Discrimination of HPV status using CT texture analysis: tumour heterogeneity in oropharyngeal squamous cell carcinomas

Lee, Jun-Young; Han, Miran; Kim, Kap Seon; Shin, Su‐Jin; Choi, Jin Wook; Ha, Eun Ju

doi:10.1007/s00234-019-02295-w

Cited by 11 publications

(11 citation statements)

References 40 publications

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“…Meyer et al reported that CT entropy was correlated with hypoxic related pathological parameter, hypoxia-inducible factor-1-alpha expression [ 23 ]. This study was concordant with previous studies that showed high entropy in malignant tumors [ 10 ].…”

Section: Discussionsupporting

confidence: 94%

“…High tumoral heterogeneity is associated with adverse biology, aggressive clinical course, and increased resistance to treatment [ 18 ]. A recent study on oropharyngeal cancer showed a correlation between texture and HPV status, showing a lower value of entropy and SD in the HPV-positive group [ 10 ]. For the correlation between texture and treatment response, texture analysis parameters are known to be associated with local failure in patients with head and neck cancer with chemoradiotherapy.…”

Section: Discussionmentioning

confidence: 99%

“…It quantifies the tumor heterogeneity using mathematical calculations of spatial patterns or arrangement of pixel intensities [ 13 ]. In head and neck cancers, it is usually used for treatment assessment [ 11 , 12 ] and correlates with human papillomavirus (HPV) status [ 10 , 14 ]. Until now, it has not been used for the discrimination of tonsil cancer from normal tonsils.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, texture analysis has been introduced and applied in lung [6,7], esophageal [8], colorectal [9], and head and neck cancers [10][11][12]. It quantifies the tumor heterogeneity using mathematical calculations of spatial patterns or arrangement of pixel intensities [13].…”

Section: Introductionmentioning

confidence: 99%

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CT texture analysis of tonsil cancer: Discrimination from normal palatine tonsils

Kim

Lee

et al. 2021

PLoS ONE

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The purposes of the study were to determine whether there are differences in texture analysis parameters between tonsil cancers and normal tonsils, and to correlate texture analysis with 18F-FDG PET/CT to investigate the relationship between texture analysis and metabolic parameters. Sixty-four patients with squamous cell carcinoma of the palatine tonsil were included. A ROI was drawn, including all slices, to involve the entire tumor. The contralateral normal tonsil was used for comparison with the tumors. Texture analysis parameters, mean, standard deviation (SD), entropy, mean positive pixels, skewness, and kurtosis were obtained using commercially available software. Parameters were compared between the tumor and the normal palatine tonsils. Comparisons were also performed among early tonsil cancer, advanced tonsil cancer, and normal tonsils. An ROC curve analysis was performed to assess discrimination of tumor from normal tonsils. Correlation between texture analysis and 18F-FDG PET/CT was performed. Compared to normal tonsils, the tumors showed a significantly lower mean, higher SD, higher entropy, lower skewness, and higher kurtosis on most filters (p<0.001). On comparisons among normal tonsils, early cancers, and advanced tonsil cancers, SD and entropy showed significantly higher values on all filters (p<0.001) between early cancers and normal tonsils. The AUC from the ROC analysis was 0.91, obtained from the entropy. A mild correlation was shown between texture parameters and metabolic parameters. The texture analysis parameters, especially entropy, showed significant differences in contrast-enhanced CT results between tumor and normal tonsils, and between early tonsil cancers and normal tonsils. Texture analysis can be useful as an adjunctive tool for the diagnosis of tonsil cancers.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CT texture analysis of tonsil cancer: Discrimination from normal palatine tonsils

Kim

Lee

et al. 2021

PLoS ONE

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“…71 Furthermore, textural histogram parameters have been found to differ significantly, depending on HPV status on pre-treatment CT studies, with Lee et al finding the entropy parameter could be used to predict priary tumour HPV status with an accuracy of 80% in the training and 75% in the validation data sets. 72–74 Additionally, evaluation of ADC-derived histograms for HPV- oral cavity SCC and OPC have been found to display a more normal distribution (compatible with greater histological heterogeneity) as compared to HPV+ oral cavity SCC and OPC, which is characterised by greater kurtosis and skewness (thought to reflect more homogenous histological features). 75 Despite promising data, the use of radiomics remains at an early stage with persistent challenges pertaining to reproducibility and generalisability, which limit its use to the research domain.…”

Section: Structural Imaging With Ct and Mrimentioning

confidence: 98%

Imaging of human papilloma virus associated oropharyngeal squamous cell carcinoma and its impact on diagnosis, prognostication, and response assessment

Touska

Connor

2022

The British Journal of Radiology

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The clinical behaviour and outcomes of patients with oropharyngeal cancer (OPC) may be dichotomised according to their association with human papilloma virus (HPV) infection. Patients with HPV-associated disease (HPV +OPC) have a distinct demographic profile, clinical phenotype and demonstrate considerably better responses to chemo-radiotherapy. This has led to a reappraisal of staging and treatment strategies for HPV +OPC, which are underpinned by radiological data. Structural modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI) can provide accurate staging information. These can be combined with ultrasound-guided tissue sampling and functional techniques (such as diffusion-weighted MRI and 18F-FDG PET-CT) to monitor response to treatment, derive prognostic information, and to identify individuals who might benefit from intensification or deintensification strategies. Furthermore, advanced MRI techniques, such as intravoxel incoherent motion and perfusion MRI as well as application of artificial intelligence and radiomic techniques, have shown promise in treatment response monitoring and prognostication. The following review will consider the contemporary role and knowledge on imaging in HPV +OPC.

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Artificial intelligence for HPV status prediction based on disease‐specific images in head and neck cancer: A systematic review and meta‐analysis

Song,

Chen,

Tang

et al. 2023

Journal of Medical Virology

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Accurate early detection of the human papillomavirus (HPV) status in head and neck cancer (HNC) is crucial to identify at‐risk populations, stratify patients, personalized treatment options, and predict prognosis. Artificial intelligence (AI) is an emerging tool to dissect imaging features. This systematic review and meta‐analysis aimed to evaluate the performance of AI to predict the HPV positivity through the HPV‐associated diseased images in HNC patients. A systematic literature search was conducted in databases including Ovid‐MEDLINE, Embase, and Web of Science Core Collection for studies continuously published from inception up to October 30, 2022. Search strategies included keywords such as “artificial intelligence,” “head and neck cancer,” “HPV,” and “sensitivity & specificity.” Duplicates, articles without HPV predictions, letters, scientific reports, conference abstracts, or reviews were excluded. Binary diagnostic data were then extracted to generate contingency tables and then used to calculate the pooled sensitivity (SE), specificity (SP), area under the curve (AUC), and their 95% confidence interval (CI). A random‐effects model was used for meta‐analysis, four subgroup analyses were further explored. Totally, 22 original studies were included in the systematic review, 15 of which were eligible to generate 33 contingency tables for meta‐analysis. The pooled SE and SP for all studies were 79% (95% CI: 75−82%) and 74% (95% CI: 69−78%) respectively, with an AUC of 0.83 (95% CI: 0.79−0.86). When only selecting one contingency table with the highest accuracy from each study, our analysis revealed a pooled SE of 79% (95% CI: 75−83%), SP of 75% (95% CI: 69−79%), and an AUC of 0.84 (95% CI: 0.81−0.87). The respective heterogeneities were moderate (I2 for SE and SP were 51.70% and 51.01%) and only low (35.99% and 21.44%). This evidence‐based study showed an acceptable and promising performance for AI algorithms to predict HPV status in HNC but was not comparable to the routine p16 immunohistochemistry. The exploitation and optimization of AI algorithms warrant further research. Compared with previous studies, future studies anticipate to make progress in the selection of databases, improvement of international reporting guidelines, and application of high‐quality deep learning algorithms.

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Discrimination of HPV status using CT texture analysis: tumour heterogeneity in oropharyngeal squamous cell carcinomas

Cited by 11 publications

References 40 publications

CT texture analysis of tonsil cancer: Discrimination from normal palatine tonsils

CT texture analysis of tonsil cancer: Discrimination from normal palatine tonsils

Imaging of human papilloma virus associated oropharyngeal squamous cell carcinoma and its impact on diagnosis, prognostication, and response assessment

Artificial intelligence for HPV status prediction based on disease‐specific images in head and neck cancer: A systematic review and meta‐analysis

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