PurposeCharacterize and monitor treatment response in human papillomavirus (HPV) head and neck squamous cell carcinoma (HNSCC) using intra‐treatment (intra‐TX) imaging metrics derived from intravoxel incoherent motion (IVIM) diffusion‐weighted magnetic resonance imaging (DW‐MRI).Materials and MethodsThirty‐four (30 HPV positive [+] and 4 HPV negative [‐]) HNSCC patients underwent a total of 136 MRI including multi‐b value DW‐MRI (pretreatment [pre‐TX] and intra‐TX weeks 1, 2, and 3) at 3.0 Tesla. All patients were treated with chemo‐radiation therapy. Monoexponential (yielding apparent diffusion coefficient [ADC]) and bi‐exponential (yielding perfusion fraction [f], diffusion [D], and pseudo‐diffusion [D*] coefficients) fits were performed on a region of interest and voxel‐by‐voxel basis, on metastatic neck nodes. Response was assessed using RECISTv1.1. The relative percentage change in D, f, and D* between the pre‐ and intra‐TX weeks were used for hierarchical clustering. A Wilcoxon rank‐sum test was performed to assess the difference in metrics within and between the complete response (CR) and non‐CR groups.ResultsThe delta (Δ) change in volume (V)1wk‐0wk for the CR group differed significantly (P = 0.016) from the non‐CR group, while not for V2wk‐0wk and V3wk‐0wk (P > 0.05). The mean increase in ΔD3wk‐0wk for the CR group was significantly higher (P = 0.017) than the non‐CR group. ADC and D showed an increasing trend at each intra‐TX week when compared with pre‐TX in CR group (P < 0.003). Hierarchical clustering demonstrated the existence of clusters in HPV + patients.ConclusionAfter appropriate validation in a larger population, these IVIM imaging metrics may be useful for individualized treatment in HNSCC patients. Level of Evidence: 2J. Magn. Reson. Imaging 2017;45:1013–1023

Recent innovations in quantitative magnetic resonance imaging (MRI) measurement methods have led to improvements in accuracy, repeatability, and acquisition speed, and have prompted renewed interest to reevaluate the medical value of quantitative T1. The purpose of this study was to determine the bias and reproducibility of T1 measurements in a variety of MRI systems with an eye toward assessing the feasibility of applying diagnostic threshold T1 measurement across multiple clinical sites. We used the International Society of Magnetic Resonance in Medicine/National Institute of Standards and Technology (ISMRM/NIST) system phantom to assess variations of T1 measurements, using a slow, reference standard inversion recovery sequence and a rapid, commonly-available variable flip angle sequence, across MRI systems at 1.5 tesla (T) (two vendors, with number of MRI systems n = 9) and 3 T (three vendors, n = 18). We compared the T1 measurements from inversion recovery and variable flip angle scans to ISMRM/NIST phantom reference values using Analysis of Variance (ANOVA) to test for statistical differences between T1 measurements grouped according to MRI scanner manufacturers and/or static field strengths. The inversion recovery method had minor over- and under-estimations compared to the NMR-measured T1 values at both 1.5 T and 3 T. Variable flip angle measurements had substantially greater deviations from the NMR-measured T1 values than the inversion recovery measurements. At 3 T, the measured variable flip angle T1 for one vendor is significantly different than the other two vendors for most of the samples throughout the clinically relevant range of T1. There was no consistent pattern of discrepancy between vendors. We suggest establishing rigorous quality control procedures for validating quantitative MRI methods to promote confidence and stability in associated measurement techniques and to enable translation of diagnostic threshold from the research center to the entire clinical community.

The purpose of this study was to identify the optimal tracer kinetic model from dynamic contrastenhanced T 1 -weighted magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model (2CXM), compartmental tissue uptake model (CTUM), extended Tofts model (ETM), and standard Tofts model (TM) were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way Analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUC's were compared. ETM performed the best with the lowest AICc and highest weights (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of K trans in PTCs with aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78±0.29 vs. 0.34±0.18 min −1 ,

To determine the ability of diffusion-weighted imaging (DWI) and dynamic contrastenhanced magnetic resonance imaging (DCE-MRI) to predict long-term response of brain metastases prior to and within 72 hours of stereotactic radiosurgery (SRS). METHODS: In this prospective pilot study, multiple b-value DWI and T1-weighted DCE-MRI were performed in patients with brain metastases before and within 72 hours following SRS. Diffusion-weighted images were analyzed using the monoexponential and intravoxel incoherent motion (IVIM) models. DCE-MRI data were analyzed using the extended Tofts pharmacokinetic model. The parameters obtained with these methods were correlated with brain metastasis outcomes according to modified Response Assessment in Neuro-Oncology Brain Metastases criteria. RESULTS: We included 25 lesions from 16 patients; 16 patients underwent pre-SRS MRI and 12 of 16 patients underwent both pre-and early (within 72 hours) post-SRS MRI. The perfusion fraction (f) derived from IVIM early post-SRS was higher in lesions demonstrating progressive disease than in lesions demonstrating stable disease, partial response, or complete response (q = .041). Pre-SRS extracellular extravascular volume fraction, v e , and volume transfer coefficient, K trans , derived from DCE-MRI were higher in nonresponders versus responders (q = .041). CONCLUSIONS: Quantitative DWI and DCE-MRI are feasible imaging methods in the pre-and early (within 72 hours) post-SRS evaluation of brain metastases. DWI-and DCE-MRI-derived parameters demonstrated physiologic changes (tumor cellularity and vascularity) and offer potentially useful biomarkers that can predict treatment response. This allows for initiation of alternate therapies within an effective time window that may help prevent disease progression.

AIMTo noninvasively investigate tumor cellularity measured using diffusion-weighted magnetic resonance imaging (DW-MRI) and glucose metabolism measured by 18F-labeled fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) during radiation therapy (RT) for human papillomavirus negative (HPV-) head and neck squamous cell carcinoma (HNSCC).METHODSIn this prospective study, 6 HPV- HNSCC patients underwent a total of 34 multimodality imaging examinations DW-MRI at 1.5 T Philips MRI scanner [(n = 24) pre-, during- (2-3 wk), and post-treatment (Tx), and 18F-FDG PET/CT pre- and post-Tx (n = 10)]. All patients received RT. Monoexponential modeling of the DW-MRI data yielded the imaging metric apparent diffusion coefficient (ADC) and the mean of standardized uptake value (SUV) was measured from 18F-FDG PET uptake. All patients had a clinical follow-up as the standard of care and survival status was documented at 1 year.RESULTSThere was a strong negative correlation between the mean of pretreatment ADC (ρ = -0.67, P = 0.01) and the pretreatment 18F-FDG PET SUV. The percentage (%) change in delta (∆) ADC for primary tumors and neck nodal metastases between pre- and Wk2-3 Tx were as follows: 75.4% and 61.6%, respectively, for the patient with no evidence of disease, 27.5% and 32.7%, respectively, for those patients who were alive with disease, and 26.9% and 7.31%, respectively, for those who were dead with disease.CONCLUSIONThese results are preliminary in nature and are indicative, and not definitive, trends rendered by the imaging metrics due to the small sample size of HPV- HNSCC patients in a Meixoeiro Hospital of Vigo Experience.

Objectives. Biologically guided radiotherapy needs an understanding of how different functional imaging techniques interact and link together. We analyse three functional imaging techniques that can be useful tools for achieving this objective. Materials and Methods. The three different imaging modalities from one selected patient are ADC maps, DCE-MRI, and 18F-FDG PET/CT, because they are widely used and give a great amount of complementary information. We show the relationship between these three datasets and evaluate them as markers for tumour response or hypoxia marker. Thus, vascularization measured using DCE-MRI parameters can determine tumour hypoxia, and ADC maps can be used for evaluating tumour response. Results. ADC and DCE-MRI include information from 18F-FDG, as glucose metabolism is associated with hypoxia and tumour cell density, although 18F-FDG includes more information about the malignancy of the tumour. The main disadvantage of ADC maps is the distortion, and we used only low distorted regions, and extracellular volume calculated from DCE-MRI can be considered equivalent to ADC in well-vascularized areas. Conclusion. A dataset for achieving the biologically guided radiotherapy must include a tumour density study and a hypoxia marker. This information can be achieved using only MRI data or only PET/CT studies or mixing both datasets.

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