Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Diagnosis and management of immune checkpoint inhibitor-induced dry mouth and salivary gland dysfunction in oncology patients: a systematic review.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer·2026
Same author

Implementation-focused consensus recommendations for oral cavity cancer prevention and early detection in Latin America and the Caribbean: A Delphi study.

Oral oncology·2026
Same author

Pathology-informed Generative Adversarial Network Augmentation Improves Classification of Peripheral Nerve Sheath Tumors by Modeling Morphological Variability: A Pilot Investigation.

Head and neck pathology·2026
Same author

Non-Metastatic Squamous Cell Carcinoma of the Oropharynx: Primary Surgery or (Chemo)radiotherapy?

Head & neck·2026
Same author

Strategies and successes of smoking cessation methods in head and neck cancer patients: a systematic review.

Cancer metastasis reviews·2026
Same author

Reply to Borewad et al. Comment on "Rao et al. The Oncological Outcome of Postoperative Radiotherapy in Patients with Node-Negative Early-Stage (T1/T2/N0) Oral Squamous Cell Carcinoma and Perineural Invasion: A Meta-Analysis. <i>Cancers</i> 2025, <i>17</i>, 862".

Cancers·2026
Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026
Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026
Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026
Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026
Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026
Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jan 13, 2026

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis
06:57

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

Published on: June 14, 2019

11.1K

Radiomic-Based Machine Learning Classifiers for HPV Status Prediction in Oropharyngeal Cancer: A Systematic Review

Anna Luíza Damaceno Araújo1,2, Luiz Paulo Kowalski1,3, Alan Roger Santos-Silva4

  • 1Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo 05508-020, Brazil.

Diagnostics (Basel, Switzerland)
|January 10, 2026
PubMed
Summary
This summary is machine-generated.

Radiomic machine learning models show promise for predicting human papillomavirus (HPV) status in oropharyngeal cancer. However, high risk of bias and methodological inconsistencies currently limit their clinical use.

Keywords:
HPVimagingmachine learningoropharyngeal cancerradiomics

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

482
RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma
10:26

RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma

Published on: March 11, 2014

28.1K

Related Experiment Videos

Last Updated: Jan 13, 2026

Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis
06:57

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

Published on: June 14, 2019

11.1K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

482
RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma
10:26

RNAscope for In situ Detection of Transcriptionally Active Human Papillomavirus in Head and Neck Squamous Cell Carcinoma

Published on: March 11, 2014

28.1K

Area of Science:

  • Oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Oropharyngeal squamous cell carcinoma (OPSCC) is strongly associated with human papillomavirus (HPV) infection.
  • Accurate HPV status prediction is crucial for OPSCC treatment stratification and prognosis.
  • Radiomics and machine learning (ML) offer potential non-invasive methods for HPV status determination.

Purpose of the Study:

  • To systematically review and synthesize evidence on radiomic-based ML models for predicting HPV status in OPSCC.
  • To assess the reliability, methodological quality, and clinical applicability of these models.
  • To identify gaps in current research and guide future investigations.

Main Methods:

  • Systematic review following PRISMA 2020 guidelines, registered in PROSPERO.
  • PICOS framework used to define the review question: "Can radiomic-based ML models accurately predict HPV status in OPSCC?"
  • Comprehensive search of electronic and gray literature databases.
  • Inclusion of retrospective cohort studies evaluating radiomics for HPV prediction.
  • Risk of bias assessment using PROBAST tool.
  • Data synthesis based on imaging modality, ML architecture, and external validation.
  • Meta-analysis performed for externally validated models.

Main Results:

  • Twenty-four studies involving 8627 patients were analyzed.
  • Common imaging modalities included CT, MRI, CE-CT, and 18F-FDG PET.
  • Widely used ML models were logistic regression, random forest, XGBoost, and CNNs.
  • Most datasets exhibited class imbalance, with a predominance of HPV+ cases.
  • Only eight studies reported external validation.
  • AUROC values varied widely, from 0.59 to 0.87 (internal) and 0.48 to 0.91 (external).
  • High risk of bias was prevalent, often due to p16-only testing, insufficient sample size, or poor handling of class imbalance.
  • Deep learning models demonstrated moderate performance (sensitivity: 0.61; specificity: 0.65) with significant heterogeneity.
  • Traditional ML models showed higher and more consistent performance (sensitivity: 0.72; specificity: 0.77).

Conclusions:

  • Radiomic-based ML models hold potential for predicting HPV status in OPSCC.
  • Significant methodological heterogeneity and a high risk of bias currently impede widespread clinical adoption.
  • Further high-quality research with robust external validation is necessary to improve reliability and clinical applicability.