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

Demographic and imaging features of oral squamous cell cancer in Serbia: a retrospective cross-sectional study

BACKGROUND

The mortality of oral squamous cell cancer (OSCC) in Serbia increased in the last decade. Recent studies on the Serbian population focused mainly on the epidemiological aspect of OSCC. This study aimed to investigate the demographic and imaging features of OSCC in the Serbian population at the time of diagnosis.

METHODS

We retrospectively analyzed computed tomography (CT) images of 276 patients with OSCC diagnosed between 2017 and 2022. Age, gender, tumor site, tumor volume (CT-TV, in cm3), depth of invasion (CT-DOI, in mm), and bone invasion (CT-BI, in %) were evaluated. TNM status and tumor stage were also analyzed. All parameters were analyzed with appropriate statistical tests.

RESULTS

The mean age was 62.32 ± 11.39 and 63.25 ± 11.71 for males and females, respectively. Male to female ratio was 1.63:1. The tongue (36.2%), mouth floor (21.0%), and alveolar ridge (19.9%) were the most frequent sites of OSCC. There was a significant gender-related difference in OSCC distribution between oral cavity subsites (Z=-4.225; p < 0.001). Mean values of CT-TV in males (13.8 ± 21.5) and females (5.4 ± 6.8) were significantly different (t = 4.620; p < 0.001). CT-DOI also differed significantly (t = 4.621; p < 0.001) between males (14.4 ± 7.4) and females (10.7 ± 4.4). CT-BI was detected in 30.1%, the most common in the alveolar ridge OSCC. T2 tumor status (31.4%) and stage IVA (28.3%) were the most dominant at the time of diagnosis. Metastatic lymph nodes were detected in 41.1%.

CONCLUSION

Our findings revealed significant gender-related differences in OSCC imaging features. The predominance of moderate and advanced tumor stages indicates a long time interval to the OSCC diagnosis.

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 Approach for Cervical Lymph Node Metastases from Unknown Primary Tumor

Neck swelling due to lymph node (LN) metastasis is one of the initial symptoms of head and neck cancer, and in some cases, the primary tumor is not clinically evident. The purpose of imaging for LN metastasis from an unknown primary site is to identify the primary tumor or detect its absence, which leads to the correct diagnosis and optimal treatment. The authors discuss diagnostic imaging approaches for identifying the primary tumor in cases of unknown primary cervical LN metastases. The distribution and characteristics of LN metastases may help locate the primary site. Unknown primary LN metastasis often occurs at nodal levels II and III, and in recent reports, these were mostly related to human papillomavirus (HPV)-positive squamous cell carcinoma of the oropharynx. Another characteristic imaging finding suggestive of metastasis from HPV-associated oropharyngeal cancer is a cystic change in LN metastases. Other characteristic imaging findings such as calcification may help predict the histologic type and locate the primary site. In cases of LN metastases at nodal levels IV and VB, a primary lesion located outside the head and neck region must also be considered. One clue for detecting the primary lesion at imaging is the disruption of anatomic structures, which can help in identifying small mucosal lesions or submucosal tumors at each subsite. Additionally, fluorine 18 fluorodeoxyglucose PET/CT may help identify a primary tumor. These imaging approaches for identifying primary tumors enable prompt identification of the primary site and assist clinicians in making the correct diagnosis. ^© RSNA, 2023 Quiz questions for this article are available through the Online Learning Center.

Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review

Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.

ADC Values of Cytologically Benign and Cytologically Malignant 18 F-FDG PET-Positive Lymph Nodes of Head and Neck Squamous Cell Carcinoma

Simple Summary

In squamous cell carcinoma of the head and neck, 18F-fluordeoxyglucose positron emission tomography (FDG-PET), diffusion-weighted magnetic resonance imaging (DW-MRI) and ultrasound-guided fine needle aspiration are commonly used imaging tools for nodal staging (N-staging). Although FDG-PET has good performance in nodal detection, it is still difficult to distinguish between PET-positive reactive and malignant nodes for the purpose of selecting nodes to be aspirated. DW-MRI can help to detect small lymph node metastases, and an inverse correlation with FDG uptake is expected. We found a mild negative correlation between SUVmax and ADC. Comparing the apparent diffusion coefficient (ADC) values between PET-positive and PET-negative nodes, ADC was significantly higher in PET-negative nodes. Whereas no significantly lower ADC value of cytological malignant nodes could be found overall, in the subgroup of non-HPV-related nodes, the ADC values of cytologically malignant PET-positive nodes were significantly lower than in cytologically benign nodes. This finding might be helpful in selecting nodes for puncture.

Abstract

Nodal staging (N-staging) in head and neck squamous cell carcinoma (HNSCC) is essential for treatment planning and prognosis. 18F-fluordeoxyglucose positron emission tomography (FDG-PET) has high performance for N-staging, although the distinction between cytologically malignant and reactive PET-positive nodes, and consequently, the selection of nodes for ultrasound-guided fine needle aspiration cytology (USgFNAC), is challenging. Diffusion-weighted magnetic resonance imaging (DW-MRI) can help to detect nodal metastases. We aim to investigate the potential of the apparent diffusion coefficient (ADC) as a metric to distinguish between cytologically reactive and malignant PET-positive nodes in order to improve node selection criteria for USgFNAC. PET-CT, real-time image-fused USgFNAC and DW-MRI to calculate ADC were available for 78 patients offered for routine N-staging. For 167 FDG-positive nodes, differences in the ADC between cytologically benign and malignant PET-positive nodes were evaluated, and both were compared to the ADC values of PET-negative reference nodes. Analyses were also performed in subsets of nodes regarding HPV status. A mild negative correlation between SUVmax and ADC was found. No significant differences in ADC values were observed between cytologically malignant and benign PET-positive nodes overall. Within the subset of non-HPV-related nodes, ADC_b0-200-1000 was significantly lower in cytologically malignant PET-positive nodes when compared to benign PET-positive nodes. ADC_b0-1000 and ADCb_0-200-1000 were significantly lower (p = 0.018, 0.016, resp.) in PET-negative reference nodes than in PET-positive nodes. ADC was significantly higher in PET-negative reference nodes than in PET-positive nodes. The non-HPV-related subgroup showed significantly (p = 0.03) lower ADC values in cytologically malignant than in cytologically benign PET-positive nodes, which should help inform the node selection procedure for puncture.

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.

Role of MR Imaging in Head and Neck Squamous Cell Carcinoma

Routine and advanced MR imaging sequences are used for locoregional spread, nodal, and distant staging of head and neck squamous cell carcinoma, aids treatment planning, predicts treatment response, differentiates recurrence for postradiation changes, and monitors patients after chemoradiotherapy.

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.

Multiparametric MRI Evaluation of Oropharyngeal Squamous Cell Carcinoma. A Mono-Institutional Study

The aim of this paper is to define the pre-treatment radiological characteristics of oropharyngeal squamous cell carcinoma (OPSCC) using morphological and non-morphological magnetic resonance imaging (MRI), based on HPV status, in a single-institution cohort. In total, 100 patients affected by OPSCC were prospectively enrolled in the present study. All patients underwent 1.5T MR with standard sequences, including diffusion-weighted imaging with and intravoxel incoherent motion (IVIM-DWI) technique and a dynamic contrast-enhanced (DCE) MRI. For all patients, human papillomavirus (HPV) status was available. No statistically significant differences in the volume of primary tumors (PTs) and lymph nodes (LNs) were observed based on HPV status. When comparing the two patient groups, no significant differences were found for the PT radiologic characteristics (presence of well-defined borders, exophytic growth, ulceration, and necrosis) and LN morphology (solid/cystic/necrotic). Tumor subsite, smoking status, and alcohol intake significantly differed based on HPV status, as well as ADC and D_t values of both PTs and LNs. We detected no significant difference in DCE-MRI parameters by HPV status. Based on a multivariate logistic regression model, the combination of clinical factors, such as tumor subsite and alcohol habits, with the perfusion-free diffusion coefficient D_t of LNs, may help to accurately discriminate OPSCC by HPV status.

[Artificial intelligence in otorhinolaryngology]

Hintergrund

Die fortschreitende Digitalisierung ermöglicht zunehmend den Einsatz von künstlicher Intelligenz (KI). Sie wird Gesellschaft und Medizin in den nächsten Jahren maßgeblich beeinflussen.

Ziel der Arbeit

Darstellung des gegenwärtigen Einsatzspektrums von KI in der Hals-Nasen-Ohren-Heilkunde und Skizzierung zukünftiger Entwicklungen bei der Anwendung dieser Technologie.

Material und Methoden

Es erfolgte die Auswertung und Diskussion wissenschaftlicher Studien und Expertenanalysen.

Ergebnisse

Durch die Verwendung von KI kann der Nutzen herkömmlicher diagnostischer Werkzeuge in der Hals-Nasen-Ohren-Heilkunde gesteigert werden. Zudem kann der Einsatz dieser Technologie die chirurgische Präzision in der Kopf-Hals-Chirurgie weiter erhöhen.

Schlussfolgerungen

KI besitzt ein großes Potenzial zur weiteren Verbesserung diagnostischer und therapeutischer Verfahren in der Hals-Nasen-Ohren-Heilkunde. Allerdings ist die Anwendung dieser Technologie auch mit Herausforderungen verbunden, beispielsweise im Bereich des Datenschutzes.

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.

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.

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.

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.

Diffusion MR Imaging in the Head and Neck: Principles and Applications

Diffusion imaging is a functional MR imaging tool that creates tissue contrast representative of the random, microscopic translational motion of water molecules within human body tissues. Long considered a cornerstone MR imaging sequence for brain imaging, diffusion-weighted imaging (DWI) increasingly is used for head and neck imaging. This review reports the current state of diffusion techniques for head and neck imaging, including conventional DWI, DWI trace with apparent diffusion coefficient map, diffusion tensor imaging, intravoxel incoherent motion, and diffusion kurtosis imaging. This article describes background physics, reports supportive evidence and potential pitfalls, highlights technical advances, and details practical clinical applications.

Applications of radiomics in precision diagnosis, prognostication and treatment planning of head and neck squamous cell carcinomas

Recent advancements in computational power, machine learning, and artificial intelligence technology have enabled automated evaluation of medical images to generate quantitative diagnostic and prognostic biomarkers. Such objective biomarkers are readily available and have the potential to improve personalized treatment, precision medicine, and patient selection for clinical trials. In this article, we explore the merits of the most recent addition to the “-omics” concept for the broader field of head and neck cancer – “Radiomics”. This review discusses radiomics studies focused on (molecular) characterization, classification, prognostication and treatment guidance for head and neck squamous cell carcinomas (HNSCC). We review the underlying hypothesis, general concept and typical workflow of radiomic analysis, and elaborate on current and future challenges to be addressed before routine clinical application.

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.