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.

Magnetic resonance imaging based radiomics prediction of Human Papillomavirus infection status and overall survival in oropharyngeal squamous cell carcinoma.

OBJECTIVES: Human papillomavirus- (HPV) positive oropharyngeal squamous cell carcinoma (OPSCC) differs biologically and clinically from HPV-negative OPSCC and has a better prognosis. This study aims to analyze the value of magnetic resonance imaging (MRI)-based radiomics in predicting HPV status in OPSCC and aims to develop a prognostic model in OPSCC including HPV status and MRI-based radiomics. MATERIALS AND METHODS: Manual delineation of 249 primary OPSCCs (91 HPV-positive and 159 HPV-negative) on pretreatment native T1-weighted MRIs was performed and used to extract 498 radiomic features per delineation. A logistic regression (LR) and random forest (RF) model were developed using univariate feature selection. Additionally, factor analysis was performed, and the derived factors were combined with clinical data in a predictive model to assess the performance on predicting HPV status. Additionally, factors were combined with clinical parameters in a multivariable survival regression analysis. RESULTS: Both feature-based LR and RF models performed with an AUC of 0.79 in prediction of HPV status. Fourteen of the twenty most significant features were similar in both models, mainly concerning tumor sphericity, intensity variation, compactness, and tumor diameter. The model combining clinical data and radiomic factors (AUC = 0.89) outperformed the radiomics-only model in predicting OPSCC HPV status. Overall survival prediction was most accurate using the combination of clinical parameters and radiomic factors (C-index = 0.72). CONCLUSION: Predictive models based on MR-radiomic features were able to predict HPV status with sufficient performance, supporting the role of MRI-based radiomics as potential imaging biomarker. Survival prediction improved by combining clinical features with MRI-based radiomics.

External validation of an MR-based radiomic model predictive of locoregional control in oropharyngeal cancer.

OBJECTIVES: To externally validate a pre-treatment MR-based radiomics model predictive of locoregional control in oropharyngeal squamous cell carcinoma (OPSCC) and to assess the impact of differences between datasets on the predictive performance. METHODS: Radiomic features, as defined in our previously published radiomics model, were extracted from the primary tumor volumes of 157 OPSCC patients in a different institute. The developed radiomics model was validated using this cohort. Additionally, parameters influencing performance, such as patient subgroups, MRI acquisition, and post-processing steps on prediction performance will be investigated. For this analysis, matched subgroups (based on human papillomavirus (HPV) status of the tumor, T-stage, and tumor subsite) and a subgroup with only patients with 4-mm slice thickness were studied. Also the influence of harmonization techniques (ComBat harmonization, quantile normalization) and the impact of feature stability across observers and centers were studied. Model performances were assessed by area under the curve (AUC), sensitivity, and specificity. RESULTS: Performance of the published model (AUC/sensitivity/specificity: 0.74/0.75/0.60) drops when applied on the validation cohort (AUC/sensitivity/specificity: 0.64/0.68/0.60). The performance of the full validation cohort improves slightly when the model is validated using a patient group with comparable HPV status of the tumor (AUC/sensitivity/specificity: 0.68/0.74/0.60), using patients acquired with a slice thickness of 4 mm (AUC/sensitivity/specificity: 0.67/0.73/0.57), or when quantile harmonization was performed (AUC/sensitivity/specificity: 0.66/0.69/0.60). CONCLUSION: The previously published model shows its generalizability and can be applied on data acquired from different vendors and protocols. Harmonization techniques and subgroup definition influence performance of predictive radiomics models. KEY POINTS: • Radiomics, a noninvasive quantitative image analysis technique, can support the radiologist by enhancing diagnostic accuracy and/or treatment decision-making. • A previously published model shows its generalizability and could be applied on data acquired from different vendors and protocols.

Quantitative Diffusion-Weighted Imaging Analyses to Predict Response to Neoadjuvant Immunotherapy in Patients with Locally Advanced Head and Neck Carcinoma.

BACKGROUND: Neoadjuvant immune checkpoint blockade (ICB) prior to surgery may induce early pathological responses in head and neck squamous cell carcinoma (HNSCC) patients. Routine imaging parameters fail to diagnose these responses early on. Magnetic resonance (MR) diffusion-weighted imaging (DWI) has proven to be useful for detecting HNSCC tumor mass after (chemo)radiation therapy. METHODS: 32 patients with stage II-IV, resectable HNSCC, treated at a phase Ib/IIa IMCISION trial (NCT03003637), were retrospectively analyzed using MR-imaging before and after two doses of single agent nivolumab (anti-PD-1) (n = 6) or nivolumab with ipilimumab (anti-CTLA-4) ICB (n = 26). The primary tumors were delineated pre- and post-treatment. A total of 32 features were derived from the delineation and correlated with the tumor regression percentage in the surgical specimen. RESULTS: MR-DWI data was available for 24 of 32 patients. Smaller baseline tumor diameter (p = 0.01-0.04) and higher sphericity (p = 0.03) were predictive of having a good pathological response to ICB. Post-treatment skewness and the change in skewness between MRIs were negatively correlated with the tumor's regression (p = 0.04, p = 0.02). CONCLUSION: Pre-treatment DWI tumor diameter and sphericity may be quantitative biomarkers for the prediction of an early pathological response to ICB. Furthermore, our data indicate that ADC skewness could be a marker for individual response evaluation.

Early Response Prediction of Multiparametric Functional MRI and 18F-FDG-PET in Patients with Head and Neck Squamous Cell Carcinoma Treated with (Chemo)Radiation.

BACKGROUND: Patients with locally-advanced head and neck squamous cell carcinoma (HNSCC) have variable responses to (chemo)radiotherapy. A reliable prediction of outcomes allows for enhancing treatment efficacy and follow-up monitoring. METHODS: Fifty-seven histopathologically-proven HNSCC patients with curative (chemo)radiotherapy were prospectively included. All patients had an MRI (DW,-IVIM, DCE-MRI) and 18F-FDG-PET/CT before and 10 days after start-treatment (intratreatment). Primary tumor functional imaging parameters were extracted. Univariate and multivariate analysis were performed to construct prognostic models and risk stratification for 2 year locoregional recurrence-free survival (LRFFS), distant metastasis-free survival (DMFS) and overall survival (OS). Model performance was measured by the cross-validated area under the receiver operating characteristic curve (AUC). RESULTS: The best LRFFS model contained the pretreatment imaging parameters ADC_kurtosis, Kep and SUV_peak, and intratreatment imaging parameters change (Δ) Δ-ADC_skewness, Δ-f, Δ-SUV_peak and Δ-total lesion glycolysis (TLG) (AUC = 0.81). Clinical parameters did not enhance LRFFS prediction. The best DMFS model contained pretreatment ADC_kurtosis and SUV_peak (AUC = 0.88). The best OS model contained gender, HPV-status, N-stage, pretreatment ADC_skewness, D, f, metabolic-active tumor volume (MATV), SUV_mean and SUV_peak (AUC = 0.82). Risk stratification in high/medium/low risk was significantly prognostic for LRFFS (p = 0.002), DMFS (p < 0.001) and OS (p = 0.003). CONCLUSIONS: Intratreatment functional imaging parameters capture early tumoral changes that only provide prognostic information regarding LRFFS. The best LRFFS model consisted of pretreatment, intratreatment and Δ functional imaging parameters; the DMFS model consisted of only pretreatment functional imaging parameters, and the OS model consisted ofHPV-status, gender and only pretreatment functional imaging parameters. Accurate clinically applicable risk stratification calculators can enable personalized treatment (adaptation) management, early on during treatment, improve counseling and enhance patient-specific post-therapy monitoring.

Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN.

BACKGROUND : Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN). METHODS: The dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively. RESULTS: The average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes. CONCLUSION: An automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.

Repeatability of IVIM biomarkers from diffusion-weighted MRI in head and neck: Bayesian probability versus neural network.

PURPOSE: The intravoxel incoherent motion (IVIM) model for DWI might provide useful biomarkers for disease management in head and neck cancer. This study compared the repeatability of three IVIM fitting methods to the conventional nonlinear least-squares regression: Bayesian probability estimation, a recently introduced neural network approach, IVIM-NET, and a version of the neural network modified to increase consistency, IVIM-NETmod . METHODS: Ten healthy volunteers underwent two imaging sessions of the neck, two weeks apart, with two DWI acquisitions per session. Model parameters (ADC, diffusion coefficient Dt , perfusion fraction fp , and pseudo-diffusion coefficient Dp ) from each fit method were determined in the tonsils and in the pterygoid muscles. Within-subject coefficients of variation (wCV) were calculated to assess repeatability. Training of the neural network was repeated 100 times with random initialization to investigate consistency, quantified by the coefficient of variance. RESULTS: The Bayesian and neural network approaches outperformed nonlinear regression in terms of wCV. Intersession wCV of Dt in the tonsils was 23.4% for nonlinear regression, 9.7% for Bayesian estimation, 9.4% for IVIM-NET, and 11.2% for IVIM-NETmod . However, results from repeated training of the neural network on the same data set showed differences in parameter estimates: The coefficient of variances over the 100 repetitions for IVIM-NET were 15% for both Dt and fp , and 94% for Dp ; for IVIM-NETmod , these values improved to 5%, 9%, and 62%, respectively. CONCLUSION: Repeatabilities from the Bayesian and neural network approaches are superior to that of nonlinear regression for estimating IVIM parameters in the head and neck.

Adherence to pretreatment and intratreatment imaging of head and neck squamous cell carcinoma patients undergoing (chemo) radiotherapy in a research setting.

PURPOSE: The emerge of improved personalized treatment adaptations and outcome prediction is accompanied with increasing non-invasive assessments in early treatment phase, leading to increased patient burden. This study assessed the adherence of patients with head and neck squamous cell carcinoma (HNSCC) to undergo pretreatment and research-related intratreatment imaging, and assessed which factors caused drop-out. METHOD: Between 2013 and 2019, advanced-staged HNSCC patients were prospectively included, underwent (chemo) radiotherapy with curative intent and planned for both pre-treatment and intratreatment sequential 18F-FDG-PET/CT, 18F-FDG-PET/MRI and thereafter MRI (including DWI/DCE). Drop-out-factors were described as healthcare-related (logistics and imaging-system defects) and patient-related (psychological, physical, not-specified). Common Toxicity Criteria (CTC) were routinely scored by radiation/medical oncologists throughout the first 3 weeks, and compared between patient drop-outs and who complete imaging. RESULTS: Ninety-seven patients (mean age 61 ± 6.8 years) were included; 95 patients (97.9%) underwent pretreatment imaging and 63 (64.9%) intratreatment imaging. For 18F-FDG-PET/CT, 18F-FDG-PET/MRI and MRI pretreatment drop-outs were 2, 10 and 3 patients and for intratreatment drop-outs were 34, 39 and 35 patients, respectively. Patient-related drop-out-factors were physical (n = 16, e.g. dysphagia), psychological (n = 6, e.g. claustrophobia) and non-specified (n = 12). Healthcare-related drop-out-factors were logistics (n = 6) and 18F-FDG-PET/CT-/MRI-system defects (n = 2). The CTC mucosal toxicity was significantly higher (p = 0.023) at week 2 of (chemo)radiotherapy in patient drop-outs than with complete imaging. CONCLUSIONS: The drop-out frequency of advanced-staged HNSCC patients for imaging during (chemo)radiotherapy in a research-setting was high and mainly patient-related. Treatment of patient-related inconveniences, communication of rationale and healthcare-related imaging protocol efficiency improvements may contribute to improved adherence.

Multiparametric functional MRI and 18F-FDG-PET for survival prediction in patients with head and neck squamous cell carcinoma treated with (chemo)radiation.

OBJECTIVES: To assess (I) correlations between diffusion-weighted (DWI), intravoxel incoherent motion (IVIM), dynamic contrast-enhanced (DCE) MRI, and 18F-FDG-PET/CT imaging parameters capturing tumor characteristics and (II) their predictive value of locoregional recurrence-free survival (LRFS) and overall survival (OS) in patients with head and neck squamous cell carcinoma (HNSCC) treated with (chemo)radiotherapy. METHODS: Between 2014 and 2018, patients with histopathologically proven HNSCC, planned for curative (chemo) radiotherapy, were prospectively included. Pretreatment clinical, anatomical, and functional imaging parameters (obtained by DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT) were extracted for primary tumors (PT) and lymph node metastases. Correlations and differences between parameters were assessed. The predictive value of LRFS and OS was assessed, performing univariable, multivariable Cox and CoxBoost regression analyses. RESULTS: In total, 70 patients were included. Significant correlations between 18F-FDG-PET parameters and DWI-/DCE volume parameters were found (r > 0.442, p < 0.002). The combination of HPV (HR = 0.903), intoxications (HR = 1.065), PT ADCGTV (HR = 1.252), Ktrans (HR = 1.223), and Ve (HR = 1.215) was predictive for LRFS (C-index = 0.546; p = 0.023). N-stage (HR = 1.058), HPV positivity (HR = 0.886), hypopharyngeal tumor location (HR = 1.111), ADCGTV (HR = 1.102), ADCmean (HR = 1.137), D* (HR = 0.862), Ktrans (HR = 1.106), Ve (HR = 1.195), SUVmax (HR = 1.094), and TLG (HR = 1.433) were predictive for OS (C-index = 0.664; p = 0.046). CONCLUSIONS: Functional imaging parameters, performing DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT, yielded complementary value in capturing tumor characteristics. More specific, intoxications, HPV-negative status, large tumor volume-related parameters, high permeability (Ktrans), and high extravascular extracellular space (Ve) parameters were predictive for adverse locoregional recurrence-free survival and adverse overall survival. Low cellularity (high ADC) and high metabolism (high SUV) were additionally predictive for decreased overall survival. These different predictive factors added to estimated locoregional and overall survival. KEY POINTS: • Parameters of DWI/IVIM, DCE-MRI, and 18F-FDG-PET/CT were able to capture complementary tumor characteristics. • Multivariable analysis revealed that intoxications, HPV negativity, large tumor volume and high vascular permeability (Ktrans), and extravascular extracellular space (Ve) were complementary predictive for locoregional recurrence. • In addition to predictive parameters for locoregional recurrence, also high cellularity (low ADC) and high metabolism (high SUV) were complementary predictive for overall survival.

The Additional Value of Ultrafast DCE-MRI to DWI-MRI and 18F-FDG-PET to Detect Occult Primary Head and Neck Squamous Cell Carcinoma.

To evaluate diagnostic accuracy of qualitative analysis and interobserver agreement of single ultrafast-DCE, DWI or 18F-FDG-PET and the combination of modalities for the detection of unknown primary tumor (UPT) in patients presenting with cervical lymph node metastasis from squamous cell carcinoma (SCC). Between 2014-2019, patients with histologically proven cervical lymph node metastasis of UPT SCC were prospectively included and underwent DWI, ultrafast-DCE, and 18F-FDG-PET/CT. Qualitative assessment was performed by two observers per modality. Interobserver agreement was calculated using the proportion specific agreement. Diagnostic accuracy of combined use of DWI, ultrafast-DCE and 18F-FDG-PET/CT was assessed. Twenty-nine patients were included (20 males. [68%], median age 60 years). Nine (31%) primary tumors remained occult. Ultrafast-DCE added reader confidence for suspicious locations (one additional true positive (5%), 2 decisive true malignant (10%). The per-location analysis showed highest specific positive agreement for ultrafast-DCE (77.6%). The per-location rating showed highest sensitivity (95%, 95%CI = 75.1-99.9, YI = 0.814) when either one of all modalities was scored positive, and 97.4% (95%CI = 93.5-99.3, YI = 0.774) specificity when co-detected on all. The per-patient analysis showed highest sensitivity (100%) for 18F-FDG-PET/CT (YI = 0.222) and either DWI or PET (YI = 0.111). Despite highest trends, no significant differences were found. The per-patient analysis showed highest specific positive agreement when co-detected on all modalities (55.6%, 95%CI = 21.2-86.3, YI = 0.456). Ultrafast-DCE showed potential to improve detection of unknown primary tumors in addition to DWI and 18F-FDG-PET/CT in patients with cervical squamous cell carcinoma lymph node metastasis. The combined use of ultrafast-DCE, DWI and 18F-FDG-PET/CT yielded highest sensitivity.

Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma.

BACKGROUND: Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (18F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy. METHODS: Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent 18F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order 18F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with 18F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients' outcome. RESULTS: Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764). CONCLUSIONS: Combining HPV-status, first-order 18F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care. TRIAL REGISTRATION: Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946.

Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures.

OBJECTIVES: Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. MATERIALS AND METHODS: Native T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). RESULTS: In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). CONCLUSIONS: MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. KEY POINTS: • MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.

Repeatability of arterial input functions and kinetic parameters in muscle obtained by dynamic contrast enhanced MR imaging of the head and neck.

BACKGROUND: Quantification of pharmacokinetic parameters in dynamic contrast enhanced (DCE) MRI is heavily dependent on the arterial input function (AIF). In the present patient study on advanced stage head and neck squamous cell carcinoma (HNSCC) we have acquired DCE-MR images before and during chemo radiotherapy. We determined the repeatability of image-derived AIFs and of the obtained kinetic parameters in muscle and compared the repeatability of muscle kinetic parameters obtained with image-derived AIF's versus a population-based AIF. MATERIALS AND METHODS: We compared image-derived AIFs obtained from the internal carotid, external carotid and vertebral arteries. Pharmacokinetic parameters (ve, Ktrans, kep) in muscle-located outside the radiation area-were obtained using the Tofts model with the image-derived AIFs and a population averaged AIF. Parameter values and repeatability were compared. Repeatability was calculated with the pre- and post-treatment data with the assumption of no DCE-MRI measurable biological changes between the scans. RESULTS: Several parameters describing magnitude and shape of the image-derived AIFs from the different arteries in the head and neck were significantly different. Use of image-derived AIFs led to higher pharmacokinetic parameters compared to use of a population averaged AIF. Median muscle pharmacokinetic parameters values obtained with AIFs in external carotids, internal carotids, vertebral arteries and with a population averaged AIF were respectively: ve (0.65, 0.74, 0.58, 0.32), Ktrans (0.30, 0.21, 0.13, 0.06), kep (0.41, 0.32, 0.24, 0.18). Repeatability of pharmacokinetic parameters was highest when a population averaged AIF was used; however, this repeatability was not significantly different from image-derived AIFs. CONCLUSION: Image-derived AIFs in the neck region showed significant variations in the AIFs obtained from different arteries, and did not improve repeatability of the resulting pharmacokinetic parameters compared with the use of a population averaged AIF. Therefore, use of a population averaged AIF seems to be preferable for pharmacokinetic analysis using DCE-MRI in the head and neck area.

Predictive value of quantitative diffusion-weighted imaging and 18-F-FDG-PET in head and neck squamous cell carcinoma treated by (chemo)radiotherapy.

BACKGROUND AND PURPOSE: In head and neck squamous cell carcinoma (HNSCC) (chemo)radiotherapy is increasingly used to preserve organ functionality. The purpose of this study was to identify predictive pretreatment DWI- and 18F-FDG-PET/CT-parameters for treatment failure (TF), locoregional recurrence (LR) and death in HNSCC patients treated by (chemo)radiotherapy. MATERIALS AND METHODS: We retrospectively included 134 histologically proven HNSCC patients treated with (chemo)radiotherapy between 2012-2017. In 58 patients pre-treatment DWI and 18F-FDG-PET/CT were performed, in 31 patients DWI only and in 45 patients 18F-FDG-PET/CT only. Primary tumor (PT) and largest lymph node (LN) metastasis were quantitatively assessed for TF, LR and death. Multivariate analysis was performed for 18F-FDG-PET/CT and DWI separately and thereafter combined. In patients with both imaging modalities, positive and negative predictive value in TF and differences in LR and death, were assessed. RESULTS: Mean follow-up was 25.6 months (interquartile-range; 14.0-37.1 months). Predictors of treatment failure, corrected for TNM-stage and HPV-status, were SUVmax-PT, ADCmax-PT, total lesion glycolysis (TLG-LN), ADCp20-LN (P = 0.049, P = 0.024, P = 0.031, P = 0.047, respectively). TLG-PT was predictive for LR (P = 0.003). Metabolic active tumor volume (MATV-PT) (P = 0.003), ADCGTV-PT (P < 0.001), ADCSD (P = 0.048) were significant predictors for death. In patients with both imaging modalities SUVmax-PT remained predictive for treatment failure (P = 0.049), TLG-LN for LR (P = 0.003) and ADCGTV-PT for death (P < 0.001). Higher predictive value for treatment failure was found for the combination of SUVmax-PT and ADCmax-PT, compared to either one separately. CONCLUSION: Both DWI- and 18F-FDG-PET/CT-parameters appear to have predictive value for treatment failure, locoregional recurrence and death. Combining SUVmax-PT and ADCmax-PT resulted in better prediction of treatment failure compared to single parameter assessment.

Functional imaging early during (chemo)radiotherapy for response prediction in head and neck squamous cell carcinoma; a systematic review.

This systematic review gives an extensive overview of the current state of functional imaging during (chemo)radiotherapy to predict locoregional control (LRC) and overall survival (OS) for head and neck squamous cell carcinoma. MEDLINE and EMBASE were searched for literature until April 2018 assessing the predictive performance of functional imaging (computed tomography perfusion (CTp), MRI and positron-emission tomography (PET)) within 4 weeks after (chemo)radiotherapy initiation. Fifty-two studies (CTp: n = 4, MRI: n = 19, PET: n = 26, MRI/PET: n = 3) were included involving 1623 patients. Prognostic information was extracted according the PRISMA protocol. Pooled estimation and subgroup analyses were performed for comparable parameters and outcome. However, the heterogeneity of included studies limited the possibility for comparison. Early tumoral changes from (chemo)radiotherapy can be captured by functional MRI and 18F-FDG-PET and could allow for personalized treatment adaptation. Lesions showed potentially prognostic intratreatment changes in perfusion, diffusion and metabolic activity. Intratreatment ADCmean increase (decrease of diffusion restriction) and low SUVmax (persistent low or decrease of 18F-FDG uptake) were most predictive of LRC. Intratreatment persistent high or increase of perfusion on CT/MRI (i.e. blood flow, volume, permeability) also predicted LRC. Low SUVmax and total lesion glycolysis (TLG) predicted favorable OS. The optimal timing to perform functional imaging to predict LRC or OS was 2-3 weeks after treatment initiation.

Diagnostic value of diffusion-weighted imaging and 18F-FDG-PET/CT for the detection of unknown primary head and neck cancer in patients presenting with cervical metastasis.

BACKGROUND AND PURPOSE: Head and neck squamous cell carcinoma (HNSCC) may present with cervical metastases without an apparent primary tumor. Detecting the primary tumor results in more targeted treatment. Acquisition of DWI is improving with less artifacts and image distortion. We assessed the diagnostic value of DWI and 18F-FDG-PET/CT for detecting primary tumors in patients presenting with nodal metastasis of an unknown primary HNSCC. MATERIALS AND METHODS: For this retrospective study we included 31 patients (male/female ratio = 23/8, median age = 66 years, age range = 40-80 years) who presented with a pathologically proven cervical nodal metastasis from HNSCC without overt primary tumor location between January 2013 and November 2016 and underwent both DWI and 18F-FDG-PET/CT. Both modalities were assessed qualitatively and quantitatively. With ROC analysis we determined the optimal cut-off for imaging parameters in separating occult malignancy from benign tissue. RESULTS: Qualitative analysis of MRI including DWI resulted in a sensitivity of 81.3% (95%CI) = 53.7-95.0) and specificity of 73.3% (95%CI = 44.8-91.1). With qualitative scoring of 18F-FDG-PET/CT a sensitivity and specificity of 93.8% (95%CI = 67.8-99.7) and 73.3% (95%CI = 44.8-91.1) were found. With quantitative analysis sensitivity and specificity of SUVmax were 81.3% (95%CI = 53.6-95.0) and 93.3% (95%CI = 66.0-99.7), respectively. Combining DWI and 18F-FDG-PET/CT resulted in a sensitivity of 93.8% (95%CI = 67.7-99.7%) and specificity of 60.0% (95%CI = 32.9-82.5%). CONCLUSION: In this study on HNSCC patients presenting with clinically UP lesions the diagnostic accuracy of qualitative analysis with DWI and 18F-FDG-PET/CT and quantitative analysis of 18F-FDG-PET/CT using SUVmax were high. Adding DWI did not improve the accuracy of 18F-FDG-PET/CT.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis.

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

The value of subtraction MRI in detection of amyloid-related imaging abnormalities with oedema or effusion in Alzheimer's patients: An interobserver study.

BACKGROUND: Immunotherapeutic treatments targeting amyloid-ß plaques in Alzheimer's disease (AD) are associated with the presence of amyloid-related imaging abnormalities with oedema or effusion (ARIA-E), whose detection and classification is crucial to evaluate subjects enrolled in clinical trials. PURPOSE: To investigate the applicability of subtraction MRI in the ARIA-E detection using an established ARIA-E-rating scale. METHODS: We included 75 AD patients receiving bapineuzumab treatment, including 29 ARIA-E cases. Five neuroradiologists rated their brain MRI-scans with and without subtraction images. The accuracy of evaluating the presence of ARIA-E, intraclass correlation coefficient (ICC) and specific agreement was calculated. RESULTS: Subtraction resulted in higher sensitivity (0.966) and lower specificity (0.970) than native images (0.959, 0.991, respectively). Individual rater detection was excellent. ICC scores ranged from excellent to good, except for gyral swelling (moderate). Excellent negative and good positive specific agreement among all ARIA-E imaging features was reported in both groups. Combining sulcal hyperintensity and gyral swelling significantly increased positive agreement for subtraction images. CONCLUSION: Subtraction MRI has potential as a visual aid increasing the sensitivity of ARIA-E assessment. However, in order to improve its usefulness isotropic acquisition and enhanced training are required. The ARIA-E rating scale may benefit from combining sulcal hyperintensity and swelling. KEY POINTS: • Subtraction technique can improve detection amyloid-related imaging-abnormalities with edema/effusion in Alzheimer's patients. • The value of ARIA-E detection, classification and monitoring using subtraction was assessed. • Validation of an established ARIA-E rating scale, recommendations for improvement are reported. • Complementary statistical methods were employed to measure accuracy, inter-rater-reliability and specific agreement.

Intravoxel incoherent motion magnetic resonance imaging in head and neck cancer: A systematic review of the diagnostic and prognostic value.

Intravoxel incoherent motion (IVIM) imaging is increasingly applied in the assessment of head and neck cancer (HNC). Our purpose was to determine the diagnostic and prognostic performance of IVIM in HNC by performing a critical review of the literature. Pubmed and EMBASE were searched until May 2016. Study and patients characteristics, imaging protocol and diagnostic or prognostic outcomes were extracted by 2 independent reviewers. The studied IVIM parameters were diffusion coefficient (D), pseudodiffusion coefficient (D∗), and perfusion fraction (f). We included 10 diagnostic studies, 5 prognostic studies and 2 studies assessing both. Studies were very heterogeneous in terms of applied b-values, imaging protocols, outcome measurements and reference standards; therefore we did not perform a meta-analysis. The most commonly used sequence was "spin-echo planar imaging". A median of 10.5 b-values (range, 3-17) were used. All but three studies included at least 4 b-values below b=200s/mm2. By combining IVIM-parameters squamous cell carcinomas, lymphomas, malignant salivary gland tumors, Warthin's tumors and pleomorphic adenomas could be differentiated with a sensitivity of 85-87% and specificity of 80-100%. Low pre-treatment D or f and an increase in D during treatment were associated with a favorable response to treatment. D∗ appeared to be the parameter with the lowest prognostic value. Future research should focus on finding the optimal IVIM protocol, using uniformly accepted study methods and larger patient populations.