Correlation between Human Papillomavirus Status and Quantitative MR Imaging Parameters including Diffusion-Weighted Imaging and Texture Features in Oropharyngeal Carcinoma

Diagnostic Performance of Radiomics Modeling in Predicting the Human Papillomavirus Status of Oropharyngeal Cancer: A Systematic Review and Meta-Analysis

In this review, we sought to assess the diagnostic performance and methodological quality of studies utilizing radiomics for the prediction of human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma. A comprehensive literature search of PubMed, Ovid, Cochrane, Web of Science, and Scopus from inception until June 7, 2022, was performed to identify eligible studies. Strict inclusion and exclusion criteria were applied to the identified studies. Data collection was performed by two independent reviewers with disagreements resolved by consensus review with a third reviewer. In total, 14 articles were chosen, with a total of 15 radiomics models. Of the included studies, 12 models reported sensitivity, with a mean of 0.778 (standard deviation (SD) = 0.073). Similarly, 12 models reported specificity, with a mean of 0.751 (SD = 0.111). The area under the curve (AUC) was reported by all 15 models, with a mean of 0.814 (SD = 0.081). Finally, accuracy was reported by eight models, with a mean of 0.768 (SD = 0.044). A meta-analysis was performed on eight studies that reported AUCs with confidence intervals (CIs), returning a pooled AUC of 0.764 (95% CI = 0.758 to 0.770). The Radiomics Quality Score (RQS) was applied to each included study as a measure of quality. RQS ranged from -1 to 22, with a mean of 13.4 and an intraclass coefficient of 0.874. Radiomics modeling has shown promise in serving as a diagnostic indicator for HPV status in patients with oropharyngeal cancer. Nevertheless, the quality of research methodologies in this area is a limiting factor for its broader clinical application and highlights the need for enhanced funding to support further research efforts.

Positron Emission Tomography-Magnetic Resonance Imaging, a New Hybrid Imaging Modality for Dentomaxillofacial Malignancies-A Systematic Review

Background/Objectives: Emerging hybrid imaging modalities, like Positron Emission Tomography/Computed Tomography (PET/CT) and Positron Emission Tomography/Magnetic Resonance Imaging (PET/MRI), are useful for assessing head and neck cancer (HNC) and its prognosis during follow-up. PET/MRI systems enable simultaneous PET and MRI scans within a single session. These combined PET/MRI scanners merge MRI's better soft tissue contrast and the molecular metabolic information offered by PET. Aim: To review scientific articles on the use of hybrid PET/MRI techniques in diagnosing dentomaxillofacial malignancies. Method: The available literature on the use of PET/MRI for the diagnosis of dentomaxillofacial malignancies in four online databases (Scopus, PubMed, Web of Science, and the Cochrane Library) was searched. Eligible for this review were original full-text articles on PET/MRI imaging, published between January 2010 and November 2024, based on experimental or clinical research involving humans. Results: Out of the 783 articles retrieved, only twelve articles were included in this systematic review. Nearly half of the articles (5 out of 12) concluded that PET/MRI is superior to PET, MRI, and PET/CT imaging in relation to defining malignancies' size. Six articles found no statistically significant results and the diagnostic accuracy presented was similar in PET/MRI versus MRI and PET/CT images. Regarding the overall risk of bias, most articles had a moderate risk. Conclusions: The use of PET/MRI in HNC cases provides a more accurate diagnosis regarding dimensions of the tumor and thus a more accurate surgical approach if needed. Further prospective studies on a larger cohort of patients are required to obtain more accurate results on the application of hybrid PET/MRI.

Diffusion-Weighted MRI and Human Papillomavirus (HPV) Status in Oropharyngeal Cancer

BACKGROUND

This study aimed to explore the differences in quantitative diffusion-weighted (DW) MRI parameters in oropharyngeal squamous cell carcinoma (OPC) based on Human Papillomavirus (HPV) status before and during radiotherapy (RT).

METHODS

Echo planar DW sequences acquired before and during (chemo)radiotherapy (CRT) of 178 patients with histologically proven OPC were prospectively analyzed. The volumetric region of interest (ROI) was manually drawn on the apparent diffusion coefficient (ADC) map, and 105 DW-MRI radiomic parameters were extracted. Change in ADC values (Δ ADC) was calculated as the difference between baseline and during RT at week 4, normalized by the baseline values.

RESULTS

Pre-treatment first-order 10th percentile ADC and Gray Level co-occurrence matrix (GLCM)-correlation were significantly lower in HPV-positive compared with HPV-negative tumors (82.4 × 10-5 mm2/s vs. 90.3 × 10-5 mm2/s, p = 0.03 and 0.18 vs. 0.30, p < 0.01). In the fourth week of RT, all first-order ADC values were significantly higher in HPV-positive tumors (p < 0.01). Δ ADC mean was significantly higher for the HPV-positive compared with the HPV-negative OPC group (95% vs. 55%, p < 0.01). A predictive model for HPV status based on smoking status, alcohol consumption, GLCM correlation, and mean ADC and 10th percentile ADC values yielded an area under the curve of 0.77 (95% CI 0.70-0.84).

CONCLUSIONS

Our results highlight the potential of DW-MR imaging as a non-invasive biomarker for the prediction of HPV status, although its current role remains supplementary to pathological confirmation.

Interpretable survival network for progression risk analysis of multimodality imaging biomarkers in poor-prognosis head and neck cancers

This study explores the predictive utility of multi-time point, multi-modality quantitative imaging biomarkers (QIBs) and clinical factors in patients with poor-prognosis head and neck cancers (HNCs) using interpretable machine learning. We examined 93 patients with p16 + oropharyngeal squamous cell carcinoma or locally advanced p16- HNCs enrolled in a phase II adaptive radiation dose escalation trial. FDG-PET and multiparametric MRI scans were conducted before radiation therapy and at the 10th fraction (2 weeks). A survival network analyzed MRI and PET-derived biomarkers such as gross tumor volume (GTV), blood volume (BV), and metabolic tumor volume (MTV50), along with clinical factors to predict local (LF) and distant failures (DF). Feature attributions and interactions were assessed using Expected Gradients (EG) and Expected Hessian (EH). Through rigorous cross-validation, the model for predicting LF, incorporating biomarkers like p16 status and radiation boost, achieved a c-index of 0.758. Similarly, the DF prediction model showed a c-index of 0.695. The analysis of feature attributions and interactions enhanced understanding of important features and complex factor interplays, potentially guiding more personalized and intensified treatment approaches for HNC patients.

Image-based multi-omics analysis for oral science: Recent progress and perspectives

OBJECTIVES

The diagnosis and treatment of oral and dental diseases rely heavily on various types of medical imaging. Deep learning-mediated multi-omics analysis can extract more representative features than those identified through traditional diagnostic methods. This review aims to discuss the applications and recent advances in image-based multi-omics analysis in oral science and to highlight its potential to enhance traditional diagnostic approaches for oral diseases.

STUDY SELECTION, DATA, AND SOURCES

A systematic search was conducted in the PubMed, Web of Science, and Google Scholar databases, covering all available records. This search thoroughly examined and summarized advances in image-based multi-omics analysis in oral and maxillofacial medicine.

CONCLUSIONS

This review comprehensively summarizes recent advancements in image-based multi-omics analysis for oral science, including radiomics, pathomics, and photographic-based omics analysis. It also discusses the ongoing challenges and future perspectives that could provide new insights into exploiting the potential of image-based omics analysis in the field of oral science.

CLINICAL SIGNIFICANCE

This review article presents the state of image-based multi-omics analysis in stomatology, aiming to help oral clinicians recognize the utility of combining omics analyses with imaging during diagnosis and treatment, which can improve diagnostic accuracy, shorten times to diagnosis, save medical resources, and reduce disparity in professional knowledge among clinicians.

The radiological features of HPV-positive vs HPV-negative OPSCC at a South African hospital

Background

Studies have found that, at presentation, human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) has a less advanced primary tumour, more advanced lymph node spread and commonly has cystic metastatic lymph nodes in comparison to HPV-negative OPSCC.

Objectives

To compare the radiological features of HPV-positive and HPV-negative OPSCC in South African patients.

Method

A retrospective cross-sectional study was conducted at a large South African hospital. Eligibility required a histologically proven OPSCC between 2007 and 2023; a p16 antigen test and, if positive, a confirmatory HPV DNA PCR test and a baseline pre-treatment contrast enhanced neck CT scan. All eligible HPV-positive OPSCC patients and a random sample of eligible HPV-negative OPSCC patients were enrolled.

Results

Twenty-one HPV-positive and 55 HPV-negative OPSCC patients were recruited. There was no statistically significant difference in the tumour epicentre location, local advancement (≥ T3 in 67% and 71%, respectively, p = 0.54), mean primary tumour size (41 mm vs. 39 mm, p = 0.73), lymph node spread (bilateral or more in 67% vs. 82%, p = 0.22) or morphologically cystic lymph nodes (10% and 4%, p = 0.61).

Conclusion

There was no statistically significant difference in the CT imaging appearances of HPV-positive and HPV-negative OPSCC in the studied sample of South African patients.

Contribution

This study documents the radiological features of OPSCC in a small South African sample population, where HPV-positive and HPV-negative OPSCC could not be distinguished on CT criteria and did not display the classic features described in the literature.

MRI for Differentiation between HPV-Positive and HPV-Negative Oropharyngeal Squamous Cell Carcinoma: A Systematic Review

Human papillomavirus (HPV) is an important risk factor for oropharyngeal squamous cell carcinoma (OPSCC). HPV-positive (HPV+) cases are associated with a different pathophysiology, microstructure, and prognosis compared to HPV-negative (HPV-) cases. This review aimed to investigate the potential of magnetic resonance imaging (MRI) to discriminate between HPV+ and HPV- tumours and predict HPV status in OPSCC patients. A systematic literature search was performed on 15 December 2022 on EMBASE, MEDLINE ALL, Web of Science, and Cochrane according to PRISMA guidelines. Twenty-eight studies (n = 2634 patients) were included. Five, nineteen, and seven studies investigated structural MRI (e.g., T1, T2-weighted), diffusion-weighted MRI, and other sequences, respectively. Three out of four studies found that HPV+ tumours were significantly smaller in size, and their lymph node metastases were more cystic in structure than HPV- ones. Eleven out of thirteen studies found that the mean apparent diffusion coefficient was significantly higher in HPV- than HPV+ primary tumours. Other sequences need further investigation. Fourteen studies used MRI to predict HPV status using clinical, radiological, and radiomics features. The reported areas under the curve (AUC) values ranged between 0.697 and 0.944. MRI can potentially be used to find differences between HPV+ and HPV- OPSCC patients and predict HPV status with reasonable accuracy. Larger studies with external model validation using independent datasets are needed before clinical implementation.

Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques

OBJECTIVES

To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC).

METHODS

This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set.

RESULTS

A total of 154 patients (59.2 ± 9.1 years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657).

CONCLUSIONS

The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set.

CLINICAL RELEVANCE STATEMENT

Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance.

KEY POINTS

• Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance.

Machine learning based on magnetic resonance imaging and clinical parameters helps predict mesenchymal-epithelial transition factor expression in oral tongue squamous cell carcinoma: a pilot study

OBJECTIVES

This study aimed to develop machine learning models to predict phosphorylated mesenchymal-epithelial transition factor (p-MET) expression in oral tongue squamous cell carcinoma (OTSCC) using magnetic resonance imaging (MRI)-derived texture features and clinical features.

METHODS

Thirty-four patients with OTSCC were retrospectively collected. Texture features were derived from preoperative MR images, including T2WI, apparent diffusion coefficient mapping, and contrast-enhanced (ce)-T1WI. Dimension reduction was performed consecutively with reproducibility analysis and an information gain algorithm. Five machine learning methods-AdaBoost, logistic regression (LR), naïve Bayes (NB), random forest (RF), and support vector machine (SVM)-were adopted to create models predicting p-MET expression. Their performance was assessed with fivefold cross-validation.

RESULTS

In total, 22 and 12 cases showed low and high p-MET expression, respectively. After dimension reduction, 3 texture features (ADC-Minimum, ce-T1WI-Imc2, and ce-T1WI-DependenceVariance) and 2 clinical features (depth of invasion [DOI] and T-stage) were selected with good reproducibility and best correlation with p-MET expression levels. The RF model yielded the best overall performance, correctly classifying p-MET expression status in 87.5% of OTSCCs with an area under the receiver operating characteristic curve of 0.875.

CONCLUSION

Differences in p-MET expression in OTSCCs can be noninvasively reflected in MRI-based texture features and clinical parameters. Machine learning can potentially predict biomarker expression levels, such as MET, in patients with OTSCC.

Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma

Background and purpose

Diffusion-weighted imaging (DWI) is a promising technique for response assessment in head-and-neck cancer. Recently, we optimized Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM), an extension of the conventional apparent diffusion coefficient (ADC) model, for the head and neck. In the current study, we describe the first application in a group of patients with human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma. The aim of this study was to relate ADC and NG-IVIM DWI parameters to HPV status and clinical treatment response.

Materials and methods

Thirty-six patients (18 HPV-positive, 18 HPV-negative) were prospectively included. Presence of progressive disease was scored within one year. The mean pre-treatment ADC and NG-IVIM parameters in the gross tumor volume were compared between HPV-positive and HPV-negative patients. In HPV-negative patients, ADC and NG-IVIM parameters were compared between patients with and without progressive disease.

Results

ADC, the NG-IVIM diffusion coefficient D, and perfusion fraction f were significantly higher, while pseudo-diffusion coefficient D* and kurtosis K were significantly lower in the HPV-negative compared to HPV-positive patients. In the HPV-negative group, a significantly lower D was found for patients with progressive disease compared to complete responders. No relation with ADC was observed.

Conclusion

The results of our single-center study suggest that ADC is related to HPV status, but not an independent response predictor. The NG-IVIM parameter D, however, was independently associated to response in the HPV-negative group. Noteworthy in the opposite direction as previously thought based on ADC.

Radiomics Features in Predicting Human Papillomavirus Status in Oropharyngeal Squamous Cell Carcinoma: A Systematic Review, Quality Appraisal, and Meta-Analysis

We sought to determine the diagnostic accuracy of radiomics features in predicting HPV status in oropharyngeal squamous cell carcinoma (SCC) compared to routine paraclinical measures used in clinical practice. Twenty-six articles were included in the systematic review, and thirteen were used for the meta-analysis. The overall sensitivity of the included studies was 0.78, the overall specificity was 0.76, and the overall area under the ROC curve was 0.84. The diagnostic odds ratio (DOR) equaled 12 (8, 17). Subgroup analysis showed no significant difference between radiomics features extracted from CT or MR images. Overall, the studies were of low quality in regard to radiomics quality score, although most had a low risk of bias based on the QUADAS-2 tool. Radiomics features showed good overall sensitivity and specificity in determining HPV status in OPSCC, though the low quality of the included studies poses problems for generalizability.

18F-FDG PET/CT Parameters Enhance MRI Radiomics for Predicting Human Papilloma Virus Status in Oropharyngeal Squamous Cell Carcinoma

PURPOSE

Predicting human papillomavirus (HPV) status is critical in oropharyngeal squamous cell carcinoma (OPSCC) radiomics. In this study, we developed a model for HPV status prediction using magnetic resonance imaging (MRI) radiomics and 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) parameters in patients with OPSCC.

MATERIALS AND METHODS

Patients with OPSCC who underwent 18F-FDG PET/CT and contrast-enhanced MRI before treatment between January 2012 and February 2020 were enrolled. Training and test sets (3:2) were randomly selected. 18F-FDG PET/CT parameters and MRI radiomics feature were extracted. We developed three light-gradient boosting machine prediction models using the training set: Model 1, MRI radiomics features; Model 2, 18F-FDG PET/CT parameters; and Model 3, combination of MRI radiomics features and 18F-FDG PET/CT parameters. Area under the receiver operating characteristic curve (AUROC) values were used to analyze the performance of the models in predicting HPV status in the test set.

RESULTS

A total of 126 patients (118 male and 8 female; mean age: 60 years) were included. Of these, 103 patients (81.7%) were HPV-positive, and 23 patients (18.3%) were HPV-negative. AUROC values in the test set were 0.762 [95% confidence interval (CI), 0.564-0.959], 0.638 (95% CI, 0.404-0.871), and 0.823 (95% CI, 0.668-0.978) for Models 1, 2, and 3, respectively. The net reclassification improvement of Model 3, compared with that of Model 1, in the test set was 0.119.

CONCLUSION

When combined with an MRI radiomics model, 18F-FDG PET/CT exhibits incremental value in predicting HPV status in patients with OPSCC.

The Prediction of Biological Features Using Magnetic Resonance Imaging in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis

Magnetic resonance imaging (MRI) is an indispensable, routine technique that provides morphological and functional imaging sequences. MRI can potentially capture tumor biology and allow for longitudinal evaluation of head and neck squamous cell carcinoma (HNSCC). This systematic review and meta-analysis evaluates the ability of MRI to predict tumor biology in primary HNSCC. Studies were screened, selected, and assessed for quality using appropriate tools according to the PRISMA criteria. Fifty-eight articles were analyzed, examining the relationship between (functional) MRI parameters and biological features and genetics. Most studies focused on HPV status associations, revealing that HPV-positive tumors consistently exhibited lower ADCmean (SMD: 0.82; p < 0.001) and ADCminimum (SMD: 0.56; p < 0.001) values. On average, lower ADCmean values are associated with high Ki-67 levels, linking this diffusion restriction to high cellularity. Several perfusion parameters of the vascular compartment were significantly associated with HIF-1α. Analysis of other biological factors (VEGF, EGFR, tumor cell count, p53, and MVD) yielded inconclusive results. Larger datasets with homogenous acquisition are required to develop and test radiomic-based prediction models capable of capturing different aspects of the underlying tumor biology. Overall, our study shows that rapid and non-invasive characterization of tumor biology via MRI is feasible and could enhance clinical outcome predictions and personalized patient management for HNSCC.

Imaging of human papilloma virus (HPV) related oropharynx tumour: what we know to date

The tumours of head and neck district are around 3% of all malignancies and squamous cell carcinoma is the most frequent histotype, with rapid increase during the last two decades because of the increment of the infection due to human papilloma virus (HPV). Even if the gold standard for the diagnosis is histological examination, including the detection of viral DNA and transcription products, imaging plays a fundamental role in the detection and staging of HPV + tumours, in order to assess the primary tumour, to establish the extent of disease and for follow-up. The main diagnostic tools are Computed Tomography (CT), Positron Emission Tomography-Computed Tomography (PET-CT) and Magnetic Resonance Imaging (MRI), but also Ultrasound (US) and the use of innovative techniques such as Radiomics have an important role. Aim of our review is to illustrate the main imaging features of HPV + tumours of the oropharynx, in US, CT and MRI imaging. In particular, we will outline the main limitations and strengths of the various imaging techniques, the main uses in the diagnosis, staging and follow-up of disease and the fundamental differential diagnoses of this type of tumour. Finally, we will focus on the innovative technique of texture analysis, which is increasingly gaining importance as a diagnostic tool in aid of the radiologist.

The Use of Radiomic Features to Predict Human Papillomavirus (HPV) Status in Head and Neck Tumors: A Review

Head and neck cancers represent a significant source of morbidity and mortality across the world. The individual genetic makeup of each tumor can help to determine the course of treatment and can help clinicians predict prognosis. Non-invasive tools to determine the genetic status of these tumors, particularly p16 (human papillomavirus (HPV)) status could prove extremely valuable to treating clinicians and surgeons. The field of radiomics is a burgeoning area of radiology practice that aims to provide quantitative biomarkers that can be derived from radiological images and could prove useful in determining p16 status non-invasively. In this review, we summarize the current evidence for the use of radiomics to determine the HPV status of head and neck tumors. .

Contemporary Imaging and Reporting Strategies for Head and Neck Cancer: MRI, FDG PET/MRI, NI-RADS, and Carcinoma of Unknown Primary-AJR Expert Panel Narrative Review

CT, MRI, and FDG PET/CT play major roles in the diagnosis, staging, treatment planning, and surveillance of head and neck cancers. Nonetheless, an evolving understanding of head and neck cancer pathogenesis, advances in imaging techniques, changing treatment regimens, and a lack of standardized guidelines have led to areas of uncertainty in the imaging of head and neck cancer. This narrative review aims to address four issues in the contemporary imaging of head and neck cancer. The first issue relates to the standard and advanced sequences that should be included in MRI protocols for head and neck cancer imaging. The second issue relates to approaches to surveillance imaging after treatment of head and neck cancer, including the choice of imaging modality, the frequency of surveillance imaging, and the role of standardized reporting through the Neck Imaging Reporting and Data System. The third issue relates to the role of imaging in the setting of neck carcinoma of unknown primary. The fourth issue relates to the role of simultaneous PET/MRI in head and neck cancer evaluation. The authors of this review provide consensus opinions for each issue.

Development and Testing of a Machine Learning Model Using 18F-Fluorodeoxyglucose PET/CT-Derived Metabolic Parameters to Classify Human Papillomavirus Status in Oropharyngeal Squamous Carcinoma

OBJECTIVE

To develop and test a machine learning model for classifying human papillomavirus (HPV) status of patients with oropharyngeal squamous cell carcinoma (OPSCC) using ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET-derived parameters in derived parameters and an appropriate combination of machine learning methods in patients with OPSCC.

MATERIALS AND METHODS

This retrospective study enrolled 126 patients (118 male; mean age, 60 years) with newly diagnosed, pathologically confirmed OPSCC, that underwent ¹⁸F-FDG PET-computed tomography (CT) between January 2012 and February 2020. Patients were randomly assigned to training and internal validation sets in a 7:3 ratio. An external test set of 19 patients (16 male; mean age, 65.3 years) was recruited sequentially from two other tertiary hospitals. Model 1 used only PET parameters, Model 2 used only clinical features, and Model 3 used both PET and clinical parameters. Multiple feature transforms, feature selection, oversampling, and training models are all investigated. The external test set was used to test the three models that performed best in the internal validation set. The values for area under the receiver operating characteristic curve (AUC) were compared between models.

RESULTS

In the external test set, ExtraTrees-based Model 3, which uses two PET-derived parameters and three clinical features, with a combination of MinMaxScaler, mutual information selection, and adaptive synthetic sampling approach, showed the best performance (AUC = 0.78; 95% confidence interval, 0.46-1). Model 3 outperformed Model 1 using PET parameters alone (AUC = 0.48, p = 0.047) and Model 2 using clinical parameters alone (AUC = 0.52, p = 0.142) in predicting HPV status.

CONCLUSION

Using oversampling and mutual information selection, an ExtraTree-based HPV status classifier was developed by combining metabolic parameters derived from ¹⁸F-FDG PET/CT and clinical parameters in OPSCC, which exhibited higher performance than the models using either PET or clinical parameters alone.

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

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 chemoradiotherapy. This has led to a reappraisal of staging and treatment strategies for HPV+OPC, which are underpinned by radiological data. Structural modalities, such as CT and 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-fludeoxyglucose positron emission tomography-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.

Multifactorial Model Based on DWI-Radiomics to Determine HPV Status in Oropharyngeal Squamous Cell Carcinoma

Background: Oropharyngeal squamous cell carcinoma (OPSCC) associated with human papillomavirus (HPV) has higher rates of locoregional control and a better prognosis than HPV-negative OPSCC. These differences are due to some unique biological characteristics that are also visible through advanced imaging modalities. We investigated the ability of a multifactorial model based on both clinical factors and diffusion-weighted imaging (DWI) to determine the HPV status in OPSCC. Methods: The apparent diffusion coefficient (ADC) and the perfusion-free tissue diffusion coefficient D were derived from DWI, both in the primary tumor (PT) and lymph node (LN). First- and second-order radiomic features were extracted from ADC and D maps. Different families of machine learning (ML) algorithms were trained on our dataset using five-fold cross-validation. Results: A cohort of 144 patients was evaluated retrospectively, which was divided into a training set (n = 95) and a validation set (n = 49). The 50th percentile of DPT, the inverse difference moment of ADCLN, smoke habits, and tumor subsite (tonsil versus base of the tongue) were the most relevant predictors. Conclusions: DWI-based radiomics, together with patient-related parameters, allowed us to obtain good diagnostic accuracies in differentiating HPV-positive from HPV-negative patients. A substantial decrease in predictive power was observed in the validation cohort, underscoring the need for further analyses on a larger sample size.

Amide proton transfer imaging for the determination of human papillomavirus status in patients with oropharyngeal squamous cell carcinoma

The aim of this study was to investigate the utility of amide proton transfer (APT) imaging for the determination of human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (SCC). Thirty-one patients with oropharyngeal SCC were retrospectively evaluated. All patients underwent amide proton transfer imaging using a 3T magnetic resonance (MR) unit. Patients were divided into HPV-positive and -negative groups depending on the pathological findings in their primary tumor. In APT imaging, the primary tumor was delineated with a polygonal region of interest (ROI). Signal information in the ROI was used to calculate the mean, standard deviation (SD) and coefficient of variant (CV) of the APT signals (APT mean, APT SD, and APT CV, respectively). The value of APT CV in the HPV-positive group (0.43 ± 0.04) was significantly lower than that in the HPV-negative group (0.48 ± 0.04) (P = .01). There was no significant difference in APT mean (P = .82) or APT SD (P = .13) between the HPV-positive and -negative groups. Receiver operating characteristic (ROC) curve analysis of APT CV had a sensitivity of 0.75, specificity of 0.8, positive predictive value of 0.75, negative predictive value of 0.8, accuracy of 0.77 and area under the curve (AUC) of 0.8. The APT signal in the HPV-negative group was considered heterogeneous compared to the HPV-positive group. This information might be useful for the determination of HPV status in patients with oropharyngeal SCC.

Diffusion-Weighted Imaging to Assess HPV-Positive versus HPV-Negative Oropharyngeal Squamous Cell Carcinoma: The Importance of b-Values

BACKGROUND AND PURPOSE

Controversy exists as to whether ADC histograms are capable to distinguish human papillomavirus-positive (HPV+) from human papillomavirus-negative (HPV-) oropharyngeal squamous cell carcinoma. We investigated how the choice of b-values influences the capability of ADC histograms to distinguish between the two tumor types.

MATERIALS AND METHODS

Thirty-four consecutive patients with histologically proved primary oropharyngeal squamous cell carcinoma (11 HPV+ and 23 HPV-) underwent 3T MR imaging with a single-shot EPI DWI sequence with 6 b-values (0, 50, 100, 500, 750, 1000 s/mm²). Monoexponentially calculated perfusion-sensitive (including b=0 s/mm²) and perfusion-insensitive/true diffusion ADC maps (with b ≥ 100 s/mm² as the lowest b-value) were generated using Matlab. The choice of b-values included 2 b-values (ADC_b0-1000, ADC_b100-1000, ADC_b500-1000, ADC_b750-1000) and 3-6 b-values (ADC_b0-750-1000, ADC_{b0-500-750-1000}, ADC_{b0-50-100-1000}, ADC_{b0-50-100-750-1000}, ADC_{b0-50-100-500-750-1000}). Readers blinded to the HPV- status contoured all tumors. ROIs were then copied onto ADC maps, and their histograms were compared.

RESULTS

ADC histogram metrics in HPV+ and HPV- oropharyngeal squamous cell carcinoma changed significantly depending on the b-values. The mean ADC was lower, and skewness was higher in HPV+ than in HPV- oropharyngeal squamous cell carcinoma only for ADC_b0-1000, ADC_b0-750-1000, and ADC_{b0-500-750-1000} (P < .05), allowing distinction between the 2 tumor types. Kurtosis was significantly higher in HPV+ versus HPV- oropharyngeal squamous cell carcinoma for all b-value combinations except 2 perfusion-insensitive maps (ADC_b500-1000 and ADC_b750-1000). Among all b-value combinations, kurtosis on ADC_b0-1000 had the highest diagnostic performance to distinguish HPV+ from HPV- oropharyngeal squamous cell carcinoma (area under the curve = 0.893; sensitivity = 100%, specificity = 82.6%). Acquiring multiple b-values for ADC calculation did not improve the distinction between HPV+ and HPV- oropharyngeal squamous cell carcinoma.

CONCLUSIONS

The choice of b-values significantly affects ADC histogram metrics in oropharyngeal squamous cell carcinoma. Distinguishing HPV+ from HPV- oropharyngeal squamous cell carcinoma is best possible on the ADC_b0-1000 map.

The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment

Purpose

Human papillomavirus (HPV) status assessment is crucial for decision making in oropharyngeal cancer patients. In last years, several articles have been published investigating the possible role of radiomics in distinguishing HPV-positive from HPV-negative neoplasms. Aim of this review was to perform a systematic quality assessment of radiomic studies published on this topic.

Methods

Radiomics studies on HPV status prediction in oropharyngeal cancer patients were selected. The Radiomic Quality Score (RQS) was assessed by three readers to evaluate their methodological quality. In addition, possible correlations between RQS% and journal type, year of publication, impact factor, and journal rank were investigated.

Results

After the literature search, 19 articles were selected whose RQS median was 33% (range 0–42%). Overall, 16/19 studies included a well-documented imaging protocol, 13/19 demonstrated phenotypic differences, and all were compared with the current gold standard. No study included a public protocol, phantom study, or imaging at multiple time points. More than half (13/19) included feature selection and only 2 were comprehensive of non-radiomic features. Mean RQS was significantly higher in clinical journals.

Conclusion

Radiomics has been proposed for oropharyngeal cancer HPV status assessment, with promising results. However, these are supported by low methodological quality investigations. Further studies with higher methodological quality, appropriate standardization, and greater attention to validation are necessary prior to clinical adoption.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00234-022-02959-0.

Radiomics outperforms clinical factors in characterizing human papilloma virus (HPV) for patients with oropharyngeal squamous cell carcinomas

Purpose:To utilize radiomic features extracted from CT images to characterize Human Papilloma Virus (HPV) for patients with oropharyngeal cancer squamous cell carcinoma (OPSCC).Methods:One hundred twenty-eight OPSCC patients with known HPV-status (60-HPV+ and 68-HPV-, confirmed by immunohistochemistry-P16-protein testing) were retrospectively studied. Radiomic features (11 feature-categories) were extracted in 3D from contrast-enhanced (CE)-CT images of gross-tumor-volumes using 'in-house' software ('ROdiomiX') developed and validated following the image-biomarker-standardization-initiative (IBSI) guidelines. Six clinical factors were investigated: Age-at-Diagnosis, Gender, Total-Charlson, Alcohol-Use, Smoking-History, and T-Stage. A Least-Absolute-Shrinkage-and-Selection-Operation (Lasso) technique combined with a Generalized-Linear-Model (Lasso-GLM) were applied to perform regularization in the radiomic and clinical feature spaces to identify the ranking of optimal feature subsets with most representative information for prediction of HPV. Lasso-GLM models/classifiers based on clinical factors only, radiomics only, and combined clinical and radiomics (ensemble/integrated) were constructed using random-permutation-sampling. Tests of significance (One-way ANOVA), average Area-Under-Receiver-Operating-Characteristic (AUC), and Positive and Negative Predictive values (PPV and NPV) were computed to estimate the generalization-error and prediction performance of the classifiers.Results:Five clinical factors, including T-stage, smoking status, and age, and 14 radiomic features, including tumor morphology, and intensity contrast were found to be statistically significant discriminators between HPV positive and negative cohorts. Performances for prediction of HPV for the 3 classifiers were: Radiomics-Lasso-GLM: AUC/PPV/NPV=0.789/0.755/0.805; Clinical-Lasso-GLM: 0.676/0.747/0.672, and Integrated/Ensemble-Lasso-GLM: 0.895/0.874/0.844. Results imply that the radiomics-based classifier enabled better characterization and performance prediction of HPV relative to clinical factors, and that the combination of both radiomics and clinical factors yields even higher accuracy characterization and predictive performance.Conclusion:Albeit subject to confirmation in a larger cohort, this pilot study presents encouraging results in support of the role of radiomic features towards characterization of HPV in patients with OPSCC.

Application of first-order feature analysis of DWI-ADC in rare malignant mesenchymal tumours of the maxillofacial region

BACKGROUND

To research the first-order features of apparent diffusion coefficient (ADC) values on diffusion-weighted magnetic resonance imaging (DWI) in maxillofacial malignant mesenchymal tumours.

METHODS

The clinical data of 12 patients with rare malignant mesenchymal tumours of the maxillofacial region (6 cases of sarcoma and 6 cases of lymphoma) treated in the hospital from May 2018 to June 2020 and were confirmed by postoperative pathology were retrospectively analyzed. The patients were all examined by 1.5T magnetic resonance imaging. PyRadiomics were used to extract radiomics imaging first-order features. Group differences in quantitative variables were examined using independent-samples t-tests.

RESULTS

The voxels number of ADC_mean and ADC_median of sarcoma tissues were 44.9124 and 44.2064, respectively, significantly higher than those in lymphoma tissues (ADC_mean (- 68.8379) and ADC_median (- 74.0045)), the difference considered statistically significant, so do the ADC_kurt and ADC_skew.

CONCLUSIONS

The statistical difference of ADC_mean and ADC_median is significant, it is consistent with the outcome of the manual measurement of the ADC mean value of the most significant cross-section of twelve cases of lymphoma. Development of tumour volume based on the ADC parameter map of DWI demonstrates that the first-order ADC radiomics features analysis can provide new imaging markers for the differentiation of maxillofacial sarcoma and lymphoma. Therefore, first-order ADC features of ADC_kurt combined ADC_skew may improve the diagnosis level.

Advances in Imaging for HPV-Related Oropharyngeal Cancer: Applications to Radiation Oncology

While there has been an overall decline of tobacco and alcohol-related head and neck cancer in recent decades, there has been an increased incidence of HPV-associated oropharyngeal cancer (OPC). Recent research studies and clinical trials have revealed that the cancer biology and clinical progression of HPV-positive OPC is unique relative to its HPV-negative counterparts. HPV-positive OPC is associated with higher rates of disease control following definitive treatment when compared to HPV-negative OPC. Thus, these conditions should be considered unique diseases with regards to treatment strategies and survival. In order to sufficiently characterize HPV-positive OPC and guide treatment strategies, there has been a considerable effort to diagnose, prognose, and track the treatment response of HPV-associated OPC through advanced imaging research. Furthermore, HPV-positive OPC patients are prime candidates for radiation de-escalation protocols, which will ideally reduce toxicities associated with radiation therapy and has prompted additional imaging research to detect radiation-induced changes in organs at risk. This manuscript reviews the various imaging modalities and current strategies for tackling these challenges as well as provides commentary on the potential successes and suggested improvements for the optimal treatment of these tumors.

Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges

The application of imaging biomarkers in oncology is still in its infancy, but with the expansion of radiomics and radiogenomics a revolution is expected in this field. This may be of special interest in head and neck cancer, since it can promote precision medicine and personalization of treatment by overcoming several intrinsic obstacles in this pathology. Our goal is to provide the medical oncologist with the basis to approach these disciplines and appreciate their main uses in clinical research and clinical practice in the medium term. Aligned with this objective we analyzed the most relevant studies in the field, also highlighting novel opportunities and current challenges.

Histogram analysis of arterial spin labeling perfusion data to determine the human papillomavirus status of oropharyngeal squamous cell carcinomas

PURPOSE

To evaluate the correlation between histogram parameters derived from pseudo-continuous arterial spin labeling (PCASL) and human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC).

METHODS

This study included a total of 58 patients (HPV-positive: n = 45; -negative: n = 13) from a prospective cohort of consecutive patients aged ≥ 18 years, who were newly diagnosed with oropharyngeal squamous cell carcinoma. All patients were required to have undergone pre-treatment MRI with PCASL to measure regional perfusion. The region of interest was drawn by two radiologists, encompassing the entire tumor volume on all corresponding slices. Differences in the histogram parameters derived from tumor blood flow (TBF) in ASL were assessed for HPV-positive and -negative patients. Receiver operating characteristic curve analysis was performed to determine the best differentiating parameters, and a leave-one-out cross-validation was used.

RESULTS

Patients with HPV-positive OPSCC showed a significantly lower overall standard deviation and 95th percentile value of tumor blood flow (P < .007). The standard deviation of TBF was the single best predictive parameter. Leave-one-out cross-validation tests revealed that the area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were 0.745, 75.9%, 75.6%, and 76.9%, respectively.

CONCLUSION

PCASL revealed differences in perfusion parameters according to HPV status in patients with OPSCC, reflecting their distinct histopathology.

The impact of human papilloma virus status on the prediction of head and neck cancer chemoradiotherapy outcomes using the pre-treatment apparent diffusion coefficient

Objective:

To determine the impact of Human Papilloma Virus (HPV) oropharyngeal cancer (OPC) status on the prediction of head and neck squamous cell cancer (HNSCC) chemoradiotherapy (CRT) outcomes with pre-treatment quantitative diffusion-weighted magnetic resonance imaging (DW-MRI).

Methods:

Following ethical approval, 65 participants (53 male, age 59.9 ± 7.86) underwent pre-treatment DW-MRI in this prospective cohort observational study. There were 46 HPV OPC and 19 other HNSCC cases with Stage III/IV HNSCC. Regions of interest (ROIs) (volume, largest area, core) at the primary tumour (n = 57) and largest pathological node (n = 59) were placed to analyse ADC_mean and ADC_min. Unpaired t-test or Mann–Whitney test evaluated the impact of HPV OPC status and clinical parameters on their prediction of post-CRT 2 year locoregional and disease-free survival (LRFS and DFS). Multivariate logistic regression compared significant variables with 2 year outcomes.

Results:

On univariate analysis of all participants, the primary tumour area ADC_mean was predictive of 2 year LRFS (p = 0.04). However, only the HPV OPC diagnosis (LFRS p = 0.03; DFS p = 0.02) predicted outcomes on multivariate analysis. None of the pre-treatment ADC values were predictive of 2 year DFS in the HPV OPC subgroup (p = 0.21–0.68). Amongst participants without 2 year disease-free survival, HPV-OPC was found to have much lower primary tumour ADC_mean values than other HNSCC.

Conclusion:

Knowledge of HPV OPC status is required in order to determine the impact of the pre-treatment ADC values on post-CRT outcomes in HNSCC.

Advances in knowledge:

Pre-treatment ADC_mean and ADC_min values acquired using different ROI methods are not predictive of 2 year survival outcomes in HPV OPC.

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

Prediction of Human Papillomavirus (HPV) Association of Oropharyngeal Cancer (OPC) Using Radiomics: The Impact of the Variation of CT Scanner

Simple Summary

Recent studies exploring the application of radiomics features in medicine have shown promising results. However, variation in imaging parameters may impact the robustness of these features. Feature robustness may then in turn affect the prediction performance of the machine learning models built upon these features. While numerous studies have tested feature robustness against a variety of imaging parameters, the extent to which feature robustness affects predictions remains unclear. A particularly notable application of radiomics in clinical oncology is the prediction of Human Papillomavirus (HPV) association in Oropharyngeal cancer. In this study we explore how CT scanner type affects the performance of radiomics features for HPV association prediction and highlight the need to implement precautionary approaches so as to minimize this effect.

Abstract

Studies have shown that radiomic features are sensitive to the variability of imaging parameters (e.g., scanner models), and one of the major challenges in these studies lies in improving the robustness of quantitative features against the variations in imaging datasets from multi-center studies. Here, we assess the impact of scanner choice on computed tomography (CT)-derived radiomic features to predict the association of oropharyngeal squamous cell carcinoma with human papillomavirus (HPV). This experiment was performed on CT image datasets acquired from two different scanner manufacturers. We demonstrate strong scanner dependency by developing a machine learning model to classify HPV status from radiological images. These experiments reveal the effect of scanner manufacturer on the robustness of radiomic features, and the extent of this dependency is reflected in the performance of HPV prediction models. The results of this study highlight the importance of implementing an appropriate approach to reducing the impact of imaging parameters on radiomic features and consequently on the machine learning models, without removing features which are deemed non-robust but may contain learning information.

Prognostic Value of Apparent Diffusion Coefficient in Oropharyngeal Carcinoma

Purpose

To investigate clinical and radiological factors predicting worse outcome after (chemo)radiotherapy ([C]RT) in oropharyngeal squamous cell carcinoma (OPSCC) with a focus on apparent diffusion coefficient (ADC).

Methods

This retrospective study included 67 OPSCC patients, treated with (C)RT with curative intent and diagnosed during 2013–2017. Human papilloma virus (HPV) association was detected with p16 immunohistochemistry. Of all 67 tumors, 55 were p16 positive, 9 were p16 negative, and in 3 the p16 status was unknown. Median follow-up time was 38 months. We analyzed pretreatment magnetic resonance imaging (MRI) for factors predicting disease-free survival (DFS) and locoregional recurrence (LRR), including primary tumor volume and the largest metastasis. Crude and p16-adjusted hazard ratios were analyzed using Cox proportional hazards model. Interobserver agreement was evaluated.

Results

Disease recurred in 13 (19.4%) patients. High ADC predicted poor DFS, but not when the analysis was adjusted for p16. A break in RT (hazard ratio, HR = 3.972, 95% confidence interval, CI 1.445–10.917, p = 0.007) and larger metastasis volume (HR = 1.041, 95% CI 1.007–1.077, p = 0.019) were associated with worse DFS. A primary tumor larger than 7 cm³ was associated with increased LRR rate (HR = 4.861, 1.042–22.667, p = 0.044). Among p16-positive tumors, mean ADC was lower in grade 3 tumors compared to lower grade tumors (0.736 vs. 0.883; p = 0.003).

Conclusion

Low tumor ADC seems to be related to p16 positivity and therefore should not be used independently to evaluate disease prognosis or to choose patients for treatment deintensification.

Radiomics of apparent diffusion coefficient maps to predict histologic grade in squamous cell carcinoma of the oral tongue and floor of mouth: a preliminary study

BACKGROUND

Histologic grade assessment plays an important part in the clinical decision making and prognostic evaluation of squamous cell carcinoma (SCC) of the oral tongue and floor of mouth (FOM).

PURPOSE

To assess the value of apparent diffusion coefficient (ADC)-based radiomics in discriminating between low- and high-grade SCC of the oral tongue and FOM.

MATERIAL AND METHODS

We included data from 88 patients (training cohort: n = 59; testing cohort: n = 29) who underwent diffusion-weighted imaging with a 3.0-T magnetic resonance imaging scanner before treatment. A total of 526 radiomics features were extracted from ADC maps to construct a radiomics signature with least absolute shrinkage and selection operator logistic regression. Receiver operating characteristic curves and areas under the curve (AUCs) were used to evaluate the performance of radiomic signature.

RESULTS

Five features were selected to construct the radiomics signature for predicting histologic grade. The ADC-based radiomics signature performed well for discriminating between low- and high-grade tumors, with AUCs of 0.83 in both cohorts. Based on the cut-off value of the training cohort, the radiomics signature achieved accuracies of 0.78 and 0.79, sensitivities of 0.65 and 0.71, and specificities of 0.85 and 0.82 in the training and testing cohorts, respectively.

CONCLUSION

ADC-based radiomics can be a useful and promising non-invasive method for predicting histologic grade of SCC of the oral tongue and FOM.

Oxidative Stress and the Intersection of Oncogenic Signaling and Metabolism in Squamous Cell Carcinomas

Squamous cell carcinomas (SCCs) arise from both stratified squamous and non-squamous epithelium of diverse anatomical sites and collectively represent one of the most frequent solid tumors, accounting for more than one million cancer deaths annually. Despite this prevalence, SCC patients have not fully benefited from recent advances in molecularly targeted therapy or immunotherapy. Rather, decades old platinum-based or radiation regimens retaining limited specificity to the unique characteristics of SCC remain first-line treatment options. Historically, a lack of a consolidated perspective on genetic aberrations driving oncogenic transformation and other such factors essential for SCC pathogenesis and intrinsic confounding cellular heterogeneity in SCC have contributed to a critical dearth in effective and specific therapies. However, emerging evidence characterizing the distinct genomic, epigenetic, and metabolic landscapes of SCC may be elucidating unifying features in a seemingly heterogeneous disease. In this review, by describing distinct metabolic alterations and genetic drivers of SCC revealed by recent studies, we aim to establish a conceptual framework for a previously unappreciated network of oncogenic signaling, redox perturbation, and metabolic reprogramming that may reveal targetable vulnerabilities at their intersection.

Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging

BACKGROUND AND PURPOSE

Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY).

METHODS

Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient.

RESULTS

The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively.

CONCLUSIONS

Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.

Pre-treatment PET/MRI based FDG and DWI imaging parameters for predicting HPV status and tumor response to chemoradiotherapy in primary oropharyngeal squamous cell carcinoma (OPSCC)

OBJECTIVES

To determine the feasibility of pre-treatment primary tumor FDG-PET and DWI-MR imaging parameters in predicting HPV status and the second aim was to assess the feasibility of those imaging parameters to predict response to therapy.

MATERIAL AND METHODS

We retrospectively analyzed primary tumors in 33 patients with proven OPSCC. PET/MRI was performed before and 6 months after chemo-radiotherapy for assessing treatment response. PET Standardized uptake value (SUVmax), total lesion glycolysis (TLG), metabolic tumor volume (MTV), and apparent diffusion coefficient (ADC) from pre-treatment measurements were assessed and compared to the clinicopathological characteristics (T stages, N stages, tumor grades, HPV and post-treatment follow up). HPV was correlated to the clinicopathological characteristics.

RESULTS

ADCmean was significantly lower in patients with HPV^+ve than HPV^-ev, (P = 0.001), cut off value of (800 ± 0.44*10^-3mm²/s) with 76.9% sensitivity, and 72.2% specificity is able to differentiate between the two groups. No significant differences were found between FDG parameters (SUVmax, TLG, and MTV), and HPV status, (P = 0.873, P = 0.958, and P = 0.817), respectively. Comparison between CR and NCR groups; ADCmean, TLG, and MTV were predictive parameters of treatment response, (P = 0.017, P = 0.013, and P = 0.014), respectively. HPV^+ve group shows a higher probability of lymph nodes involvement, (P = 0.006) CONCLUSION: Our study found that pretreatment ADC of the primary tumor can predict HPV status and treatment response. On the other hand, metabolic PET parameters (TLG, and MTV) were able to predict primary tumor response to therapy.

Functional Imaging to Predict Treatment Response in Head and Neck Cancer: How Close are We to Biologically Adaptive Radiotherapy?

It is increasingly recognised that head and neck cancer represents a spectrum of disease with a differential response to standard treatments. Although prognostic factors are well established, they do not reliably predict response. The ability to predict response early during radiotherapy would allow adaptation of treatment: intensifying treatment for those not responding adequately or de-intensifying remaining therapy for those likely to achieve a complete response. Functional imaging offers such an opportunity. Changes in parameters obtained with functional magnetic resonance imaging or positron emission tomography-computed tomography during treatment have been found to be predictive of disease control in head and neck cancer. Although many questions remain unanswered regarding the optimal implementation of these techniques, current, maturing and future studies may provide the much-needed homogeneous cohorts with larger sample sizes and external validation of parameters. With a stepwise and collaborative approach, we may be able to develop imaging biomarkers that allow us to deliver personalised, biologically adaptive radiotherapy for head and neck cancer.

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

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.

Prediction of Human Papillomavirus Status and Overall Survival in Patients with Untreated Oropharyngeal Squamous Cell Carcinoma: Development and Validation of CT-Based Radiomics

BACKGROUND AND PURPOSE

Human papillomavirus is a prognostic marker for oropharyngeal squamous cell carcinoma. We aimed to determine the value of CT-based radiomics for predicting the human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma.

MATERIALS AND METHODS

Eighty-six patients with oropharyngeal squamous cell carcinoma were retrospectively collected and grouped into training (n = 61) and test (n = 25) sets. For human papillomavirus status and overall survival prediction, radiomics features were selected via a random forest-based algorithm and Cox regression analysis, respectively. Relevant features were used to build multivariate Cox regression models and calculate the radiomics score. Human papillomavirus status and overall survival prediction were assessed via the area under the curve and concordance index, respectively. The models were validated in the test and The Cancer Imaging Archive cohorts (n = 78).

RESULTS

For prediction of human papillomavirus status, radiomics features yielded areas under the curve of 0.865, 0.747, and 0.834 in the training, test, and validation sets, respectively. In the univariate Cox regression, the human papillomavirus status (positive: hazard ratio, 0.257; 95% CI, 0.09-0.7; P = .008), T-stage (≥III: hazard ratio, 3.66; 95% CI, 1.34-9.99; P = .011), and radiomics score (high-risk: hazard ratio, 3.72; 95% CI, 1.21-11.46; P = .022) were associated with overall survival. The addition of the radiomics score to the clinical Cox model increased the concordance index from 0.702 to 0.733 (P = .01). Validation yielded concordance indices of 0.866 and 0.720.

CONCLUSIONS

CT-based radiomics may be useful in predicting human papillomavirus status and overall survival in patients with oropharyngeal squamous cell carcinoma.

Machine Learning-Based MRI Texture Analysis to Predict the Histologic Grade of Oral Squamous Cell Carcinoma

OBJECTIVE. This study aimed to explore the performance of machine learning (ML)-based MRI texture analysis in discriminating between well-differentiated (WD) oral squamous cell carcinoma (OSCC) and moderately or poorly differentiated OSCC. MATERIALS AND METHODS. The study enrolled 80 patients with pathologically confirmed OSCC (18 WD OSCCs and 62 moderately or poorly differentiated OSCCs) who underwent pretreatment MRI. ROIs were manually delineated to cover the entire tumor to the greatest possible extent on T2-weighted imaging and contrast-enhanced T1-weighted imaging, and 1118 texture features were extracted. Dimension reduction was performed using reproducibility analysis by two radiologists, collinearity analysis, and feature selection with a minimum-redundancy maximum-relevance algorithm. Models were created using random forest (RF), artificial neural network, and logistic regression (LR) alone and with a synthetic minority oversampling technique (SMOTE). Classifier performance was assessed using 10-fold cross-validation. RESULTS. Dimension reduction steps yielded eight texture features, including four features from each sequence. None of the clinical variables was selected. Among the eight texture features, five and seven texture features showed significant differences between the two groups in the actual data and balanced data, respectively (p < 0.05). All classifiers with SMOTE achieved better performances than those alone. The RF classifier with SMOTE achieved the best performance with an area under the ROC curve of 0.936 and accuracy of 86.3%. CONCLUSION. ML-based MRI texture analysis provides a promising noninvasive approach for predicting the histologic grade of OSCC.

Pretreatment DWI with Histogram Analysis of the ADC in Predicting the Outcome of Advanced Oropharyngeal Cancer with Known Human Papillomavirus Status Treated with Chemoradiation

BACKGROUND AND PURPOSE

The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has increased in the period from the 1970s to 2004, due to increase of infection with human papilloma virus (HPV). This study aimed to examine the role of histogram analysis of the ADC in treatment response and survival prediction of patients with oropharyngeal squamous cell carcinoma and known human papillomavirus status.

MATERIALS AND METHODS

This was a retrospective single-center study. Following inclusion and exclusion criteria, data for 59 patients affected by T2-T4 (according to the 8th edition of the AJCC Cancer Staging Manual) oropharyngeal squamous cell carcinoma were retrieved. Twenty-eight had human papillomavirus-positive oropharyngeal squamous cell carcinoma, while 31 had human papillomavirus-negative oropharyngeal squamous cell carcinoma. All patients underwent a pretreatment MR imaging. Histogram analysis of ADC maps obtained by DWI (b = 0-1000 mm/s²) was performed on the central section of all of tumors. The minimum follow-up period was 2 years. Histogram ADC parameters were associated with progression-free survival and overall survival. Univariable and multivariable Cox models were applied to the data; P values were corrected using the Benjamini-Hochberg method.

RESULTS

At univariable analysis, both human papillomavirus status and mean ADC were associated with progression-free survival (hazard ratio = 0.267, P < .05, and hazard ratio  = 1.0028, P ≤ .05, respectively), while only human papillomavirus status was associated with overall survival (hazard ratio  = 0.213, P ≤ .05) before correction. At multivariable analysis, no parameter was included (in fact, human papillomavirus status lost significance after correction). If we separated the patients into 2 subgroups according to human papillomavirus status, ADC entropy was associated with overall survival in the human papillomavirus-negative group (hazard ratio  = 4.846, P = .01).

CONCLUSIONS

ADC and human papillomavirus status are related to progression-free survival in patients treated with chemoradiation for advanced oropharyngeal squamous cell carcinoma; however, this association seems to result from the strong association between ADC and human papillomavirus status.

Prediction of the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma by FDG-PET imaging dataset using deep learning analysis: A hypothesis-generating study

PURPOSE

To assess the diagnostic accuracy of imaging-based deep learning analysis to differentiate between human papillomavirus (HPV) positive and negative oropharyngeal squamous cell carcinomas (OPSCCs) using FDG-PET images.

METHODS

One hundred and twenty patients with OPSCC who underwent pretreatment FDG-PET/CT were included and divided into the training 90 patients and validation 30 patients cohorts. In the training session, 2160 FDG-PET images were analyzed after data augmentation process by a deep learning technique to create a diagnostic model to discriminate between HPV-positive and HPV-negative OPSCCs. Validation cohort data were subsequently analyzed for confirmation of diagnostic accuracy in determining HPV status by the deep learning-based diagnosis model. In addition, two radiologists evaluated the validation cohort image-data to determine the HPV status based on each tumor's imaging findings.

RESULTS

In deep learning analysis with training session, the diagnostic model using training dataset was successfully created. In the validation session, the deep learning diagnostic model revealed sensitivity of 0.83, specificity of 0.83, positive predictive value of 0.88, negative predictive value of 0.77, and diagnostic accuracy of 0.83, while the visual assessment by two radiologists revealed 0.78, 0.5, 0.7, 0.6, and 0.67 (reader 1), and 0.56, 0.67, 0.71, 0.5, and 0.6 (reader 2), respectively. Chi square test showed a significant difference between deep learning- and radiologist-based diagnostic accuracy (reader 1: P = 0.016, reader 2: P = 0.008).

CONCLUSIONS

Deep learning diagnostic model with FDG-PET imaging data can be useful as one of supportive tools to determine the HPV status in patients with OPSCC.

Texture Analysis of Multi-Shot Echo-planar Diffusion-Weighted Imaging in Head and Neck Squamous Cell Carcinoma: The Diagnostic Value for Nodal Metastasis

Accurate assessment of nodal metastasis in head and neck squamous cell carcinoma (SCC) is important, and diffusion-weighted imaging (DWI) has emerged as a potential technique in differentiating benign from malignant lymph nodes (LNs). This study aims to evaluate the diagnostic performance of texture analysis using apparent diffusion coefficient (ADC) data of multi-shot echo-planar imaging-based DWI (msEPI-DWI) in predicting metastatic LNs of head and neck SCC. 36 patients with pathologically proven head and neck SCC were included in this study. A total of 204 MRI-detected LNs, including 176 subcentimeter-sized LNs, were assigned to metastatic or benign groups. Texture features of LNs were compared using independent t-test. Hierarchical cluster analysis was performed to exclude redundant features. Multivariate logistic regression and receiver operating characteristic analysis were performed to assess the diagnostic performance. The discriminative texture features for predicting metastatic LNs were complexity, energy and roundness. Areas under the curves (AUCs) for diagnosing metastasis in all/subcentimeter-sized LNs were 0.829/0.767 using complexity, 0.699/0.685 using energy and 0.671/0.638 using roundness, respectively. The combination of three features resulted in higher AUC values of 0.836/0.781. In conclusion, texture analysis of ADC data using msEPI-DWI could be a useful tool for nodal staging in head and neck SCC.

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

PURPOSE

To evaluate the diagnostic performance of texture analysis for discriminating human papillomavirus (HPV) status in patients with oropharyngeal squamous cell carcinoma (OPSCC) in the primary tumours and metastatic lymph nodes.

METHODS

Ninety-five patients with primary tumour and 91 with metastatic lymph nodes with confirmed HPV status, who underwent pretreatment contrast-enhanced CT (CECT), were included as the discovery population. CT texture analysis was performed using commercially available software. Differences between HPV-positive and HPV-negative groups were analysed using the χ² test (or Mann-Whitney U test) and independent t test (or Fisher's exact test). ROC curve analysis was performed to discriminate HPV status according to heterogeneity parameters. Diagnostic accuracy was evaluated in the separate validation population (n = 36) from an outside hospital.

RESULTS

HPV positivity was 52.6% for primary tumours and 56.0% for metastatic lymph nodes. The entropy and standard deviation (SD) values in the HPV-positive group were significantly lower. Entropy using the medium filter was the best discriminator between HPV-positive and HPV-negative primary OPSCCs (AUC, 0.85) and SD without the filter for metastatic lymph nodes (AUC, 0.82). Diagnostic accuracy of entropy for the primary tumour was 80.0% in the discovery group and 75.0% in the validation group. In cases of metastatic lymph node, the accuracy of SD was 79.1% and 78.8%, respectively.

CONCLUSION

Significant differences were found in heterogeneity parameters from texture analysis of pretreatment CECT, according to HPV status. Texture analysis could be used as an adjunctive tool for diagnosis of HPV status in clinical practice.

Quantitative diffusion magnetic resonance imaging for prediction of human papillomavirus status in head and neck squamous-cell carcinoma: A systematic review and meta-analysis

PURPOSE

Head and neck squamous-cell carcinoma (HNSCC) related to human papillomavirus (HPV) infection represents a distinct biological and prognostic subtype compared to the HPV-negative form. Prior studies suggest a correlation between the apparent diffusion coefficient (ADC) values on diffusion-weighted imaging (DWI) of primary tumor lesion and HPV status in HNSCC. In this meta-analysis, we compared the average ADC of primary lesion between HPV-positive and HPV-negative HNSCC.

METHODS

A comprehensive literature search of PubMed and Embase was performed. Studies comparing the average ADC on echo-planar DWI of primary tumor lesions between HPV-positive and HPV-negative HNSCC were included. The standardized mean difference was calculated using fixed- and random-effects models. Tau-squared estimates of total heterogeneity and Higgins inconsistency index (I² test) were determined.

RESULTS

A total of five studies, pooling data of 264 patients, were included for meta-analysis. Among these five studies, three had included oral cavity, hypopharyngeal, and/or laryngeal HNSCC in addition to oropharyngeal subsite. Primary lesions were comprised of 185 HPV-negative and 79 HPV-positive HNSCC. The meta-analysis showed lower average ADC values in HPV-positive HNSCC compared to the HPV-negative form, with a standardized mean difference of 0.961 (95% confidence interval 0.644-1.279; p < 0.0001). Since there was no significant heterogeneity in analysis (p = 0.3852), both random- and fixed-effects models resulted in the same estimates of overall effect.

CONCLUSIONS

HPV-positive HNSCC primary lesions have a lower average ADC compared to the HPV-negative form, highlighting the potential application of quantitative diffusion magnetic resonance imaging as a noninvasive imaging biomarker for prediction of HPV status.