Evidence map›Paper›PMID 38001684›Full record

ArticleCancers2023

A Radiomics-Based Machine Learning Perspective on the Parotid Gland as a Potential Surrogate Marker for HPV in Oropharyngeal Cancer.

Gordian Prasse, Agnes Glaas, Hans-Jonas Meyer, Veit Zebralla, Andreas Dietz, Kathrin Hering, Thomas Kuhnt, Timm Denecke

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 1 institution in 1 country.

Gordian PrasseDepartment of Radiology, University of Leipzig, 04103 Leipzig, Germany.ORCID 0000-0002-9210-9710
Agnes GlaasDepartment of Radiation Therapy, University of Leipzig, 04103 Leipzig, Germany.
Hans-Jonas MeyerDepartment of Radiology, University of Leipzig, 04103 Leipzig, Germany.ORCID 0000-0001-8489-706X
Veit ZebrallaDepartment of Otorhinolaryngology, Head and Neck Surgery, University of Leipzig, 04103 Leipzig, Germany.ORCID 0000-0001-5519-7566
Andreas DietzDepartment of Otorhinolaryngology, Head and Neck Surgery, University of Leipzig, 04103 Leipzig, Germany.
Kathrin HeringDepartment of Radiation Therapy, University of Leipzig, 04103 Leipzig, Germany.
Thomas KuhntDepartment of Radiation Therapy, University of Leipzig, 04103 Leipzig, Germany.
Timm DeneckeDepartment of Radiology, University of Leipzig, 04103 Leipzig, Germany.ORCID 0000-0003-3313-3208
Leipzig University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn treatment of oropharyngeal squamous cell carcinoma (OPSCC), human papillomavirus status (HPV) plays a crucial role. The HPV-positive subtype tends to affect younger patients and is associated with a more favorable prognosis. HPV-associated lesions have been described in the parotid gland, which is included in routine imaging for OPSCC. This work aims to explore the ability of an ML system to classify HPV status based on imaging of the parotid gland, which is routinely depicted on staging imaging.

methodsUsing a radiomics approach, we investigate the ability of five contemporary machine learning (ML) models to distinguish between HPV-positive and HPV-negative OPSCC based on non-contrast computed tomography (CT) data of tumor volume (TM), locoregional lymph node metastasis (LNM), and the parotid gland (Parotid). After exclusion of cases affected by streak artefacts, 53 patients (training set: 39; evaluation set: 14) were retrospectively evaluated. Classification performances were tested for significance against random optimistic results.

resultsThe best results are AUC 0.71 by XGBoost (XGB) for TM, AUC 0.82 by multi-layer perceptron (MLP) for LNM, AUC 0.76 by random forest (RF) for Parotid, and AUC 0.86 by XGB for a combination of all three regions of interest (ROIs).

conclusionsThe results suggest involvement of the parotid gland in HPV infections of the oropharyngeal region. While the role of HPV in parotid lesions is under active discussion, the migration of the virus from the oral cavity to the parotid gland seems plausible. The imaging of the parotid gland offers the benefit of fewer streak artifacts due to teeth and dental implants and the potential to screen for HPV in cases of an absent or unlocatable tumor. Future investigation can be directed to validation of the results in independent datasets and to the potential of improvement of current classification models by addition of information based on the parotid gland.

Indexed as

HPVmachine learningOPSCCparotid glandradiomics

Identifiers

PMID38001684
PMCPMC10670647
OpenAlexW4388694767

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.