Standard Deviation Of Apparent Diffusion Coefficient Research Articles

MRI Features Associated with Histology of Benign Prostatic Hyperplasia Nodules: Generation of a Predictive Model.

Background: Histologic phenotypic variation of benign prostatic hyperplasia (BPH) has been hypothesized to underlie response to medical therapy. We evaluate preoperative MRI of robot-assisted simple prostatectomy (RASP) specimens and determine imaging features associated with histologic phenotype. Materials and Methods: All patients undergoing RASP from November 2015 to November 2019 with a multiparametric MRI ≤1 year before RASP were included. Patients without identifiable BPH nodules on histologic specimens were excluded. Histology slides were obtained from whole mount adenoma specimens and corresponding MRI were reviewed and graded independently by a blinded expert in BPH histopathology (D.W.S.) and an experienced radiologist specializing in prostate imaging (D.N.C.), respectively. Each nodule was assigned a phenotypic score on a 5-point Likert scale (1 = predominantly glandular; 5 = predominantly stromal) by each reviewer. Scores were compared using the sign test and univariate analysis. Signal intensity relative to background transition zone and nodule texture were noted on T2, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging sequences. Univariate and multivariate stepwise linear regression analysis were conducted to identify MRI features associated with histology score. All analyses were performed using Statistical Analysis System (version 9.4). Results: A total of 99 prostate nodules in 29 patients were included. Median phenotypic scores by histology and MRI were comparable (2, interquartile range [IQR] 2-3 vs 2, IQR 2-4, respectively; p = 0.63). Histology scores were positively correlated with MRI scores (Pearson's correlation 0.84, p < 0.0001). Multivariate stepwise linear regression analysis showed that low apparent diffusion coefficient (ADC) signal intensity (p < 0.001) and DCE wash-in (p = 0.03) were positively associated with more stromal histology, whereas ADC standard deviation (p = 0.03), DCE wash-out (p = 0.001), and heterogeneous T2 texture (p = 0.003) were associated with more glandular histology. Conclusion: There is a strong correlation between MRI features and the histologic phenotype of BPH nodules. MRI may provide a noninvasive method to determine underlying BPH nodule histology.

Differentiation of Angiomyolipoma With Minimal Fat from Clear Cell Renal Cell Carcinoma Using Non-contrast Multiparametric Magnetic Resonance Imaging.

This study was conducted to ascertain the optimal combination of non-contrast magnetic resonance (MR) imaging sequences for the differential diagnosis between small angiomyolipoma (AML) with minimal fat and clear cell renal cell carcinoma (CCRCC). Thirty-nine patients with pathologically proven AML with minimal fat (n=6) or CCRCC (n=33) measuring 4 cm or less were included. All underwent MR imaging before partial nephrectomy or percutaneous biopsy. Four quantitative parameters of tumors were evaluated: signal intensity (SI) index of T1W- gradient-echo imaging, SI index of T2- fat suppression imaging (T2-SI index), apparent diffusion coefficient (ADC) value, and standard deviation (SD) of ADC. These quantitative parameters were compared using Wilcoxon rank-sum test and receiver operating characteristic (ROC) curve analyses. The optimal combination of quantitative parameters was sought using logistic regression analysis. Comparison of quantitative parameters showed that the T2-SI index (median, AML with minimal fat vs. CCRCC; 0.74 vs. 1.27, p<0.001), ADC value (1.12 vs. 1.75, p=0.005), and SD of ADC (104 vs. 233, p<0.001) were significantly lower in AML with minimal fat than CCRCC. From the ROC curve analysis, the highest area under the curve (1.000; 100% sensitivity; 100% specificity) was obtained using the logistic regression model with the SD of ADC and T2-SI index or ADC value as explanatory variables. SD of ADC combined with T2-SI index or ADC value exhibited the highest diagnostic performance for differentiating small AML with minimal fat from CCRCC.

Meta-Analysis of Apparent Diffusion Coefficient in Pediatric Medulloblastoma, Ependymoma, and Pilocytic Astrocytoma.

Medulloblastoma, ependymoma, and pilocytic astrocytoma are common pediatric posterior fossa tumors. These tumors show overlapping characteristics on conventional MRI scans, making diagnosis difficult. To investigate whether apparent diffusion coefficient (ADC) values differ between tumor types and to identify optimum cut-off values to accurately classify the tumors using different performance metrics. Systematic review and meta-analysis. Seven studies reporting ADC in pediatric posterior fossa tumors (115 medulloblastoma, 68 ependymoma, and 86 pilocytic astrocytoma) were included following PubMed and ScienceDirect searches. Diffusion weighted imaging (DWI) was performed on 1.5 and 3T across multiple institution and vendors. The combined mean and standard deviation of ADC were calculated for each tumor type using a random-effects model, and the effect size was calculated using Hedge's g. Sensitivity/specificity, weighted classification accuracy, balanced classification accuracy. A P value < 0.05 was considered statistically significant, and a Hedge's g value of >1.2 was considered to represent a large difference. The mean (± standard deviation) ADCs of medulloblastoma, ependymoma, and pilocytic astrocytoma were 0.76 ± 0.16, 1.10 ± 0.10, and 1.49 ± 0.16 mm2 /sec× 10-3 . To maximize sensitivity and specificity using the mean ADC, the cut-off was found to be 0.96 mm2 /sec× 10-3 for medulloblastoma and ependymoma and 1.26 mm2 /sec× 10-3 for ependymoma and pilocytic astrocytoma. The meta-analysis showed significantly different ADC distributions for the three posterior fossa tumors. The cut-off values changed markedly (up to 7%) based on the performance metric used and the prevalence of the tumor types. There were significant differences in ADC between tumor types. However, it should be noted that only summary statistics from each study were analyzed and there were differences in how regions of interest were defined between studies. 1 TECHNICAL EFFICACY: Stage 3.

Correlation between molecular prognostic factors and magnetic resonance imaging intravoxel incoherent motion histogram parameters in breast cancer

ObjectiveTo explore the efficacy of the quantitative parameter histogram analysis of intravoxel incoherent motion (IVIM) for different molecular prognostic factors of breast cancer. Materials and methodsA total of 72 patients with breast cancer who were confirmed by surgical pathology and underwent preoperative magnetic resonance imaging (MRI) were analyzed retrospectively. A region of interest (ROI) was drawn in each slice of the IVIM images. Whole-tumor histogram parameters were obtained with Firevoxel's software by accumulating all ROIs. Next, Kolmogorov-Smirnov test, Student's t-test, Mann-Whitney U test, receiver operating characteristic curve analysis and spearman rank correlation analysis were used to assess the relationship between histogram parameters and molecular prognostic factors of breast cancer. ResultsAmong estrogen receptor (ER)-negative ROCs, the apparent diffusion coefficient (ADC) 10th percentile had the highest ROC of 0.792, with a cut-off value of 0.788 × 10−3 mm2/s, and sensitivity and specificity of 0.714 and 0.867, respectively. The negative correlation between lymph node metastasis status and ADC standard deviation was significant (ρ = −0.44, the correlation coefficients was represented by ρ). Positive correlations were observed between hormonal expression of ER and progesterone receptor (PR) with heterogeneity metrics of ADC or perfusion fraction (f), such as ADC inhomogeneity (ρ = 0.37, ρ = 0.29) and f skewness (ρ = 0.32, ρ = 0.28). Negative correlations were observed with numerical metrics, such as the ADC median (ρ = −0.31, ρ = −0.34) and f 45th percentile (ρ = −0.35, ρ = −0.28). The positive correlations between human epidermal receptor factor-2 (HER2) and pseudo-diffusivity (Dp) numerical metrics, Ki-67 expression, and heterogeneity metrics of Dp were high. ConclusionsThe ADC 10th percentile had the largest area under the curve in the ER-negative ROC analysis, and the ADC standard deviation was the most valuable in the correlation analysis of lymph node metastasis. Whole-lesion quantitative histogram parameters of IVIM could, therefore, provide a scientific basis for radiomics to further guide clinical practice in the prognosis of breast cancer.

Histogram Analysis Based on Apparent Diffusion Coefficient Maps of Bone Marrow in Multiple Myeloma: An Independent Predictor for High-risk Patients Classified by the Revised International Staging System

The revised International Staging System (R-ISS) is the current risk stratifier for patients with newly diagnosed multiple myeloma (NDMM). We used histogram analysis based on apparent diffusion coefficient (ADC) maps of bone marrow to predict high-risk NDMM patients staged as R-ISS stage III. Sixty-one NDMM patients were recruited prospectively and underwent whole-body diffusion-weighted MRI. Mean ADC and four ADC-based histogram parameters of representative background bone marrow were quantified with TexRAD software, including ADC entropy, ADC standard deviation (SD), ADC skewness and ADC kurtosis. Diagnostic performance to discriminate R-ISS III from I/II disease was evaluated by receiver-operating characteristics curve (ROC). Univariate and multivariate analysis using stepwise logistic regression model was performed to identify predictors for R-ISS III. ADC entropy of background marrow showed the highest areas under the ROC (0.784, sensitivity=93.3%, specificity=63.0%) for the detection of R-ISS stage III disease. Multivariate analysis showed that increased ADC entropy (>3.612) of background marrow can independently predict R-ISS stage III disease in the overall patients (Model 1 corrected for diffuse infiltration [DI] pattern: odds ratio [OR], 10.647; p=0.008; Model 2 corrected for mean ADC: OR, 10.485; p=0.010) and in the subgroup with DI pattern (OR, 7.043; p=0.037). ADC entropy of background marrow may serve as a sensitive imaging biomarker facilitating the detection of high-risk NDMM patients staged as R-ISS stage III. Increased ADC entropy of background marrow can independently predict R-ISS stage III in the overall patients and in the subgroup with DI pattern.

Comparison of conventional magnetic resonance imaging and diffusion-weighted imaging in the differentiation of bone plasmacytoma from bone metastasis in the extremities

PurposeTo compare conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) in the differentiation of bone plasmacytoma from bone metastasis in the extremities. Materials and methodsA total of 65 patients with 27 bone plasmacytomas (11 men; mean age, 63.6±8.2 [SD] years) and 38 patients with bone metastases (20 men; mean age, 64.1±11.5 [SD] years) were retrospectively included. Plasmacytomas and metastases were compared for size, peritumoral edema, signal intensity (SI), SI pattern, apparent diffusion coefficient (ADC) values and standard deviation (SD) of ADC. Receiver operating characteristic analysis with area under the curve (AUC) was used to calculate sensitivity, specificity, and accuracy of MRI and DWI for the diagnosis of plasmacytoma according to a defined cut-off value. ResultsOn conventional MRI, plasmacytomas showed less peritumoral edema (22% vs. 71%; P<0.001), were more often hyperintense on T1-weighted image (48% vs. 18%; P=0.022) and more homogeneous on T2-weighted image (78% vs. 26%; P<0.001) and contrast-enhanced T1-weighted images (70% vs. 25%; P=0.001) than bone metastases. Mean ADC value and SD of ADC were significantly lower in bone plasmacytomas (760.1±196.9 [SD] μm2/s and 161.5±62.7 [SD], respectively) than in bone metastases (1214.2±382.6 [SD] μm2/s and 277.0±110.3 [SD], respectively) (P<0.001). Using an ADC value≤908.3μm2/s, DWI yielded 88% sensitivity and 78% specificity for the diagnosis of plasmacytoma. ADC value yielded best area under the curve (AUC=0.913), followed by SD of ADC (AUC=0.814) and homogeneity on T2-weighted images (AUC=0.757). The combination of conventional MRI and DWI (AUC=0.894) showed improved diagnostic performance over conventional MRI alone (AUC= 0.843) for discriminating between plasmacytoma and metastasis. ConclusionConventional MRI in combination with DWI can be useful to discriminate between bone plasmacytoma and bone metastasis in the extremities.

Diffusion weighted imaging in high-grade gliomas: A histogram-based analysis of apparent diffusion coefficient profile.

PurposeGlioblastoma and anaplastic astrocytoma represent the most commonly encountered high-grade-glioma (HGG) in adults. Although both neoplasms are very distinct entities in context of epidemiology, clinical course and prognosis, their appearance in conventional magnetic resonance imaging (MRI) is very similar. In search for additional information aiding the distinction of potentially confusable neoplasms, histogram analysis of apparent diffusion coefficient (ADC) maps recently proved to be auxiliary in a number of entities. Therefore, our present exploratory retrospective study investigated whether ADC histogram profile parameters differ significantly between anaplastic astrocytoma and glioblastoma, reflect the proliferation index Ki-67, or are associated with the prognostic relevant MGMT (methylguanine-DNA methyl-transferase) promotor methylation status.MethodsPre-surgical ADC volumes of 56 HGG patients were analyzed by histogram-profiling. Association between extracted histogram parameters and neuropathology including WHO-grade, Ki-67 expression and MGMT promotor methylation status was investigated due to comparative and correlative statistics.ResultsGrade IV gliomas were more heterogeneous than grade III tumors. More specifically, ADCmin and the lowest percentile ADCp10 were significantly lower, whereas ADCmax, ADC standard deviation and Skewness were significantly higher in the glioblastoma group. ADCmin, ADCmax, ADC standard deviation, Kurtosis and Entropy of ADC histogram were significantly correlated with Ki-67 expression. No significant difference could be revealed by comparison of ADC histogram parameters between MGMT promotor methylated and unmethylated HGG.ConclusionsADC histogram parameters differ significantly between glioblastoma and anaplastic astrocytoma and show distinct associations with the proliferative activity in both HGG. Our results suggest ADC histogram profiling as promising biomarker for differentiation of both, however, further studies with prospective multicenter design are wanted to confirm and further elaborate this hypothesis.

Pretreatment Apparent Diffusion Coefficient Cannot Predict Histopathological Features and Response to Neoadjuvant Radiochemotherapy in Rectal Cancer: A Meta-Analysis

Aim: Our purpose was to perform a systemic literature review and meta-analysis regarding use of apparent diffusion coefficient (ADC) for prediction of histopathological features in rectal cancer (RC) and to prove if ADC can predict treatment response to neoadjuvant radiochemotherapy (NARC) in RC. Methods: MEDLINE library, EMBASE, Cochrane, and SCOPUS database were screened for associations between ADC and histopathology and/or treatment response in RC up to June 2020. Authors, year of publication, study design, number of patients, mean value, and standard deviation of ADC were acquired. The methodological quality of the collected studies was checked according to the Quality Assessment of Diagnostic Studies instrument. The meta-analysis was undertaken by using the RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used to account the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated. Results: Overall, 37 items (2,015 patients) were included. ADC values of tumors with different T and N stages and grades overlapped strongly. ADC cannot distinguish RC with a high- and low-carcinoembryonic antigen level. Regarding KRAS status, ADC cannot discriminate mutated and wild-type RC. ADC did not correlate significantly with expression of vascular endothelial growth factor and hypoxia-inducible factor 1a. ADC correlates with Ki 67, with the calculated correlation coefficient: −0.52. The ADC values in responders and nonresponders overlapped significantly. Conclusion: ADC correlates moderately with expression of Ki 67 in RC. ADC cannot discriminate tumor stages, grades, and KRAS status in RC. ADC cannot predict therapy response to NARC in RC.

Is the standard deviation of the apparent diffusion coefficient a potential tool for the preoperative prediction of tumor grade in endometrial cancer?

The tumor histological grade is closely related to the prognosis of endometrial cancer (EC). The use of the apparent diffusion coefficient (ADC), tumor volume, and MRI-based texture analysis has allowed exciting advances in predicting EC grade before surgery. However, whether this constitutes a simple, convenient, and powerful diagnostic method remains unknown. To explore the utility of standard deviation (SD) of the ADC (ADCSD) for predicting the tumor grade in patients with EC. We retrospectively evaluated 138 patients with EC. All patients underwent unenhanced MRI and diffusion-weighted imaging (DWI). The mean ADC value (ADCmean) and SD were obtained using a freehand region of interest traced on the ADC map. Spearman's linear correlation coefficients were calculated to analyze the correlations between the indexes (including ADCSD and the ADCmean) and the Ki-67 index. The Kruskal-Wallis and Mann-Whitney U tests were used to compare differences in the index results among tumor grades. A significant difference in ADCSD was observed among the tumor grades (P=0.000), and the ADCSD value was significantly higher for high-grade EC than for low-grade tumors (289.7 vs. 216.3×10-6mm2 /s, P=0.000). A statistically significant positive correlation was observed between ADCSD and the Ki-67 index (r=0.364, P=0.000). According to the receiver operating characteristic curve, ADCSD ≥240.2×10-6mm2 /s predicted high-grade EC with a sensitivity, specificity, and accuracy of 73.1%, 80.2%, and 77.5%, respectively. Based on the intratumor heterogeneity of EC, ADCSD represents a potential method for the preoperative prediction of high-grade EC, although further studies are needed.

Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions

BackgroundThe purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.MethodsMEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength.The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions.ResultsThe acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10− 3 mm2/s and the mean value of the benign lesions was 1.5 × 10− 3 mm2/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10− 3 mm2/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area).ConclusionAn ADC threshold of 1.00 × 10− 3 mm2/s can be recommended for distinguishing breast cancers from benign lesions.

Value of whole-liver apparent diffusion coefficient histogram analysis for quantification of liver fibrosis stages

To explore whether volumetric acquisition of histogram analysis of apparent diffusion coefficient (ADC) maps could quantify liver fibrosis stages. 84 patients underwent MRI exams including DWI before surgery or liver biopsy were included. Histogram analysis of whole liver ADC maps were performed and parameters including ADC mean, ADC median, ADC skewness, ADC kurtosis, ADC standard deviation (SD), ADC 5th, 10th, 25th, 75th, 90th and 95th percentiles were calculated. The correlations between different histopathologic grades and histogram analysis parameters were determined. For the significant parameters, receiver operating characteristics (ROC) analyses were further established to evaluate the potential diagnostic performance for differentiating liver fibrosis stages. The ADC mean and ADC median, ADC SD, ADC5th, 10th, 25th, 75th, 90th and 95th percentile were significantly lower with the increasing grade of liver fibrosis (rho: from − 0.580 to − 0.230). ADC skewness and ADC kurtosis were significantly higher with the increasing grade of liver fibrosis (rho: 0.243, 0.347, respectively). Areas under the ROC curve (AUC) of ADC mean and ADC 75th percentile for identification liver fibrosis (F1–F4), moderate to advanced liver fibrosis (F2–F4), advanced liver fibrosis (F3–F4), cirrhosis (F4) were 0.87, 0.83, 0.82, 0.76 and 0.88, 0.82, 0.80, 0.74, respectively. Histogram analysis of volumetric acquisition of liver ADC maps has a potential value for quantifying liver fibrosis grades.

The application of the local histograms of apparent difusion coefficient in differentiation of brain astrocytomas

Background/Aim. Microstructural diversity of brain astrocytomas makes their diagnostics and differentiation by using the diffusion weighted imaging (DWI) difficult. In this study we used the histogram-based positioning of regions of interests on the apparent diffusion coefficient (ADC) maps in order to restrict the determination of diffusion parameters to regions of interest (ROI) corresponding to maximum cellularity. Success of ADC standard deviation (?ADC) and kurtosis (K) in differentiation of brain astrocytomas was evaluated. Methods. The thirtyone patients (16 women and 15 men, median age 37 years, age range 6?72 years) with suspected supratentorial astrocytomas were included in the retrospective study. The magnetic resonance imaging (MRI) examinations were performed using the 1.5 T MR system (Avanto; Siemens, Erlangen, Germany) and 8-channel phased array head coil. The DWI images were acquired in three orthogonal directions for the b-values 0, 500 and 1000 s mm-2. The histogram calculations and determination of diffusion parameters were performed using the MIPAV software package and the statistical analysis was done in the Openstat software. Results. The ADC values enabled differentiation of diffuse astrocytomas (DA) from a high-grade astrocytoma (HGA), but not between the classes of HGA. In addition, the ?ADC value provided discrimination between the anaplastic astrocytoma (AA) and glioblastoma multiforme (GBM) with 100% of sensitivity and 89% of specificity . The kurtosis value can also differentiate between the grades AA and GBM although with the lower sensitivity and specificity. Conclusion. The histogram analysis of tumor region on the ADC maps can provide a guidance for an appropriate choice of the ROIs. The parameters which characterize diffusion of such defined ROIs, as well as their combination can improve differentiation of brain astrocytomas.

Diffusion and perfusion MR parameters to assess preoperative short-course radiotherapy response in locally advanced rectal cancer: a comparative explorative study among Standardized Index of Shape by DCE-MRI, intravoxel incoherent motion- and diffusion kurtosis imaging-derived parameters.

To assess preoperative short-course radiotherapy (SCR) tumor response in locally advanced rectal cancer (LARC) by means of Standardized Index of Shape (SIS) by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters derived from diffusion-weighted MRI (DW-MRI). Thirty-four patients with LARC who underwent MRI scans before and after SCR followed by delayed surgery, retrospectively, were enrolled. SIS, ADC, IVIM parameters [tissue diffusion (Dt), pseudo-diffusion (Dp), perfusion fraction (fp)] and DKI parameters [mean diffusivity (MD), mean of diffusional kurtosis (MK)] were calculated for each patient. IVIM parameters were estimated using two methods, namely conventional biexponential fitting (CBFM) and variable projection (VARPRO). After surgery, the pathological TNM and tumor regression grade (TRG) were estimated. For each parameter, percentage changes between before and after SCR were evaluated. Furthermore, an artificial neural network was trained for outcome prediction. Nonparametric sample tests and receiver operating characteristic curve (ROC) analysis were performed. Fifteen patients were classified as responders (TRG ≤ 2) and 19 as not responders (TRG > 3). Seven patients had TRG 1 (pathological complete response, pCR). Mean and standard deviation values of pre-treatment CBFM Dp and mean value of VARPRO Dp pre-treatment showed statistically significant differences to predict pCR. (p value at Mann-Whitney test was 0.05, 0.03 and 0.008, respectively.) Exclusively SIS percentage change showed significant differences between responder and non-responder patients after SCR (p value << 0.001) and to assess pCR after SCR (p value << 0.001). The best results to predict pCR were obtained by VARPRO Fp mean value pre-treatment with area under ROC of 0.84, a sensitivity of 96.4%, a specificity of 71.4%, a positive predictive value (PPV) of 92.9%, a negative predictive value (NPV) of 83.3% and an accuracy of 91.2%. The best results to assess after treatment complete pathological response were obtained by SIS with an area under ROC of 0.89, a sensitivity of 85.7%, a specificity of 92.6%, a PPV of 75.0%, a NPV of 96.1% and an accuracy of 91.2%. Moreover, the best results to differentiate after treatment responders vs. non-responders were obtained by SIS with an area under ROC of 0.94, a sensitivity of 93.3%, a specificity of 84.2%, a PPV of 82.4%, a NPV of 94.1% and an accuracy of 88.2%. Promising initial results were obtained using a decision tree tested with all ADC, IVIM and DKI extracted parameter: we reached high accuracy to assess pathological complete response after SCR in LARC (an accuracy of 85.3% to assess pathological complete response after SCR using VARPRO Dp mean value post-treatment, ADC standard deviation value pre-treatment, MD standard deviation value post-treatment). SIS is a hopeful DCE-MRI angiogenic biomarker to assess preoperative treatment response after SCR with delayed surgery. Furthermore, an important prognostic role was obtained by VARPRO Fp mean value pre-treatment and by a decision tree composed by diffusion parameters derived by DWI and DKI to assess pathological complete response.

Apparent diffusion coefficients in prostate cancer: correlation with molecular markers Ki-67, HIF-1α and VEGF.

Prostate cancer (PCa) is the second most common cancer in men. The Gleason score (GS) and biomarkers play important roles in the diagnosis and treatment of patients with PCa. The purpose of this study was to investigate the relationship between the apparent diffusion coefficient (ADC) and the molecular markers Ki-67, hypoxia-inducible factor-1α (HIF-1α) and vascular endothelial growth factor (VEGF) in PCa. Thirty-nine patients with 39 lesions, who had been diagnosed with PCa, were enrolled in this study. All patients underwent diffusion-weighted magnetic resonance imaging (DW-MRI) (b = 800s/mm2 ). The expression of Ki-67, HIF-1α and VEGF was assessed by immunohistochemistry. Statistical analysis was applied to analyze the association between ADC and prostate-specific antigen (PSA), GS and the expression of Ki-67, HIF-1α and VEGF. The group differences in ADC among different grades of Ki-67, HIF-1α and VEGF were also analyzed. The mean ± standard deviation of ADC was (0.76 ± 0.27) × 10-3 mm2 /s. ADC correlated negatively with PSA and GS (p < 0.05). The Ki-67 staining index (SI), HIF-1α expression and VEGF expression in PCa were correlated inversely with ADC, controlling for age (r = -0.332, p < 0.05; r = -0.662, p < 0.0005; and r = -0.714, p < 0.0005, respectively). ADC showed a significant difference among different grades of Ki-67 (F = 9.164, p = 0.005), HIF-1α (F = 40.333, p < 0.0005) and VEGF (F = 22.048, p < 0.0005). In conclusion, ADC was correlated with PSA, GS, and Ki-67, HIF-1α and VEGF expression in patients with PCa. ADC may be used to evaluate tumor proliferation, hypoxia and angiogenesis in PCa.

Feasibility study of reduced field of view diffusion-weighted magnetic resonance imaging in head and neck tumors.

Background Reduced field of view (rFOV) imaging may be used to improve the quality of diffusion-weighted imaging (DWI) in the head and neck (HN) region. Purpose To evaluate the feasibility of rFOV-DWI in patients affected by HN tumors, through a comparison with conventional full FOV (fFOV) DWI. Material and Methods Twenty-two patients with histologically-proven malignant or benign tumors of the head and neck were included in a retrospective study. All patients underwent pre-treatment magnetic resonance imaging (MRI) studies including rFOV-DWI and fFOV-DWI. The apparent diffusion coefficient (ADC) value distributions inside tumor and muscle were derived and the mean, standard deviation (SD), and kurtosis were calculated. Image distortion was quantitatively and qualitatively evaluated, as well as the capability of lesion identification. The Wilcoxon test was used to compare all variables. Agreements between the ADC estimations were assessed by Bland-Altman plots. Results Image distortion and lesion identification scores were both higher for rFOV-DWI compared to fFOV-DWI. A reduction in ADC values with rFOV-DWI emerged for both lesion and muscle, with a mean percentage difference in ADC of 6.2% in the lesions and 24.9% in the muscle. The difference in SD of ADC was statistically significant in the lesions, indicating a higher ADC homogeneity for rFOV DWI ( P = 0.005). Conclusion The application of rFOV DWI in patients affected by HN tumors is feasible and promising, based on both qualitative and quantitative analyses. This technique has potential for improving the diagnostic accuracy of fFOV-DWI for the study of specific tumoral areas.

Tumor apparent diffusion coefficient as an imaging biomarker to predict tumor aggressiveness in patients with estrogen-receptor-positive breast cancer.

The purpose of this retrospective study was to evaluate whether tumor apparent diffusion coefficient (ADC) was correlated with pathologic biomarkers such as tumor cellularity, Ki67, tumor-infiltrating lymphocytes (TILs), and peritumoral lymphocytic infiltrate (PLI) in patients with estrogen receptor (ER)-positive breast cancer. The study was approved by the institutional review board and informed consent was waived. From July 2014 to December 2014, we reviewed 140 ER-positive tumors in 138 consecutive patients (range, 28-77 years; mean, 52 years) who underwent preoperative breast MRI and definitive surgery. All patients underwent diffusion-weighted imaging with a 3T scanner. Two radiologists drew the region of interest of the entire tumor and obtained the mean and pixel-based histogram of ADC. On pathology, two pathologists reviewed tumor cellularity, Ki67, TILs, and PLI. Multiple linear regression analysis was used to determine pathologic variables independently associated with ADC. Tumors with high tumor cellularity and high Ki67 had significantly lower ADCs than those with low tumor cellularity and low Ki67 (P < 0.05 for all). Tumors without PLI had significantly higher standard deviation than those with PLI (0.23 ± 0.08 versus 0.18 ± 0.05; P < 0.001). Median ADC was negatively correlated with tumor cellularity (r = -0.441), and Ki67 (r = -0.382). The standard deviation of ADC was also negatively correlated with the degree of PLI (r = -0.319). On multivariate linear regression analysis, tumor cellularity and Ki67 were independently associated with tumor ADC. Tumor ADC would be an MRI biomarker for the prediction of tumor aggressiveness indicators such as Ki67, tumor cellularity, and PLI in ER-positive breast cancer. Copyright © 2016 John Wiley & Sons, Ltd.

Can volumetric ADC measurement help predict response to Y90 radioembolization in HCC?

To compare changes in RECIST, anatomical volume, mRECIST, and volumetric diffusion-weighted Imaging parameters (3D apparent diffusion coefficient (ADC) measurements), with pathological analysis of hepatocellular carcinoma (HCC) treated by (90)Yttrium radioembolization (Y90). 21 patients were treated by Y90 as a sole treatment modality for solitary, >2 cm HCC that underwent liver transplantation. MRI at baseline, 1 and 3 months post-Y90, and tumor pathological findings on explants were reviewed in all patients. Compared to baseline (RECIST/volume: 3.6 cm/17.7 cm(3)), RECIST and volume were not modified after Y90 (1 month, p = 0.28/0.09 RECIST/tumor volume; 3 months, p = 0.28/0.54). In contrast, mRECIST (3.3-1.4 cm, p < 0.001), mean ADC (0.185-1.093 mm(2)/s × 10(-3), p = 0.04), and ADC standard deviation (STD) (0.041-0.201 mm(2)/s × 10(-3), p = 0.0496) changed as earlier as 1 month post-Y90. ADC STD % change was higher in ADC responding lesions than non-responding lesions at 1 month (p = 0.002) and 3 months (p = 0.008). All lesions exhibited necrosis on pathological analysis (11 partially viable, 10 complete pathological necrosis (CPN)) but no imaging criterion was able to predict CPN. mRECIST (±ADC) at 1 (κ ± ADC = 0.08/0.06) or 3 months (κ = -0.06/-0.06) were poor predictors of pathological response. As soon as 1 month post-treatment, mRECIST and volumetric ADC performed better than traditional size RECIST or volumetric parameters in detecting imaging response to Y90; however, CPN cannot be predicted by any criteria. Improvements in methodologies to assess response and identification of better surrogates are awaited.

Emphysematous changes and normal variation in smokers and COPD patients using diffusion 3He MRI

Introduction: This study aims to quantify global and regional changes of diffusive motion of 3He gas within the lung, as determined by hyperpolarized 3He MR apparent diffusion coefficient (ADC) measurement, in non-smokers, smokers and chronic obstructive pulmonary disease (COPD) patients. Methods: Age-matched groups of six healthy non-smokers, five healthy smokers and five patients with COPD. The experiments were performed with approval from the local Research Ethics Committee. Diffusion imaging was performed following hyperpolarized 3He gas inhalation, producing ADC maps. Mean and standard deviation of the ADCs were used to compare the subject groups and assess regional variations within individuals. Results: The intra-individual standard deviation of ADC in the healthy smokers was significantly larger than that of the non-smoking group ( P < 0.02). Compared to the non-smoking group, COPD patients had significantly higher mean and standard deviation of ADC ( P < 0.01). The mean ADC in the anterior half of the chest was systematically higher than in the posterior half in the healthy non-smoking subject group. Discussion: This study suggests that there are regional trends in the ADC values of healthy volunteers that may have implications for the clinical interpretation of ADC values. Less homogeneous ADC values have been detected in asymptomatic smokers, indicative of damage to the distal air spaces.