Dataset Information

Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status.

ABSTRACT: We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically proved OPSCC, who underwent head and neck MRIs consisting of axial T1WI, T2WI, CE-T1WI, and apparent diffusion coefficient (ADC) maps from diffusion-weighted imaging (DWI). The median age was 59 years (the range being 35 to 85 years), and 83.3% of patients were male. The imaging data were randomised into a training set (32 HPV-positive and 8 HPV-negative OPSCC) and a test set (16 HPV-positive and 4 HPV-negative OPSCC) in each fold. 1618 quantitative features were extracted from manually delineated regions-of-interest of primary tumour and one definite lymph node in each sequence. After feature selection by using the least absolute shrinkage and selection operator (LASSO), three different machine-learning classifiers (logistic regression, random forest, and XG boost) were trained and compared in the setting of various combinations between four sequences. The highest diagnostic accuracies were achieved when using all sequences, and the difference was significant only when the combination did not include the ADC map. Using all sequences, logistic regression and the random forest classifier yielded higher accuracy compared with the that of the XG boost classifier, with mean area under curve (AUC) values of 0.77, 0.76, and 0.71, respectively. The machine-learning classifier of non-invasive and quantitative radiomics signature could guide the classification of the HPV status.

SUBMITTER: Suh CH

PROVIDER: S-EPMC7568530 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status.

Suh Chong Hyun CH Lee Kyung Hwa KH Choi Young Jun YJ Chung Sae Rom SR Baek Jung Hwan JH Lee Jeong Hyun JH Yun Jihye J Ham Sungwon S Kim Namkug N

Scientific reports 20201016 1

We investigated the ability of machine-learning classifiers on radiomics from pre-treatment multiparametric magnetic resonance imaging (MRI) to accurately predict human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC). This retrospective study collected data of 60 patients (48 HPV-positive and 12 HPV-negative) with newly diagnosed histopathologically proved OPSCC, who underwent head and neck MRIs consisting of axial T1WI, T2WI, CE-T1WI, and apparent diff ...[more]

PMID: 33067484

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Project description:The global rise of HPV(+) oropharyngeal squamous cell carcinoma (OPSCC) has generated considerable interest underlying its etiology and management. Despite an overall decline in head and neck malignancies, the incidence in OPSCC has by contrast sharply risen, with HPV(+) subtypes now comprising 80% of all OPSCC.1,2 Relative to HPV(-) OPSCC, HPV(+) patients are more responsive to chemoradiation and harbor a 52% risk-of-death reduction.3 Despite this distinct outcome, treatment regimens remain the same for all OPSCC subtypes (smoking-driven and virus-driven), rather than an adaptive approach to what most consider distinct diseases. The short-term effects (e.g., mucositis, odynophagia) and long-term toxicities (e.g., xerostomia, dysphagia, ototoxicity) from treatment substantially affect quality of life, and rival the impact of the cancer itself. Recently published as well as ongoing trials are actively examining deintensification approaches2,4-9, with the goal of diminishing treatment sequelae for HPV(+) subtypes. While deintensification may decrease chemoradiation-related toxicities, it nonetheless may also undertreat a meaningful percentage of HPV(+) patients who may then recur. In examining national trials, 19% of HPV(+) patients had disease progression after therapy, with a two-year survival rate of 60%.10 Current methods to ascertain HPV status involve p16 staining. However, on comparison with gold standard E6/E7 expression by qPCR, p16 harbored a 15% false positivity rate11, suggesting limited utility as a sole biomarker for deintensification. Improved molecular stratification would greatly enhance the clinician’s ability to precisely tailor treatment while minimizing the risk of jeopardizing outcomes. One approach encompasses in-depth proteomic profiling of HPV(+) OPSCC to reveal distinct protein expression profiles and delineate clinically relevant upstream pathways. In turn, these proteomic differences may distinguish higher-risk disease (cases predisposed to recurrence that may benefit from treatment intensification) from lower-risk phenotypes (cases whose treatment response is sufficiently robust to warrant deintensification). Here, two HPV(+) OPSCC cohorts stratified by treatment response are compared via a hybrid data dependent acquisition/data independent acquisition (DDA/DIA) approach via mass spectrometry. We focused on detection of low-abundance proteins to highlight proteomic signatures that can be potentially exploited for treatment stratification.

Dataset Information

Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status.

Publications

Oropharyngeal squamous cell carcinoma: radiomic machine-learning classifiers from multiparametric MR images for determination of HPV infection status.

Similar Datasets

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