Comprehensive population-averaged arterial input function for dynamic contrast-enhanced vmagnetic resonance imaging of head and neck cancer

Quantitative analysis from ultrafast dynamic contrast-enhanced breast MRI using population-based versus individual arterial input functions, and comparison with semi-quantitative analysis

PURPOSE

To evaluate the value of inline quantitative analysis of ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a population-based arterial input function (P-AIF) compared with offline quantitative analysis with an individual AIF (I-AIF) and semi-quantitative analysis for diagnosing breast cancer.

METHODS

This prospective study included 99 consecutive patients with 109 lesions (85 malignant and 24 benign). Model-based parameters (Ktrans, kep, and ve) and model-free parameters (washin and washout) were derived from CAIPIRINHA-Dixon-TWIST-VIBE (CDTV) DCE-MRI. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. The AUC and F1 score were assessed for semi-quantitative and two quantitative analyses.

RESULTS

kep from inline quantitative analysis with P-AIF for diagnosing breast cancer provided an AUC similar to kep from offline quantitative analysis with I-AIF (0.782 vs 0.779, p = 0.954), higher compared to washin from semi-quantitative analysis (0.782 vs 0.630, p = 0.034). Furthermore, the inline quantitative analysis with P-AIF achieved the larger F1 score (0.920) compared with offline quantitative analysis with I-AIF (0.780) and semi-quantitative analysis (0.480). There were no statistically significant differences for kep values between the two quantitative analysis schemes (p = 0.944).

CONCLUSION

The inline quantitative analysis with P-AIF from CDTV in characterizing breast lesions could offer similar diagnostic accuracy to offline quantitative analysis with I-AIF, and higher diagnostic accuracy to semi-quantitative analysis.

Quantitative dynamic contrast-enhanced magnetic resonance imaging in head and neck cancer: A systematic comparison of different modelling approaches

Background and purpose

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) describes tissue microvasculature and has prognostic and predictive potential in radiotherapy for head and neck cancer (HNC). However, lack in standardization of DCE-MRI hinders comparison of studies and clinical implementation. This study investigated the accuracy and robustness of the population arterial input function (AIF), correlations between pharmacokinetic parameters and their association to T stage and human papillomavirus (HPV) status for HNC.

Materials and methods

DCE-MRI was acquired for 44 HNC patients. Population AIFs were calculated with six different approaches. DCE-MRI was analysed in primary and lymph node tumours using Tofts model (TM) with population AIFs and individual AIFs, extended TM (ETM) with individual AIFs, Brix model (BM), and areas under the curve (AUCs). Intraclass correlation, concordance correlation, Pearson correlation and Whitney Mann U test helped examining the robustness and accuracy of population AIF, correlations between DCE-MRI parameters and their association to T stage and HPV status, respectively.

Results

The population AIF was robust but differed from individual AIFs. There was significant correlation between KtransTM/ETM and ve, TM/ETM, and KtransTM/ETM and Kep, TM/ETM. ABrix and AUCs correlated for lymph nodes. Kep, Brix correlated with ABrix, KtransTM/ETM and Kep, TM/ETM for primary tumours. Kep, TM significantly decreased with increasing T stage. Both the correlations and the parameters' association to T stage were stronger for HPV negative lesions.

Conclusions

Individual AIF was preferred for accurate pharmacokinetic modelling of DCE-MRI. DCE-MRI parameters and their correlations were affected by the lesion type, HPV status and T staging.

Influence of Different Measurement Methods of Arterial Input Function on Quantitative Dynamic Contrast-Enhanced MRI Parameters in Head and Neck Cancer

BACKGROUND

Head and neck cancer (HNC) is the sixth most prevalent cancer worldwide. Dynamic contrast-enhanced MRI (DCE-MRI) helps in diagnosis and prognosis. Quantitative DCE-MRI requires an arterial input function (AIF), which affects the values of pharmacokinetic parameters (PKP).

PURPOSE

To evaluate influence of four individual AIF measurement methods on quantitative DCE-MRI parameters values (K^trans , v_e , k_ep , and v_p ), for HNC and muscle.

STUDY TYPE

Prospective.

POPULATION

A total of 34 HNC patients (23 males, 11 females, age range 24-91) FIELD STRENGTH/SEQUENCE: A 3 T; 3D SPGR gradient echo sequence with partial saturation of inflowing spins.

ASSESSMENT

Four AIF methods were applied: automatic AIF (AIFa) with up to 50 voxels selected from the whole FOV, manual AIF (AIFm) with four voxels selected from the internal carotid artery, both conditions without (Mc-) or with (Mc+) motion correction. Comparison endpoints were peak AIF values, PKP values in tumor and muscle, and tumor/muscle PKP ratios.

STATISTICAL TESTS

Nonparametric Friedman test for multiple comparisons. Nonparametric Wilcoxon test, without and with Benjamini Hochberg correction, for pairwise comparison of AIF peak values and PKP values for tumor, muscle and tumor/muscle ratio, P value ≤ 0.05 was considered statistically significant.

RESULTS

Peak AIF values differed significantly for all AIF methods, with mean AIFmMc+ peaks being up to 66.4% higher than those for AIFaMc+. Almost all PKP values were significantly higher for AIFa in both, tumor and muscle, up to 76% for mean K^trans values. Motion correction effect was smaller. Considering tumor/muscle parameter ratios, most differences were not significant (0.068 ≤ Wilcoxon P value ≤ 0.8).

DATA CONCLUSION

We observed important differences in PKP values when using either AIFa or AIFm, consequently choice of a standardized AIF method is mandatory for DCE-MRI on HNC. From the study findings, AIFm and inflow compensation are recommended. The use of the tumor/muscle PKP ratio should be of interest for multicenter studies.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 1.

Establishing ADC-Based Histogram and Texture Features for Early Treatment-Induced Changes in Head and Neck Squamous Cell Carcinoma

The purpose of this study was to assess baseline variability in histogram and texture features derived from apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (DW-MRI) examinations and to identify early treatment-induced changes to these features in patients with head and neck squamous cell carcinoma (HNSCC) undergoing definitive chemoradiation. Patients with American Joint Committee on Cancer Stage III–IV (7^th edition) HNSCC were prospectively enrolled on an IRB-approved study to undergo two pre-treatment baseline DW-MRI examinations, performed 1 week apart, and a third early intra-treatment DW-MRI examination during the second week of chemoradiation. Forty texture and six histogram features were derived from ADC maps. Repeatability of the features from the baseline ADC maps was assessed with the intra-class correlation coefficient (ICC). A Wilcoxon signed-rank test compared average baseline and early treatment feature changes. Data from nine patients were used for this study. Comparison of the two baseline ADC maps yielded 11 features with an ICC ≥ 0.80, indicating that these features had excellent repeatability: Run Gray-Level Non-Uniformity, Coarseness, Long Zone High Gray-Level, Variance (Histogram Feature), Cluster Shade, Long Zone, Variance (Texture Feature), Run Length Non-Uniformity, Correlation, Cluster Tendency, and ADC Median. The Wilcoxon signed-rank test resulted in four features with significantly different early treatment-induced changes compared to the baseline values: Run Gray-Level Non-Uniformity (p = 0.005), Run Length Non-Uniformity (p = 0.005), Coarseness (p = 0.006), and Variance (Histogram) (p = 0.006). The feasibility of histogram and texture analysis as a potential biomarker is dependent on the baseline variability of each metric, which disqualifies many features.

Dynamic Contrast-Enhanced MRI

Dynamic contrast-enhanced MRI in pre-clinical imaging allows the in-vivo monitoring of vascular, physiological properties in normal and diseased tissue. There is considerable variation in the methods employed owing to the different questions that can be asked and answered about the physiologic alterations as well as morphologic changes in tissue. Here we review the typical decisions in the design and execution of a dynamic contrast-enhanced MRI study in mice although the findings can easily be transferred to other species. Emphasis is placed on highlighting the many pitfalls that wait for the unaware pre-clinical MRI practitioner and that go often unmentioned in the abundant literature dealing with dynamic contrast-enhanced MRI in animal models.

Dynamic contrast-enhanced MRI detects acute radiotherapy-induced alterations in mandibular microvasculature: prospective assessment of imaging biomarkers of normal tissue injury

Normal tissue toxicity is an important consideration in the continued development of more effective external beam radiotherapy (EBRT) regimens for head and neck tumors. The ability to detect EBRT-induced changes in mandibular bone vascularity represents a crucial step in decreasing potential toxicity. To date, no imaging modality has been shown to detect changes in bone vascularity in real time during treatment. Based on our institutional experience with multi-parametric MRI, we hypothesized that DCE-MRI can provide in-treatment information regarding EBRT-induced changes in mandibular vascularity. Thirty-two patients undergoing EBRT treatment for head and neck cancer were prospectively imaged prior to, mid-course, and following treatment. DCE-MRI scans were co-registered to dosimetric maps to correlate EBRT dose and change in mandibular bone vascularity as measured by K_trans and V_e. DCE-MRI was able to detect dose-dependent changes in both K_trans and V_e in a subset of patients. One patient who developed ORN during the study period demonstrated decreases in K_trans and V_e following treatment completion. We demonstrate, in a prospective imaging trial, that DCE-MRI can detect dose-dependent alterations in mandibular bone vascularity during chemoradiotherapy, providing biomarkers that are physiological correlates of acute of acute mandibular vascular injury and recovery temporal kinetics.

Assessment of repeatability and treatment response in early phase clinical trials using DCE-MRI: comparison of parametric analysis using MR- and CT-derived arterial input functions

OBJECTIVES

Pharmacokinetic (PK) modelling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data requires a reliable measure of the arterial input function (AIF) to robustly characterise tumour vascular properties. This study compared repeatability and treatment-response effects of DCE-MRI-derived PK parameters using a population-averaged AIF and three patient-specific AIFs derived from pre-bolus MRI, DCE-MRI and dynamic contrast computed tomography (DC-CT) data.

METHODS

The four approaches were compared in 13 patients with abdominal metastases. Baseline repeatability [Bland-Altman statistics; coefficient of variation (CoV)], cohort percentage change and p value (paired t test) and number of patients with significant DCE-MRI parameter change post-treatment (limits of agreement) were assessed.

RESULTS

Individual AIFs were obtained for all 13 patients with pre-bolus MRI and DC-CT-derived AIFs, but only 10/13 patients had AIFs measurable from DCE-MRI data. The best CoV (7.5 %) of the transfer coefficient between blood plasma and extravascular extracellular space (K (trans)) was obtained using a population-averaged AIF. All four AIF methods detected significant treatment changes: the most significant was the DC-CT-derived AIF. The population-based AIF was similar to or better than the pre-bolus and DCE-MRI-derived AIFs.

CONCLUSIONS

A population-based AIF is the recommended approach for measuring cohort and individual effects since it has the best repeatability and none of the PK parameters derived using measured AIFs demonstrated an improvement in treatment sensitivity.

KEY POINTS

• Pharmacokinetic modelling of DCE-MRI data requires a reliable measure of AIF. • Individual MRI-DCE-derived AIFs cannot reliably be extracted from patients. • All four AIF methods detected significant K (trans) changes after treatment. • A population-based AIF can be recommended for measuring cohort treatment responses in trials.

Automatic individual arterial input functions calculated from PCA outperform manual and population-averaged approaches for the pharmacokinetic modeling of DCE-MR images

BACKGROUND

To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on principal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters.

METHODS

The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results.

RESULTS

Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61).

CONCLUSION

The automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate.