Clinicopathological Study of Oncocytomas of Head and Neck Region: A Systematic Review.

Background: Salivary gland oncocytomas are infrequent benign salivary gland tumors with few reported cases.

Aims: This study aimed to systematically review case reports and case series studies on oncocytomas in the head and neck region.

Materials & Methods: Electronic searches were performed in PubMed, Scopus, Web of Science, Embase, and LILACS databases.

Clinicopathological Profile of HPV-related Multiphenotypic Sinonasal Carcinoma: A Systematic Review.

Purpose: This study aimed to perform a systematic review of the clinicopathological, prognostic features, and HPV genotyping patterns of HPV-related multiphenotypic sinonasal carcinoma (HMSC).

Methods: This study adhered to the PRISMA 2020 guidelines and was registered in the PROSPERO database. We included case reports, case series studies, and cohort studies of HMSC indexed in the PubMed, Web of Science, Scopus, Embase, and LILACS databases published between 2017 and 2025.

Mucinous adenocarcinoma of the base of the tongue: A case report.

A 50-year-old male patient presented with a nodular lesion at the right base of the tongue. An incisional biopsy was performed, and the histopathological evaluation revealed an ill-defined multicystic papillary proliferation of mucin-producing cells. Immunohistochemically, tumor cells were positive for CK7 and negative for CK20, p40, p63, SOX10, SMA, and calponin.

Oral and Maxillofacial Low-Grade Myofibroblastic Sarcoma: A Systematic Review.

Background: This systematic review (SR) aimed to summarize the clinical, histopathological, and immunohistochemical features of low-grade myofibroblastic sarcoma (LGMS) in the oral and maxillofacial region (OMR), as well as treatment protocols, recurrence, follow-up, and metastasis rates. It follows PRISMA 2022 guidelines and is registered in PROSPERO (CRD42023409758).

Methods: An electronic search included PubMed, EMBASE, Scopus, Web of Science, LILACS, Google Scholar, and ProQuest databases, and bias risk was assessed using the Joanna Briggs Institute tool.

Clinicopathological Analysis of Oral Paracoccidioidomycosis in Women.

Unlabelled: Paracoccidioidomycosis (PCM) is a deep systemic mycosis caused by Paracoccidioides brasiliensis. PCM affects predominantly men in their fifth and sixth decades of life, with low prevalence in women. The reasons for this discrepancy are not fully understood, but oestrogen may influence the transformation of the fungus and modulate the immune response.

Artificial intelligence in healthcare applications targeting cancer diagnosis-part I: data structure, preprocessing and data organization.

Background: Machine learning techniques hold significant potential to support the diagnosis and prognosis of diseases. However, the success of these approaches is heavily dependent on rigorous data acquisition, preprocessing and data organization.

Methods: This article reviews the literature to evaluate key factors in dataset construction, focusing on data structure, preprocessing, and data organization, particularly in the context of imaging data.

Clinicopathological, Prognostic and Molecular Profile of Salivary Gland Intraductal Carcinoma: A Systematic Review.

Purpose: This study aimed to conduct a systematic review summarizing the clinicopathological, prognostic, and molecular features of salivary gland intraductal carcinoma (SGIC).

Methods: This study followed the PRISMA 2020 guidelines and was registered in the PROSPERO database. It included case reports, case series studies, and cohort studies of SGIC indexed in the PubMed, Web of Science, Scopus, and Embase databases published between 1983 and 2024.

Deep Convolutional Neural Network for Accurate Classification of Myofibroblastic Lesions on Patch-Based Images.

Objective: This study aimed to implement and evaluate a Deep Convolutional Neural Network for classifying myofibroblastic lesions into benign and malignant categories based on patch-based images.

Methods: A Residual Neural Network (ResNet50) model, pre-trained with weights from ImageNet, was fine-tuned to classify a cohort of 20 patients (11 benign and 9 malignant cases). Following annotation of tumor regions, the whole-slide images (WSIs) were fragmented into smaller patches (224 × 224 pixels).

Development and Evaluation of a Convolutional Neural Network for Microscopic Diagnosis Between Pleomorphic Adenoma and Carcinoma Ex-Pleomorphic Adenoma.

Aims: To develop a model capable of distinguishing carcinoma ex-pleomorphic adenoma from pleomorphic adenoma using a convolutional neural network architecture.

Methods And Results: A cohort of 83 Brazilian patients, divided into carcinoma ex-pleomorphic adenoma (n = 42) and pleomorphic adenoma (n = 41), was used for training a convolutional neural network. The whole-slide images were annotated and fragmented into 743 869 (carcinoma ex-pleomorphic adenomas) and 211 714 (pleomorphic adenomas) patches, measuring 224 × 224 pixels.

Intraoral Salivary Gland Cystadenomas: A Case Series Study and Literature Review.

Background: Salivary gland cystadenoma (SGCA) is a rare benign tumor that predominantly occurs in the parotid gland. SGCAs affecting the minor salivary glands are uncommon and often resemble, clinically and histopathologically, other salivary gland lesions.

Methods: This study aimed to describe a series of four cases of SGCA affecting intraoral sites and performed a literature review of well-reported SGCA published in the English-language literature.

Feasibility study of ResNet-50 in the distinction of intraoral neural tumors using histopathological images.

Background: Neural tumors are difficult to distinguish based solely on cellularity and often require immunohistochemical staining to aid in identifying the cell lineage. This article investigates the potential of a Convolutional Neural Network for the histopathological classification of the three most prevalent benign neural tumor types: neurofibroma, perineurioma, and schwannoma.

Methods: A model was developed, trained, and evaluated for classification using the ResNet-50 architecture, with a database of 30 whole-slide images stained in hematoxylin and eosin (106, 782 patches were generated from and divided among the training, validation, and testing subsets, with strategies to avoid data leakage).

Artificial intelligence and radiomics in the diagnosis of intraosseous lesions of the gnathic bones: A systematic review.

Background: The purpose of this systematic review (SR) is to gather evidence on the use of machine learning (ML) models in the diagnosis of intraosseous lesions in gnathic bones and to analyze the reliability, impact, and usefulness of such models. This SR was performed in accordance with the PRISMA 2022 guidelines and was registered in the PROSPERO database (CRD42022379298).

Methods: The acronym PICOS was used to structure the inquiry-focused review question "Is Artificial Intelligence reliable for the diagnosis of intraosseous lesions in gnathic bones?" The literature search was conducted in various electronic databases, including PubMed, Embase, Scopus, Cochrane Library, Web of Science, Lilacs, IEEE Xplore, and Gray Literature (Google Scholar and ProQuest).

Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas.

Background: Odontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis.