10th Percentile Apparent Diffusion Coefficient Research Articles

Repeatability and reproducibility of prostate apparent diffusion coefficient values on a 1.5 T magnetic resonance linear accelerator

Background and PurposeIntegrated magnetic resonance linear accelerator (MR-Linac) systems offer potential for biologically based adaptive radiation therapy using apparent diffusion coefficient (ADC). Accurate tracking of longitudinal ADC changes is key to establishing ADC-driven dose adaptation. Here, we report repeatability and reproducibility of intraprostatic ADC using deformable image registration (DIR) to correct for inter-fraction prostate changes. Materials and MethodsThe study included within-fraction repeat ADC measurements for three consecutive fractions for 20 patients with prostate cancer treated on a 1.5 T MR-Linac. We deformably registered successive fraction T2-weighted images and applied the deformation vector field to corresponding ADC maps to align to fraction 2. We delineated gross tumour volume (GTV), peripheral zone (PZ) and prostate clinical target volume (CTV) regions-of-interest (ROIs) on T2-weighted MRI and copied to ADC maps. We computed intraclass correlation coefficients (ICC) and percent repeatability coefficient (%RC) to determine within-fraction repeatability and between-fraction reproducibility for individual voxels, mean and 10th percentile ADC values per ROI. ResultsThe ICC between repeats and fractions was excellent for mean and 10th percentile ADC in all ROIs (ICC > 0.86), and moderate repeatability and reproducibility existed for individual voxels (ICC > 0.542). Similarly, low %RC within-fraction (4.2–17.9 %) mean and 10th percentile ADC existed, with greater %RC between fractions (10.2–36.8 %). Higher %RC existed for individual voxel within-fraction (21.7–30.6 %) and between-fraction (32.1–34.5 %) ADC. ConclusionsResults suggest excellent ADC repeatability and reproducibility in clinically relevant ROIs using DIR to correct between-fraction anatomical changes. We established the precision of voxel-level ADC tracking for future biologically based adaptation implementation.

Apparent diffusion coefficient values predict response to brachytherapy in bulky cervical cancer

BackgroundDiffusion-weighted magnetic resonance imaging (DWI) provides a measurement of tumor cellularity. We evaluated the potential of apparent diffusion coefficient (ADC) values obtained from post-external beam radiation therapy (EBRT) DWI and prior to brachytherapy (BT) to predict for complete metabolic response (CMR) in bulky cervical cancer.MethodsClinical and DWI (b value = 500 s/mm2) data were obtained from patients undergoing interstitial BT with high-risk clinical target volumes (HR-CTVs) > 30 cc. Volumes were contoured on co-registered T2 weighted images and 90th percentile ADC values were calculated. Patients were stratified by CMR (defined by PET-CT at three months post-BT). Relation of CMR with 90th percentile ADC values and other clinical factors (International Federation of Gynecology and Obstetrics (FIGO) stage, histology, tumor and HR-CTV size, pre-treatment hemoglobin, and age) was assessed both in univariate and multivariate logistic regression analyses. Youden’s J statistic was used to identify a threshold value.ResultsAmong 45 patients, twenty-eight (62%) achieved a CMR. On univariate analysis for CMR, only 90th percentile ADC value was significant (p = 0.029) while other imaging and clinical factors were not. Borderline significant factors were HR-CTV size (p = 0.054) and number of chemotherapy cycles (p = 0.078). On multivariate analysis 90th percentile ADC (p < 0.0001) and HR-CTV size (p < 0.003) were highly significant. Patients with 90th percentile ADC values above 2.10 × 10− 3 mm2/s were 5.33 (95% CI, 1.35–24.4) times more likely to achieve CMR.ConclusionsClinical DWI may serve to risk-stratify patients undergoing interstitial BT for bulky cervical cancer.

Differentiation of bladder cancer stages using the vesical imaging -reporting and data system and apparent diffusion coefficient.

T stage is closely related to the treatment and prognosis of patients with bladder cancer (BC). However, preoperative T staging is still challenging. Multiparametric magnetic resonance imaging (mpMRI) may be valuable. This study was performed to explore the value of the Vesical Imaging-Reporting and Data System (VI-RADS) and the volumetric apparent diffusion coefficient (ADC) histogram parameters in detecting T2 stage and below stage (≤T2 stage) from T3 stage and above stage (≥T3 stage) BCs. The study included 62 patients (mean age, males vs. females: 62.1±10.9 vs. 61.8±11.7 years) with BC pathologically confirmed by partial or radical cystectomy. All of the tumors were scored normatively by two radiologists using the VI-RADS scoring system by two radiologists. The volumetric ADC histogram of each lesion was obtained from the ADC maps. The Cochran-Armitage test was used to examine the relevance between VI-RADS scores and T stages. The Mann-Whitney U test was used to compare the histogram parameters between ≤T2 stage and ≥T3 stage BCs. A receiver operating characteristic (ROC) curve was used to assess the predictive power of each model. The minimum ADC; mean ADC; median ADC; maximum ADC; and 10th, 25th, 75th, and 90th percentile ADC of ≤T2 stage BCs were significantly higher than those of ≥T3 stage BCs, while skewness and kurtosis had opposite results. VI-RADS achieved the highest area under the curve (AUC) of 0.834 among all parameters. The combination of VI-RADS, skewness and kurtosis yield a significantly higher AUC than VI-RADS alone (0.915 vs. 0.834, P=0.0478). VI-RADS and volume ADC histogram analysis can effectively discriminate between ≤T2 stage and ≥T3 stage BCs, and the volumetric ADC histogram can provide further information to supplement VI-RADS.

Whole-tumor ADC histogram analysis for differentiating endometriosis-related tumors: seromucinous borderline tumor, clear cell carcinoma and endometrioid carcinoma.

To investigate the feasibility of whole-tumor apparent diffusion coefficient (ADC) histogram analysis for improving the differentiation of endometriosis-related tumors: seromucinous borderline tumor (SMBT), clear cell carcinoma (CCC) and endometrioid carcinoma (EC). Clinical features, solid component ADC (ADCSC) and whole-tumor ADC histogram-derived parameters (volume, the ADCmean, 10th, 50th and 90th percentile ADCs, inhomogeneity, skewness, kurtosis and entropy) were compared among 22 SMBTs, 42 CCCs and 21 ECs. Statistical analyses were performed using chi-square test, one-way ANOVA or Kruskal-Wallis test, and receiver operating characteristic curves. A significantly higher ADCSC and smaller volume were associated with SMBT than with CCC/EC. The ADCmean was significantly higher in CCC than in EC. The 10th percentile ADC was significantly lower in EC than in SMBT/CCC. The 50th and 90th percentile ADCs were significantly higher in CCC than in SMBT/EC. For differentiating SMBT from CCC, AUCs of the ADCSC, volume, and 50th and 90th percentile ADCs were 0.97, 0.86, 0.72 and 0.81, respectively. For differentiating SMBT from EC, AUCs of the ADCSC, volume and 10th percentile ADC were 0.97, 0.71 and 0.72, respectively. For differentiating CCC from EC, AUCs of the ADCmean and 10th, 50th and 90th percentile ADCs were 0.79, 0.72, 0.81 and 0.85, respectively. Whole-tumor ADC histogram analysis was valuable for differentiating endometriosis-related tumors, and the 90th percentile ADC was optimal in differentiating CCC from EC.

Diffusion-Weighted MRI for Predicting Pathologic Complete Response in Neoadjuvant Immunotherapy.

Simple SummaryImmunotherapy targets patients’ immune systems to fight cancer. The aim of this retrospective study is to assess tumor response to pre-operative immunotherapy and predict pathologic complete response using MRI at an early treatment time- point. Based on our analysis with a cohort from the multi-center I-SPY 2 clinical trial, we found diffusion-weighted MRI is superior to dynamic contrast-enhanced MRI, where the latter is a standard and most-commonly used MRI modality, in assessing immunotherapy, while no significant difference was observed in the control cohort where only standard chemotherapy was provided.This study tested the hypothesis that a change in the apparent diffusion coefficient (ADC) measured in diffusion-weighted MRI (DWI) is an independent imaging marker, and ADC performs better than functional tumor volume (FTV) for assessing treatment response in patients with locally advanced breast cancer receiving neoadjuvant immunotherapy. A total of 249 patients were randomized to standard neoadjuvant chemotherapy with pembrolizumab (pembro) or without pembrolizumab (control). DCE-MRI and DWI, performed prior to and 3 weeks after the start of treatment, were analyzed. Percent changes of tumor ADC metrics (mean, 5th to 95th percentiles of ADC histogram) and FTV were evaluated for the prediction of pathologic complete response (pCR) using a logistic regression model. The area under the ROC curve (AUC) estimated for the percent change in mean ADC was higher in the pembro cohort (0.73, 95% confidence interval [CI]: 0.52 to 0.93) than in the control cohort (0.63, 95% CI: 0.43 to 0.83). In the control cohort, the percent change of the 95th percentile ADC achieved the highest AUC, 0.69 (95% CI: 0.52 to 0.85). In the pembro cohort, the percent change of the 25th percentile ADC achieved the highest AUC, 0.75 (95% CI: 0.55 to 0.95). AUCs estimated for percent change of FTV were 0.61 (95% CI: 0.39 to 0.83) and 0.66 (95% CI: 0.47 to 0.85) for the pembro and control cohorts, respectively. Tumor ADC may perform better than FTV to predict pCR at an early treatment time-point during neoadjuvant immunotherapy.

Apparent diffusion coefficient values and response to brachytherapy in bulky cervical cancer: A retrospective study (169)

<b>Objectives:</b> Local control can be difficult to achieve for patients with bulky cervical cancers. Cellularity inferred from apparent diffusion coefficient (ADC) values measured by diffusion MRI (dMRI) has been shown to predict tumor outcomes in other cancers. This study aimed to evaluate whether distributions of ADC from post-external beam radiation therapy (EBRT)/ prebrachytherapy (BT) dMRI can be used to predict a complete metabolic response (CR) to definitive chemoradiation in patients with bulky cervical cancer. <b>Methods:</b> A retrospective, single-institution study was conducted among patients with cervical cancer requiring interstitial BT for bulky disease (>30cc). Gross residual cervical cancer volume on post-EBRT/pre-BT single b-value clinical dMRI was contoured in 3D Slicer and reviewed by a radiation oncologist for accuracy; 90th percentile ADC values were generated from these contours. Patients were stratified based on response to BT (defined as CR on PET-CT at three months post-BT). Two-tailed t-tests were used to compare ADC values between the CR and non-CR groups. <b>Results:</b> Among a total of 47 patients, 29 (62%) were CR. The 90th percentile ADC value was significantly (p = 0.014) higher in CR (2.46 ± 0.44 µm2/ms, <i>n</i> = 29) than non-CR (2.14 ± 0.36 µm2/ms, <i>n</i> = 18). <b>Conclusions:</b> Clinical dMRI may serve to risk-stratify patients undergoing ISBT for bulky residual cervical cancer. ADC values in tumors are associated with tissue cellularity, and a higher 90th percentile ADC value may represent a higher degree of tumor necrosis post-EBRT. If our results can be replicated in larger prospective studies, percentile ADC values from post-EBRT dMRI may be used to predict a complete response to ISBT. As standard practice includes post-EBRT/ pre-BT imaging with tumor contouring, this could allow radiation oncologists to identify patients in whom escalation or additional therapy may be desired, based on the individual likelihood of complete response.

The role of apparent diffusion coefficient histogram metrics for differentiating pediatric medulloblastoma histological variants and molecular groups

Medulloblastoma, a high-grade embryonal tumor, is the most common primary brain malignancy in the pediatric population. Molecular medulloblastoma groups have documented clinically and biologically relevant characteristics. Several authors have attempted to differentiate medulloblastoma molecular groups and histology variants using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps. However, literature on the use of ADC histogram analysis in medulloblastomas is still scarce. This study presents data from a sizable group of pediatric patients with medulloblastoma from a single institution to determine the performance of ADC histogram metrics for differentiating medulloblastoma variants and groups based on both histological and molecular features. In this retrospective study, we evaluated the distribution of absolute and normalized ADC values of medulloblastomas. Tumors were manually segmented and diffusivity metrics calculated on a pixel-by-pixel basis. We calculated a variety of first-order histogram metrics from the ADC maps, including entropy, minimum, 10th percentile, 90th percentile, maximum, mean, median, skewness and kurtosis, to differentiate molecular and histological variants. ADC values of the tumors were also normalized to the bilateral cerebellar cortex and thalami. We used the Kruskal-Wallis and Mann-Whitney U tests to evaluate differences between the groups. We carried out receiver operating characteristic (ROC) curve analysis to evaluate the areas under the curves and to determine the cut-off values for differentiating tumor groups. We found 65 children with confirmed histopathological diagnosis of medulloblastoma. Mean age was 8.3 ± 5.8 years, and 60% (n = 39) were male. One child was excluded because histopathological variant could not be determined. In terms of medulloblastoma variants, tumors were classified as classic (n = 47), desmoplastic/nodular (n = 9), large/cell anaplastic (n = 6) or as having extensive nodularity (n = 2). Seven other children were excluded from the study because of incomplete imaging or equivocal molecular diagnosis. Regarding medulloblastoma molecular groups, there were: wingless (WNT) group (n = 7), sonic hedgehog (SHH) group (n = 14) and non-WNT/non-SHH (n = 36). Our results showed significant differences among the molecular groups in terms of the median (P = 0.002), mean (P = 0.003) and 90th percentile (P = 0.002) ADC histogram metrics. No significant differences among the various medulloblastoma histological variants were found. ADC histogram analysis can be implemented as a complementary tool inthe preoperative evaluation of medulloblastoma in children. This technique can provide valuable information for differentiating among medulloblastoma molecular groups. ADC histogram metrics can help predict medulloblastoma molecular classification preoperatively.

Relationship between Apparent Diffusion Coefficient Distribution and Cancer Grade in Prostate Cancer and Benign Prostatic Hyperplasia.

The aim of this paper was to assess the associations between prostate cancer aggressiveness and histogram-derived apparent diffusion coefficient (ADC) parameters and determine which ADC parameters may help distinguish among stromal hyperplasia (SH), glandular hyperplasia (GH), and low-grade, intermediate-grade, and high-grade prostate cancers. The mean, median, minimum, maximum, and 10th and 25th percentile ADC values were determined from the ADC histogram and compared among two benign prostate hyperplasia (BPH) groups and three Gleason score (GS) groups. Seventy lesions were identified in 58 patients who had undergone proctectomy. Thirty-nine lesions were prostate cancers (GS 6 = 7 lesions, GS 7 = 19 lesions, GS 8 = 11 lesions, GS 9 = 2 lesions), and thirty-one lesions were BPH (SH = 15 lesions, GH = 16 lesions). There were statistically significant differences in 10th percentile and 25th percentile ADC values when comparing GS 6 to GS 7 (p < 0.05). The 10th percentile ADC values yielded the highest area under the curve (AUC). Tenth and 25th percentile ADCs can be used to more accurately differentiate lesions with GS 6 from those with GS 7 than other ADC parameters. Our data indicate that the major challenge with ADC mapping is to differentiate between SH and GS 6, and SH and GS 7.

Diffusion-weighted imaging of breast invasive lobular carcinoma: comparison with invasive carcinoma of no special type using a histogram analysis.

To investigate the imaging findings and visibility of breast invasive lobular carcinoma (ILC) on diffusion-weighted imaging (DWI) and compare quantitative apparent diffusion coefficient (ADC) metrics of ILC and invasive carcinoma of no special type (NST) using a histogram analysis. We performed an observational retrospective study of 629 consecutive women with pathologically proven ILC and invasive ductal carcinoma of NST, who underwent 3-T MRI including DWI, between January 2017 and August 2020. After propensity score matching, 71 women were allocated to each group. On DWI, 9 (12.7%) lesions of ILC and 4 (5.6%) invasive carcinomas of the NST were not visualized. For the tumor visibility on DWI, tumor size, tumor ADC value, and background diffusion grade were significantly associated with the visibility score in both groups (all P<0.05), whereas the mean background ADC value was not significant (P>0.05). The mean ADC (1.226×10-3 vs. 1.052×10-3 mm2/s, P<0.001), median ADC (1.222×10-3 vs. 1.051×10-3 mm2/s, P=0.002), maximum ADC (1.758×10-3 vs. 1.504×10-3 mm2/s, P<0.001), minimum ADC (0.717×10-3 vs. 0.649×10-3 mm2/s, P=0.003), 90th percentile ADC (1.506×10-3 vs. 1.292×10-3 mm2/s, P<0.001) and 10th percentile ADC (0.956×10-3 vs. 0.818×10-3 mm2/s, P=0.008) were higher in ILC than in invasive carcinoma of NST. Additionally, the ADC difference value of the ILC was higher than that of invasive carcinoma of NST (1.04×10-3 vs. 0.855×10-3 mm2/s, P=0.027). On DWI, the visibility of ILC was lower compared to invasive carcinoma of NST. ILC showed higher quantitative ADC values and higher ADC difference values.

Longitudinal evaluation of apparent diffusion coefficient values as a predictor of Prostate Cancer Research International Active Surveillance reclassification.

This study aimed to evaluate the effectiveness of apparent diffusion coefficient (ADC) parameters in distinguishing between Prostate Cancer Research International Active Surveillance (PRIAS) non-reclassification and reclassification groups during active surveillance (AS) of prostate cancer. We included 55 patients who fulfilled the PRIAS criteria and underwent ≥ 2 magnetic resonance imaging (MRI) including diffusion-weighted imaging with an interval of ≤ 3years between baseline and second MRI. A mono-exponential fitting model was used to automatically create ADC maps with minimum b-values of 0 and maximum of 2000s/mm2. For detectable lesions on ADC maps, the lesions were manually segmented on each slice of the ADC maps. For undetectable lesions, the corresponding normal-appearing zone of the lobe on each slice of ADC maps was segmented. The ADC data for each slice were summed to obtain the 25th, 50th, and 75th percentile ADC values of the histogram at baseline and second MRI. These ADC parameters at baseline and second MRI, and the changes of ADC parameters from baseline to second MRI were compared between PRIAS non-reclassification and reclassification groups. The PRIAS reclassification group had significantly lower 25th, 50th, and 75th percentile ADC values at second MRI compared to the non-reclassification group. The non-reclassification group had significantly lower changes in ADC values in these percentiles compared to the reclassification group. The ADC parameters at second MRI and the changes from baseline to second MRI may be effective distinguishing factors between PRIAS non-reclassification and reclassification groups.

Whole tumor volumetric ADC analysis: relationships with histopathological differentiation of hepatocellular carcinoma.

The aim of this study was to investigate the relationships between values obtained from whole tumor volumetric apparent diffusion coefficient (ADC) measurements and histopathological grade in patients with hepatocellular carcinoma (HCC). Fifty-one naïve patients with HCC were included in the study. The tumors were classified according to the Edmondson-Steiner grade and separated as well-differentiated and non-well-differentiated (moderately and poorly differentiated). The ADC parameters of groups were compared by applying Mann-Whitney U test. The correlation between tumors' histopathological stage and whole tumor ADC parameters was investigated using Spearman's Rank Correlation Coefficient. The receiver operating characteristic curve analysis (ROC) was applied to calculate the area under curve (AUC) with intersection point of ADC parameters and curve. Mean and percentile ADC values of well-differentiated tumors were significantly higher than those of non-well-differentiated tumors (p < 0.05). The strongest correlation between histopathological grade and ADC parameters was 75th percentile ADC (r = -0.501), 50th percentile ADC (r = -0.476) and mean ADC (r = -0.465). Mean, 75th and 50th percentile ADC values used for the distinction of groups gave the highest AUC at ROC analysis (0.781, 0.781, 0.767, respectively). When threshold values of mean, 75th and 50th percentile ADC values were applied (1516mm2/s, 1194mm2/s, and 1035mm2/s) sensitivity was calculated as 0.73, 0.91, 0.83, respectively, and specificity was calculated as 0.82, 0.61, and 0.68, respectively. A correlation between whole tumor volumetric ADC values and HCCs' histopathological grade was detected in this study. 75th percentile, 50th percentile and mean ADC values are determined as highly sensitive and specific tests when the threshold values are applied for distinguishing between well-differentiated tumors and moderately/poorly differentiated tumors. When all these findings are evaluated together, HCCs' volumetric ADC values might be a useful noninvasive predictive parameters for histopathological grade in patients with HCC.

Pathological characteristics and risk stratification in patients with stage I endometrial cancer: utility of apparent diffusion coefficient histogram analysis.

Accurate pre-operative prediction of risk stratification using a non-invasive imaging tool is clinically important for planning optimal treatment strategies, particularly in early-stage endometrial cancer (EC). This study aimed to investigate the utility of apparent diffusion coefficient (ADC) histogram analysis in evaluating the pathological characteristics and risk stratification in patients with Stage I EC. Between October 2009 and December 2014, a total of 108 patients with surgically proven Stage I EC (endometrioid type = 91; non-endometrioid type = 17) excluding stage ≥II that underwent preoperative 3T-diffusion-weighted imaging without administration of contrast medium were enrolled in this retrospective study. Risk stratification was divided into four risk categories based on the ESMO-ESGO-ESTRO Guidelines: low, intermediate, high-intermediate, and high risk. The ADC histogram parameters (minimum, mean [ADCmean], 10th-90th percentile, and maximum [ADCmax]) of the tumor were generated using an in-house software. The ADC histogram parameters were compared between patients with endometrioid type and non-endometrioid type, between Stage IA and IB, between histological grades, and evaluated for differentiating non-high risk group from high risk group. Inter-reader agreement for tumor ADC measurements was also evaluated. Statistical analyses were performed using the Student's t-test, Mann-Whitney U test, receiver operating characteristics (ROC) analysis, or intraclass correlation coefficient (ICC). In differentiating endometrioid type from non-endometrioid type EC, all ADC histogram parameters were statistically significant (p < 0.05). In differentiating histological grades, 90th percentile ADC and ADCmax showed significantly higher values in tumor Grade III than in tumor Grade I-II (p < 0.05). In differentiating superficial myometrial invasion from deep myometrial invasion, all ADC histogram parameters were statistically significant (p < 0.05), except ADCmax. In differentiating non-high risk group from high risk group, ADCmean, 75th-90th percentile ADC, and ADCmax were statistically significant (p < 0.05). For predicting the high risk group, the area under the ROC curve of ADCmax was 0.628 and the highest among other histogram parameters. All histogram parameters revealed moderate to good inter-reader reliability (ICC = 0.581‒0.769). The ADC histogram analysis as reproducible tool may be useful for evaluating the pathological characteristics and risk stratification in patients with early-stage EC. ADC histogram analysis may be useful for evaluating risk stratification in early-stage endometrial cancer patients.

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.

Magnetic resonance imaging and diffusion-weighted imaging-based histogram analyses in predicting glypican 3-positive hepatocellular carcinoma

PurposeWe aimed to investigate the potential MR imaging findings in predicting glypican-3 (GPC3)-positive hepatocellular carcinomas (HCCs), with special emphasis on diffusion-weighted imaging (DWI)-based histogram analyses. MethodsForty-three patients with pathologically-confirmed GPC3-negative HCCs and 100 patients with GPC3-positive HCCs were retrospectively evaluated using contrast-enhanced MRI and DWI. Clinical characteristics and MRI features including DWI-based histogram features were assessed and compared between the two groups. Univariate and multivariate analyses were used to identify the significant clinico-radiologic variables associated with GPC3 expressions that were then incorporated into a predictive nomogram. Nomogram performance was evaluated based on calibration, discrimination, and decision curve analyses. ResultsFeatures significantly related to GPC3-positive HCCs at univariate analyses were serum alpha-fetoprotein (AFP) levels >20 ng/mL (P < 0.0001), absence of enhancing capsule (P = 0.040), peritumoral enhancement appearance on the arterial phase (P = 0.049), as well as lower mean (P = 0.0278), median (P = 0.0372) and 75th percentile (P = 0.0085) apparent diffusion coefficient (ADC) values. At multivariate analysis, the AFP levels (odds ratio, 11.236; P < 0.0001) and 75th percentile ADC values (odds ratio, 1.009; P = 0.033) were independent risk factors associated with GPC3-positive HCCs. When both criteria were combined, both sensitivity (79.0 %) and specificity (79.1 %) greater than 75 % were achieved, and satisfactory predictive nomogram performance was obtained with a C-index of 0.804 (95 % confidence interval, 0.729−0.866). Decision curve analysis further confirmed the clinical usefulness of the nomogram. ConclusionsElevated serum AFP levels and lower 75th percentile ADC values were helpful in differentiating GPC3-positive and GPC3-negative HCCs. The combined nomogram achieved satisfactory preoperative risk prediction of GPC3 expression in HCC patients.

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.

Mean Apparent Diffusion Coefficient Is a Sufficient Conventional Diffusion-weighted MRI Metric to Improve Breast MRI Diagnostic Performance: Results from the ECOG-ACRIN Cancer Research Group A6702 Diffusion Imaging Trial.

Background The Eastern Cooperative Oncology Group and American College of Radiology Imaging Network Cancer Research Group A6702 multicenter trial helped confirm the potential of diffusion-weighted MRI for improving differential diagnosis of suspicious breast abnormalities and reducing unnecessary biopsies. A prespecified secondary objective was to explore the relative value of different approaches for quantitative assessment of lesions at diffusion-weighted MRI. Purpose To determine whether alternate calculations of apparent diffusion coefficient (ADC) can help further improve diagnostic performance versus mean ADC values alone for analysis of suspicious breast lesions at MRI. Materials and Methods This prospective trial (ClinicalTrials.gov identifier: NCT02022579) enrolled consecutive women (from March 2014 to April 2015) with a Breast Imaging Reporting and Data System category of 3, 4, or 5 at breast MRI. All study participants underwent standardized diffusion-weighted MRI (b = 0, 100, 600, and 800 sec/mm2). Centralized ADC measures were performed, including manually drawn whole-lesion and hotspot regions of interest, histogram metrics, normalized ADC, and variable b-value combinations. Diagnostic performance was estimated by using the area under the receiver operating characteristic curve (AUC). Reduction in biopsy rate (maintaining 100% sensitivity) was estimated according to thresholds for each ADC metric. Results Among 107 enrolled women, 81 lesions with outcomes (28 malignant and 53 benign) in 67 women (median age, 49 years; interquartile range, 41-60 years) were analyzed. Among ADC metrics tested, none improved diagnostic performance versus standard mean ADC (AUC, 0.59-0.79 vs AUC, 0.75; P = .02-.84), and maximum ADC had worse performance (AUC, 0.52; P < .001). The 25th-percentile ADC metric provided the best performance (AUC, 0.79; 95% CI: 0.70, 0.88), and a threshold using median ADC provided the greatest reduction in biopsy rate of 23.9% (95% CI: 14.8, 32.9; 16 of 67 BI-RADS category 4 and 5 lesions). Nonzero minimum b value (100, 600, and 800 sec/mm2) did not improve the AUC (0.74; P = .28), and several combinations of two b values (0 and 600, 100 and 600, 0 and 800, and 100 and 800 sec/mm2; AUC, 0.73-0.76) provided results similar to those seen with calculations of four b values (AUC, 0.75; P = .17-.87). Conclusion Mean apparent diffusion coefficient calculated with a two-b-value acquisition is a simple and sufficient diffusion-weighted MRI metric to augment diagnostic performance of breast MRI compared with more complex approaches to apparent diffusion coefficient measurement. © RSNA, 2020 Online supplemental material is available for this article.

Effective apparent diffusion coefficient parameters for differentiation between mass-forming autoimmune pancreatitis and pancreatic ductal adenocarcinoma.

To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) parameters by region of interest (ROI) methods in differentiating mass-forming autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). The institutional review board approved this retrospective study and the requirement for informed consent was waived. Twenty-three patients with mass-forming AIP and 144 patients with PDAC underwent diffusion-weighted imaging with b-values of 0 s/mm2 and 800 s/mm2. The minimum, maximum, and mean ADC values obtained by placing ROIs within lesions and percentile ADC values (10th, 25th, 50th, 75th, and 90th) from entire-lesion histogram analysis were compared between the two groups by using Mann-Whitney U tests. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. The minimum, maximum, and mean ADC values were significantly different between mass-forming AIP and PDAC groups. ROC curve analysis showed that the maximum ADC had the highest diagnostic performance (0.92), while the minimum ADC value had the lowest diagnostic performance (0.72). The AUC of minimum ADC was significantly lower than that of maximum or mean ADC (P < 0.0001, P < 0.0001). The AUC was lowest in 10th percentile ADC value and highest in 90th percentile value. The AUC increased along with the increase of percentile values. Either the maximum or mean ADC value was effective in differentiating mass-forming AIP from the PDAC group, while the minimum ADC value might not be recommended.

Combined dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging to predict neoadjuvant chemotherapy effect in FIGO stage IB2-IIA2 cervical cancers.

To explore the value of histogram analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) quantitative parameters and apparent diffusion coefficient (ADC) values in predicting the neoadjuvant chemotherapy (NACT) response for cervical cancers. Sixty-three patients with pathologically proved stage IB2-IIA2 cervical cancer from March 2013 to January 2017 were retrospectively analyzed. They were divided into two groups on the basis of therapeutic response: the significant response (SR) group, which contains complete response patients and partial response patients, and nonsignificant response (non-SR) group, which contains progressive diseases and stable diseases. Clinical characteristics, DCE-MRI parameters (Ktrans, Kep, Ve), and ADC values before NACT were analyzed and compared between the two groups. SR group and non-SR group were documented in 35 and 28 patients. The mean Ktrans value, 90th percentile Ktrans value, maximal Ktrans value, and 90th percentile ADC value of tumors in SR were significantly higher than those in non-SR group (P = 0.012, P = 0.022, P = 0.005, P = 0.033, respectively), and the mean Ve value and 10th percentile Ve value of tumors were significantly lower in SR group (P = 0.041, P = 0.033, respectively). Kep values did not significantly differ between SR and non-SR. The 90th percentile Ktrans value combined with the 90th percentile ADC value had the highest area under the curve at 0.740 (P = 0.003) to predict NACT effectiveness. Histogram analysis of DCE-MRI multi-parameters combined with ADC values may serve as sensitive indicators for predicting NACT effectiveness in cervical cancers.

Clear Cell Renal Cell Carcinoma Growth Correlates with Baseline Diffusion-weighted MRI in Von Hippel-Lindau Disease.

Background Identification of markers to aid in understanding the growth kinetics of Von Hippel-Lindau (VHL)-associated clear cell renal cell carcinoma (ccRCC) has the potential to allow individualization of patient care, thereby helping prevent unnecessary screening and optimizing intervention. Purpose To determine whether the degree of restricted diffusion at baseline MRI holds predictive potential for the growth rate of VHL-associated ccRCC. Materials and Methods Patients with VHL disease who underwent surgical resection of tumors between November 2014 and October 2017 were analyzed retrospectively in this HIPAA-compliant study. The change in ccRCC volume between two time points and apparent diffusion coefficient (ADC) at baseline was calculated by using segmentations by two readers at nephrographic-phase CT and diffusion-weighted MRI, respectively. Intraclass correlation coefficient was used to assess agreement between readers. Repeated-measures correlation was used to investigate relationships between ADC (histogram parameters) and tumor size at baseline with growth rate and volume doubling time (VDT). Predictive performance of the ADC parameter with highest correlation and tumor size at baseline was reviewed to differentiate tumors based on their VDT (≤1 year or >1 year). Results Forty-six patients (mean age, 46 years ± 7 [standard deviation]; 25 women) with 100 ccRCCs were evaluated. Interreader agreement resulted in mean κ scores of 0.89, 0.82, and 0.93 for mean ADC, baseline tumor volume, and follow-up tumor volume, respectively. ADC percentiles correlated negatively with tumor growth rate but correlated positively with VDT. Lower ADC values demonstrated stronger correlations. The 25th percentile ADC had the strongest correlation with growth rate (ρ = -0.52, P < .001) and VDT (ρ = 0.60, P < .001) and enabled prediction of VDT (≤1 year or >1 year) with an area under the receiver operating characteristic curve of 0.86 (sensitivity, 67%; specificity, 89%) (P < .001). Conclusion Apparent diffusion coefficient at baseline was negatively correlated with tumor growth rate. Diffusion-weighted MRI may be useful to identify clear cell renal cell carcinomas with higher growth rates. © RSNA, 2020See also the editorial by Goh and Prezzi in this issue.

Diffusion-weighted MRI in the assessment of nephroblastoma: results of a multi-center trial.

To assess the value of diffusion-weighted MRI in the pre-therapeutic evaluation of pediatric renal cortical tumors. This IRB-approved, retrospective multi-center study included 122 pediatric patients with 130 renal tumors, who underwent MRI including DWI before neoadjuvant chemotherapy and nephrectomy. Two radiologists independently assessed each tumor volumetrically, and apparent diffusion coefficient (ADC) values were calculated on a voxel-wise basis, including parameters derived from histogram and texture analysis. Inter-reader agreement was excellent (ICC 0.717-0.975). For both readers, patients with locally aggressive tumor growth (SIOP 3 stage) or with metastases (M1) had significantly lower 12.5th-percentile ADC values (p ≤ 0.028) compared to those with lower-stage tumors, and the parameter energy differed significantly between patients with M1 and those with M0 status (p ≤ 0.028). Contrast and homogeneity differed significantly between benign nephroblastomatosis and malignant nephroblastoma (p ≤ 0.045, both readers). As compared to all other subtypes, the blastemal subtype demonstrated significantly higher skewness (p ≤ 0.022, both readers) and the diffuse anaplastic subtype demonstrated significantly higher 75th-percentile ADC values (p ≤ 0.042, both readers). Diffusion-weighted MRI may be of value in identifying benign nephroblastomatosis and assessing nephroblastoma subtypes. Therefore, further research is warranted to assess its value in risk stratification for pediatric patients with renal tumors in the future.