AIMC Topic: Carcinoma, Squamous Cell

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FTIR-based machine learning for prediction of malignant transformation in oral epithelial dysplasia.

The Analyst
Oral squamous cell carcinoma (OSCC) is an aggressive cancer with a poor prognosis. Oral epithelial dysplasia (OED) is a precancerous lesion associated with an increased risk of malignant transformation (MT) into OSCC. However, current histopathologic...

Oral cavity carcinoma detection using BAT algorithm-optimized machine learning models with transfer learning and random sampling.

Computers in biology and medicine
BACKGROUND: Oral cavity carcinoma remains a major public health concern, where early and accurate detection is vital for improving patient outcomes and survival rates. Current diagnostic systems often face challenges such as limited feature selection...

Single-cell and spatial transcriptomics reveals an anti-tumor neutrophil subgroup in microwave thermochemotherapy-treated lip cancer.

International journal of oral science
Microwave thermochemotherapy (MTC) has been applied to treat lip squamous cell carcinoma (LSCC), but a deeper understanding of its therapeutic mechanisms and molecular biology is needed. To address this, we used single-cell transcriptomics (scRNA-seq...

Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma.

BMC medical genomics
BACKGROUND: One of the most common and prevalent cancers is laryngeal squamous cell carcinoma (LSCC), which poses a great threat to the life and health of the patient. Nonetheless, it has been demonstrated that ubiquitination is crucial for the devel...

Multiparameter MRI-based model integrating radiomics and deep learning for preoperative staging of laryngeal squamous cell carcinoma.

Scientific reports
The accurate preoperative staging of laryngeal squamous cell carcinoma (LSCC) provides valuable guidance for clinical decision-making. The objective of this study was to establish a multiparametric MRI model using radiomics and deep learning (DL) to ...

Cytopathological quantification of NORs using artificial intelligence to oral cancer screening.

Brazilian oral research
Oral squamous cell carcinoma (OSCC) remains the most prevalent neoplasm of the head and neck. In recent decades, the incidence and prevalence of OSCC have not significantly changed, highlighting the critical need to develop and implement new risk ass...

Development and validation of a CT-based deep learning radiomics signature to predict lymph node metastasis in oropharyngeal squamous cell carcinoma: a multicentre study.

Dento maxillo facial radiology
OBJECTIVES: Lymph node metastasis (LNM) is a pivotal determinant that influences the treatment strategies and prognosis for oropharyngeal squamous cell carcinoma (OPSCC) patients. This study aims to establish and verify a deep learning (DL) radiomics...

[Clinical study of cervical lymph node metastasis in oral tongue squamous carcinoma by a machine learning model based on contrast-enhanced CT radiomics].

Shanghai kou qiang yi xue = Shanghai journal of stomatology
PURPOSE: To investigate the value of machine learning model based on enhanced CT imaging features and clinical parameters in predicting cervical lymph node metastasis in patients with tongue squamous cell carcinoma (TSCC).

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

Pathologica
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 ...

Oropharyngeal Cancer Staging Health Record Extraction Using Artificial Intelligence.

JAMA otolaryngology-- head & neck surgery
IMPORTANCE: Accurate, timely, and cost-effective methods for staging oropharyngeal cancers are crucial for patient prognosis and treatment decisions, but staging documentation is often inaccurate or incomplete. With the emergence of artificial intell...