectively reviewed. STCOs were identified in axial, coronal, and sagittal planes, with segmentation performed in the axial plane. The AI model was trained to detect arterial calcifications, phleboliths, tonsilloliths, styloid ligament ossification, osteoma cutis, antroliths, laryngeal cartilage calcifications, sialoliths, lymph node calcifications, and rhinoliths as well as a single combined class. Data were split into training (80%), testing (10%), and validation (10%) sets, and performance was evaluated using sensitivity, precision, and F1-score.

Results: In the single-class model, sensitivity, precision, and F1-score were 0.98, 0.91, and 0.94, respectively. In the multi-class model, these values were 0.88, 0.80, and 0.84.

Conclusion: The AI system achieved high accuracy in detecting STCOs, with superior results in single-class classification. AI-assisted CBCT evaluation may improve diagnostic efficiency, facilitate multidisciplinary collaboration, and support clinical decision-making.