A Digital Workflow for Automated Assessment of Tumor-Infiltrating Lymphocytes in Oral Squamous Cell Carcinoma Using QuPath and a StarDist-Based Model.

The search for reliable prognostic markers in oral squamous cell carcinoma (OSCC) remains a critical need. Tumor-infiltrating lymphocytes (TILs), particularly T lymphocytes, play a pivotal role in the immune response against tumors and are strongly correlated with favorable prognoses. Computational pathology has proven highly effective for histopathological image analysis, automating tasks such as cell detection, classification, and segmentation.

A machine learning approach to predict HPV positivity of oropharyngeal squamous cell carcinoma.

HPV status is an important prognostic factor in oropharyngeal squamous cell carcinoma (OPSCC), with HPV-positive tumors associated with better overall survival. To determine HPV status, we rely on the immunohistochemical investigation for expression of the P16 protein, which must be associated with molecular investigation for the presence of viral DNA. We aim to define a criterion based on image analysis and machine learning to predict HPV status from hematoxylin/eosin stain.

The oral microbiome and its role in oral squamous cell carcinoma: a systematic review of microbial alterations and potential biomarkers.

Background: Oral squamous cell carcinoma (OSCC) is one of the most common cancers worldwide. Despite advances in diagnosis and treatment, the incidence of OSCC is increasing, and the mortality rate remains high. This systematic review aims to examine the potential association between the composition of the oral microbiota and OSCC.

RNA-Based Next-Generation Sequencing in Non-Small Cell Lung Cancer patients: data from Campania, Italy.

Objective: ALK, ROS1, NTRK, and RET gene fusions and MET exon 14 skipping alterations represent fundamental predictive biomarkers for advanced non-small cell lung cancer (NSCLC) patients to ensure the best treatment choice. In this scenario, RNA-based NGS approach has emerged as an extremely useful tool for detecting these alterations. In this study, we report our NGS molecular records on ALK, ROS1, NTRK, and RET gene fusions and MET exon 14 skipping alterations detected by using a narrow RNA-based NGS panel, namely SiRe fusion.

Leveraging deep learning for identification and segmentation of "CAF-1/p60-positive" nuclei in oral squamous cell carcinoma tissue samples.

In the current study, we introduced a unique method for identifying and segmenting oral squamous cell carcinoma (OSCC) nuclei, concentrating on those predicted to have significant CAF-1/p60 protein expression. Our suggested model uses the StarDist architecture, a deep-learning framework designed for biomedical image segmentation tasks. The training dataset comprises painstakingly annotated masks created from tissue sections previously stained with hematoxylin and eosin (H&E) and then restained with immunohistochemistry (IHC) for p60 protein.