AIMC Topic: Carcinoma, Squamous Cell
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Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.
Scientific reports
Dec 30, 2024
Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significant management implications. We assessed the performance of radiomics-based models in distinguishing ...
Comparative investigation of lung adenocarcinoma and squamous cell carcinoma transcriptome to reveal potential candidate biomarkers: An explainable AI approach.
Computational biology and chemistry
Dec 27, 2024
Patients with Non-Small Cell Lung Cancer (NSCLC) present a variety of clinical symptoms, such as dyspnea and chest pain, complicating accurate diagnosis. NSCLC includes subtypes distinguished by histological characteristics, specifically lung adenoca...
Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study.
Cancer immunology, immunotherapy : CII
Dec 21, 2024
BACKGROUND: Existing biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell non-small cell lung cancer (sqNSCLC) did not have enough accuracy. We used data from the ORIENT-3 stud...
Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.
European radiology
Dec 20, 2024
OBJECTIVES: Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial inte...
MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation.
Computers in biology and medicine
Dec 10, 2024
Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Manual analysis for detecting squamous cell carcinoma in histopathological images is time-consuming and may be subjective. Squamous cell carcinoma diagn...
Integrated Proteomics and Machine Learning Approach Reveals PYCR1 as a Novel Biomarker to Predict Prognosis of Sinonasal Squamous Cell Carcinoma.
International journal of molecular sciences
Dec 10, 2024
Sinonasal squamous cell carcinoma (SNSCC) is a rare tumor with a high 5-year mortality rate. However, proteomic technologies have not yet been utilized to identify SNSCC-associated proteins, which could be used as biomarkers. In this study, we aimed ...
Predicting cancer content in tiles of lung squamous cell carcinoma tumours with validation against pathologist labels.
Computers in biology and medicine
Dec 4, 2024
BACKGROUND: A growing body of research is using deep learning to explore the relationship between treatment biomarkers for lung cancer patients and cancer tissue morphology on digitized whole slide images (WSIs) of tumour resections. However, these W...
Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers.
Frontiers in immunology
Dec 3, 2024
INTRODUCTION: Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived...
Lymph Node Metastasis Prediction From In Situ Lung Squamous Cell Carcinoma Histopathology Images Using Deep Learning.
Laboratory investigation; a journal of technical methods and pathology
Nov 13, 2024
Lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer, represents a significant portion of lung cancer cases with distinct histologic patterns impacting prognosis and treatment. The current pathological assessment methods face ...
Development and Validation of Machine Learning Models for Predicting Tumor Progression in OSCC.
Oral diseases
Oct 27, 2024
OBJECTIVES: Development of a prediction model using machine learning (ML) method for tumor progression in oral squamous cell carcinoma (OSCC) patients would provide risk estimation for individual patient outcomes.
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