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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:PurposeThere is a lack of biomarkers for accurately prognosticating outcome in both human papillomavirus-related (HPV+) and tobacco- and alcohol-related (HPV-) oropharyngeal squamous cell carcinoma (OPSCC). The aims of this study were to i) develop and evaluate radiomic features within (intratumoral) and around tumor (peritumoral) on CT scans to predict HPV status; ii) investigate the prognostic value of the radiomic features for both HPV- and HPV+ patients, including within individual AJCC eighth edition-defined stage groups; and iii) develop and evaluate a clinicopathologic imaging nomogram involving radiomic, clinical, and pathologic factors for disease-free survival (DFS) prediction for HPV+ patients.Experimental designThis retrospective study included 582 OPSCC patients, of which 462 were obtained from The Cancer Imaging Archive (TCIA) with available tumor segmentation and 120 were from Cleveland Clinic Foundation (CCF, denoted as SCCF) with HPV+ OPSCC. We subdivided the TCIA cohort into training (ST, 180 patients) and validation (SV, 282 patients) based on an approximately 3:5 ratio for HPV status prediction. The top 15 radiomic features that were associated with HPV status were selected by the minimum redundancy-maximum relevance (MRMR) using ST and evaluated on SV. Using 3 of these 15 top HPV status-associated features, we created radiomic risk scores for both HPV+ (RRSHPV+) and HPV- patients (RRSHPV-) through a Cox regression model to predict DFS. RRSHPV+ was further externally validated on SCCF. Nomograms for the HPV+ population (Mp+RRS) were constructed. Both RRSHPV+ and Mp+RRS were used to prognosticate DFS for the AJCC eighth edition-defined stage I, stage II, and stage III patients separately.ResultsRRSHPV+ was prognostic for DFS for i) the whole HPV+ population [hazard ratio (HR) = 1.97, 95% confidence interval (CI): 1.35-2.88, p < 0.001], ii) the AJCC eighth stage I population (HR = 1.99, 95% CI: 1.04-3.83, p = 0.039), and iii) the AJCC eighth stage II population (HR = 3.61, 95% CI: 1.71-7.62, p < 0.001). HPV+ nomogram Mp+RRS (C-index, 0.59; 95% CI: 0.54-0.65) was also prognostic of DFS (HR = 1.86, 95% CI: 1.27-2.71, p = 0.001).ConclusionCT-based radiomic signatures are associated with both HPV status and DFS in OPSCC patients. With additional validation, the radiomic signature and its corresponding nomogram could potentially be used for identifying HPV+ OPSCC patients who might be candidates for therapy deintensification.

Project description:Introduction"Radiomics" extracts and mines a large number of medical imaging features in a non-invasive and cost-effective way. The underlying assumption of radiomics is that these imaging features quantify phenotypic characteristics of an entire tumor. In order to enhance applicability of radiomics in clinical oncology, highly accurate and reliable machine-learning approaches are required. In this radiomic study, 13 feature selection methods and 11 machine-learning classification methods were evaluated in terms of their performance and stability for predicting overall survival in head and neck cancer patients.MethodsTwo independent head and neck cancer cohorts were investigated. Training cohort HN1 consisted of 101 head and neck cancer patients. Cohort HN2 (n?=?95) was used for validation. A total of 440 radiomic features were extracted from the segmented tumor regions in CT images. Feature selection and classification methods were compared using an unbiased evaluation framework.ResultsWe observed that the three feature selection methods minimum redundancy maximum relevance (AUC?=?0.69, Stability?=?0.66), mutual information feature selection (AUC?=?0.66, Stability?=?0.69), and conditional infomax feature extraction (AUC?=?0.68, Stability?=?0.7) had high prognostic performance and stability. The three classifiers BY (AUC?=?0.67, RSD?=?11.28), RF (AUC?=?0.61, RSD?=?7.36), and NN (AUC?=?0.62, RSD?=?10.52) also showed high prognostic performance and stability. Analysis investigating performance variability indicated that the choice of classification method is the major factor driving the performance variation (29.02% of total variance).ConclusionOur study identified prognostic and reliable machine-learning methods for the prediction of overall survival of head and neck cancer patients. Identification of optimal machine-learning methods for radiomics-based prognostic analyses could broaden the scope of radiomics in precision oncology and cancer care.

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.

Project description:Background and purposeSemantic imaging features have been used for molecular subclassification of high-grade gliomas. Radiomics-based prediction of molecular subgroups has the potential to strategize and individualize therapy. Using MRI texture features, we propose to distinguish between IDH wild type and IDH mutant type high grade gliomas.MethodsBetween 2013 and 2020, 100 patients were retrospectively analyzed for the radiomics study. Immunohistochemistry of the pathological specimen was used to initially identify patients for the IDH mutant/wild phenotype and was then confirmed by Sanger's sequencing. Image texture analysis was performed on contrast-enhanced T1 (T1C) and T2 weighted (T2W) MR images. Manual segmentation was performed on MR image slices followed by single-slice multiple sampling image augmentation. Both whole tumor multislice segmentation and single-slice multiple sampling approaches were used to arrive at the best model. Radiomic features were extracted, which included first-order features, second-order (GLCM-Grey level co-occurrence matrix), and shape features. Feature enrichment was done using LASSO (Least Absolute Shrinkage and Selection Operator) regression, followed by radiomic classification using Support Vector Machine (SVM) and a 10-fold cross-validation strategy for model development. The area under the Receiver Operator Characteristic (ROC) curve and predictive accuracy were used as diagnostic metrics to evaluate the model to classify IDH mutant and wild-type subgroups.ResultsMultislice analysis resulted in a better model compared to the single-slice multiple-sampling approach. A total of 164 MR-based texture features were extracted, out of which LASSO regression identified 14 distinctive GLCM features for the endpoint, which were used for further model development. The best model was achieved by using combined T1C and T2W MR images using a Quadratic Support Vector Machine Classifier and a 10-fold internal cross-validation approach, which demonstrated a predictive accuracy of 89% with an AUC of 0.89 for each IDH mutant and IDH wild subgroup.ConclusionA machine learning classifier of radiomic features extracted from multiparametric MRI images (T1C and T2w) provides important diagnostic information for the non-invasive prediction of the IDH mutant or wild-type phenotype of high-grade gliomas and may have potential use in either escalating or de-escalating adjuvant therapy for gliomas or for using targeted agents in the future.

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.

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