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Herpes simplex type 1 (HSV‑1) is a widespread pathogen responsible for orolabial lesions. It is an enveloped, double-stranded DNA (dsDNA) virus belonging to the family Herpesviridae. Once the virus infects a host cell, its double‑stranded DNA genome is delivered into the nucleus, where a coordinated cascade of immediate‑early, early, and late gene expression directs viral DNA replication, structural protein synthesis, and virion assembly. After primary infection of epithelial cells, HSV-1...

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Use of Interferon-γ Enzyme-linked Immunospot Assay to Characterize Novel T-cell Epitopes of Human Papillomavirus

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Investigation of Machine and Deep Learning Techniques to Detect HPV Status.

Efstathia Petrou¹, Konstantinos Chatzipapas², Panagiotis Papadimitroulas¹

¹3dmi Research Group, Department of Medical Physics, School of Medicine, University of Patras, 26504 Rion, Greece.

Journal of Personalized Medicine

|July 27, 2024

Summary

Deep learning models analyzing computed tomography (CT) scans show promise for non-invasive human papillomavirus (HPV) detection in head and neck cancers (HNCs), achieving 90% accuracy. Radiomic features offer a complementary approach.

Keywords:

HPV status artificial intelligence cancer deep learning ensemble models

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Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis

Published on: June 14, 2019

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Head and neck cancers (HNCs) often involve human papillomavirus (HPV) infection.
Accurate HPV status detection is crucial for HNC diagnosis and treatment.
Current detection methods can be invasive, necessitating exploration of non-invasive alternatives.

Purpose of the Study:

To investigate non-invasive methods for HPV detection in HNCs.
To compare the efficacy of Deep Learning (DL) models analyzing CT scans versus machine learning (ML) models using radiomic features for HPV detection.

Main Methods:

A modified ResNet-18 DL model was trained on CT data from 50 HNC patients to predict HPV status.
Radiomic features were extracted from CT images and used to train four ML models (KNN, logistic regression, decision tree, random forest).

Main Results:

The CT-based DL model achieved 90% accuracy in classifying HPV status.
Among ML models, K-Nearest Neighbors showed the highest accuracy at 80%.
Ensemble methods combining DL and ML models yielded moderate accuracy improvements (70-90%).

Conclusions:

CT scans analyzed by DL models present a promising non-invasive method for HPV detection in HNC.
Radiomic features provide a complementary, though less accurate, approach in this study.
Future research should focus on larger datasets and integrating DL with radiomic techniques.