Whole-lesion Apparent Diffusion Coefficient Histogram Research Articles

Incremental prognostic value of ADC histogram analysis in patients with high-risk prostate cancer receiving adjuvant hormonal therapy after radical prostatectomy.

To investigate the incremental prognostic value of preoperative apparent diffusion coefficient (ADC) histogram analysis in patients with high-risk prostate cancer (PCa) who received adjuvant hormonal therapy (AHT) after radical prostatectomy (RP). Sixty-two PCa patients in line with the criteria were enrolled in this study. The 10th, 50th, and 90th percentiles of ADC (ADC10, ADC50, ADC90), the mean value of ADC (ADCmean), kurtosis, and skewness were obtained from the whole-lesion ADC histogram. The Kaplan-Meier method and Cox regression analysis were used to analyze the relationship between biochemical recurrence-free survival (BCR-fs) and ADC parameters and other clinicopathological factors. Prognostic models were constructed with and without ADC parameters. The median follow-up time was 53.4 months (range, 41.1-79.3 months). BCR was found in 19 (30.6%) patients. Kaplan-Meier curves showed that lower ADCmean, ADC10, ADC50, and ADC90 and higher kurtosis could predict poorer BCR-fs (all p<0.05). After adjusting for clinical parameters, ADC50 and kurtosis remained independent prognostic factors for BCR-fs (HR: 0.172, 95% CI: 0.055-0.541, p=0.003; HR: 7.058, 95% CI: 2.288-21.773, p=0.001, respectively). By adding ADC parameters to the clinical model, the C index and diagnostic accuracy for the 24- and 36-month BCR-fs were improved. ADC histogram analysis has incremental prognostic value in patients with high-risk PCa who received AHT after RP. Combining ADC50, kurtosis and clinical parameters can improve the accuracy of BCR-fs prediction.

MRI features and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer for differentiating epidermal growth factor receptor mutation status

To explore the utility of magnetic resonance imaging (MRI) characteristics and whole-lesion apparent diffusion coefficient histogram analysis of brain metastasis from non-small cell lung cancer (NSCLC) in the differentiation of epidermal growth factor receptor (EGFR) mutation status. Forty-eight patients with brain metastases from NSCLC were enrolled in this retrospective study. Patients were subtyped into EGFR mutation (23 cases) and wild-type (25 cases) groups. Whole-lesion histogram metrics were derived from the apparent diffusion coefficient (ADC) maps, and imaging features were evaluated according to conventional MRI. Student's t-test or Mann-Whitney U-test, chi-squared test, and receiver operating characteristic (ROC) curve analysis were performed to discriminate the two groups and to determine the diagnostic efficacy of ADC histogram parameters. EGFR mutation group had more multiple brain metastases, less peritumoural brain oedema (PTBO), and lower peritumoural brain oedema index (PTBO-I) than EGFR wild-type group (all p<0.05). In addition, 90th and 75th percentiles of ADC and maximum ADC in the EGFR mutation group were significantly higher than in the EGFR wild-type group (all p<0.05). Ninetieth percentile of ADC had the highest area under the curve (AUC; 0.711), and it was found to outperform 75th percentile of ADC (AUC, 0.662; p=0.039) and maximum ADC (AUC, 0.681). Whole-lesion ADC histogram analysis and MRI features of brain metastasis from NSCLC are expected to be potential biomarkers to non-invasively differentiate the EGFR mutation status.

The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer

BackgroundThis study aims to estimate the amount of axillary lymph node (ALN) involvement in early-stage breast cancer utilizing a field of view (FOV) optimized and constrained undistorted single-shot (FOCUS) diffusion-weighted imaging (DWI) approach, as well as a whole-lesion histogram analysis.MethodsThis retrospective analysis involved 81 individuals with invasive breast cancer. The patients were divided into three groups: N0 (negative ALN metastasis), N1–2 (low metastatic burden with 1–2 ALNs), and N≥3 (heavy metastatic burden with ≥ 3 ALNs) based on their sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Histogram parameters of apparent diffusion coefficient (ADC) depending basically on FOCUS DWI were performed using 3D-Slicer software for whole lesions. The typical histogram characteristics for N0, N1–2, and N≥ 3 were compared to identify the significantly different parameters. To determine the diagnostic efficacy of significantly different factors, the area under their receiver operating characteristic (ROC) curves was examined.ResultsThere were significant differences in the energy, maximum, 90 percentile, range, and lesion size among N0, N1–2, and N≥ 3 groups (P < 0.05). The energy differed significantly between N0 and N1–2 groups (P < 0.05), and some certain ADC histogram parameters and lesion sizes differed significantly between N0 and N≥3, or N1–2 and N≥3 groups. For ROC analysis, the energy yielded the best diagnostic performance in distinguishing N0 and N1–2 groups from N≥3 group with an AUC value of0.853. All parameters revealed excellent inter-observer agreement with inter-reader consistencies data ranging from0.919 to 0.982.ConclusionBy employing FOCUS DWI method, the analysis of whole-lesion ADC histogram quantitatively provides a non-invasive way to evaluate the degree of ALN metastatic spread in early-stage breast cancer.

Discrimination between microcystic meningioma and atypical meningioma using whole-lesion apparent diffusion coefficient histogram analysis

To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in discriminating microcystic meningioma (MCM) from atypical meningioma (AM). Clinical and preoperative MRI data of 20 patients with MCM and 26 patients with AM were analysed retrospectively. Whole-lesion apparent diffusion coefficient (ADC) histogram analysis was performed on each patient's lesion to obtain histogram parameters, including mean, variance, skewness, kurtosis, the 1st (ADCp1), 10th (ADCp10), 50th (ADCp50), 90th (ADCp90), and 99th (ADCp99) percentiles of ADC. The differences between the ADC histogram parameters of the two tumours were compared, and the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of statistically significant parameters in distinguishing the two tumours. The mean, ADCp1, ADCp10, ADCp50, and ADCp90 of MCM were greater than those of AM, and significant differences were observed in these parameters between MCM and AM (all p<0.05). ROC analysis showed that the mean had the highest area under the curve value (AUC) in distinguishing the two tumours (AUC=0.852), when using 120.46×10-6 mm2/s as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for discriminating the two groups were 84.6%, 75%, 80.4%, 81.5%, and 78.9%, respectively. Histogram analysis based on whole-lesion ADC maps was useful for discriminating between MCM from AM preoperatively, with the mean being the most promising potential parameter.

Whole-lesion ADC histogram analysis versus single-slice ADC measurement for the differentiation of benign and malignant soft tissue tumors

PurposeTo evaluate and compare the diagnostic performances of whole-lesion apparent diffusion coefficient (ADC) histogram analysis and single-slice ADC measurement in the differentiation of benign and malignant soft tissue tumors. MethodsFifty-three patients (mean age: 48.5 ± 21.4) with soft tissue tumors (27 benign and 26 malignant) were evaluated with diffusion-weighted MRI. Whole-lesion ADC histogram parameters (mean, median, 10th percentile, 90th percentile, minimum, maximum, range, mean absolute deviation, interquartile range, kurtosis, skewness, root mean squared, variance and inhomogeneity) of the lesions were measured using the whole solid tumor volume region of interest (ROI). In other sessions, five ROIs were manually drawn on the tumor slices, and mean ADC and minimum ADC of the measurements were calculated. Diagnostic accuracies of the two methods were assessed and compared. ResultsMean, median, minimum, 10th percentile, 90th percentile, range, root mean squared and inhomogeneity of ADC histogram analysis, and mean ADC and minimum ADC of single-slice ADC measurement were significantly different between malignant and benign lesions (p < 0.001 – p = 0.002). Among the ADC histogram parameters, 10th percentile had the highest diagnostic performance (AUC = 0.825) followed by mean (AUC = 0.792) and median (AUC = 0.789). For the single-slice ADC measurement, the AUC of mean ADC and minimum ADC were 0.842 and 0.786, respectively. Mean ADC of single-slice measurement had a similar diagnostic performance with the 10th percentile, mean, and median of ADC histogram analysis (p = 0.070–1.000). ConclusionsBoth whole-lesion ADC histogram analysis and single-slice ADC measurement can differentiate benign and malignant soft tissue tumors with similar diagnostic performances.

Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer.

PurposeTo assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer.MethodsIn total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29–85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10th, 25th, 50th, 75th, and 90th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student’s t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests.ResultsA statistically significant difference existed in the 10th and 25th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029).ConclusionsOur findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.

Whole-lesion histogram analysis of apparent diffusion coefficient for the prediction of pathological complete response to neoadjuvant chemotherapy in different subtypes of breast cancer

Objective To investigate the value of whole-lesion histogram parameters of apparent diffusion coefficient (ADC) in evaluating and predicting the pathological complete response(PCR) to neoadjuvant chemotherapy (NAC) in different subtypes of breast cancer. Methods This retrospective study included 117 patients with breast cancer who underwent MRI examination before NAC prior to surgery from January 2016 to December 2017 in the First Affiliated Hospital of Nanjing University. All cases were divided into Luminal B, HER2 positive (n=21) and triple negative (n=26) groups. The surgical pathology after chemotherapy was evaluated by Miller-Payne (M-P) system and the patients were divided into PCR group and non-PCR (nPCR) group. Firevoxel software was used to generate the whole-lesion ADC histogram. The parameters included mean (ADCmean), skewness, kurtosis, the minimum (ADCmin), the maximum (ADCmax), 10th percentile(ADC10%), 50th percentile (ADC50%) and 90th percentile (ADC90%). The two independent samples t test or Mann-Whitney U test was used to compare the differences between PCR and nPCR groups in each subtype. The diagnostic performance of statistically different ADC parameters for predicting PCR was evaluated by receiver operating characteristic (ROC) curve. Results Kurtosis was significantly higher in PCR group than that in nPCR group in HER2 positive subtype (P=0.039). It achieved an area under the curve (AUC) of 0.813 with sensitivity of 100% and specificity of 68.7% at the optimal cutoff value (1.861) for differentiating PCR from nPCR cases. In triple negative subtype, ADCmean and ADC50% were smaller in PCR group than those in nPCR group (P=0.028,0.013). They achieved AUCs of 0.800, 0.842, respectively. When ADCmean of 1.030×10-3 mm2/s and ADC50% of 0.976×10-3 mm2/s were used as cutoff value to differentiate PCR from nPCR, the sensitivities were 75.0%, 80.0% and the specificities were 83.3%, 83.3%, respectively. Conclusion Kurtosis can predict post-NAC PCR in patients with HER2 positive breast cancer, while ADC50% has a high value in predicting post NAC PCR of triple negative breast cancer patients. Key words: Breast neoplasms; Molecular subtype; Neoadjuvant chemotherapy; Magnetic resonance imaging

Diagnostic performance of whole-lesion apparent diffusion coefficient histogram analysis metrics for differentiating benign and malignant breast lesions: a systematic review and diagnostic meta-analysis.

Although whole-lesion apparent diffusion coefficient (ADC) histogram has been increasingly used for breast lesions, it has not been routinely used in clinical practice as an emergent promising imaging tool. To evaluate the performance of whole-lesion ADC histogram analysis metrics for differentiating benign and malignant breast lesions. A systematic PubMed/EMBASE/Cochrane electronic database search was performed for original diagnostic studies from 1 January 1970 to 2 January 2019. Summary estimates of diagnostic accuracy were generated and meta-regression was performed to explore sources of heterogeneity according to study and magnetic resonance imaging characteristics. Five original articles involving 493 patients were included in the meta-analysis. The pooled sensitivity and specificity of whole-lesion ADC histogram analysis were 0.85 (95% confidence interval [CI] = 0.81-0.89) and 0.79 (95% CI = 0.72-0.84) for distinguishing benign and malignant breast lesions, respectively. The area under the curve (AUC) was 0.9178. No publication bias was detected (P = 0.51). In subgroup analysis, the summary sensitivity and specificity of 50th percentile ADC value were 0.81 (95% CI = 0.71-0.88) and 0.86 (95% CI = 0.74-0.94), respectively. Meta-regression analysis indicated no covariates were sources of heterogeneity (P > 0.05). Whole-lesion ADC histogram analysis demonstrated good diagnostic performance for differentiating between benign and malignant breast lesions, with 50th percentile ADC value showing higher diagnostic accuracy than other histogram parameters. Given the limited number of studies included in the analysis, the findings from our meta-analysis will need further confirmation in future research.

Histological grades of rectal cancer: whole-volume histogram analysis of apparent diffusion coefficient based on reduced field-of-view diffusion-weighted imaging.

To explore the role of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) derived from reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) technique in discriminating histological grades of rectal carcinoma. Altogether, 49 patients with rectal cancer were enrolled in this retrospective study. All patients received preoperative 3.0 T MR scan. Histogram parameters from rFOV DWI were calculated and correlated with histological differentiation of rectal cancer. The parameters were compared between different histological grades of rectal cancer by independent Student's t-test or Man-Whitney U-test. The Spearman correlation test analyzed correlations between histological grade and histogram parameters. The diagnostic performance of individual parameters for distinguishing poorly from well-/moderately differentiated tumors was assessed by receiver operating characteristic curve (ROC) analysis. There were significant differences for ADCmean, 25th, 50th, 75th, 90th, 95th percentiles, skewness, and kurtosis of rFOV DWI sequence between well-, moderately, and poorly differentiated rectal cancers (P<0.05). Significant correlations were noted between histological grades and the above histogram parameters (r=0.679, 0.540, 0.701, 0.730, 0.669, 0.574, -0.730, and -0.760 respectively, P<0.001). Among the individual histogram parameter, kurtosis achieved the highest AUC of 0.882 with an optimal cutoff value of 1.934 in distinguishing poorly from well-/moderately differentiated rectal cancers. The combination of ADCmean, 75th percentile, and kurtosis yielded the highest AUC of 0.927 with a sensitivity of 88.00% and a sensitivity of 91.7% using logistic regression. Quantitative whole-lesion ADC histogram analysis based on the rFOV DWI technique could help differentiate histological grades of rectal cancer. The combination of ADCmean, 75th percentile, and kurtosis may be the best choice.

Whole-lesion ADC histogram analysis and the spondyloarthritis research consortium of canada (SPARCC) MRI index in evaluating the disease activity of ankylosing spondylitis.

Conventional MRI is limited in quantitative evaluation of ankylosing spondylitis (AS) activity states. A comparison of the effectiveness of the whole-lesion apparent diffusion coefficient (ADC) histogram analysis with the Spondyloarthritis Research Consortium of Canada (SPARCC) MRI index in evaluating the disease activity of AS might aid in this assessment. To compare the effectiveness of the whole-lesion ADC histogram analysis with the SPARCC MRI index in evaluating the disease activity states of AS. Prospective. A total of 57 AS patients and 27 healthy matched volunteers were included. 3.0T MR including a diffusion-weighted imaging (DWI) sequence (b = 0, 1000 s/mm2 ). One-way analysis of variance (ANOVA) and Scheffe's post-hoc was used to compare the parameters among different groups. A receiver operating characteristic (ROC) analysis and the Spearman rank correlation were performed to test the diagnostic performance of all parameters in distinguishing different disease activity states and determining the correlations between them. AS disease activity states was evaluated according to the Ankylosing Spondylitis Disease Activity Score (ASDAS). Initial DWI images and corresponding ADC maps were imported into our in-house software. Regions of interest (ROIs) were drawn in all slices and the relevant parameters were derived simultaneously. The SPARCC MRI index scores were counted artificially based on T2 -PDW-SPAIR images. The ADCmean , ADC percentiles, and SPARCC MRI index of the active group were significantly higher than the inactive and control groups (all P < 0.001). The 90th percentile could differentiate the inactive from the control group and the low disease activity group from the inactive group (P = 0.011 and 0.006, respectively). The 50th percentile of the high disease activity group was significantly higher than the low group (P = 0.004), while the SPARCC MRI index of the very high disease activity group was higher than the high group (P < 0.001). The whole-volume ADC histogram analysis was superior to the SPARCC MRI index in assessing AS activity states. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:114-126.

Cervical lymphadenopathy: can the histogram analysis of apparent diffusion coefficient help to differentiate between lymphoma and squamous cell carcinoma in patients with unknown clinical primary tumor?

To retrospectively evaluate the value of whole-lesion histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating between lymphoma and metastatic squamous cell carcinoma (SCC) of unknown clinical primary in neck nodes. A total of 39 patients, 20 affected by lymphoma and 19 affected by metastatic non-nasopharyngeal SCC, were included in this retrospective study. All patients underwent MR imaging with a 1.5 T scanner system, including diffusion-weighted imaging (DWI) with three different b values (b = 0, 500 and 800s/mm2). The entire tumor volume was manually delineated on the ADC maps, using the T2-weighted images and DWIs with b = 800s/mm2 as a guide to the lesion location. The Mann-Whitney rank-sum test for independent samples was performed to compare the histogram parameters of patients with lymphoma and SCC. The SCCs showed significantly higher median ADC (ADCmedian) and mean ADC (ADCmean) values, compared to lymphomas (p < 0.001), while they exhibited lower kurtosis and skewness without reaching significance (p = 0.066 and 0.148, respectively). The ADCmean and ADCmedian had the best discriminative powers for differentiating lymphoma and SCC, with an area under the curve of 87% and 85%, respectively. The optimal cutoff values for ADCmean and ADCmedian as predictors for lymphoma were ≤ 0.83 × 10-3 mm2/s and ≤ 0.73 × 10-3 mm2/s, respectively. The whole-lesion ADC histogram analysis of cervical lymphadenopathy may help to discriminate lymphomas from non-nasopharyngeal SCC in patients with unknown clinical primary tumor.

Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers

BackgroundWhole-lesion apparent diffusion coefficient (ADC) histogram analysis has been introduced and proved effective in assessment of multiple tumors. However, the application of whole-volume ADC histogram analysis in gastrointestinal tumors has just started and never been reported in T and N staging of gastric cancers.MethodsEighty patients with pathologically confirmed gastric carcinomas underwent diffusion weighted (DW) magnetic resonance imaging before surgery prospectively. Whole-lesion ADC histogram analysis was performed by two radiologists independently. The differences of ADC histogram parameters among different T and N stages were compared with independent-samples Kruskal-Wallis test. Receiver operating characteristic (ROC) analysis was performed to evaluate the performance of ADC histogram parameters in differentiating particular T or N stages of gastric cancers.ResultsThere were significant differences of all the ADC histogram parameters for gastric cancers at different T (except ADCmin and ADCmax) and N (except ADCmax) stages. Most ADC histogram parameters differed significantly between T1 vs T3, T1 vs T4, T2 vs T4, N0 vs N1, N0 vs N3, and some parameters (ADC5%, ADC10%, ADCmin) differed significantly between N0 vs N2, N2 vs N3 (all P < 0.05). Most parameters except ADCmax performed well in differentiating different T and N stages of gastric cancers. Especially for identifying patients with and without lymph node metastasis, the ADC10% yielded the largest area under the ROC curve of 0.794 (95% confidence interval, 0.677–0.911). All the parameters except ADCmax showed excellent inter-observer agreement with intra-class correlation coefficients higher than 0.800.ConclusionWhole-volume ADC histogram parameters held great potential in differentiating different T and N stages of gastric cancers preoperatively.

Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT.

PurposeTo explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram and texture analysis in predicting tumor recurrence of advanced cervical cancer treated with concurrent chemo-radiotherapy (CCRT).Methods36 women with pathologically confirmed advanced cervical squamous carcinomas were enrolled in this prospective study. 3.0 T pelvic MR examinations including diffusion weighted imaging (b = 0, 800 s/mm2) were performed before CCRT (pre-CCRT) and at the end of 2nd week of CCRT (mid-CCRT). ADC histogram and texture features were derived from the whole volume of cervical cancers.ResultsWith a mean follow-up of 25 months (range, 11 ∼ 43), 10/36 (27.8%) patients ended with recurrence. Pre-CCRT 75th, 90th, correlation, autocorrelation and mid-CCRT ADCmean, 10th, 25th, 50th, 75th, 90th, autocorrelation can effectively differentiate the recurrence from nonrecurrence group with area under the curve ranging from 0.742 to 0.850 (P values range, 0.001 ∼ 0.038).ConclusionsPre- and mid-treatment whole-lesion ADC histogram and texture analysis hold great potential in predicting tumor recurrence of advanced cervical cancer treated with CCRT.

Whole-lesion histogram analysis of the apparent diffusion coefficient: Evaluation of the correlation with subtypes of mucinous breast carcinoma.

To evaluate the utility of the whole-lesion histogram apparent diffusion coefficient (ADC) for characterizing the heterogeneity of mucinous breast carcinoma (MBC) and to determine which ADC metrics may help to best differentiate subtypes of MBC. This retrospective study involved 52 MBC patients, including 37 pure MBC (PMBC) and 15 mixed MBC (MMBC). The PMBC patients were subtyped into PMBC-A (20 cases) and PMBC-B (17 cases) groups. All patients underwent preoperative diffusion-weighted imaging (DWI) at 1.5T and the whole-lesion ADC assessments were generated. Histogram-derived ADC parameters were compared between PMBC vs. MMBC and PMBC-A vs. PMBC-B, and receiver operating characteristic (ROC) curve analysis was used to determine optimal histogram parameters for differentiating these groups. The PMBC group exhibited significantly higher ADC values for the mean (P = 0.004), 25th (P = 0.004), 50th (P = 0.004), 75th (P = 0.006), and 90th percentiles (P = 0.013) and skewness (P = 0.021) than did the MMBC group. The 25th percentile of ADC values achieved the highest area under the curve (AUC) (0.792), with a cutoff value of 1.345 × 10-3 mm2 /s, in distinguishing PMBC and MMBC. The PMBC-A group showed significantly higher ADC values for the mean (P = 0.049), 25th (P = 0.015), and 50th (P = 0.026) percentiles and skewness (P = 0.004) than did the PMBC-B group. The 25th percentile of the ADC cutoff value (1.476 × 10-3 mm2 /s) demonstrated the best AUC (0.837) among the ADC values for distinguishing PMBC-A and PMBC-B. Whole-lesion ADC histogram analysis enables comprehensive evaluation of an MBC in its entirety and differentiating subtypes of MBC. Thus, it may be a helpful and supportive tool for conventional MRI. 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:391-400.

Whole-Tumor Quantitative Apparent Diffusion Coefficient Histogram and Texture Analysis to Predict Gleason Score Upgrading in Intermediate-Risk 3 + 4 = 7 Prostate Cancer.

The objective of our study was to evaluate whole-lesion quantitative apparent diffusion coefficient (ADC) for the prediction of Gleason score (GS) upgrading in 3 + 4 = 7 prostate cancer. Fifty-four patients with GS 3 + 4 = 7 prostate cancer diagnosed at systematic transrectal ultrasound (TRUS)-guided biopsy underwent 3-T MRI and radical prostatectomy (RP) between 2012 and 2014. A blinded radiologist contoured dominant tumors on ADC maps using histopathologic correlation. The whole-lesion mean ADC, ADC ratio (normalized to peripheral zone), ADC histogram, and texture analysis were compared between tumors with GS upgrading and those without GS upgrading using multivariate ROC analyses and logistic regression modeling. Tumors were upgraded to GS 4 + 3 = 7 after RP in 26% (n = 14) of the 54 patients, and tumors were downgraded after RP in none of the patients. The mean ADC, ADC ratio, 10th-centile ADC, 25th-centile ADC, and 50th-centile ADC were similar between patients with GS 3 + 4 = 7 tumors (0.99 ± 0.22, 0.58 ± 0.15, 0.77 ± 0.31, 0.94 ± 0.28, and 1.15 ± 0.24, respectively) and patients with upgraded GS 4 + 3 = 7 tumors (1.02 ± 0.18, 0.55 ± 0.11, 0.71 ± 0.26, 0.89 ± 0.20, and 1.11 ± 0.16) (p > 0.05). Regression models combining texture features improved the prediction of GS upgrading. The combination of kurtosis, entropy, and skewness yielded an AUC of 0.76 (SE = 0.07) (p < 0.001), a sensitivity of 71%, and a specificity of 73%. The combination of kurtosis, heterogeneity, entropy, and skewness yielded an AUC of 0.77 (SE = 0.07) (p < 0.001), a sensitivity of 71%, and a specificity of 78%. In this study, whole-lesion mean ADC, ADC ratio, and ADC histogram analysis were not predictive of pathologic upgrading of GS 3 + 4 = 7 prostate cancer after RP. ADC texture analysis improved accuracy.

Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.

To investigate the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in capturing breast lesion heterogeneity and determine which ADC metric may help best differentiate benign from malignant breast mass lesions at 3.0T magnetic resonance imaging (MRI). We retrospectively included 101 women with breast mass lesions (benign:malignant = 36:65) who underwent 3.0T diffusion-weighted imaging (DWI) and subsequently had histopathologic confirmation. ADC histogram parameters, including the mean, minimum, maximum, 10th/25th/50th/75th/90th percentile, skewness, kurtosis, and entropy ADCs, were derived for the whole-lesion volume in each patient. Mann-Whitney U-test, univariate and multivariate logistic regression, area under the receiver-operating characteristic curve (Az ), intraclass correlation coefficient (ICC), and Bland-Altman test were used for statistical analysis. Mean, minimum, maximum, and 10th/25th/50th/75th/90th percentile ADCs were significantly lower (all P < 0.0001), while skewness and entropy ADCs were significantly higher (P < 0.001 and P = 0.001, respectively) in malignant lesions compared with benign ones. The Az values of minimum and 25th percentile ADCs were significantly higher than that of mean ADC (P = 0.0194 and P = 0.0154, respectively) or that of median ADC (P = 0.0300 and P = 0.0401, respectively), indicating that minimum and 25th percentile ADCs may be more accurate for lesion discrimination. Multivariate logistic regression showed that the minimum ADC was the unique independent predictor of breast malignancy. Minimum and 25th percentile ADCs had excellent interobserver agreement (ICC = 0.943 and 0.989, respectively; narrow width of 95% limits of agreement). These results suggest that whole-lesion ADC histogram analysis may facilitate the differentiation between benign and malignant breast mass lesions.