Correlations Between DCE MRI and Histopathological Parameters in Head and Neck Squamous Cell Carcinoma

BACKGROUND: Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) can characterize perfusion and vascularization of tissues. DCE MRI parameters can differentiate between malignant and benign lesions and predict tumor grading. The purpose of this study was to correlate DCE MRI findings and various histopathological parameters in head and neck squamous cell carcinoma (HNSCC). PATIENTS AND METHODS: Sixteen patients with histologically proven HNSCC (11 cases primary tumors and in 5 patients with local tumor recurrence) were included in the study. DCE imaging was performed in all cases and the following parameters were estimated: K^trans, V_e, K_ep, and iAUC. The tumor proliferation index was estimated on Ki 67 antigen stained specimens. Microvessel density parameters (stained vessel area, total vessel area, number of vessels, and mean vessel diameter) were estimated on CD31 antigen stained specimens. Spearman''s non-parametric rank sum correlation coefficients were calculated between DCE and different histopathological parameters. RESULTS: The mean values of DCE perfusion parameters were as follows: K^trans 0.189 ± 0.056 min⁻¹, K_ep 0.390 ± 0.160 min⁻¹, V_e 0.548 ± 0.119%, and iAUC 22.40 ± 12.57. Significant correlations were observed between K_ep and stained vessel areas (r = 0.51, P = .041) and total vessel areas (r = 0.5118, P = .043); between V_e and mean vessel diameter (r = −0.59, P = .017). Cell count had a tendency to correlate with V_e (r = −0.48, P = .058). In an analysis of the primary HNSCC only, a significant inverse correlation between K^trans and KI 67 was identified (r = −0.62, P = .041). Our analysis showed significant correlations between DCE parameters and histopathological findings in HNSCC.Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) has been reported as a technique which is able to characterize perfusion and vascularization of tissues [1], [2]. It has been shown that DCE MRI can be helpful to differentiate between malignant and benign lesions [1]. For example, Yuan et al. reported that lung cancer had a larger volume transfer constant (K^trans) and a lower volume of the extravascular extracellular leakage space (V_e) in comparison to benign lesions [3]. Similar results were reported by Li et al. for breast lesions [4]. Furthermore, according to Cho et al., DCE MRI parameters can be used to distinguish prostatic cancer from benign changes [5]. Moreover, DCE MRI parameters can also predict tumor grading. As reported previously, K^trans correlated well with Gleason score in prostatic cancer [5], [6]. According to other reports, K^trans and V_e correlated with glioma grade [7], [8].DCE MRI parameters were also associated with prognosis in several malignancies [9], [10]. Koo et al. showed that breast cancers with higher K^trans or lower V_e had poor prognostic factors and were often of the triple-negative subtype [10].According to the literature, DCE MRI parameters can predict response to therapy in different tumors. For instance, some authors mentioned that low pretreatment K^trans in regional lymph node metastases in head and neck cancer was associated with a poor response to concurrent chemoradiation therapy [11].Furthermore, Andersen et al. showed that DCE MR parameters obtained prior to chemoradiotherapy predicted survival of patients with cervical cancer [12].Presumably, DCE MRI parameters may be based on tissue composition, such as cellularity and vascular density. However, in this regard there are contradictory data in the literature. While some studies identified significant correlations between DCE MRI and histopathological parameters, others did not [13], [14], [15], [16].The purpose of this study was to correlate DCE MRI findings and various histopathological parameters in head and neck squamous cell carcinoma (HNSCC).     

Proving of a Mathematical Model of Cell Calculation Based on Apparent Diffusion Coefficient

OBJECTIVES: Recently, Atuegwu et al. proposed a mathematical model based on ADC_mean and ADC_min to calculation of cellularity. Our purpose was to compare the calculated cellularity according to the formula with the estimated cell count by histopathology in different tumors. METHODS: For this study, we re-analyzed our previous data regarding associations between ADC parameters and histopathological findings. Overall, 134 patients with different tumors were acquired for the analysis. For all tumors, the number of tumor cells was calculated according to Atuegwu et al. 2013. We performed a correlation analysis between the calculated and estimated cellularity. Thereby, Pearson's correlation coefficient was used and P < .05 was taken to indicate statistical significance in all instances. RESULTS: The estimated and calculated cellularity correlated well together in HNSCC (r = 0.701, P = .016) and lymphomas (r = 0.661, P = .001), and moderately in rectal cancer (r = 0.510, P = .036). There were no statistically significant correlations between the estimated and calculated cellularity in uterine cervical cancer, meningiomas, and in thyroid cancer. CONCLUSION: The proposed formula for cellularity calculation does not apply for all tumors. It may be used for HNSCC, cerebral lymphomas and rectal cancer, but not for uterine cervical cancer, meningioma, and thyroid cancer. Furthermore, its usefulness should be proved for other tumors.     

Can Imaging Parameters Provide Information Regarding Histopathology in Head and Neck Squamous Cell Carcinoma? A Meta-Analysis

OBJECT: Our purpose was to provide data regarding relationships between different imaging and histopathological parameters in HNSCC. METHODS: MEDLINE library was screened for associations between different imaging parameters and histopathological features in HNSCC up to December 2017. Only papers containing correlation coefficients between different imaging parameters and histopathological findings were acquired for the analysis. RESULTS: Associations between ¹⁸F-FDG positron emission tomography (PET) and KI 67 were reported in 8 studies (236 patients). The pooled correlation coefficient was 0.20 (95% CI = [?0.04; 0.44]). Furthermore, in 4 studies (64 patients), associations between ¹⁸F-fluorothymidine PET and KI 67 were analyzed. The pooled correlation coefficient between SUV_max and KI 67 was 0.28 (95% CI = [?0.06; 0.94]). In 2 studies (23 patients), relationships between KI 67 and dynamic contrast-enhanced magnetic resonance imaging were reported. The pooled correlation coefficient between K_trans and KI 67 was ?0.68 (95% CI = [?0.91; ?0.44]). Two studies (31 patients) investigated correlation between apparent diffusion coefficient (ADC) and KI 67. The pooled correlation coefficient was ?0.61 (95% CI = [?0.84; ?0.38]). In 2 studies (117 patients), relationships between ¹⁸F-FDG PET and p53 were analyzed. The pooled correlation coefficient was 0.0 (95% CI = [?0.87; 0.88]). There were 3 studies (48 patients) that investigated associations between ADC and tumor cell count in HNSCC. The pooled correlation coefficient was ?0.53 (95% CI = [?0.74; ?0.32]). Associations between ¹⁸F-FDG PET and HIF-1α were investigated in 3 studies (72 patients). The pooled correlation coefficient was 0.44 (95% CI = [?0.20; 1.08]). CONCLUSIONS: ADC may predict cell count and proliferation activity, and SUV_max may predict expression of HIF-1α in HNSCC. SUV_max cannot be used as surrogate marker for expression of KI 67 and p53.     

Whole Tumor Histogram-profiling of Diffusion-Weighted Magnetic Resonance Images Reflects Tumorbiological Features of Primary Central Nervous System Lymphoma

PURPOSE: Diffusion weighted imaging (DWI) quantifies motion of hydrogen nuclei in biological tissues and hereby has been used to assess the underlying tissue microarchitecture. Histogram-profiling of DWI provides more detailed information on diffusion characteristics of a lesion than the standardly calculated values of the apparent diffusion coefficient (ADC)—minimum, mean and maximum. Hence, the aim of our study was to investigate, which parameters of histogram-profiling of DWI in primary central nervous system lymphoma can be used to specifically predict features like cellular density, chromatin content and proliferative activity. PROCEDURES: Pre-treatment ADC maps of 21 PCNSL patients (8 female, 13 male, 28–89 years) from a 1.5T system were used for Matlab-based histogram profiling. Results of histopathology (H&E staining) and immunohistochemistry (Ki-67 expression) were quantified. Correlations between histogram-profiling parameters and neuropathologic examination were calculated using SPSS 23.0. RESULTS: The lower percentiles (p10 and p25) showed significant correlations with structural parameters of the neuropathologic examination (cellular density, chromatin content). The highest percentile, p90, correlated significantly with Ki-67 expression, resembling proliferative activity. Kurtosis of the ADC histogram correlated significantly with cellular density. CONCLUSIONS: Histogram-profiling of DWI in PCNSL provides a comprehensible set of parameters, which reflect distinct tumor-architectural and tumor-biological features, and hence, are promising biomarkers for treatment response and prognosis.     

Histogram Analysis Parameters Apparent Diffusion Coefficient for Distinguishing High and Low-Grade Meningiomas: A Multicenter Study

Low grade meningiomas have better prognosis than high grade meningiomas. The aim of this study was to measure apparent diffusion coefficient (ADC) histogram analysis parameters in different meningiomas in a large multicenter sample and to analyze the possibility of several parameters for predicting tumor grade and proliferation potential. Overall, 148 meningiomas from 7 institutions were evaluated in this retrospective study. Grade 1 lesions were diagnosed in 101 (68.2%) cases, grade 2 in 41 (27.7%) patients, and grade 3 in 6 (4.1%) patients. All tumors were investigated by MRI (1.5 T scanner) by using diffusion weighted imaging (b values of 0 and 1000 s/mm²). For every lesion, the following parameters were calculated: mean ADC, maximum ADC, minimum ADC, median ADC, mode ADC, ADC percentiles P10, P25, P75, P90, kurtosis, skewness, and entropy. The comparison of ADC values was performed by Mann–Whitney-U test. Correlation between different ADC parameters and KI 67 was calculated by Spearman's rank correlation coefficient. Grade 2/3 meningiomas showed statistically significant lower ADC histogram analysis parameters in comparison to grade 1 tumors, especially ADC median. A threshold value of 0.82 for ADC median to predict tumor grade was estimated (sensitivity?=?82.2%, specificity?=?63.8%, accuracy?=?76.4%, positive and negative predictive values were 83% and 62.5%, respectively).All ADC parameters except maximum ADC showed weak significant correlations with KI 67, especially ADC P25 (P?=??.340, P?=?.0001).     

Nuclear Overhauser Enhancement Imaging of Glioblastoma at 7 Tesla: Region Specific Correlation with Apparent Diffusion Coefficient and Histology

Objective

To explore the correlation between Nuclear Overhauser Enhancement (NOE)-mediated signals and tumor cellularity in glioblastoma utilizing the apparent diffusion coefficient (ADC) and cell density from histologic specimens. NOE is one type of chemical exchange saturation transfer (CEST) that originates from mobile macromolecules such as proteins and might be associated with tumor cellularity via altered protein synthesis in proliferating cells.

Patients and Methods

For 15 patients with newly diagnosed glioblastoma, NOE-mediated CEST-contrast was acquired at 7 Tesla (asymmetric magnetization transfer ratio (MTR_asym) at 3.3ppm, B1 = 0.7 μT). Contrast enhanced T1 (CE-T1), T2 and diffusion-weighted MRI (DWI) were acquired at 3 Tesla and coregistered. The T2 edema and the CE-T1 tumor were segmented. ADC and MTR_asym values within both regions of interest were correlated voxelwise yielding the correlation coefficient r_Spearman (r_Sp). In three patients who underwent stereotactic biopsy, cell density of 12 specimens per patient was correlated with corresponding MTR_asym and ADC values of the biopsy site.

Results

Eight of 15 patients showed a weak or moderate positive correlation of MTR_asym and ADC within the T2 edema (0.16≤r_Sp≤0.53, p<0.05). Seven correlations were statistically insignificant (p>0.05, n = 4) or yielded r_Sp≈0 (p<0.05, n = 3). No trend towards a correlation between MTR_asym and ADC was found in CE-T1 tumor (-0.31<r_Sp<0.28, p<0.05, n = 9; p>0.05, n = 6). The biopsy-analysis within CE-T1 tumor revealed a strong positive correlation between tumor cellularity and MTR_asym values in two of the three patients (r_Sp ^patient3 = 0.69 and r_Sp ^patient15 = 0.87, p<0.05), while the correlation of ADC and cellularity was heterogeneous (r_Sp ^patient3 = 0.545 (p = 0.067), r_Sp ^patient4 = -0.021 (p = 0.948), r_Sp ^patient15 = -0.755 (p = 0.005)).

Discussion

NOE-imaging is a new contrast promising insight into pathophysiologic processes in glioblastoma regarding cell density and protein content, setting itself apart from DWI. Future studies might be based on the assumption that NOE-mediated CEST visualizes cellularity more accurately than ADC, especially in the CE-T1 tumor region.     

Clinical Utility of Multimodality Imaging with Dynamic Contrast-Enhanced MRI,Diffusion-Weighted MRI,and 18F-FDG PET/CT for the Prediction of Neck Control in Oropharyngeal or Hypopharyngeal Squamous Cell Carcinoma Treated with Chemoradiation

The clinical usefulness of pretreatment imaging techniques for predicting neck control in patients with oropharyngeal or hypopharyngeal squamous cell carcinoma (OHSCC) treated with chemoradiation remains unclear. In this prospective study, we investigated the role of pretreatment dynamic contrast-enhanced perfusion MR imaging (DCE-PWI), diffusion-weighted MR imaging (DWI), and [¹⁸F]fluorodeoxyglucose-positron emission tomography (¹⁸F-FDG PET)/CT derived imaging markers for the prediction of neck control in OHSCC patients treated with chemoradiation. Patients with untreated OHSCC scheduled for chemoradiation between August, 2010 and July, 2012 were eligible for the study. Clinical variables and the following imaging parameters of metastatic neck lymph nodes were examined in relation to neck control: transfer constant, volume of blood plasma, and volume of extracellular extravascular space (V_e) on DCE-PWI; apparent diffusion coefficient (ADC) on DWI; maximum standardized uptake value, metabolic tumor volume, and total lesion glycolysis on ¹⁸F-FDG PET/CT. There were 69 patients (37 with oropharynx SCC and 32 with hypopharynx SCC) with successful pretreatment DCE-PWI and DWI available for analysis. After a median follow-up of 31 months, 25 (36.2%) participants had neck failure. Multivariate analysis identified hemoglobin level <14.3 g/dL (P = 0.019), V_e <0.23 (P = 0.040), and ADC >1.14×10⁻³ mm²/s (P = 0.003) as independent prognostic factors for 3-year neck control. A prognostic scoring system was formulated by summing up the three significant predictors of neck control. Patients with scores of 2–3 had significantly poorer neck control and overall survival rates than patients with scores of 0–1. We conclude that hemoglobin levels, V_e, and ADC are independent pretreatment prognostic factors for neck control in OHSCC treated with chemoradiation. Their combination may identify a subgroup of patients at high risk of developing neck failure.     

18F-FDG PET Metabolic Parameters and MRI Perfusion and Diffusion Parameters in Hepatocellular Carcinoma: A Preliminary Study

Objectives

Glucose metabolism, perfusion, and water diffusion may have a relationship or affect each other in the same tumor. The understanding of their relationship could expand the knowledge of tumor characteristics and contribute to the field of oncologic imaging. The purpose of this study was to evaluate the relationships between metabolism, vasculature and cellularity of advanced hepatocellular carcinoma (HCC), using multimodality imaging such as ¹⁸F-FDG positron emission tomography (PET), dynamic contrast enhanced (DCE)-MRI, and diffusion weighted imaging(DWI).

Materials and Methods

Twenty-one patients with advanced HCC underwent ¹⁸F-FDG PET, DCE-MRI, and DWI before treatment. Maximum standard uptake values (SUV_max) from ¹⁸F-FDG-PET, variables of the volume transfer constant (K^trans) from DCE-MRI and apparent diffusion coefficient (ADC) from DWI were obtained for the tumor and their relationships were examined by Spearman’s correlation analysis. The influence of portal vein thrombosis on SUV_max and variables of K^trans and ADC was evaluated by Mann-Whitney test.

Results

SUV_max showed significant negative correlation with K^trans _max (ρ = −0.622, p = 0.002). However, variables of ADC showed no relationship with variables of K^trans or SUV_max (p>0.05). Whether portal vein thrombosis was present or not did not influence the SUV _max and variables of ADC and K^trans (p>0.05).

Conclusion

In this study, SUV was shown to be correlated with K_trans in advanced HCCs; the higher the glucose metabolism a tumor had, the lower the perfusion it had, which might help in guiding target therapy.     

Parameterizing the Logistic Model of Tumor Growth by DW-MRI and DCE-MRI Data to Predict Treatment Response and Changes in Breast Cancer Cellularity during Neoadjuvant Chemotherapy

Diffusion-weighted and dynamic contrast-enhanced magnetic resonance imaging (MRI) data of 28 patients were obtained pretreatment, after one cycle, and after completion of all cycles of neoadjuvant chemotherapy (NAC). For each patient at each time point, the tumor cell number was estimated using the apparent diffusion coefficient and the extravascular extracellular (v_e) and plasma volume (v_p) fractions. The proliferation/death rate was obtained using the number of tumor cells from the first two time points in conjunction with the logistic model of tumor growth, which was then used to predict tumor cellularity at the conclusion of NAC. The Pearson correlation coefficient between the predicted and the experimental number of tumor cells measured at the end of NAC was 0.81 (P = .0043). The proliferation rate estimated after the first cycle of therapy was able to separate patients who went on to achieve pathologic complete response from those who did not (P = .021) with a sensitivity and specificity of 82.4% and 72.7%, respectively. These data provide preliminary results indicating that incorporating readily available quantitative MRI data into a simple model of tumor growth can lead to potentially clinically relevant information for predicting an individual patient's response to NAC.     

Apparent Diffusion Coefficient (ADC) Value: A Potential Imaging Biomarker That Reflects the Biological Features of Rectal Cancer

Objective

We elected to analyze the correlation between the pre-treatment apparent diffusion coefficient (ADC) and the clinical, histological, and immunohistochemical status of rectal cancers.

Materials and Methods

Forty-nine rectal cancer patients who received surgical resection without neoadjuvant therapy were selected that underwent primary MRI and diffusion-weighted imaging (DWI). Tumor ADC values were determined and analyzed to identify any correlations between these values and pre-treatment CEA or CA19-9 levels, and/or the histological and immunohistochemical properties of the tumor.

Results

Inter-observer agreement of confidence levels from two separate observers was suitable for ADC measurement (k  =  0.775). The pre-treatment ADC values of different T stage tumors were not equal (p  =  0.003). The overall trend was that higher T stage values correlated with lower ADC values. ADC values were also significantly lower for the following conditions: tumors with the presence of extranodal tumor deposits (p  =  0.006) and tumors with CA19-9 levels ≥ 35 g/ml (p  =  0.006). There was a negative correlation between Ki-67 LI and the ADC value (r  =  −0.318, p  =  0.026) and between the AgNOR count and the ADC value (r  =  −0.310, p  =  0.030).

Conclusion

Significant correlations were found between the pre-treatment ADC values and T stage, extranodal tumor deposits, CA19-9 levels, Ki-67 LI, and AgNOR counts in our study. Lower ADC values were associated with more aggressive tumor behavior. Therefore, the ADC value may represent a useful biomarker for assessing the biological features and possible relationship to the status of identified rectal cancers.     

Uninterpretable Dynamic Susceptibility Contrast-Enhanced Perfusion MR Images in Patients with Post-Treatment Glioblastomas: Cross-Validation of Alternative Imaging Options

Purpose

The purpose of this study was to evaluate the accuracy of diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) perfusion MR imaging for distinguishing tumor recurrence from post-treatment effect as alternatives to dynamic-susceptibility contrast-enhanced (DSC) perfusion MR imaging when the DSC image is uninterpretable.

Materials and Methods

This retrospective study was approved by our institutional review board. Seventy one post-treatment glioblastoma patients who showed enlarged contrast-enhancing lesions on follow-up MR images after concurrent chemoradiotherapy and uninterpretable DSC images for corresponding enhancing lesions, underwent additional DWI and DCE MR imaging. The primary outcome was the frequency of interpretable DWI and DCE MR cases in these 71 patients. The secondary outcome was the area under the receiver operating characteristic curve (AUC) of DWI and DCE imaging parameters for distinguishing tumor recurrence from post-treatment effect in selected patients with interpretable DWI and DCE images. The imaging parameters were quantified as 10% cumulative histogram cutoff of apparent diffusion coefficient (ADC10) and 90% cumulative histogram cutoff of initial area under the time signal intensity curve (IAUC90). The AUCs were cross-validated by using leave-one-out method.

Results

Of the 71 patients, the uninterpretable DSC images were associated with treatment-related hemorrhage within the corresponding enhancing lesions (n = 54, 76.1%) and a near skull base location (n = 17, 23.9%). The frequencies of interpretable DWI and DCE image were 51 (71.8%) and 59 (83.1%) of the 71 cases with uninterpretable DSC images, respectively. Of the 45 selected patients with interpretable DWI and DCE images, the combination of DWI with DCE imaging showed a superior diagnostic performance than DWI or DCE imaging alone for differentiating tumor recurrence from post-treatment effect (cross-validated AUC: 0.78 versus 0.55 and 0.73 for reader 1; cross-validated AUC: 0.78 versus 0.53 and 0.75 for reader 2, respectively). Cross-validated accuracy of the single and combined imaging parameters also showed the highest for the combination of DWI with DCE MR imaging (72.9% for reader 1; 72.5% for reader 2) and the lowest for DWI alone (54.0% for reader 1; 56.4% for reader 2). Inter-reader agreement for DCE imaging was higher than that for DWI (intraclass correlation coefficient: 0.95 versus 0.87).

Conclusion

DCE MR imaging could be a superior and more reproducible imaging biomarker than DWI for differentiating tumor recurrence from post-treatment effect in patients with post-treatment glioblastoma when DSC MR images are not interpretable.     

Simultaneous [18F]FDG-PET/MRI: Correlation of Apparent Diffusion Coefficient (ADC) and Standardized Uptake Value (SUV) in Primary and Recurrent Cervical Cancer

Objectives

Previous non–simultaneous PET/MR studies have shown heterogeneous results about the correlation between standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs). The aim of this study was to investigate correlations in patients with primary and recurrent tumors using a simultaneous PET/MRI system which could lead to a better understanding of tumor biology and might play a role in early response assessment.

Methods

We included 31 patients with histologically confirmed primary (n = 14) or recurrent cervical cancer (n = 17) who underwent simultaneous whole-body ¹⁸F-FDG-PET/MRI comprising DWI. Image analysis was performed by a radiologist and a nuclear physician who identified tumor margins and quantified ADC and SUV. Pearson correlations were calculated to investigate the association between ADC and SUV.

Results

92 lesions were detected. We found a significant inverse correlation between SUV_max and ADC_min (r = -0.532, p = 0.05) in primary tumors as well as in primary metastases (r = -0.362, p = 0.05) and between SUV_mean and ADC_min (r = -0.403, p = 0.03). In recurrent local tumors we found correlations for SUV_max and ADC_min (r = -0.747, p = 0.002) and SUV_mean and ADC_min (r = -0.773, p = 0.001). Associations for recurrent metastases were not significant (p>0.05).

Conclusions

Our study demonstrates the feasibility of fast and reliable measurement of SUV and ADC with simultaneous PET/MRI. In patients with cervical cancer we found significant inverse correlations for SUV and ADC which could play a major role for further tumor characterization and therapy decisions.

Key Point 1

This study investigates the correlation of functional parameters in a simultaneous PET/MRI.

Key Point 2

We found significant inverse correlations between ADC and SUV in cervical carcinoma which could increase knowledge about tumor biology.     

Quantitative Multi-Parametric Magnetic Resonance Imaging of Tumor Response to Photodynamic Therapy

ObjectiveThe aim of this study was to characterize response to photodynamic therapy (PDT) in a mouse cancer model using a multi-parametric quantitative MRI protocol and to identify MR parameters as potential biomarkers for early assessment of treatment outcome.MethodsCT26.WT colon carcinoma tumors were grown subcutaneously in the hind limb of BALB/c mice. Therapy consisted of intravenous injection of the photosensitizer Bremachlorin, followed by 10 min laser illumination (200 mW/cm²) of the tumor 6 h post injection. MRI at 7 T was performed at baseline, directly after PDT, as well as at 24 h, and 72 h. Tumor relaxation time constants (T₁ and T₂) and apparent diffusion coefficient (ADC) were quantified at each time point. Additionally, Gd-DOTA dynamic contrast-enhanced (DCE) MRI was performed to estimate transfer constants (K^trans) and volume fractions of the extravascular extracellular space (v_e) using standard Tofts-Kermode tracer kinetic modeling. At the end of the experiment, tumor viability was characterized by histology using NADH-diaphorase staining.ResultsThe therapy induced extensive cell death in the tumor and resulted in significant reduction in tumor growth, as compared to untreated controls. Tumor T₁ and T₂ relaxation times remained unchanged up to 24 h, but decreased at 72 h after treatment. Tumor ADC values significantly increased at 24 h and 72 h. DCE-MRI derived tracer kinetic parameters displayed an early response to the treatment. Directly after PDT complete vascular shutdown was observed in large parts of the tumors and reduced uptake (decreased K^trans) in remaining tumor tissue. At 24 h, contrast uptake in most tumors was essentially absent. Out of 5 animals that were monitored for 2 weeks after treatment, 3 had tumor recurrence, in locations that showed strong contrast uptake at 72 h.ConclusionDCE-MRI is an effective tool for visualization of vascular effects directly after PDT. Endogenous contrast parameters T₁, T₂, and ADC, measured at 24 to 72 h after PDT, are also potential biomarkers for evaluation of therapy outcome.     

A Pilot Study of Diffusion-Weighted MRI in Patients Undergoing Neoadjuvant Chemoradiation for Pancreatic Cancer

PURPOSE: In the current study we examined the ability of diffusion MRI (dMRI) to predict pathologic response in pancreatic cancer patients receiving neoadjuvant chemoradiation. METHODS: We performed a prospective pilot study of dMRI in patients with resectable pancreatic cancer. Patients underwent dMRI prior to neoadjuvant chemoradiation. Surgical specimens were graded according to the percent tumor cell destruction. Apparent diffusion coefficient (ADC) maps were used to generate whole-tumor derived ADC histogram distributions and mean ADC values. The primary objective of the study was to correlate ADC parameters with pathologic and CT response. RESULTS: Ten of the 12 patients enrolled on the study completed chemoradiation and had surgery. Three were found to be unresectable at the time of surgery and no specimen was obtained. Out of the 7 patients who underwent pancreaticoduodenectomy, 3 had a grade III histopathologic response (> 90% tumor cell destruction), 2 had a grade IIB response (51% to 90% tumor cell destruction), 1 had a grade IIA response (11% to 50% tumor cell destruction), and 1 had a grade I response (> 90% viable tumor). Median survival for patients with a grade III response, grade I-II response, and unresectable disease were 25.6, 18.7, and 6.1 months, respectively. There was a significant correlation between pre-treatment mean tumor ADC values and the amount of tumor cell destruction after chemoradiation with a Pearson correlation coefficient of 0.94 (P = .001). Mean pre-treatment ADC was 161 × 10^− 5 mm²/s (n = 3) in responding patients (> 90% tumor cell destruction) compared to 125 × 10^− 5 mm²/s (n = 4) in non-responding patients (> 10% viable tumor). CT imaging showed no significant change in tumor size in responders or non-responders. CONCLUSIONS: dMRI may be useful to predict response to chemoradiation in pancreatic cancer. In our study, tumors with a low ADC mean value at baseline responded poorly to standard chemoradiation and would be candidates for intensified therapy.     

不同分化程度的原发性肝癌MRI初步临床研究

目的:探讨原发性肝癌不同分化程度、细胞类型的MRI图像特征、参数。方法:回顾性分析肝硬化再生结节27例、肝细胞癌75例81处病灶(高度分化15例,中度分化40例,低度分化26例)、肝内胆管癌20例的病理结果及MRI影像学数据,比较再生结节、肝细胞癌和肝内胆管癌之间,以及肝细胞癌各种组织分化程度之间的ADC值、强化程度差异。结果:常规MRI平扫结合LAVA可准确诊断大部分原发性肝癌病例。再生结节、肝细胞癌及肝内胆管癌ADC值均值的具有显著统计学差异(P0.01);肝细胞癌高分化组、中分化组及低分化组ADC值均值均具有显著统计学差异(P0.01),但中、低分化肝细胞癌ADC值均值的差异无实际临床意义;高分化肝细胞癌与肝内胆管癌ADC值均值的差异无统计学意义(P=0.27)。结论:常规MRI结合DWI、多期动态增强扫描有助于区分原发性肝癌各种组织分化程度及细胞类型。     

Repeatability of Quantitative MRI Measurements in Normal Breast Tissue

PURPOSE: To evaluate the variability and repeatability of repeated magnetic resonance imaging (MRI) measurements in normal breast tissues between and within subjects. METHODS: Eighteen normal premenopausal subjects underwent two contrast-enhanced MRI scans within 72 hours or during the same menstrual phase in two consecutive months. A subset of nine women also completed diffusion-weighted imaging (DWI). Fibroglandular tissue (FGT) density and FGT enhancement were measured on the contrast-enhanced MRI. Apparent diffusion coefficient (ADC) values were computed from DWI. Between- and within-subject coefficients of variation (bCV and wCV, respectively) were assessed. Repeatability of all measurements was assessed by the coefficient of repeatability (CR) and Bland-Altman plots. RESULTS: The bCV of FGT density and FGT enhancement at visit 1 and visit 2 ranged from 47% to 63%. The wCV was 13% for FGT density, 22% for FGT enhancement, and 11% for ADC. The CRs of FGT density and FGT enhancement were 0.15 and 0.19, respectively, and for ADC, it was 6.1 x 10^-4 mm²/s. CONCLUSIONS: We present an estimate of the variability and repeatability of MR measurements in normal breasts. These estimates provide the basis for understanding the normal variation of healthy breast tissue in MRI and establishing thresholds for agreement between measurements.     

Diffusion-Weighted Imaging Using a Readout-Segmented,Multishot EPI Sequence at 3 T Distinguishes between Morphologically Differentiated and Undifferentiated Subtypes of Thyroid Carcinoma—A Preliminary Study

BACKGROUND: Thyroid carcinomas represent the most frequent endocrine malignancies. Recent studies were able to distinguish malignant from benign nodules of the thyroid gland with diffusion-weighted imaging (DWI). Although this differentiation is undoubtedly helpful, presurgical discrimination between well-differentiated and undifferentiated carcinomas would be crucial to define the optimal treatment algorithm. Therefore, the aim of this study was to investigate if readout-segmented multishot echo planar DWI is able to differentiate between differentiated and undifferentiated subtypes of thyroid carcinomas. PATIENTS AND METHODS: Fourteen patients with different types of thyroid carcinomas who received preoperative DWI were included in our study. In all lesions, apparent diffusion coefficient (ADC)min, ADCmean, ADCmax, and D were estimated on the basis of region of interest measurements after coregistration with T1-weighted, postcontrast images. All tumors were resected and analyzed histopathologically. Ki-67 index, p53 synthesis, cellularity, and total and average nucleic areas were estimated using ImageJ version 1.48. RESULTS: Analysis of variance revealed a statistically significant difference in ADCmean values between differentiated and undifferentiated thyroid carcinomas (P = .022). Spearman Rho calculation identified significant correlations between ADCmax and cell count (r = 0.541, P = .046) as well as between ADCmax and total nuclei area (r = 0.605, P = .022). CONCLUSION: DWI can distinguish between differentiated and undifferentiated thyroid carcinomas.     

Diffusion-Weighted MRI as a Biomarker of Tumor Radiation Treatment Response Heterogeneity: A Comparative Study of Whole-Volume Histogram Analysis versus Voxel-Based Functional Diffusion Map Analysis

RATIONALE: Treatment of glioblastoma (GBM) remains challenging due in part to its histologic intratumoral heterogeneity that contributes to its overall poor treatment response. Our goal was to evaluate a voxel-based biomarker, the functional diffusion map (fDM), as an imaging biomarker to detect heterogeneity of tumor response in a radiation dose escalation protocol using a genetically engineered murine GBM model. EXPERIMENTAL DESIGN: Twenty-four genetically engineered murine GBM models [Ink4a-Arf^-/-/Pten^loxp/loxp/Ntv-a RCAS/PDGF(+)/Cre(+)] were randomized in four treatment groups (n = 6 per group) consisting of daily doses of 0, 1, 2, and 4 Gy delivered for 5 days. Contrast-enhanced T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scans were acquired for tumor delineation and quantification of apparent diffusion coefficient (ADC) maps, respectively. MRI experiments were performed daily for a week and every 2 days thereafter. For each animal, the area under the curve (AUC) of the percentage change of the ADC (AUC_ADC) and that of the increase in fDM values (AUC_fDM+) were determined within the first 5 days following therapy initiation. RESULTS: Animal survival increased with increasing radiation dose. Treatment induced a dose-dependent increase in tumor ADC values. The strongest correlation between survival and ADC measurements was observed using the AUC_fDM+ metric (R² = 0.88). CONCLUSION: This study showed that the efficacy of a voxel-based imaging biomarker (fDM) was able to detect spatially varying changes in tumors, which were determined to be a more sensitive predictor of overall response versus whole-volume tumor measurements (AUC_ADC). Finally, fDM provided for visualization of treatment-associated spatial heterogeneity within the tumor.     

Multimodality Imaging to Predict Response to Systemic Treatment in Patients with Advanced Colorectal Cancer

Aim

Aim of this study was to investigate the potential of ¹⁸F-FDG PET, diffusion weighted imaging (DWI) and susceptibility-weighted (T2*) MRI to predict response to systemic treatment in patients with colorectal liver metastases. The predictive values of pretreatment measurements and of early changes one week after start of therapy, were evaluated.

Methods

Imaging was performed prior to and one week after start of first line chemotherapy in 39 patients with colorectal liver metastases. ¹⁸F-FDG PET scans were performed on a PET/CT scanner and DWI and T2* were performed on a 1.5T MR scanner. The maximum standardized uptake values (SUV), total lesion glycolysis (TLG), apparent diffusion coefficient (ADC) and T2* value were assessed in the same lesions. Up to 5 liver metastases per patient were analyzed. Outcome measures were progression free survival (PFS), overall survival (OS) and size response.

Results

Pretreatment, high SUV_max, high TLG, low ADC and high T2* were associated with a shorter OS. Low pretreatment ADC value was associated with shorter PFS. After 1 week a significant drop in SUV_max and rise in ADC were observed. The drop in SUV was correlated with the rise in ADC (r=-0.58, p=0.002). Neither change in ADC nor in SUV was predictive of PFS or OS. T2* did not significantly change after start of treatment.

Conclusion

Pretreatment SUV_max, TLG, ADC, and T2* values in colorectal liver metastases are predictive of patient outcome. Despite sensitivity of DWI and ¹⁸F-FDG PET for early treatment effects, change in these parameters was not predictive of long term outcome.