Hans Jonas Meyer

Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis

The purpose of this meta-analysis was to provide clinical evidence regarding relationship between ADC and cellularity in different tumors based on large patient data.Medline library was screened for associations between ADC and cell count in different tumors up to September 2016. Only publications in English were extracted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) was used for the research.Overall, 39 publications with 1530 patients were included into the analysis. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients.The pooled correlation coefficient for all studies was ρ = -0.56 (95 % CI = [-0.62; -0.50]),. Correlation coefficients ranged from ρ=-0.25 (95 % CI = [-0.63; 0.12]) in lymphoma to ρ=-0.66 (95 % CI = [-0.85; -0.47]) in glioma. Other coefficients were as follows: ovarian cancer, ρ = -0.64 (95% CI = [-0.76; -0.52]); lung cancer, ρ = -0.63 (95 % CI = [-0.78; -0.48]); uterine cervical cancer, ρ = -0.57 (95 % CI = [-0.80; -0.34]); prostatic cancer, ρ = -0.56 (95 % CI = [-0.69; -0.42]); renal cell carcinoma, ρ = -0.53 (95 % CI = [-0.93; -0.13]); head and neck squamous cell carcinoma, ρ = -0.53 (95 % CI = [-0.74; -0.32]); breast cancer, ρ = -0.48 (95 % CI = [-0.74; -0.23]); and meningioma, ρ = -0.45 (95 % CI = [-0.73; -0.17]).The purpose of this meta-analysis was to provide clinical evidence regarding relationship between ADC and cellularity in different tumors based on large patient data. Medline library was screened for associations between ADC and cell count in different tumors up to September 2016. Only publications in English were extracted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) was used for the research. Overall, 39 publications with 1530 patients were included into the analysis. The following data were extracted from the literature: authors, year of publication, number of patients, tumor type, and correlation coefficients. The pooled correlation coefficient for all studies was ρ = -0.56 (95 % CI = [−0.62; −0.50]),. Correlation coefficients ranged from ρ =−0.25 (95 % CI = [−0.63; 0.12]) in lymphoma to ρ=−0.66 (95 % CI = [−0.85; −0.47]) in glioma. Other coefficients were as follows: ovarian cancer, ρ = −0.64 (95% CI = [−0.76; −0.52]); lung cancer, ρ = −0.63 (95 % CI = [−0.78; −0.48]); uterine cervical cancer, ρ = −0.57 (95 % CI = [−0.80; −0.34]); prostatic cancer, ρ = −0.56 (95 % CI = [−0.69; −0.42]); renal cell carcinoma, ρ = −0.53 (95 % CI = [−0.93; −0.13]); head and neck squamous cell carcinoma, ρ = −0.53 (95 % CI = [-0.74; −0.32]); breast cancer, ρ = −0.48 (95 % CI = [−0.74; −0.23]); and meningioma, ρ = -0.45 (95 % CI = [−0.73; −0.17]).

Simultaneous 18F-FDG-PET/MRI: Associations between diffusion, glucose metabolism and histopathological parameters in patients with head and neck squamous cell carcinoma

OBJECTIVES To analyze possible associations between functional simultaneous (18)F-FDG-PET/MR imaging parameters and histopathological parameters in head and neck squamous cell carcinoma (HNSCC). MATERIAL AND METHODS 11 patients (2 female, 9 male; mean age 56.0years) with biopsy-proven primary HNSCC underwent simultaneous (18)F-FDG-PET/MRI with a dedicated head and neck protocol including diffusion weighted imaging. For each tumor, glucose metabolism was estimated with standardized uptake values (SUV) and diffusion restriction was calculated using apparent diffusion coefficients (ADC). The tumor proliferation index was estimated on Ki 67 antigen stained specimens. Cell count, total nucleic area, and average nucleic area were estimated in each case. Pearsons correlation coefficient was used to analyze possible associations between the estimated parameters. RESULTS The mean SUVmax value was 24.41±6.51, and SUVmean value 15.01±4.07. Mean values (×10(-3)mm(2)s(-1)) of ADC parameters were as follows: ADCmin: 0.65±0.20; ADCmean: 1.28±0.18; and ADCmax: 2.16±0.35. Histopathological analysis identified the following results: cell count 1069.82±388.66, total nucleic area 150771.09±61177.12μm(2), average nucleic area 142.90±57.27μm(2) and proliferation index 49.09±22.67%. ADCmean correlated with Ki 67 level (r=-0.728, p=0.011) and total nucleic area (r=-0.691, p=0.019) and tended to correlate with average nucleic area (r=-0.527, p=0.096). ADCmax correlated with Ki 67 level (r=-0.633, p=0.036). SUVmax also tended to correlate with average nucleic area (r=0.573, p=0.066). Combined parameter SUVmax/ADCmin correlated with average nucleic area (r=0.627, p=0.039). CONCLUSION ADC and SUV values showed significant correlations with different histopathological parameters and can be used as biological markers in HNSCC.

Histogram Analysis of Diffusion Weighted Imaging at 3T is Useful for Prediction of Lymphatic Metastatic Spread, Proliferative Activity, and Cellularity in Thyroid Cancer

Pre-surgical diffusion weighted imaging (DWI) is increasingly important in the context of thyroid cancer for identification of the optimal treatment strategy. It has exemplarily been shown that DWI at 3T can distinguish undifferentiated from well-differentiated thyroid carcinoma, which has decisive implications for the magnitude of surgery. This study used DWI histogram analysis of whole tumor apparent diffusion coefficient (ADC) maps. The primary aim was to discriminate thyroid carcinomas which had already gained the capacity to metastasize lymphatically from those not yet being able to spread via the lymphatic system. The secondary aim was to reflect prognostically important tumor-biological features like cellularity and proliferative activity with ADC histogram analysis. Fifteen patients with follicular-cell derived thyroid cancer were enrolled. Lymph node status, extent of infiltration of surrounding tissue, and Ki-67 and p53 expression were assessed in these patients. DWI was obtained in a 3T system using b values of 0, 400, and 800 s/mm2. Whole tumor ADC volumes were analyzed using a histogram-based approach. Several ADC parameters showed significant correlations with immunohistopathological parameters. Most importantly, ADC histogram skewness and ADC histogram kurtosis were able to differentiate between nodal negative and nodal positive thyroid carcinoma. Conclusions: histogram analysis of whole ADC tumor volumes has the potential to provide valuable information on tumor biology in thyroid carcinoma. However, further studies are warranted.

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: Ktrans, Ve, Kep, 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. Spearmans 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: Ktrans 0.189 ± 0.056 min−1, Kep 0.390 ± 0.160 min−1, Ve 0.548 ± 0.119%, and iAUC 22.40 ± 12.57. Significant correlations were observed between Kep and stained vessel areas (r = 0.51, P = .041) and total vessel areas (r = 0.5118, P = .043); between Ve and mean vessel diameter (r = −0.59, P = .017). Cell count had a tendency to correlate with Ve (r = −0.48, P = .058). In an analysis of the primary HNSCC only, a significant inverse correlation between Ktrans and KI 67 was identified (r = −0.62, P = .041). Our analysis showed significant correlations between DCE parameters and histopathological findings in HNSCC.

Parameters of simultaneous 18 F-FDG-PET/MRI predict tumor stage and several histopathological features in uterine cervical cancer

The purpose of this study was to analyze associations between apparent diffusion coefficient (ADC) and standardized uptake values (SUV) values and different histopathological parameters in uterine cervical cancer. 21 patients with primary uterine cervical cancer were involved into the study. All patients underwent a whole body simultaneous18F-FDG PET/MRI. Mean and maximum SUV were noted (SUVmean and SUVmax). In all tumors minimal, mean, and maximal ADC values (ADCmin, ADCmean, and ADCmax) were estimated. Combined parameters were calculated: SUVmax/SUVmean, ADCmin/ ADCmean, SUVmax/ADCmin and SUVmax/ADCmean. In all cases the diagnosis was confirmed histopathologically by tumor biopsy. Histological slices were stained by hematoxilin and eosin, MIB 1 monoclonal antibody, and p16. All histopathological images were digitalized and analyzed by using a ImageJ software 1.48v. The following parameters were estimated: cell count, proliferation index KI 67, total and average nucleic areas, epithelial and stromal areas. Spearmans correlation coefficient was used to analyze associations between ADC and SUV values and histological parameters. P values ≤ 0.05 were considered as statistically significant. ADCmin and ADCmin/ ADCmean were statistically significant lower in N positive tumors. KI 67 correlated statistically significant with SUVmax (r = 0.59, p = 0.005), SUVmean (0.45, p = 0.04), ADCmin (r = −0.48, p = 0.03), SUVmax/ADCmin (r = 0.71, p = 0.001), SUVmax/ADCmean (0.75, p = 0.001). SUVmax correlated well with epithelial area (r = 0.71, p = 0.001) and stromal areas (r = −0.71, p = 0.001). SUV values, ADCmin, SUVmax/ADCmin and SUVmax/ADCmean correlated statistically significant with KI 67 and can be used to estimate the proliferation potential of tumors. SUV values correlated strong with epithelial area of tumor reflected metabolic active areas.

Histogram analysis parameters identify multiple associations between DWI and DCE MRI in head and neck squamous cell carcinoma

OBJECTIVE Nowadays, multiparametric investigations of head and neck squamous cell carcinoma (HNSCC) are established. These approaches can better characterize tumor biology and behavior. Diffusion weighted imaging (DWI) can by means of apparent diffusion coefficient (ADC) quantitatively characterize different tissue compartments. Dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) reflects perfusion and vascularization of tissues. Recently, a novel approach of data acquisition, namely histogram analysis of different images is a novel diagnostic approach, which can provide more information of tissue heterogeneity. The purpose of this study was to analyze possible associations between DWI, and DCE parameters derived from histogram analysis in patients with HNSCC. MATERIALS AND METHODS Overall, 34 patients, 9 women and 25 men, mean age, 56.7±10.2years, with different HNSCC were involved in the study. DWI was obtained by using of an axial echo planar imaging sequence with b-values of 0 and 800s/mm2. Dynamic T1w DCE sequence after intravenous application of contrast medium was performed for estimation of the following perfusion parameters: volume transfer constant (Ktrans), volume of the extravascular extracellular leakage space (Ve), and diffusion of contrast medium from the extravascular extracellular leakage space back to the plasma (Kep). Both ADC and perfusion parameters maps were processed offline in DICOM format with custom-made Matlab-based application. Thereafter, polygonal ROIs were manually drawn on the transferred maps on each slice. For every parameter, mean, maximal, minimal, and median values, as well percentiles 10th, 25th, 75th, 90th, kurtosis, skewness, and entropy were estimated. RESULTS Сorrelation analysis identified multiple statistically significant correlations between the investigated parameters. Ve related parameters correlated well with different ADC values. Especially, percentiles 10 and 75, mode, and median values showed stronger correlations in comparison to other parameters. Thereby, the calculated correlation coefficients ranged from 0.62 to 0.69. Furthermore, Ktrans related parameters showed multiple slightly to moderate significant correlations with different ADC values. Strongest correlations were identified between ADC P75 and Ktrans min (p=0.58, P=0.0007), and ADC P75 and Ktrans P10 (p=0.56, P=0.001). Only four Kep related parameters correlated statistically significant with ADC fractions. Strongest correlation was found between Kep max and ADC mode (p=-0.47, P=0.008). CONCLUSION Multiple statistically significant correlations between, DWI and DCE MRI parameters derived from histogram analysis were identified in HNSCC.

Thrombotic events as incidental finding on computed tomography in intensive care unit patients

INTRODUCTION Intensive care unit (ICU) patients are a risk group to develop thrombosis and/or thromboembolism. The purpose of this study was to analyze the frequency and localization of clinically silent thrombotic events (TE) detected on CT. MATERIALS AND METHODS From 2006 to 2013 a total of 370 patients from the ICU of our university clinic were investigated by postcontrast CT. In all cases CT was performed for detecting septic foci. There were 135 women and 235 men. CT scans included cervical, thoracic, abdominal, and pelvic regions. CT images of all patients were re-interpreted by 2 radiologists by consensus. Only thromboses detected for the first time on CT were included into the analysis. Collected data were evaluated by means of descriptive statistics. Frequencies and localizations of TE in surgical and non surgical patients were analyzed by Chi-square test. Significance level was p<0.05. RESULTS In 31.9% several TE were diagnosed. There were venous thrombosis (89.8%), cardiac thrombus (2.6%), and pulmonary embolism (7.6%). More often jugular veins were affected followed by brachiocephalic veins, and iliac veins. The frequency of TE in surgical patients was 31.1%, and 32.1% in non surgical patients. Patients after surgery had more often thrombosis of extremities veins in comparison to non surgical patients. In 61.9% of all TE the identified thrombotic complications were not diagnosed at the time of CT investigations. CONCLUSION TE can be identified in 31.9% of ICU patients as incidental finding on CT. There were venous thromboses, pulmonary embolism, and cardiac thrombus. Most frequently neck and thoracic veins were affected. 61.9% of all TE were not diagnosed at the time of CT investigations. Radiologists should check carefully CT scans for presence of different TE.

The frequency of incidental pulmonary embolism in different CT examinations

OBJECTIVE Pulmonary embolism (PE) is commonly found in patients with oncologic and non-oncologic disease. The aim of the present study is to assess how frequently suspected, incidental and unreported PE occurs in particular CT examinations. In addition, differences in embolus distribution are to be considered. METHODS In a retrospective, single-centre study that covered a 5.5-year period, every contrast-enhanced CT examination was reviewed. The study group included 7238 patients with 11,747 CT examinations. A detailed pulmonary artery obstruction index (Mastora score) was used to assess thrombus mass and distribution. RESULTS PE frequency was 3.9% in oncologic patients and 6.6% in non-oncologic patients. PE was unsuspected in 54% of all PE events. Incidental PE was mostly often found in the following CT examinations: evaluation of acute pulmonary disease and follow-up staging. The thrombus mass was higher in non-oncologic patients than in oncologic patients. Furthermore, the thrombus mass was significantly lower in unsuspected PE than in suspected PE. In addition, the thrombus mass was significantly lower in unreported PE than in incidental PE. CONCLUSION The radiologist should pay special attention to pulmonary vessels, even when not asked for PE, in the following CT examinations: evaluation of acute pulmonary disease and follow-up staging. ADVANCES IN KNOWLEDGE Particular CT indications are associated with a high frequency of PE. Whether PE is suspected or not and found or not highly depends on thrombus mass.

Diffusion Profiling via a Histogram Approach Distinguishes Low-grade from High-grade Meningiomas, Can Reflect the Respective Proliferative Potential and Progesterone Receptor Status

PurposePresurgical grading, estimation of growth kinetics, and other prognostic factors are becoming increasingly important for selecting the best therapeutic approach for meningioma patients. Diffusion-weighted imaging (DWI) provides microstructural information and reflects tumor biology. A novel DWI approach, histogram profiling of apparent diffusion coefficient (ADC) volumes, provides more distinct information than conventional DWI. Therefore, our study investigated whether ADC histogram profiling distinguishes low-grade from high-grade lesions and reflects Ki-67 expression and progesterone receptor status.ProceduresPretreatment ADC volumes of 37 meningioma patients (28 low-grade, 9 high-grade) were used for histogram profiling. WHO grade, Ki-67 expression, and progesterone receptor status were evaluated. Comparative and correlative statistics investigating the association between histogram profiling and neuropathology were performed.ResultsThe entire ADC profile (p10, p25, p75, p90, mean, median) was significantly lower in high-grade versus low-grade meningiomas. The lower percentiles, mean, and modus showed significant correlations with Ki-67 expression. Skewness and entropy of the ADC volumes were significantly associated with progesterone receptor status and Ki-67 expression. ROC analysis revealed entropy to be the most accurate parameter distinguishing low-grade from high-grade meningiomas.ConclusionsADC histogram profiling provides a distinct set of parameters, which help differentiate low-grade versus high-grade meningiomas. Also, histogram metrics correlate significantly with histological surrogates of the respective proliferative potential. More specifically, entropy revealed to be the most promising imaging biomarker for presurgical grading. Both, entropy and skewness were significantly associated with progesterone receptor status and Ki-67 expression and therefore should be investigated further as predictors for prognostically relevant tumor biological features. Since absolute ADC values vary between MRI scanners of different vendors and field strengths, their use is more limited in the presurgical setting.

Whole lesion histogram analysis of meningiomas derived from ADC values. Correlation with several cellularity parameters, proliferation index KI 67, nucleic content, and membrane permeability

PURPOSE To analyze several histopathological features and their possible correlations with whole lesion histogram analysis derived from ADC maps in meningioma. MATERIALS AND METHODS The retrospective study involved 36 patients with primary meningiomas. For every tumor, the following histogram analysis parameters of apparent diffusion coefficient (ADC) were calculated: ADCmean, ADCmax, ADCmin, ADCmedian, ADCmode, ADC percentiles: P10, P25, P75, P90, as well kurtosis, skewness, and entropy. All measures were performed by two radiologists. Proliferation index KI 67, minimal, maximal and mean cell count, total nucleic area, and expression of water channel aquaporin 4 (AQP4) were estimated. Spearmans correlation coefficient was used to analyze associations between investigated parameters. RESULTS A perfect interobserver agreement for all ADC values (0.84-0.97) was identified. All ADC values correlated inversely with tumor cellularity with the strongest correlation between P10, P25 and mean cell count (-0.558). KI 67 correlated inversely with all ADC values except ADCmin. ADC parameters did not correlate with total nucleic area. All ADC values correlated statistically significant with expression of AQP4. CONCLUSIONS ADC histogram analysis is a valid method with an excellent interobserver agreement. Cellularity parameters and proliferation potential are associated with different ADC values. Membrane permeability may play a greater role for water diffusion than cell count and proliferation activity.