Apparent Diffusion Coefficient Entropy Research Articles

Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis for Assessing Normal-Sized Lymph Node Metastasis in Cervical Cancer: Comparison Between Readout-Segmented and Single-Shot Echo-Planar Diffusion-Weighted Imaging.

To compare the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis derived from readout-segmented echo-planar imaging (RS-EPI) and single-shot echo-planar imaging (SS-EPI) diffusion-weighted imaging (DWI) in evaluating normal-sized lymph node metastasis (LNM) in cervical cancer. Seventy-six pathologically confirmed cervical cancer patients (stages IB and IIA) were enrolled, including 61 patients with non-LNM (group A) and 15 patients with normal-sized LNM (group B). The recorded tumor volume on T2-weighted imaging was the reference against which both DWIs were evaluated. Each ADC histogram parameter (including ADC max , ADC 90 , ADC median , ADC mean , ADC 10 , ADC min , ADC skewness , ADC kurtosis , and ADC entropy ) was compared between SS-EPI and RS-EPI and between the 2 groups. There was no significant difference in tumor volume between the 2 DWIs and T2-weighted imaging (both P > 0.05). Higher ADC max and ADC entropy but lower ADC 10 , ADC min and ADC skewness were found in SS-EPI than those in RS-EPI (all P < 0.05). For SS-EPI, lower ADC 90 and higher ADC kurtosis were found in group B than those in group A (both P < 0.05). For RS-EPI, lower ADC 90 and higher ADC kurtosis and ADC entropy were found in group B than those in group A (all P < 0.05). Readout-segmented echo-planar imaging ADC kurtosis showed the highest area under the curve of 0.792 in the differentiation of the 2 groups (sensitivity, 80%; specificity, 73.77%). Compared with SS-EPI, the ADC histogram parameters derived from RS-EPI were more accurate, and ADC kurtosis held great potential in differentiating normal-sized LNM in cervical 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.

Whole Volume Apparent Diffusion Coefficient (ADC) Histogram as a Quantitative Imaging Biomarker to Differentiate Breast Lesions: Correlation with the Ki-67 Proliferation Index.

Objectives To evaluate the value of the whole volume apparent diffusion coefficient (ADC) histogram in distinguishing between benign and malignant breast lesions and differentiating different molecular subtypes of breast cancers and to assess the correlation between ADC histogram parameters and Ki-67 expression in breast cancers. Methods The institutional review board approved this retrospective study. Between September 2016 and February 2019, 189 patients with 84 benign lesions and 105 breast cancers underwent magnetic resonance imaging (MRI). Volumetric ADC histograms were created by placing regions of interest (ROIs) on the whole lesion. The relationships between the ADC parameters and Ki-67 were analysed using Spearman's correlation analysis. Results Of the 189 breast lesions included, there were significant differences in patient age (P < 0.001) and lesion size (P = 0.006) between the benign and malignant lesions. The results also demonstrated significant differences in all ADC histogram parameters between benign and malignant lesions (all P < 0.001). The median and mean ADC histogram parameters performed better than the other ADC histogram parameters (AUCs were 0.943 and 0.930, respectively). The receiver operating characteristic (ROC) analysis revealed that the 10th percentile ADC value and entropy could determine the human epidermal growth factor receptor 2 (HER-2) status (both P = 0.001) and estrogen receptor (ER)/progesterone receptor (PR) status (P = 0.020 and P = 0.041, respectively). Among all breast cancer lesions, 35 tumours in the low-proliferation group (Ki − 67 < 14%) and 70 tumours in the high-proliferation group (Ki − 67 ≥ 14) were analysed with ROC curves and correlation analyses. The ROC analysis revealed that entropy and skewness could determine the Ki-67 status (P = 0.007 and P < 0.001, respectively), and there were weak correlations between ADC entropy (r = 0.383) and skewness (r = 0.209) and the Ki-67 index. Conclusion The volumetric ADC histogram could serve as an imaging marker to determine breast lesion characteristics and may be a supplemental method in predicting tumour proliferation in breast cancer.

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.

Value of conventional magnetic resonance imaging texture analysis in the differential diagnosis of benign and borderline/malignant phyllodes tumors of the breast

BackgroundThe purpose of this study was to determine the potential value of magnetic resonance imaging (MRI) texture analysis (TA) in differentiating between benign and borderline/malignant phyllodes tumors of the breast.MethodsThe preoperative MRI data of 25 patients with benign phyllodes tumors (BPTs) and 19 patients with borderline/malignant phyllodes tumors (BMPTs) were retrospectively analyzed. A gray-level histogram and gray-level cooccurrence matrix (GLCM) were used for TA with fat-suppressed T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) images, and 2- and 7-min postcontrast T1W images on dynamic contrast-enhanced MRI (DCE-T1WI2min and DCE-T1WI7min) between BPTs and BMPTs. Independent sample t-test and Mann-Whitney U test were performed for intergroup comparison. A regression model was established by using binary logistic regression analysis, and receiver operating characteristic (ROC) curve analysis was carried out to evaluate diagnostic efficiency.ResultsFor ADC images, the texture parameters angular second moment (ASM), correlation, contrast, entropy and the minimum gray values of ADC images (ADCMinimum) showed significant differences between the BPT group and BMPT group (all p<0.05). The parameter entropy of FS-T2WI and the maximum gray values and kurtosis of the tumor solid region of DCE-T1WI7min also showed significant differences between these two groups. Except for ADCMinimum, angular second moment of FS-T2WI (FS-T2WIASM), and the maximum gray values of DCE-T1WI7min (DCE-T1WI7min-Maximum) of the tumor solid region, the AUC values of other positive texture parameters mentioned above were greater than 0.75. Binary logistic regression analysis demonstrated that the contrast of ADC images (ADCContrast) and entropy of FS-T2WI (FS-T2WIEntropy) could be considered independent texture variables for the differential diagnosis of BPTs and BMPTs. Combined, the AUC of these parameters was 0.891 (95% CI: 0.793–0.988), with a sensitivity of 84.2% and a specificity of up to 89.0%.ConclusionTexture analysis could be helpful in improving the diagnostic efficacy of conventional MR images in differentiating BPTs and BMPTs.

Histogram Analysis of Diffusion Weighted Imaging in Low-Grade Gliomas: in vivo Characterization of Tumor Architecture and Corresponding Neuropathology.

Background: Low-grade gliomas (LGG) in adults are usually slow growing and frequently asymptomatic brain tumors, originating from glial cells of the central nervous system (CNS). Although regarded formally as “benign” neoplasms, they harbor the potential of malignant transformation associated with high morbidity and mortality. Their complex and unpredictable tumor biology requires a reliable and conclusive presurgical magnetic resonance imaging (MRI). A promising and emerging MRI approach in this context is histogram based apparent diffusion coefficient (ADC) profiling, which recently proofed to be capable of providing prognostic relevant information in different tumor entities. Therefore, our study investigated whether histogram profiling of ADC distinguishes grade I from grade II glioma, reflects the proliferation index Ki-67, as well as the IDH (isocitrate dehydrogenase) mutation and MGMT (methylguanine-DNA methyl-transferase) promotor methylation status.Material and Methods: Pre-treatment ADC volumes of 26 LGG patients were used for histogram-profiling. WHO-grade, Ki-67 expression, IDH mutation, and MGMT promotor methylation status were evaluated. Comparative and correlative statistics investigating the association between histogram-profiling and neuropathology were performed.Results: Almost the entire ADC profile (p25, p75, p90, mean, median) was significantly lower in grade II vs. grade I gliomas. Entropy, as second order histogram parameter of ADC volumes, was significantly higher in grade II gliomas compared with grade I gliomas. Mean, maximum value (ADCmax) and the percentiles p10, p75, and p90 of ADC histogram were significantly correlated with Ki-67 expression. Furthermore, minimum ADC value (ADCmin) was significantly associated with MGMT promotor methylation status as well as ADC entropy with IDH-1 mutation status.Conclusions: ADC histogram-profiling is a valuable radiomic approach, which helps differentiating tumor grade, estimating growth kinetics and probably prognostic relevant genetic as well as epigenetic alterations in LGG.

Exploratory radiomic features from integrated 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging are associated with contemporaneous metastases in oesophageal/gastroesophageal cancer

PurposeThe purpose of this study was to determine if 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MRI) features are associated with contemporaneous metastases in patients with oesophageal/gastroesophageal cancer.MethodsFollowing IRB approval and informed consent, patients underwent a staging PET/MRI following 18F-FDG injection (326 ± 28 MBq) and 156 ± 23 min uptake time. First-order histogram and second-order grey level co-occurrence matrix features were computed for PET standardized uptake value (SUV) and MRI T1-W, T2-W, diffusion weighted (DWI) and apparent diffusion coefficient (ADC) images for the whole tumour volume. K-means clustering assessed the correlation of feature-pairs with metastases. Multivariate analysis of variance (MANOVA) was performed to assess the statistical separability of the groups identified by feature-pairs. Sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and accuracy (ACC) were calculated for these features and compared with SUVmax, ADCmean and maximum diameter alone for predicting contemporaneous metastases.ResultsTwenty patients (18 males, 2 female; median 67 years, range 52–86) comprised the final study cohort; ten patients had metastases. Lower second-order SUV entropy combined with higher second-order ADC entropy were the best feature-pair for discriminating metastatic patients, MANOVA p value <0.001 (SN = 80%, SP = 80%, PPV = 80%, NPV = 80%, ACC = 80%). SUVmax (SN = 30%, SP = 80%, PPV = 60%, NPV = 53%, ACC = 55%), ADCmean (SN = 20%, SP = 70%, PPV = 40%, NPV = 47%, ACC = 45%) and tumour maximum diameter (SN = 10%, SP = 90%, PPV = 50%, NPV = 50%, ACC = 50%) had poorer sensitivity and accuracy.ConclusionHigh ADC entropy combined with low SUV entropy is associated with a higher prevalence of metastases and a promising initial signature for future study.

Predictive markers for MGMT promoter methylation in glioblastomas.

The promoter methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) gene has been described as the most important predictor of chemotherapeutic response and patients' survival in glioblastomas (GBs). Therefore, prediction of the MGMT promoter methylation status by imaging would help to preoperatively decide the overall treatment strategy as well as surgical strategy. This study aimed to detect imaging parameters to predict MGMT promoter methylation in GBs by using a commercially available software. We investigated three imaging features (ring enhancement, tumor location, and laterality) and apparent diffusion coefficient (ADC) parameters in 48 newly diagnosed GBs treated at Keio University Hospital in 2006 or later. For ADC, texture analyses were performed. Regions of interest (ROIs) were drawn manually with reference to contrast-enhanced areas, excluding necrotic and cystic regions. Mean ADC value and ADC histogram parameters, including kurtosis, skewness, and entropy, were compared with MGMT promoter methylation. Each parameter was evaluated to determine correlation with MGMT promoter methylation, and the parameters with significant associations with the methylation status were correlated with the MGMT-positive cell ratio determined by immunohistochemistry (IHC) analysis. The mean ADC value and ADC entropy were significantly associated with MGMT promoter methylation. The combination of mean ADC value and ADC entropy predicted MGMT promoter methylation, with a PPV of 81.2% and specificity of 88.9%. The mean ADC value and ADC entropy were negatively correlated with the MGMT-positive cell ratio in the IHC analysis. This study demonstrated that texture analyses of ADC histograms in GBs were predictive of MGMT promoter methylation.

Preoperative Prediction of Solitary Fibrous Tumor/Hemangiopericytoma and Angiomatous Meningioma Using Magnetic Resonance Imaging Texture Analysis

Solitary fibrous tumor (SFT)/hemangiopericytoma (HPC) is radiologically difficult to distinguish from meningioma, especially angiomatous meningioma. This study aimed to detect texture parameters to distinguish SFT/HPC from angiomatous meningioma using magnetic resonance imaging texture analysis with commercially available software. We retrospectively investigated textural parameters in 43 newly diagnosed SFTs/HPCs, angiomatous meningiomas, and other World Health Organization (WHO) grade I meningiomas treated at Keio University Hospital. For T1 contrast-enhanced, T2, and apparent diffusion coefficient (ADC) images, texture analyses were performed. Regions of interest were drawn manually with reference to the greater signal on contrast-enhanced T1-weighted images. ADC values and texture parameters, including kurtosis, skewness, and entropy, were evaluated and compared between these 3 groups. The mean ADC value was significantly high in angiomatous meningioma, compared with SFT/HPC and other WHO grade I meningioma. ADC entropy was highest in SFT/HPC, followed by angiomatous and other WHO grade I meningioma. T2 skewness was significantly high in SFT/HPC, compared with angiomatous and other WHO grade I meningioma. T1 contrast-enhanced skewness was significantly low in angiomatous meningioma, compared with other WHO grade I meningioma. Mean ADC value distinguished SFT/HPC from angiomatous meningioma with a positive predictive value (PPV) of 62.5% and specificity of 62.5%. ADC entropy distinguished SFT/HPC from angiomatous meningioma with a PPV of 100% and specificity of 100%. ADC skewness distinguished SFT/HPC from angiomatous meningioma with a PPV of 66.7% and specificity of 71.4%. This study demonstrated that magnetic resonance imaging texture analysis was useful for distinguishing SFT/HPC from meningioma, especially angiomatous meningioma.

Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging.

To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm2) and high (1500 s/mm2) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging. One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm2, and corresponding ADC800 and ADC1500 maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated. Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC1500 entropy provided superior diagnostic performance over ADC800 entropy (p = 0.04). Independent risk factors were ADC1500 entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC1500 entropy (p = 0.022) with luminal B (HER2 positive), mean ADC1500 (p = 0.018)with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative. Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC1500 may facilitate better phenotype discrimination. • ADC metrics are associated with molecular marker status in IDC. • ADC 1500 improves differentiation of histologic phenotypes compared with ADC 800 . • ADC metrics add value to morphologic features in IDC phenotyping.

ADC Metrics From Multiparametric MRI: Histologic Downgrading of Gleason Score 9 or 10 Prostate Cancers Diagnosed at Nontargeted Transrectal Ultrasound-Guided Biopsy.

The purpose of this study was to evaluate quantitative apparent diffusion coefficient (ADC) metrics for the downgrading of Gleason score (GS) 9 or 10 prostate cancer (PCa) diagnosed by means of nontargeted transrectal ultrasound-guided biopsy. Between 2012 and 2015, 30 men with a diagnosis of GS 9 or 10 PCa at nontargeted transrectal ultrasound-guided biopsy underwent 3-T multiparametric MRI before radical prostatectomy (RP). Two radiologists blinded to the histopathologic results independently assessed multiparametric MR images using Prostate Imaging Reporting and Data System (PI-RADS) version 2. Whole-lesion ADC mean, centile, and texture features were extracted from coregistered ADC and RP maps by a third blinded radiologist. Comparisons were performed by chi-square, multivariable logistic regression, and ROC analysis. Tumors were downgraded to intermediate risk (GS 4 + 3 [n = 7] and GS 3 + 4 [n = 2]) PCa in 30.0% (9/30) of men after RP. There were no statistically significant differences between groups with respect to age (p = 0.028), prostate-specific antigen level (p = 0.018), or clinical stage (p = 0.021). PI-RADS version 2 scores did not differ between groups (p = 0.035, p = 0.091) with moderate agreement (κ = 0.48). There were no differences in mean or centile ADC (p = 0.269-0.634) between the two groups. ADC entropy was significantly lower in downgraded tumors (5.542 ± 0.721 [SD] vs 8.089 ± 1.237, p < 0.001) with no difference in kurtosis or skewness (p = 0.133, p = 0.296). The ROC AUC for the diagnosis of downgrading was 0.93 (95% CI, 0.84-1.00) with sensitivity of 85.7% and specificity of 88.9% when entropy was less than 6.31. ADC entropy was significantly lower in GS 9 and 10 tumors diagnosed by means of nontargeted transrectal ultrasound-guided biopsy that were eventually downgraded to intermediate risk (GS 7) after RP. ADC texture analysis may be useful for further risk stratification of PCa diagnosed at biopsy.

Whole-lesion Apparent Diffusion Coefficient First- and Second-Order Texture Features for the Characterization of Rectal Cancer Pathological Factors.

The objective of this study was to explore the value of whole-volume apparent diffusion coefficient (ADC) features in characterizing pathologic features of rectal cancer. A total of 50 patients who were diagnosed with rectal cancer via biopsy underwent 3-T pretreatment diffusion-weighted imaging. Apparent diffusion coefficient features, including mean, 10th-90th percentile, Entropy and Entropy(H), derived from whole-lesion volumes were compared between pathologic T1-2 and T3 stages, perineural invasion (PNI) present and absent, lymphangiovascular invasion present and absent, and pathological N0 and N+ stage groups. Entropy and Entropy(H) were significantly lower in rectal cancers at T1-2 stages than T3. The 90th percentile of rectal cancers with PNI was significantly lower than that of those without PNI. All P < 0.05. Whole-lesion ADC Entropy and Entropy(H) have potential in evaluating different T stages, and 90th percentile can be helpful for determining PNI presence of rectal cancers.

Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma.

Medulloblastomas are the most common central nervous system tumors in childhood. Treatment and prognosis strongly depend on histology and transcriptomic profiling. However, the proliferative potential also has prognostical value. Our study aimed to investigate correlations between histogram profiling of diffusion-weighted images and further microarchitectural features. Seven patients (age median 14.6years, minimum 2years, maximum 20years; 5 male, 2 female) were included in this retrospective study. Using a Matlab-based analysis tool, histogram analysis of whole apparent diffusion coefficient (ADC) volumes was performed. ADC entropy revealed a strong inverse correlation with the expression of the proliferation marker Ki67 (r = - 0.962, p = 0.009) and with total nuclear area (r = - 0.888, p = 0.044). Furthermore, ADC percentiles, most of all ADCp90, showed significant correlations with Ki67 expression (r = 0.902, p = 0.036). Diffusion histogram profiling of medulloblastomas provides valuable in vivo information which potentially can be used for risk stratification and prognostication. First of all, entropy revealed to be the most promising imaging biomarker. However, further studies are warranted.

Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI

Radiomics deals with the high throughput extraction of quantitative textural information from radiological images that not visually perceivable by radiologists. However, the biological correlation between radiomic features and different tissues of interest has not been established. To that end, we present the radiomic feature mapping framework to generate radiomic MRI texture image representations called the radiomic feature maps (RFM) and correlate the RFMs with quantitative texture values, breast tissue biology using quantitative MRI and classify benign from malignant tumors. We tested our radiomic feature mapping framework on a retrospective cohort of 124 patients (26 benign and 98 malignant) who underwent multiparametric breast MR imaging at 3 T. The MRI parameters used were T1-weighted imaging, T2-weighted imaging, dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted imaging (DWI). The RFMs were computed by convolving MRI images with statistical filters based on first order statistics and gray level co-occurrence matrix features. Malignant lesions demonstrated significantly higher entropy on both post contrast DCE-MRI (Benign-DCE entropy: 5.72 ± 0.12, Malignant-DCE entropy: 6.29 ± 0.06, p = 0.0002) and apparent diffusion coefficient (ADC) maps as compared to benign lesions (Benign-ADC entropy: 5.65 ± 0.15, Malignant ADC entropy: 6.20 ± 0.07, p = 0.002). There was no significant difference between glandular tissue entropy values in the two groups. Furthermore, the RFMs from DCE-MRI and DWI demonstrated significantly different RFM curves for benign and malignant lesions indicating their correlation to tumor vascular and cellular heterogeneity respectively. There were significant differences in the quantitative MRI metrics of ADC and perfusion. The multiview IsoSVM model classified benign and malignant breast tumors with sensitivity and specificity of 93 and 85%, respectively, with an AUC of 0.91.

Evaluation of MRI for diagnosis of extraprostatic extension in prostate cancer.

To assess the ability of magnetic resonance imaging (MRI) to diagnose extraprostatic extension (EPE) in prostate cancer. With Institutional Review Board (IRB) approval, 149 men with 170 ≥0.5 mL tumors underwent preoperative 3T MRI followed by radical prostatectomy (RP) between 2012-2015. Two blinded radiologists (R1/R2) assessed tumors using Prostate Imaging Reporting and Data System (PI-RADS) v2, subjectively evaluated for the presence of EPE, measured tumor size, and length of capsular contact (LCC). A third blinded radiologist, using MRI-RP-maps, measured whole-lesion: apparent diffusion coefficient (ADC) mean/centile and histogram features. Comparisons were performed using chi-square, logistic regression, and receiver operator characteristic (ROC) analysis. The subjective EPE assessment showed high specificity (SPEC = 75.4/91.3% [R1/R2]), low sensitivity (SENS = 43.3/43.6% [R1/R2]), and area-under (AU) ROC curve = 0.67 (confidence interval [CI] 0.61-0.73) R1 and 0.61 (CI 0.53-0.70) R2; (k = 0.33). PI-RADS v2 scores were strongly associated with EPE (P < 0.001 / P = 0.008; R1/R2) with AU-ROC curve = 0.72 (0.64-0.79) R1 and 0.61 (0.53-0.70) R2; (k = 0.44). Tumors with EPE were larger (18.8 ± 7.8 [median 17, range 6-51] vs. 18.8 ± 4.9 [12, 6-28] mm) and had greater LCC (21.1 ± 14.9 [16, 1-85] vs. 13.6 ± 6.1 [11.5, 4-30] mm); P < 0.001 and 0.002, respectively. AU-ROC for size was 0.73 (0.64-0.80) and LCC was 0.69 (0.60-0.76), respectively. Optimal SENS/SPEC for diagnosis of EPE were: size ≥15 mm = 67.7/66.7% and LCC ≥11 mm = 84.9/44.8%. 10th -centile ADC and ADC entropy were both associated with EPE (P = 0.02 and < 0.001), with AU-ROC = 0.56 (0.47-0.65) and 0.76 (0.69-0.83), respectively. Optimal SENS/SPEC for diagnosis of EPE with entropy ≥6.99 was 63.3/75.0%. 25th -centile ADC trended towards being significantly lower with EPE (P = 0.06) with no difference in other ADC metrics (P = 0.25-0.88). Size, LCC, and ADC entropy improved sensitivity but reduced specificity compared with subjective analysis with no difference in overall accuracy (P = 0.38). Measurements of tumor size, capsular contact, and ADC entropy improve sensitivity but reduce specificity for diagnosis of EPE compared to subjective assessment. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:176-185.

Whole-lesion apparent diffusion coefficient (ADC) metrics as a marker of breast tumour characterization-comparison between ADC value and ADC entropy.

To prospectively assess the role of whole-lesion apparent diffusion coefficient (ADC) metrics in the characterization of breast tumours by comparing ADC value with ADC entropy. 49 patients with 53 breast lesions underwent phased-array breast coil 1.5-T MRI. Two radiologists experienced in breast MRI, blinded to the final diagnosis, reviewed the ADC maps and placed a volume of interest on all slices including each lesion on the ADC map to obtain whole-lesion mean ADC value and ADC entropy. The mean ADC value and ADC entropy in benign and malignant lesions were compared by the Mann-Whitney U-test. Receiver-operating characteristic analysis was performed to assess the sensitivity and specificity of the two variables in the characterization of the breast lesions. The benign (n = 19) and malignant lesions (n = 34) had mean diameters of 20.8 mm (10.1-31.5 mm) and 26.4 mm (10.5-42.3 mm), respectively. The mean ADC value of the malignant lesions was significantly lower than that of the benign ones (0.87 × 10-3 vs 1.49 × 10-3 mm2 s-1; p < 0.0001). Malignant ADC entropy was higher than benign entropy, without reaching levels of statistical significance (5.4 vs 5.0; p = 0.064). At a mean ADC cut-off value of 1.16 × 10-3 mm2 s-1, the sensitivity and specificity for diagnosing malignancy became optimal (97.1% and 93.7, respectively) with an area under the curve (AUC) of 0.975. With regard to ADC entropy, the sensitivity and specificity at a cut-off of 5.18 were 67.6 and 68.7%, respectively, with an AUC of 0.664. Whole-lesion mean ADC could be a helpful index in the characterization of suspicious breast lesions, with higher sensitivity and specificity than ADC entropy. Advances in knowledge: Two separate parameters of the whole-lesion histogram were compared for their diagnostic accuracy in characterizing breast lesions. Mean ADC was found to be able to characterize breast lesions, whereas entropy proved to be unable to differentiate benign from malignant breast lesions. It is, however, likely that entropy may distinguish these two groups if a larger cohort were used, or the fact that this may be influenced by the molecular subtypes of breast cancers included.

ADC histogram analysis for prediciting highly aggressive breast carcinomas

Introduction Diffusion Weighted Imaging (DWI) has demonstrated increased potential in identifying imaging-based biomarkers for cancer diagnosis, prognosis and monitoring response to therapy. Purpose This study investigates the feasibility of apparent diffusion coefficient (ADC) histogram statistics in identifying highly aggressive breast carcinomas, by investigating correlation to established prognostic indices. Materials and methods The dataset consists of 43 histologically verified invasive ductal carcinomas (IDC) of patients undergoing breast DW-MRI at 3 T (b-values 0, 900 s/mm2). ADC maps were generated for a slice representative of lesion largest diameter. An expert radiologist delineated lesion contour on ADC map, defining the lesion region of interest to be subjected to histogram analysis. Feature extraction considered mean, standard deviation, skewness, kurtosis, entropy, maximum, minimum and range of ADC. The ability of ADC histogram features in identifying tumor grade, estrogen receptor (ER) and progesterone receptor (PR) status was investigated. Results Median value of feature ADC mean was 1.034 × 10−3 mm2/s, 0.869 × 10−3 mm2/s and 0.971 × 10−3 mm2/s in grade I, grade II and grade III lesions, respectively. Median value of ADC kurtosis was 0.176, 1.165 and 1.289 in grade I, grade II and grade III lesions, respectively. ER positive lesions demonstrated increased median value of ADC entropy (5.828), reflecting increased ADC heterogeneity, as compared to ER negative ones (5.480). Median value of ADC entropy was 5.842 and 5.486 in PR positive and PR negative lesions, respectively. Conclusion ADC histogram analysis may contribute in breast cancer prognosis by identifying high grade tumors and predicting ER and PR status.

Retrospective Assessment of Histogram-Based Diffusion Metrics for Differentiating Benign and Malignant Endometrial Lesions.

Our study aimed to retrospectively evaluate the utility of volumetric histogram-based diffusion metrics in differentiating benign from malignant endometrial abnormalities. A total of 54 patients underwent pelvic magnetic resonance imaging with diffusion-weighted imaging before endometrial tissue diagnosis. Two radiologists placed volumes of interest on the apparent diffusion coefficient (ADC) map encompassing the entire endometrium and focal endometrial lesions. The mean ADC, percentile ADC values, kurtosis, skewness, and entropy of ADC were compared between benign and malignant abnormalities. In premenopausal patients, significant independent predictors of malignancy were whole-endometrium analysis for R1, 10th to 25th ADC percentile (P = 0.012); whole-endometrium analysis for R2, mean ADC (P = 0.001) and skewness (P = 0.004); focal lesion analysis for R1, skewness (P = 0.045); focal lesion analysis for R2, 10th to 25th ADC percentile (P ≤ 0.0001). The area under the curve for malignancy was 90.0% to 97.3% and 76.1% to 77.3% for the more and less experienced radiologists, respectively. In postmenopausal patients, the only significant difference was kurtosis using whole-endometrium analysis for R1 (P = 0.042). Volumetric ADC histogram metrics may help radiologists assess the risk of malignancy in endometrial abnormalities on magnetic resonance imaging in premenopausal patients.

Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.

To investigate Haralick texture analysis of prostate MRI for cancer detection and differentiating Gleason scores (GS). One hundred and forty-seven patients underwent T2- weighted (T2WI) and diffusion-weighted prostate MRI. Cancers ≥0.5ml and non-cancerous peripheral (PZ) and transition (TZ) zone tissue were identified on T2WI and apparent diffusion coefficient (ADC) maps, using whole-mount pathology as reference. Texture features (Energy, Entropy, Correlation, Homogeneity, Inertia) were extracted and analysed using generalized estimating equations. PZ cancers (n = 143) showed higher Entropy and Inertia and lower Energy, Correlation and Homogeneity compared to non-cancerous tissue on T2WI and ADC maps (p-values: <.0001-0.008). In TZ cancers (n = 43) we observed significant differences for all five texture features on the ADC map (all p-values: <.0001) and for Correlation (p = 0.041) and Inertia (p = 0.001) on T2WI. On ADC maps, GS was associated with higher Entropy (GS 6 vs. 7: p = 0.0225; 6 vs. >7: p = 0.0069) and lower Energy (GS 6 vs. 7: p = 0.0116, 6 vs. >7: p = 0.0039). ADC map Energy (p = 0.0102) and Entropy (p = 0.0019) were significantly different in GS ≤3 + 4 versus ≥4 + 3 cancers; ADC map Entropy remained significant after controlling for the median ADC (p = 0.0291). Several Haralick-based texture features appear useful for prostate cancer detection and GS assessment. • Several Haralick texture features may differentiate non-cancerous and cancerous prostate tissue. • Tumour Energy and Entropy on ADC maps correlate with Gleason score. • T2w-image-derived texture features are not associated with the Gleason score.

The trouble with apparent diffusion coefficient papers.

This issue of Journal of Medical Radiation Sciences includes a study (Rundle-Thiele et al.)1 that investigates the correlation between magnetic resonance imaging (MRI)-based estimates of water mobility in glioblastoma tumours and expression of the prognostically important gene promoter methylguanine methyltransferase (MGMT). The authors address previous inconsistent reports regarding a correlation between apparent diffusion coefficient (ADC) and the presence of MGMT by consideration of the effect of tumour heterogeneity. Since tissue water mobility is intimately and directly dependent on tissue microstructure, tumour heterogeneity will manifest as intervoxel differences of calculated ADC. The question then is how to correlate such heterogeneous measurements with tumour type markers. For the same clinical data set, different analyses may lead to different conclusions. One approach to this problem is to consider the histogram of intra-tumour voxel ADCs. If the tumour were homogeneous one would expect a normal distribution of voxel ADCs. This is clearly not the case for glioblastoma which shows a bimodal distribution in the measurements reported by Rundle-Thiele et al. The mean of the lower ADC component of the bimodal distribution correlated strongly with presence of MGMT while the minimum ADC in the tumour did not. There are many potential methods of correlating measurements of heterogeneous tissue with properties such as pathologic status. For example, in the prostate the intervoxel ADC entropy in suspicious regions is reported to correlate more strongly with the presence of cancer than a simple mean.2 This example, and the investigation reported by Rundle-Thiele et al. emphasise the importance of recognising and accounting for tissue heterogeneity when attempting to develop markers for pathology or pathologic type. However, there is a dark undercurrent to the ADC story and the majority of publications describing clinical ADC-based analysis of diffusion-weighted MRI data. Quite separate from the inter-voxel ADC differences there are potential effects on calculated ADC from sub-voxel tissue heterogeneity, measurement technique and analysis method. This is rarely mentioned in the clinical diffusion MRI literature, and in many cases probably not recognised by authors or reviewers. To a significant extent it may be a confused usage of ADC that leads to inconsistent results when independent studies are compared. Unfortunately, the misuse of ADC is now deeply imbedded in the culture of radiology.