Apparent Diffusion Coefficient Distribution Research Articles

Apparent diffusion coefficient histogram analysis for predicting neoadjuvant chemoradiotherapy response in patients with rectal cancer.

PURPOSE This study aimed to retrospectively evaluate the apparent diffusion coefficient (ADC) histograms in predicting chemoradiotherapy (CRT) response in patients with locally advanced rectal cancer (LARC). METHODS A total of 51 patients who underwent surgery in our institution for rectal cancer following neoadjuvant CRT between November 2013 and July 2019 were enrolled. Conventional magnetic resonance (MR) and diffusion-weighted images obtained before and after CRT were evaluated retrospectively. All tumor-containing regions of interests were drawn in 3 selected axial images, and special software for histogram analysis was used to evaluate ADC distribution. ADC cutoff values from post-CRT ADC histogram were calculated from receiver operating characteristic (ROC) analysis for evaluating CRT response. RESULTS In histopathological analysis, 5 patients (9.8%) had minimal response (group 1), 31 patients (60.8%) had partial response (group 2), and 15 patients (29.4%) had complete or almost complete response (group 3). In the ADC histogram, minimum, maximum, 10th, 25th, 50th, 75th, and 90th percentile, mean ADC values, and skewness values of groups 2 and 3 showed significant changes before and after CRT, but no difference was found within group 1 values. The mean, 25th, 50th, 75th percent ADC values after CRT and skewness, and kurtosis values were significantly different between group 1 and group 3. Skewness value from the ADC histogram in postCRT magnetic resonance imaging had the best diagnostic performance with an area under the ROC curve of 0.851 (P =.003) for detecting group 3. The skewness cutoff calculated from the ROC analysis was 0.210 for evaluating CRT response. The sensitivity and specificity of the cut-off value were 100% and 61.4%, respectively. CONCLUSION The ADC histogram analysis seems to have potential application in predicting response to neoadjuvant CRT in patients with locally advanced rectal cancer.

Reproducibility of apparent diffusion coefficient measurement in normal prostate peripheral zone at 1.5T MRI

PurposeThe purpose of this study was to quantify the influence of factors of variability on apparent diffusion coefficient (ADC) estimation in the normal prostate peripheral zone (PZ). Materials and methodsFifty healthy volunteers underwent in 2017 (n = 17) or 2020 (n = 33) two-point (0, 800 s/mm²) prostate diffusion-weighted imaging in the morning on 1.5 T scanners A and B from different manufacturers. Additional five-point (50, 150, 300, 500, 800 s/mm²) acquisitions were performed on scanner B in the morning and evening. ADC was measured in PZ at midgland using ADC maps reconstructed with various b-value combinations. ADC distributions from 2017 and 2020 were compared using Wilcoxon rank sum test. ADC obtained in the same volunteers were compared using Bland Altman methodology. The 95% confidence interval upper limit of the repeatability/reproducibility coefficient defined the lowest detectable ADC difference. ResultsForty-nine participants with a mean age of 24.6 ± 3.8 [SD] years (range: 21–37 years) were finally included. ADC distributions from 2017 and 2020 were not significantly different and were combined. Despite high individual variability, there was no significant bias (10 × 10−6 mm²/s, P = 0.58) between ADC measurements made on both scanners. On scanner B, differences in lowest b-values chosen within the 0–500 s/mm² range for two-point ADC computation induced significant biases (56-109 × 10−6 mm²/s, P < 0.0001). ADC was significantly lower in the morning (bias: 33 × 10−6 mm²/s, P = 0.006). The number of b-values had little influence on ADC values. The lowest detectable ADC difference varied from 85 × 10−6 to 311 × 10−6 mm²/s across scanners, b-value combinations and periods of the day. ConclusionsThe MRI scanner, the lowest b-value used and the period of the day induce substantial variability in ADC computation.

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.

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

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

Utility of diffusion MRI characteristics of cervical lymph nodes as disease classifier between patients with head and neck squamous cell carcinoma and healthy volunteers

Diffusion MRI characteristics assessed by apparent diffusion coefficient (ADC) histogram analysis in head and neck squamous cell carcinoma (HNSCC) have been reported as helpful in classifying tumours based on diffusion characteristics. There is little reported on HNSCC lymph nodes classification by diffusion characteristics. The aim of this study was to determine whether pretreatment nodal microstructural diffusion MRI characteristics can classify diseased nodes of patients with HNSCC from normal nodes of healthy volunteers. Seventy-nine patients with histologically confirmed HNSCC prior to chemoradiotherapy, and eight healthy volunteers, underwent diffusion-weighted (DW) MRI at a 1.5-T MR scanner. Two radiologists contoured lymph nodes on DW (b =300 s/m2 ) images. ADC, distributed diffusion coefficient (DDC) and alpha (α) values were calculated by monoexponential and stretched exponential models. Histogram analysis metrics of drawn volume were compared between patients and volunteers using a Mann-Whitney test. The classification performance of each metric between the normal and diseased nodes was determined by receiver operating characteristic (ROC) analysis. Intraclass correlation coefficients determined interobserver reproducibility of each metric based on differently drawn ROIs by two radiologists. Sixty cancerous and 40 normal nodes were analysed. ADC histogram analysis revealed significant differences between patients and volunteers (p ≤0.0001 to 0.0046), presenting ADC distributions that were more skewed (1.49 for patients, 1.03 for volunteers; p=0.0114) and 'peaked' (6.82 for patients, 4.20 for volunteers; p=0.0021) in patients. Maximum ADC values exhibited the highest area under the curve ([AUC] 0.892). Significant differences were revealed between patients and volunteers for DDC and α value histogram metrics (p ≤0.0001 to 0.0044); the highest AUC were exhibited by maximum DDC (0.772) and the 25th percentile α value (0.761). Interobserver repeatability was excellent for mean ADC (ICC =0.88) and the 25th percentile α value (ICC =0.78), but poor for all other metrics. These results suggest that pretreatment microstructural diffusion MRI characteristics in lymph nodes, assessed by ADC and α value histogram analysis, can identify nodal disease.

Histographic analysis of oedema and fat in inflamed bone marrow based on quantitative MRI

ObjectiveTo demonstrate proof-of-concept for a quantitative MRI method using histographic analysis to assess bone marrow oedema and fat metaplasia in the sacroiliac joints.Materials and methodsFifty-three adolescents aged 12–23 with known or suspected sacroiliitis were prospectively recruited and underwent quantitative MRI (qMRI) scans, consisting of chemical shift-encoded (at 3 T) and diffusion-weighted imaging (at 1.5 T), plus conventional MRI (at 1.5 T) and clinical assessment. qMRI scans produced proton-density fat fraction (PDFF) and apparent diffusion coefficient (ADC) maps of the sacroiliac joints (SIJs), which were analysed using an in-house software tool enabling partially automated ROI definition and histographic analysis. Logistic regression and receiver operating characteristic (ROC) analyses assessed the predictive performance of ADC- and PDFF-based parameters in identifying active inflammation (oedema) and structural damage (fat metaplasia).ResultsADC-based parameters were associated with increased odds of oedema (all p < 0.05); ROC-AUC was higher for histographic parameters representing the upper end of the ADC distribution than for simple averages. Similarly, PDFF-based parameters were associated with increased odds of fat metaplasia (all p < 0.05); ROC area-under-the-curve was higher for histographic parameters representing the upper end of the PDFF distribution than for simple averages. Both ADC- and PDFF-based histographic parameters demonstrated excellent inter- and intra-observer agreement (ICC > 0.9).ConclusionsADC-based parameters can differentiate patients with bone marrow oedema from those without, whilst PDFF-based parameters can differentiate patients with fat metaplasia from those without. Histographic analysis might improve performance compared with simple averages such as the mean and median and offers excellent agreement within and between observers.Key Points• Quantitative MRI with histographic analysis can identify bone marrow oedema (an active inflammatory lesion) and fat metaplasia (a ‘chronic’ inflammatory lesion) in patients with spondyloarthritis.• The use of histographic analysis might improve the performance of quantitative MRI for detecting bone marrow oedema and fat metaplasia compared with simple averages such as the mean and median.• Bone marrow oedema and fat metaplasia are known to be of diagnostic and prognostic significance, and the proposed method could support clinical decisions around biologic (and other) therapies in spondyloarthritis.

Apparent diffusion coefficient and enhancement patterns in MR imaging as markers of severe or moderate ileum inflammation in Crohn's disease.

magnetic resonance enterography has been increasingly used for the diagnosis and follow-up of Crohn's disease (CD). The purpose of the study was to compare the apparent diffusion coefficient (ADC) with wall enhancement for the differentiation of severe, moderate or no inflammation activity in the ileum. a prospective, blinded study was conducted of 46 CD patients with a clinical Crohn's disease activity index (CDAI) ≥ 220 and a simple endoscopic score for Crohn's disease (ES-CD) ≥ 7, which yielded 58 inflamed segments with CD. Twenty controls were also included. All segments were characterized by four ADC readings. The two different enhancement patterns observed in inflamed segments, transmural or mucosal, were associated with severely (23) or moderately (35) active CD. the ADC value decreased from 2.79 ± 0.35 x 10-3 mm2/s for normal segments to 1.81 ± 0.39 x 10-3 mm2/s for the moderately inflamed segments and 1.15 ± 0.20 x 10-3 mm2/s for severely inflamed segments (p ≤ 0.0001). ROC curve analysis on the basis of the three ADC distributions showed a very good discrimination capability with an area under the curve of 0.95. Three groups were defined as follows: normal ileum ADC > 2.4 x 10-3 mm2/s, moderate stages of inflammation 1.5 x 10-3 mm2/s < ADC ≤ 2.4 x 10-3 mm2/s and severe stages of ADC ≤ 1.5 x 10-3 mm2/s. the ADC value reliably discriminates between normal and inflamed ileum and also distinguishes between severe and moderate inflammation.

Apparent diffusion coefficient histogram analysis for prediction of prognosis in glioblastoma

BackgroundTo investigate the potential to predict prognosis of glioblastoma (GBM) patients by analysis of the broader and lower values in the lower distribution of apparent diffusion coefficient (ADCL) (B&L-ADCL) values in the ADC histogram. BackgroundPresurgical publicly available diffusion-weighted images (DWI) and contrast-enhanced T1-weighted images from 76 GBM patients were analyzed. With applied 2-mixture normal distribution in the ADC histogram of enhanced lesions on T1-weighted images, the mean and width of ADCL were analyzed. We dichotomized the lower mean of ADCL (L-ADCL) and the broader width of ADCL (B-ADCL) at their own average. B&L-ADCL was defined as B-ADCL with L-ADCL. Progression-free survival (PFS) and overall survival (OS) were determined by using Cox proportional hazards analysis and the Kaplan–Meier method with the log-rank test. The difference between PFS and OS was calculated. ResultsSix (7.89%) patients had B&L-ADCL values. B&L-ADCL was strongly associated with poor PFS (hazard risk: 5.747; P=0.002) and OS (hazard risk: 3.331; P=0.018). There were significant differences in PFS (median, 77 vs. 302 days; P<0.001) and OS (median, 199 vs. 472 days; P=0.004) between the patents with and without B&L-ADCL. There was no significant difference in the OS–PFS duration difference between the patients with (median, 79 days) and without B&L-ADCL (median, 132 days) (P=0.348). ConclusionIn this study, B&L-ADCL from pretreatment ADC analysis predicted poor PFS. B&L-ADCL may indicate higher cellularity and heterogeneity in GBM tumor tissue.

Diffusion-weighted magnetic resonance imaging in locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy

PurposeTo analyze diffusion-weighted magnetic resonance imaging (DW-MRI) for treatment response assessment in locally advanced rectal cancer (LARC). Patients and methodsPatients with histologically proven rectal adenocarcinoma, stage II–III disease, were enrolled and underwent surgery following neoadjuvant chemoradiotherapy (nCRT). All patients were referred for a DW-MRI protocol on a 3 Tesla MR-system, consisting of axial T2-weighted and DWI sequences prior (I), during (II) and after (III) nCRT. Corresponding apparent diffusion coefficient (ADC) values were calculated. ResultsBetween February 2011 and June 2015, 37 patients participated in the study. All patients completed programmed treatment. Overall, 11 patients (29.7%) had pathologic complete response (pCR). No correlation between the mean pre- (ADC-I), during (ADC-II), post- (ADC-III) ADC and the reduction in tumor size after nCRT was recorded. No substantial difference in the ADC distribution was found between pCR and no-pCR patients. The ADC-II level significantly increased in the pCR cases (T = 1.675; p < 0.05). ConclusionADC value could be useful for discriminating between the pCR patients and the no-pCR patients. Further studies are necessary to identify the optimal MRI parameters combination to predict tumor response to nCRT. It is hoped that these data will provide the basis for a more solid scientific evidence.

The distribution of the apparent diffusion coefficient as an indicator of the response to chemotherapeutics in ovarian tumour xenografts

Diffusion-weighted magnetic resonance imaging (DW-MRI) was used to evaluate the effects of single-agent and combination treatment regimens in a spheroid-based animal model of ovarian cancer. Ovarian tumour xenografts grown in non-obese diabetic/severe-combined-immunodeficiency (NOD/SCID) mice were treated with carboplatin or paclitaxel, or combination carboplatin/paclitaxel chemotherapy regimens. After 4 weeks of treatment, tumours were extracted and underwent DW-MRI, mechanical testing, immunohistochemical and gene expression analyses. The distribution of the apparent diffusion coefficient (ADC) exhibited an upward shift as a result of each treatment regimen. The 99-th percentile of the ADC distribution (“maximum ADC”) exhibited a strong correlation with the tumour size (r2 = 0.90) and with the inverse of the elastic modulus (r2 = 0.96). Single-agent paclitaxel (n = 5) and combination carboplatin/paclitaxel (n = 2) treatment regimens were more effective in inducing changes in regions of higher cell density than single-agent carboplatin (n = 3) or the no-treatment control (n = 5). The maximum ADC was a good indicator of treatment-induced cell death and changes in the extracellular matrix (ECM). Comparative analysis of the tumours’ ADC distribution, mechanical properties and ECM constituents provides insights into the molecular and cellular response of the ovarian tumour xenografts to chemotherapy. Increased sample sizes are recommended for future studies. We propose experimental approaches to evaluation of the timeline of the tumour’s response to treatment.

Investigation of volumetric apparent diffusion coefficient histogram analysis for assessing complete response and clinical outcomes following pre-operative chemoradiation treatment for rectal carcinoma.

To investigate the relationship of pre-treatment volumetric apparent diffusion coefficient (ADC) histogram parameters with post-operative histopathologic treatment response and clinical outcomes following pre-operative chemoradiation treatment (CRT) in rectal cancer. In a Health Insurance Portability and Accountability Act compliant retrospective study, 78 rectal cancer patients treated with pre-operative CRT and rectal MRI were included. MR imaging analysis was performed using OncoTREAT (software tool). Multiple volumetric ADC histogram parameters (voxel distribution across ADC ranges, kurtosis, and skewness) were assessed. Correlation was made to post-operative pathological complete response, clinical, or radiological evidence of disease progression using the Mann-Whitney test. Post CRT, 8 patients showed pathologic complete response and 13 patients showed distant disease progression. Pre-treatment mean ADC was 1.2 × 10-3 mm2/s (range 0.3-1.99 × 10-3 mm2/s). Mean kurtosis measured was 0.56 (range -1 to 6; SD 1.36). Mean skewness was 0.3 (range -1 to 2; SD 0.69). Skewness had significant correlation (p value = 0.006) with disease progression. The mean rectal tumor volume was 24cc (range 1cc-134cc). Pre-treatment MRI tumor volume showed significant correlation (p value = 0.013) with pathologic complete response. Mean ADC and percentage voxels distribution against ADC ranges had no significant correlation with treatment response or disease outcomes. Volumetric ADC histogram analysis of pre-CRT rectal cancer MRI appears promising for prediction of post-CRT complete response and disease progression.

MiMSeg - an algorithm for automated detection of tumor tissue on NMR apparent diffusion coefficient maps.

Although there are several data analysis frameworks, both commercial and open source, supporting the detection of tumours on nuclear magnetic resonance (NMR) sequences, none of them gives satisfactory results in the case of low volume tumors. The majority of the frameworks require the detailed analysis of at least two sequences of the examined sample, or give sample specific thresholds distinguishing between the tumor and subtypes of healthy tissue. In this paper, we present a novel algorithm for the automated estimation of tumor specific cut-off values in the domain of the apparent diffusion coefficient (ADC). Once the cut-off characteristics for a particular type of tumor is estimated, their further usage on other independent samples does not require any calculations except for an easy thresholding. The proposed methodology is a combination of classical decomposition of ADC distribution into a Gaussian mixture model (GMM) with k-means clustering subsequently performed on the parameters of mixture model components, leading to the identification of ADC distributions for every tissue type. The maximum conditional probability criterion gives the final threshold estimate. The developed signal analysis pipeline was applied to the problem of Glioblastoma Multiforme grade IV brain tumor segmentation, with a dataset of 119 randomly chosen ADC maps and Leave-One-Out cross-validation procedure for population error estimate. Additionally, a comparison to standard GMM based tumor segmentation algorithms as well as to three other automated segmentation methods was performed and the obtained tumor regions were referenced to the segmentation done by a human expert. The results demonstrate the average MiMSeg similarity to the expert-curated decision measured by the Dice coefficient as equal to 89.2% (with 95% confidence interval 87.7 ÷ 90.6). The MiMSeg algorithm significantly outperforms other techniques in the case of small tumors (of volume less than 10%), obtaining similarity to the expert-curated decision at the level 86.7%, with 44.9% obtained by standard GMM, 79.0% by Self-Organising-Maps algorithm, 68.7% by Murakami’s algorithm, and 78.2% by Kang’s method.

Weekly response assessment of involved lymph nodes to radiotherapy using diffusion-weighted MRI in oropharynx squamous cell carcinoma.

Purpose:Patients with cancers of oropharynx have a favorable prognosis and are an ideal candidate for adaptive therapy. A replan to improve coverage or escalate/de-escalate dose based on morphological information alone may not be adequate as the grossly involved lymph nodes (LNs) of a subset of these patients tend to become cystic and often do not regress. Functional adaptation may be a better approach when considering replanning for these patients. The purpose of this study was to evaluate the weekly trends in treatment related morphological and physiological changes for these LNs using diffusion-weighted MRI (DW-MRI) and evaluate its implications for adaptive replanning.Methods:Ten patients with histologically proven oropharynx HNSCC undergoing concurrent chemoradiation were analyzed in this study. MR imaging protocol included axial T1w, T2w, and DW-MRI using a 3 T Philips MR scanner. The patients were scanned weekly in radiation treatment planning position using a 16 element phased-array anterior coil and a 44 element posterior coil. A total of 65 DWI and T2w scans were analyzed. DWI was performed using an optimized single-shot echo planar imaging sequence (TR/TE = 5000/65 ms, slice thickness = 5 mm; slices = 28; b values = 0 and 800 s/mm2). Quantification of the DW-MRI images was performed by calculating the apparent diffusion coefficient (ADC). T2w and DWI scans were imported into the Eclipse treatment planning system and gross tumor volumes (GTVs) corresponding to grossly involved LNs were contoured on each axial slice by physician experts. An attempt was made to remove any cystic or necrotic components so that the ADC analysis was of viable tumor only. A pixel-by-pixel fit of signal intensities within the GTVs was performed assuming monoexponential behavior. From each GTV histogram mean, median, standard deviation, skewness, and kurtosis were calculated. Absolute and percent change in weekly ADC histogram parameters and percent change in T2w GTV were also calculated.Results:For all nodes, an immediate change in ADC was observed during first 2–3 weeks after which ADC values either continued to increase or plateaued. A few nodal volumes had a slightly decreased ADC value during later weeks. Percent increase in median ADC from weeks 1 to 6 with respect to baseline was 14%, 25%, 41%, 42%, 45%, and 58%. The corresponding change in median T2 volumes was 8%, 10%, 16%, 22%, 40%, and 42%, respectively. The ADC distribution of the viable tumors was initially highly kurtotic; however, the kurtosis decreased as treatment progressed. The ADC distribution also showed a higher degree of skewness in the first 2 weeks, progressively becoming less skewed as treatment progressed so as to slowly approach a more symmetric distribution.Conclusions:Physiological changes in LNs represented by changes in ADC evaluated using DW-MRI are evident sooner than the morphological changes calculated from T2w MRI. The decisions for adaptive replanning may need to be individualized and should be based primarily on tumor functional information. The authors’ data also suggest that for many patients, week 3 maybe the optimal time to intervene and replan. Larger studies are needed to confirm their findings.

Use of multiparametric magnetic resonance microscopy for discrimination among different processing protocols and anatomical positions of Slovenian dry-cured hams

A novel multiparametric magnetic resonance microscopy (MRM) approach was applied to the Slovenian Kraški pršut dry-cured ham samples in order to evaluate its potential for discrimination among biceps femoris and semimembranosus muscle from two hams, differing in processing (salting duration) and thus in water and salt content. The approach is based on apparent diffusion coefficient (ADC) mapping as well as on longitudinal (T1) and transversal (T2) nuclear magnetic resonance relaxation time mapping. Three-dimensional maps were acquired and analyzed by one dimensional (1D) ADC, T1, and T2 distributions as well as by paired two-dimensional ADC-T1, ADC-T2 and T1–T2 distributions. The discriminating potential of the applied MRM approach was confirmed by differences among both 1D and 2D distributions of different ham samples. In addition, distribution peak positions highly correlated with the conventionally determined moisture content.

P0191 Histogram analysis of apparent diffusion coefficients after neoadjuvant chemotherapy in breast cancer

Background The purpose of our study was to evaluate change of the apparent diffusion coefficient (ADC) histogram during the neoadjuvant chemotherapy (NAC) in breast cancer, and to compare changes between pathologically verified responders and non-responders. Methods 62 patients received NAC followed by surgery. We defined responders and non-responders based on 30% reduction in tumour cells using the Miller-Payne Grading System. All patients underwent 3T magnetic resonance (MR) with diffusion-weighted imaging (DWI) before the NAC and after the completion of two cycles of NAC. ADC histogram analysis encompassing the entire tumour was performed and various ADC parameters (mean, minimum, 10th, 25th, 50th, 75th, 90th percentile, maximum ADCs, skewness, and kurtosis) were obtained. Findings Mean, minimum, 10th, 25th, 50th, and 75th percentile ADCs significantly increased after NAC ( p =0.0129, p =0.0004, p =0.0047, p =0.0002, p =0.0006, p =0.0030) and maximum ADC significantly decreased (p=0.0003). Skewness changed into less positive ( p p =0.0168) after NAC. Although there was no statistical significance, mean, minimum, 10th, 25th, 50th, 75th, and 90th percentile ADCs tended to decrease more in responders compared with non-responders. Interpretation There is significant change in distribution of ADC after NAC. ADC histogram analysis quantitatively demonstrates the alterations more precisely during the treatment course.

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.

Correlation of histogram analysis of apparent diffusion coefficient with uterine cervical pathologic finding.

The purpose of this study was to investigate the application of histogram analysis of apparent diffusion coefficient (ADC) in characterizing pathologic features of cervical cancer and benign cervical lesions. This prospective study was approved by the institutional review board, and written informed consent was obtained. Seventy-three patients with cervical cancer (33-69 years old; 35 patients with International Federation of Gynecology and Obstetrics stage IB cervical cancer) and 38 patients (38-61 years old) with normal cervix or cervical benign lesions (control group) were enrolled. All patients underwent 3-T diffusion-weighted imaging (DWI) with b values of 0 and 800 s/mm(2). ADC values of the entire tumor in the patient group and the whole cervix volume in the control group were assessed. Mean ADC, median ADC, 25th and 75th percentiles of ADC, skewness, and kurtosis were calculated. Histogram parameters were compared between different pathologic features, as well as between stage IB cervical cancer and control groups. Mean ADC, median ADC, and 25th percentile of ADC were significantly higher for adenocarcinoma (p = 0.021, 0.006, and 0.004, respectively), and skewness was significantly higher for squamous cell carcinoma (p = 0.011). Median ADC was statistically significantly higher for well or moderately differentiated tumors (p = 0.044), and skewness was statistically significantly higher for poorly differentiated tumors (p = 0.004). No statistically significant difference of ADC histogram was observed between lymphovascular space invasion subgroups. All histogram parameters differed significantly between stage IB cervical cancer and control groups (p < 0.05). Distribution of ADCs characterized by histogram analysis may help to distinguish early-stage cervical cancer from normal cervix or cervical benign lesions and may be useful for evaluating the different pathologic features of cervical cancer.

Parameterization of the Age-Dependent Whole Brain Apparent Diffusion Coefficient Histogram.

Purpose. The distribution of apparent diffusion coefficient (ADC) values in the brain can be used to characterize age effects and pathological changes of the brain tissue. The aim of this study was the parameterization of the whole brain ADC histogram by an advanced model with influence of age considered. Methods. Whole brain ADC histograms were calculated for all data and for seven age groups between 10 and 80 years. Modeling of the histograms was performed for two parts of the histogram separately: the brain tissue part was modeled by two Gaussian curves, while the remaining part was fitted by the sum of a Gaussian curve, a biexponential decay, and a straight line. Results. A consistent fitting of the histograms of all age groups was possible with the proposed model. Conclusions. This study confirms the strong dependence of the whole brain ADC histograms on the age of the examined subjects. The proposed model can be used to characterize changes of the whole brain ADC histogram in certain diseases under consideration of age effects.

Multiparametric MRI in characterizing venous thrombi and pulmonary thromboemboli acquired from patients with pulmonary embolism.

The structure of thrombi plays an important role in delaying reperfusion and re-occlusion after intravenous thrombolysis and could influence the performance of mechanical thrombectomy devices. This study aims to distinguish various thrombi groups based on their T2 and apparent diffusion coefficient (ADC) properties. 26 venous thrombi (VT), 28 acute (aPTE) and 18 chronic pulmonary thromboemboli (cPTE) and 18 sedimented clots (SC) were scanned by a three-dimensional T1 -weighted spin-echo sequence and by T2 and ADC mapping. Immunohistochemical analysis was used as a reference. Differences in the ADC, T2 and paired ADC-T2 distributions between the various thrombi group pairs were analyzed by standard statistics as well as by overlap integrals. Paired ADC-T2 distribution enabled the most efficient discrimination of structural changes between pairs of thrombi. For thrombi pairs VT-cPTE, VT-SC, aPTE-cPTE, aPTE-SC, cPTE-SC differences were statistically significant (pADC , pT 2 , pADC-T 2 < 0.05) and the values of the overlap integrals were low (OIADC ≤ 0.505, OIT2 ≤ 0.823, OIADC-T2 ≤ 0.379). Least efficient was the structural discrimination for the VT-aPTE pair (pT 2 = 0.877, OIT2 = 0.940). Multiparametric MRI is an efficient method for discriminating different thrombi groups at various stages of organization.

Influence of image registration on apparent diffusion coefficient images computed from free-breathing diffusion MR images of the abdomen.

To evaluate the influence of image registration on apparent diffusion coefficient (ADC) images obtained from abdominal free-breathing diffusion-weighted MR images (DW-MRIs). A comprehensive pipeline based on automatic three-dimensional nonrigid image registrations is developed to compensate for misalignments in DW-MRI datasets obtained from five healthy subjects scanned twice. Motion is corrected both within each image and between images in a time series. ADC distributions are compared with and without registration in two abdominal volumes of interest (VOIs). The effects of interpolations and Gaussian blurring as alternative strategies to reduce motion artifacts are also investigated. Among the four considered scenarios (no processing, interpolation, blurring and registration), registration yields the best alignment scores. Median ADCs vary according to the chosen scenario: for the considered datasets, ADCs obtained without processing are 30% higher than with registration. Registration improves voxelwise reproducibility at least by a factor of 2 and decreases uncertainty (Fréchet-Cramér-Rao lower bound). Registration provides similar improvements in reproducibility and uncertainty as acquiring four times more data. Patient motion during image acquisition leads to misaligned DW-MRIs and inaccurate ADCs, which can be addressed using automatic registration.