Computed Tomography-Based Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinoma

Ranjbar, Sara; Ning, Shuluo; Zwart, Christine M.; Wood, Christopher P.; Weindling, Steven M.; Wu, Teresa; Mitchell, J. Ross; Li, Jing; Hoxworth, Joseph M.

doi:10.1097/rct.0000000000000682

Cited by 31 publications

(23 citation statements)

References 40 publications

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“…Amongst these, nine studies focussed on the assessment of oral squamous cell carcinoma (OSCC) and seven studies focussed on the evaluation of, or differentiation between, oral potentially malignant disorders (OPMD) and OSCC. The remaining studies focussed on assessment of nasopharyngeal SCC ( n = 3), 29 – 31 laryngeal SCC ( n = 2), 32 , 33 oropharyngeal SCC ( n = 3), 34 – 36 parotid gland neoplasms ( n = 2) 37 , 38 and differentiation between sinonasal SCC from inverted papilloma ( n = 1). 39 In four studies, 40 – 42 tissue sections of HNC from various different sites (tongue, floor of mouth, soft palate, mandible, gingivae, alveolar ridge, supraglottis, maxillary sinus, nose, thyroid and parotid gland) were evaluated.…”

Section: Resultsmentioning

confidence: 99%

“…3 ). Radiology image data were used in eight studies and obtained from dynamic contrast-enhanced MRI (DCE-MRI) ( n = 3), 31 , 39 , 43 CT ( n = 2), 36 , 37 PET/CT ( n = 1), 29 US ( n = 1) 38 and plain film intraoral radiographs ( n = 1). 44 Other imaging modalities included hyperspectral imaging (HSI) ( n = 6), endoscopic/clinical imaging ( n = 5), infrared thermal imaging ( n = 1) 45 and multimodal optical imaging ( n = 1).…”

Section: Resultsmentioning

confidence: 99%

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Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

et al. 2021

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Background This paper reviews recent literature employing Artificial Intelligence/Machine Learning (AI/ML) methods for diagnostic evaluation of head and neck cancers (HNC) using automated image analysis. Methods Electronic database searches using MEDLINE via OVID, EMBASE and Google Scholar were conducted to retrieve articles using AI/ML for diagnostic evaluation of HNC (2009–2020). No restrictions were placed on the AI/ML method or imaging modality used. Results In total, 32 articles were identified. HNC sites included oral cavity (n = 16), nasopharynx (n = 3), oropharynx (n = 3), larynx (n = 2), salivary glands (n = 2), sinonasal (n = 1) and in five studies multiple sites were studied. Imaging modalities included histological (n = 9), radiological (n = 8), hyperspectral (n = 6), endoscopic/clinical (n = 5), infrared thermal (n = 1) and optical (n = 1). Clinicopathologic/genomic data were used in two studies. Traditional ML methods were employed in 22 studies (69%), deep learning (DL) in eight studies (25%) and a combination of these methods in two studies (6%). Conclusions There is an increasing volume of studies exploring the role of AI/ML to aid HNC detection using a range of imaging modalities. These methods can achieve high degrees of accuracy that can exceed the abilities of human judgement in making data predictions. Large-scale multi-centric prospective studies are required to aid deployment into clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

et al. 2021

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“…In total, we extracted 119 features per ROI: 3 Raw intensity, 26 GLCM, 10 DOST, 36 LoGHist, 12 LBP, and 32 GFB features. Extensive details on these features can be found in {42, 43, 61}. To account for sampling variability, we averaged the features over slice without losing the laterality information, leading to a total of 238 texture features (119 per hippocampus) per patient.…”

Section: Methodsmentioning

confidence: 99%

“…These models offer the potential of capturing often overlooked or hidden information of underlying disease dynamics. Our group has developed a radiomics texture analysis platform that has been previously used to characterize gene expression patterns of brain cancer {39, 40}, to aid in the diagnosis of head and neck cancers {41, 42} and breast cancer {43}.…”

Section: Introductionmentioning

confidence: 99%

Brain MR Radiomics to Differentiate Cognitive Disorders

Ranjbar

Velgos

Dueck

et al. 2019

JNP

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Subtle and gradual changes occur in the brain years prior to cognitive impairment due to age-related neurodegenerative disorders. We examined the utility of hippocampal texture analysis and volumetric features extracted from brain magnetic resonance (MR) data to differentiate between three cognitive groups (cognitively normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer’s disease (AD)), and neuropsychological Clinical Dementia Rating (CDR) scores. Data from 173 unique patients with 3T T1-weighted MR images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were analyzed. A variety of texture and volumetric features were extracted from bilateral hippocampal regions and were used to perform binary classification of cognitive groups and CDR scores. We used Diagonal Quadratic Discriminant Analysis (DQDA) in a leave one out cross validation scheme. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were used to assess the performance of models. Our results show promise for hippocampal texture analysis to distinguish between no impairment and early stages of impairment (AUCs of 0.86 for CN-MCI and 0.95 for CDR0-CDR1 models, respectively). Volumetric features were more successful at differentiating between no impairment and advanced stages of impairment (AUCs of 0.89 for CN-AD and 0.98 for CDR0-CDR2, respectively). MR radiomics may be a promising tool to classify various cognitive groups.

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“…Previously published CT-based works support the rationale for the application of radiomics in the setting of OPSCC. A recent study by Ranjbar et al has compared the classification accuracy of machine learning and of trained human eye in predicting HPV status using CT-based textural analysis and demonstrated that the accuracy of the best machine learning model was significantly better than those of the two human controls (p < 0.001 and p=0.002) [5]. Other authors managed to identify radiomic models for the prediction of HPV status and/or loco-regional control in either head-and-neck cancer as a whole [6,7] or in homogenous subsets of OPSCC patients [8,9].…”

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confidence: 99%

Association of quantitative MRI-based radiomic features with prognostic factors and recurrence rate in oropharyngeal squamous cell carcinoma

Giannitto

Marvaso

Botta

et al. 2021

neo

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Radiomics focuses on extracting a large number of quantitative imaging features and testing both their correlation with clinical characteristics and their prognostic and predictive values. We propose a radiomic approach using magnetic resonance imaging (MRI) to decode tumor phenotype and local recurrence in oropharyngeal squamous cell carcinoma (OPSCC). The contrast-enhanced T1weighted sequences from baseline MRI examinations of OPSCC patients treated between 2008 and 2016 were retrospectively selected. Radiomic features were extracted using the IBEX software, and hierarchical clustering was applied to reduce features redundancy. The association of each radiomic feature with tumor grading and stage, HPV status, loco-regional recurrence within 2 years, considered as main endpoints, was assessed by univariate analysis and then corrected for multiple testing. Statistical analysis was performed with SAS/STAT® software. Thirty-two eligible cases were identified. For each patient, 1286 radiomic features were extracted, subsequently grouped into 16 clusters. Higher grading (G3 vs. G1/G2) was associated with lower 2 values of GOH/65Percentile and GOH/85Percentile features (p = 0.04 and 0.01, respectively). Positive HPV status was associated with higher values of GOH/10Percentile (p = 0.03) and lower values of GOH/90Percentile (p = 0.03). Loco-regional recurrence within 2 years was associated with higher values of GLCM3/4-7Correlation (p = 0.04) and lower values of GLCM3/2-1InformationMeasureCorr1 (p = 0.04). Results lost the statistical significance after correction for multiple testing. T stage was significantly correlated with 9 features, 4 of which (GLCM25/180-4InformationMeasureCorr2, Shape/MeanBreadth, GLCM25/90-1InverseDiffMomentNorm and GLCM3/6-1InformationMeasureCorr1) retained statistical significance after False Discovery Rate correction. MRI-based radiomics is a feasible and promising approach for the prediction of tumor phenotype and local recurrence in OPSCC. Some radiomic features seem to be correlated with tumor characteristics and oncologic outcome however, larger collaborative studies are warranted in order to increase the statistical power and to obtain robust and validated results.

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Computed Tomography-Based Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinoma

Abstract: Useful diagnostic information regarding HPV infection can be extracted from the CT appearance of OPSCC beyond what is apparent to the trained human eye.

Cited by 31 publications

References 40 publications

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview

Brain MR Radiomics to Differentiate Cognitive Disorders

Association of quantitative MRI-based radiomic features with prognostic factors and recurrence rate in oropharyngeal squamous cell carcinoma

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