The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered f...
OBJECTIVES: To develop a lightweight deep convolutional neural network (CNN) for binary classification of oral lesions into benign and malignant or potentially malignant using standard real-time clinical images.
Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
Jan 17, 2021
BACKGROUND: Oral cancer requires early diagnosis and treatment to increase the chances of survival. This study aimed to develop an artificial neural network model that helps to predict the individuals' risk of developing oral cancer based on data on ...
Proceedings of the National Academy of Sciences of the United States of America
Dec 14, 2020
Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into t...
IMPORTANCE: Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chemotherapy is unclear for patients with other intermediate risk features.
BACKGROUND: Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic diagnosis of OSCC using artificial intelligence techniques. Though biopsy is the ultimate test for ...
AJR. American journal of roentgenology
Sep 15, 2020
This study aimed to explore the performance of machine learning (ML)-based MRI texture analysis in discriminating between well-differentiated (WD) oral squamous cell carcinoma (OSCC) and moderately or poorly differentiated OSCC. The study enrolled ...
IMPORTANCE: A tool for precisely stratifying postoperative patients with advanced oral cancer is crucial for the treatment plan, such as intensifying or deintensifying the regimen to improve their quality of life and prognosis.
OBJECTIVES: High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy (RT). Given relatively small patient sample (as compared with high dimensional imaging features), id...
OBJECTIVE: To assess the utility of deep learning analysis using F-fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET/CT) to predict disease-free survival (DFS) in patients with oral cavity squamous cell carcinoma (OCSCC).
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