Refining the predictive value of preoperative apparent diffusion coefficient (ADC) by whole-tumor analysis for facial nerve outcomes in vestibular schwannomas

BACKGROUND

Apparent diffusion coefficient (ADC) in MRI has been shown to correlate with postoperative House-Brackmann (HB) scores in patients with vestibular schwannoma despite limited methodology. To rectify limitations of single region of interest (ROI) sampling, we hypothesize that whole-tumor ADC histogram analysis will refine the predictive value of this preoperative biomarker related to postoperative facial nerve function.

METHODS

Of 155 patients who underwent resection of vestibular schwannoma (2014-2020), 125 patients were included with requisite clinical and radiographic data. After volumetric analysis and whole-tumor ADC histogram, regression tree analysis identified ADC cutoff for significant differences in HB grade. Outcomes were extent of resection, facial nerve function, hospital length of stay (LOS), and complications.

RESULTS

Regression tree analysis defined three quantitative ADC groups (× 10-6 mm2/s) as high (> 2248.77; HB 1.7), mid (1468.44-2248.77; HB 3.1), and low (< 1468.44; HB 2.3) range (p 0.04). The mid-range ADC group had significantly worse postoperative HB scores and longer hospital LOS. Large tumor volume was independently predictive of lower rates of gross total resection (p <0.0001), higher postoperative HB score (p 0.002), higher rate of complications (p 0.04), and longer LOS (p 0.003).

CONCLUSIONS

Whole-tumor histogram yielded a robust regression tree analysis that defined three ADC groups with significantly different facial nerve outcomes. This likely reflects tumor heterogeneity better than solid-tumor ROI sampling. Whole-tumor ADC warrants further study as a useful radiographic biomarker in patients with vestibular schwannoma who are considering surgical resection.

Association of peritumoral region features assessed on breast MRI and prognosis of breast cancer: a systematic review and meta-analysis

BACKGROUND

Increasing attention has been given to the peritumoral region. However, conflicting findings have been reported regarding the relationship between peritumoral region features on MRI and the prognosis of breast cancer.

PURPOSE

To evaluate the relationship between peritumoral region features on MRI and prognosis of breast cancer.

MATERIALS AND METHODS

A retrospective meta-analysis of observational studies comparing either qualitative or quantitative assessments of peritumoral MRI features on breast cancer with poor prognosis and control subjects was performed for studies published till October 2022. Pooled odds ratios (ORs) or standardized mean differences and 95% confidence intervals (CIs) were estimated by using random-effects models. The heterogeneity across the studies was measured using the statistic I2. Sensitivity analyses were conducted to test this association according to different study characteristics.

RESULTS

Twenty-four studies comprising 1853 breast cancers of poor prognosis and 2590 control participants were included in the analysis. Peritumoral edema was associated with non-luminal breast cancers (OR=3.56; 95%CI: 2.17, 5.83; p=.000), high expression of the Ki-67 index (OR=3.70; 95%CI: 2.41, 5.70; p =.000), high histological grade (OR=5.85; 95%CI: 3.89, 8.80; p=.000), lymph node metastasis (OR=2.83; 95%CI: 1.71, 4.67; p=.000), negative expression of HR (OR=3.15; 95%CI: 2.03, 4.88; p=.000), and lymphovascular invasion (OR=1.72; 95%CI: 1.28, 2.30; p=.000). The adjacent vessel sign was associated with greater odds of breast cancer with poor prognosis (OR=2.02; 95%CI: 1.68, 2.44; p=.000). Additionally, breast cancers with poor prognosis had higher peritumor-tumor ADC ratio (SMD=0.67; 95%CI: 0.54, 0.79; p=.000) and peritumoral ADCmean (SMD=0.29; 95%CI: 0.15, 0.42; p=.000). A peritumoral region of 2-20 mm away from the margin of the tumor is recommended.

CONCLUSION

The presence of peritumoral edema and adjacent vessel signs, higher peritumor-tumor ADC ratio, and peritumoral ADCmean were significantly correlated with poor prognosis of breast cancer.

CLINICAL RELEVANCE STATEMENT

MRI features of the peritumoral region can be used as a non-invasive index for the prognostic evaluation of invasive breast cancer.

KEY POINTS

• Peritumoral edema was positively associated with non-luminal breast cancer, high expression of the Ki-67 index, high histological grade, lymph node metastasis, negative expression of HR, and lymphovascular invasion. • The adjacent vessel sign was associated with greater odds of breast cancers with poor prognosis. • Breast cancers with poor prognosis had higher peritumor-tumor ADC ratio and peritumoral ADCmean.

Can machine learning models improve early detection of brain metastases using diffusion weighted imaging-based radiomics?

Background

Metastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics.

Methods

Longitudinal diffusion imaging from 116 patients previously treated with stereotactic radiosurgery (SRS) for brain metastases were retrospectively analyzed. Clinical contours from 600 metastases were extracted from radiosurgery planning computed tomography, and rigidly registered to corresponding contrast enhanced-T1 and apparent diffusion coefficient (ADC) maps. Contralateral contours located in healthy brain tissue were used as control. The dataset consisted of (I) radiomic features using ADC maps, (II) radiomic feature change calculated using timepoints before the metastasis manifested on contrast enhanced-T1, (III) primary cancer, and (IV) anatomical location. The dataset was divided into training and internal validation sets using an 80/20 split with stratification. Four classification algorithms [Linear Support Vector Machine (SVM), Random Forest (RF), AdaBoost, and XGBoost] underwent supervised classification training, with contours labeled either 'control' or 'metastasis'. Hyperparameters were optimized towards balanced accuracy. Various model metrics (receiver operating characteristic curve area scores, accuracy, recall, and precision) were calculated to gauge performance.

Results

The radiomic and clinical data set, feature engineering, and ML models developed were able to identify metastases with an accuracy of up to 87.7% on the training set, and 85.8% on an unseen test set. XGBoost and RF showed superior accuracy (XGBoost: 0.877±0.021 and 0.833±0.47, RF: 0.823±0.024 and 0.858±0.045) for training and validation sets, respectively. XGBoost and RF also showed strong area under the receiver operating characteristic curve (AUC) performance on the validation set (0.910±0.037 and 0.922±0.034, respectively). AdaBoost performed slightly lower in all metrics. SVM model generalized poorly with the internal validation set. Important features involved changes in radiomics months before manifesting on contrast enhanced-T1.

Conclusions

The proposed models using diffusion-based radiomics showed encouraging results in differentiating healthy brain tissue from metastases using clinical imaging data. These findings suggest that longitudinal diffusion imaging and ML may help improve patient care through earlier diagnosis and increased patient monitoring/follow-up. Future work aims to improve model classification metrics, robustness, user-interface, and clinical applicability.

Predictive value of apparent diffusion coefficient for neoadjuvant chemotherapy in locally advanced colorectal cancer patients

Background

The efficacy of neoadjuvant chemotherapy is closely related to the long-term prognosis of colorectal cancer (CRC) patients. Apparent diffusion coefficient (ADC) is an index in dynamic enhanced magnetic resonance imaging (MRI), reflecting the density of tumor cells. ADC has been shown to be related to the efficacy of neoadjuvant chemotherapy in other malignant tumors, but there is still a lack of relevant research in CRC patients.

Methods

A total of 128 patients with CRC treated with neoadjuvant chemotherapy in The First Affiliated Hospital of Xiamen University from January 2016 to January 2017 were retrospectively collected. According to the response after neoadjuvant chemotherapy, the patients were divided into an objective response group (n=80) and a control group (n=48). The clinical characteristics and ADC levels of the two groups were compared, and the predictive value of ADC on the efficacy of neoadjuvant chemotherapy was analyzed. The patients were followed up for 5 years to observe the difference of survival rate between the two groups, and further analyzed the correlation between ADC and survival rate.

Results

Compared with the control group, the tumor size in the objective response group was significantly reduced (3.32±1.60 vs. 5.07±2.19 cm, P=0.000); ADC significantly increased (1.23±0.18 vs. 0.98±0.18 ×10^-3 mm²/s, P=0.000); albumin significantly increased (39.32±4.14 vs. 37.46±4.18 g/L, P=0.016); the proportion of patients with poorly differentiated or undifferentiated tumor cells was significantly lower (51.25% vs. 72.92%, P=0.016); and the 5-year mortality decreased significantly (40.00% vs. 58.33%, P=0.044). ADC had the highest predictive value of objective response for locally advanced CRC patients after neoadjuvant chemotherapy, and the area under the curve (AUC) was 0.834 [95% confidence interval (CI): 0.765-0.903, P=0.000]; ADC had certain predictive value for the 5-year survival of locally advanced CRC patients, and the AUC was 0.778 (95% CI: 0.696-0.861, P=0.000). ADC >1.055×10^-3 mm²/s, tumor size <4.1 cm, and moderately or well differentiated tumors were favorable factors for patients with locally advanced CRC to obtain objective response after neoadjuvant chemotherapy (P<0.05).

Conclusions

ADC could be used as a predictor of the efficacy of neoadjuvant chemotherapy in locally advanced CRC patients.

Value of quantitative apparent diffusion coefficients in differentiating low-grade gliomas from mixed neuronal-glial tumors

Purpose

To retrospectively assess if diffusion-weighted MR imaging (DWI) and quantitative apparent-diffusion coefficient (ADC) maps could be used to differentiate between low-grade gliomas (LGGs) and mixed neuronal-glial tumors (MNGTs including Dysembryoplastic Neuroepithelial Tumor and Ganglioglioma).

Materials and methods

We retrospectively searched the clinical, pathological, and radiological databases for a span of 9 years and identified 24 patients with biopsy proven LGG. This included WHO (fourth edition) grade I and II tumors including astrocytoma, oligoastrocytoma and oligodendrogliomas. We also identified 22 patients with MNGTs (WHO grade I) including 13 patients with DNET and 9 patients with Ganglioglioma. All patients with pathologically confirmed tumors who had MRI including DWI sequence were included in the study. Regions of interest (ROIs) of 0.1-0.15 cm² were manually positioned on the ADC maps and multiple values (10^-6 mm²/s) were obtained including the ADCmean. Optimal thresholds of ADC values and ADC ratios for distinguishing low-grade gliomas from mixed neuronal-glial tumors were determined by receiver operating characteristic (ROC) curve analysis.

Results

All the four ADC measurement variables, including the minimum (ADC min), the (ADC max) maximum, the mean of ADC values (ADC mean) and the ADC ratios (ADC mean/ADCnormal) showed significant difference between the MNGTs and LGGs. The most significant difference was seen with the maximum ADC value (ADC max) of the tumor where the values for LGGs were 1317 ± 314 whereas the values for MNGTs were 2134 ± 438. In both subsets of patients with MNGTs (DNET and Ganglioglioma), this difference was statistically significant (P = .015 and P = .0066, respectively). However, there was no significant difference between the ADC values of these subtypes of MNGTs.

Conclusion

The ADC values of MNGTs are significantly higher compared to LGGs and can be helpful in radiological demarcation of these two conditions. The high ADC of MNGTs may be attributable to the presence of large extracellular spaces and their cellularity, which is much lower than that of pure glial neoplasms.

Quantitative imaging of uterine cancers with diffusion-weighted MRI and 18-fluorodeoxyglucose PET/CT

Imaging plays an important role in the diagnosis and treatment of women with uterine cervical and endometrial cancers. Quantitative imaging, through MRI, PET/CT, and hybrid PET/MRI, allows for characterization of primary tumors beyond anatomic and qualitative descriptors. MRI diffusion-weighted imaging (DWI) yields an apparent diffusion coefficient (ADC), which can be applied in both the pre-and post-treatment assessment of uterine tumors. PET/CT assesses metabolic activity, and measurement of tumor standardized uptake value (SUV) is a useful metric in the staging of uterine malignancies. Hybrid PET/MRI is an emerging modality that combines the soft tissue contrast of MRI with the molecular imaging capability of PET. This review provides an overview of these quantitative imaging modalities, and their current and potential roles in the assessment of uterine cervical and cancer.

Conformance of a 3T Radiotherapy MRI Scanner to the QIBA Diffusion Profile

Purpose

To assess the technical performance of the apparent diffusion coefficient (ADC) on a dedicated 3T radiotherapy scanner, using a standardized phantom and sequences. Investigations into factors that could impact the technical performance of ADC in the clinic were also completed, including changing the slice‐encoded imaging direction and the reference sample ADC value.

Methods

ADC acquisitions were performed monthly on an isotropic diffusion phantom over 1 year. Measurements of ADC %bias, coefficients of variation for short‐/long‐term repeatability and precision (CV_ST/CV_LT and CV_P), and b‐value dependency (Dep_b) were calculated. The measurements were then assessed according to the Quantitative Imaging Biomarker Alliance (QIBA) Diffusion Profile specifications.

Results

The average of all measurements over the year was within Profile recommended ranges. This included when testing was performed in different imaging directions, and on samples that had different ADC reference values (0.4–1.1 μm²/ms). Results in the axial plane for the central water vial included a bias of +0.05%, CV_ST /CV_LT/CV_P = 0.1%/ 0.9%/0.4% and Dep_b = 0.4%.

Conclusions

The technical performance of ADC on a radiotherapy dedicated MRI scanner over the course of 12 months was considered conformant to the QIBA Profile. Quantifying these metrics and factors that may affect the performance is essential in progressing the use of ADC clinically: ensuring that the observed change of ADC in a tissue is due to a physiological response and not measurement variability.

The diffusion-tensor imaging reveals alterations in water diffusion parameters in acute pediatric concussion

Wide-spread visualization methods which are computed tomography (CT) and magnetic resonance imaging (MRI) are not sensitive to mild traumatic brain injury (mTBI). However, mTBI may cause changes of cerebral microstructure that could be found using diffusion-tensor imaging. The aim of this study is to reveal the impact of acute mTBI (no more than 3 days after trauma) on diffusion parameters in corpus callosum, corticospinal tract, and thalamus in children (aged 14-18). Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were analyzed. Significant increase in FA and decrease in ADC were observed in thalamus. The trend to an increase in FA is observed in corpus callosum.

Machine Learning in the Differentiation of Soft Tissue Neoplasms: Comparison of Fat-Suppressed T2WI and Apparent Diffusion Coefficient (ADC) Features-Based Models

Machine learning has been widely used in the characterization of tumors recently. This article aims to explore the feasibility of the whole tumor fat-suppressed (FS) T2WI and ADC features-based least absolute shrinkage and selection operator (LASSO)-logistic predictive models in the differentiation of soft tissue neoplasms (STN). The clinical and MR findings of 160 cases with 161 histologically proven STN were reviewed, retrospectively, 75 with diffusion-weighted imaging (DWI with b values of 50, 400, and 800 s/mm²). They were divided into benign and malignant groups and further divided into training (70%) and validation (30%) cohorts. The MR FS T2WI and ADC features-based LASSO-logistic models were built and compared. The AUC of the FS T2WI features-based LASSO-logistic regression model for benign and malignant prediction was 0.65 and 0.75 for the training and validation cohorts. The model's sensitivity, specificity, and accuracy of the validation cohort were 55%, 96%, and 76.6%. While the AUC of the ADC features-based model was 0.932 and 0.955 for the training and validation cohorts. The model's sensitivity, specificity, and accuracy were 83.3%, 100%, and 91.7%. The performances of these models were also validated by decision curve analysis (DCA). The AUC of the whole tumor ADC features-based LASSO-logistic regression predictive model was larger than that of FS T2WI features (p = 0.017). The whole tumor fat-suppressed T2WI and ADC features-based LASSO-logistic predictive models both can serve as useful tools in the differentiation of STN. ADC features-based LASSO-logistic regression predictive model did better than that of FS T2WI features.

Differentiation between primary CNS lymphoma and atypical glioblastoma according to major genomic alterations using diffusion and susceptibility-weighted MR imaging

PURPOSE

We aimed to differentiate primary central nervous system lymphoma (PCNSL) from atypical glioblastoma (GB) and distinguish major genomic subtypes between these tumors using susceptibility-weighted imaging (SWI) along with diffusion-weighted imaging (DWI).

METHODS

Thirty-one immuno-competent patients with PCNSL stratified by BCL2 and MYC rearrangement, and 57 patients with atypical GB (no visible necrosis) grouped according to isocitrate dehydrogenase-1 (IDH1) mutation status underwent 3.0-Tesla MRI before treatment in this retrospective study. Region of interest analysis with apparent diffusion coefficient (ADC) and SWI signal intensity values of the tumors were normalized by dividing those of contralateral white matter. The independent-samples t-test and Kruskal-Wallis test were utilized to compare parameters. The diagnostic ability of each parameter and their optimal combination was evaluated by logistic regression analysis and receiver operating characteristic.

RESULTS

PCNSL with rearrangement of both MYC and BCL2 (n = 7) [mean relative (r) ADCmean:0.87 ± 0.06, rADCmin:0.72 ± 0.08] demonstrated significantly lower rADCmean, and rADCmin compared to other PCNSLs (n = 24) (rADCmean:1.19 ± 0.18, rADCmin:1.03 ± 0.17;p < 0.001) and GBs (p < 0.001). GB without IDH1 mutation (n = 44) (mean rSWI value:0.95 ± 0.15) demonstrated significantly lower rSWI value compared to GB with IDH1 mutation (n = 13) (rSWI value:1.13 ± 0.09;p < 0.001) and PCNSL (p < 0.001). The incorporation of rADCmean and rSWI parameters distinguished GB with IDH1 mutation [Area under the curve (AUC):0.985] with sensitivity and specificity of 94.3 and 100 % respectively; and PCNSL with rearrangement of both MYC and BCL2 (AUC:0.982) with sensitivity and specificity of 100 % and 95.4 %, respectively. CONCLUSıONS: Combined analysis of SWI and DWI could differentiate atypical GB from PCNSL and distinguish major genomic subtypes between these tumors.

Qualitative characterization of breast tumors with diffusion-weighted imaging has comparable accuracy to quantitative analysis

PURPOSE

To evaluate the applicability and accuracy of a new qualitative diffusion-weighted imaging (DWI) assessment method in the characterization of breast tumors compared to quantitative ADC measurement and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

After review board approval, MRIs of 216 consecutive women with final diagnoses (131 malignant, 85 benign) were retrospectively analyzed. Two radiologists independently scored DWI and dynamic contrast-enhanced MRI (DCE-MRI) according to malignancy probability. Qualitative assessments were performed by combined analysis of tumor morphology and diffusion signal. Quantitative data was obtained from apparent diffusion coefficient (ADC) measurements. Lastly, descriptive DWI features were evaluated and recorded. Cohen's kappa, receiver operating characteristic and multivariate analyzes were applied.

RESULTS

Of malignant tumors, 97% were visible on DWI. Qualitative and quantitative DWI assessments provided comparable sensitivities of 89-94% and 88-92% and specificities of 51-61% and 59-67%, respectively. There was no statistical difference between the accuracies of qualitative and quantitative DWI (p ≥ 0.105). Best diagnostic values were obtained with DCE-MRI (sensitivity, 99-100%; specificity, 69-71%). Inter-reader agreement was moderate (kappa = 0.597) for qualitative DWI and substantial (kappa = 0.689) for DCE-MRI (p < 0.001). Agreement between qualitative DWI and DCE-MRI scores was moderate (kappa = 0.536 and 0.442). Visual diffusion signal, mass margin and shape were the most predictive features of malignancy on multivariate analysis of qualitative assessment.

CONCLUSION

Qualitative characterization of breast tumors on DWI has comparable accuracy to quantitative ADC analysis. This method might be used to make DWI more widely available with eliminating the need to a predetermined ADC threshold in tumor characterization. However, lower accuracy and inter-reader agreement of it compared to DCE-MRI should be considered.

Evaluation of bone marrow infiltration in multiple myeloma using whole-body diffusion-weighted imaging and T1-weighted water-fat separation Dixon

Background

Multiple myeloma (MM) is a blood cancer caused by the unlimited proliferation of intramedullary plasma cells. The presence of focal lesions (FLs) is presumed to be a more relevant factor for patient outcomes and risk distribution than diffuse bone marrow signal abnormalities. Signal changes in these FLs also have a good correlation with prognosis. As the cell density increased, a lower apparent diffusion coefficient (ADC) value was found with the diffusion-weighted imaging (DWI) sequence. Therefore, whole-body magnetic resonance imaging (MRI) with DWI sequences is sensitive to cell density and viability and may be vital for disease detection and therapy response assessments. However, the correlation between the DWI signal and the degree of bone destruction and the proportion of bone marrow plasma cells (BMPC) was still unclear in patients with MM. Water-fat separation MRI is used mainly for evaluating liver and bone marrow fat quantification, and fat quantification in other diseases. Meanwhile, it is also possible to assess the extent of bone marrow invasion in medullary lesions. This study aimed to investigate the correlation between ADC values from whole-body DWI and water/fat MRI signals from T1-weighted water-fat separation in evaluating bone marrow infiltration in patients with MM.

Methods

The study included 35 patients with MM who underwent whole-body DWI and T1-weighted water-fat separation Dixon examinations before therapy. The ADC values, normalized fat signal intensity (nMfat), normalized water molecular signal intensity (nMwater), and normalized fat fraction (nFF) of the thoracolumbar spine was measured in FLs and the normal-appearing bone marrow (NABM). The differences in values were compared using the independent-samples t-test. The correlation between ADC values and water-fat MRI signals was estimated using the Pearson or Spearman correlation test. The correlation between the MRI above parameters and proportions of BMPC was also explored.

Results

Statistically significant differences were found between the mean ADC values in FLs and NABM (0.72 vs. 0.33 mm²/s, P<0.0001). Significantly elevated nMwater values and decreased nMfat and nFF values were observed in FLs; no correlations were found in NABM (P>0.05). The ADC value highly correlated with nMfat and nFF values and moderately with the nMwater value in FLs (r=-0.899, -0.834, 0.642, respectively, P<0.0001). Correlations were also observed between the proportion of BMPC and MRI parameters in MM (r=0.984, 0.716, -0.938, and -0.905, respectively, P<0.05).

Conclusions

The ADC value combined with water-fat separation parameters could be used for evaluating thoracolumbar bone marrow infiltration in MM. All parameters correlated with the proportion of BMPC, which helped assess the early response in MM therapy.

Analysis of Renal Diffusion-Weighted Imaging (DWI) Using Apparent Diffusion Coefficient (ADC) and Intravoxel Incoherent Motion (IVIM) Models

Analysis of renal diffusion-weighted imaging (DWI) data to derive markers of tissue properties requires careful consideration of the type, extent, and limitations of the acquired data. Alongside data quality and general suitability for quantitative analysis, choice of diffusion model, fitting algorithm, and processing steps can have consequences for the precision, accuracy, and reliability of derived diffusion parameters. Here we introduce and discuss important steps for diffusion-weighted image processing, and in particular give example analysis protocols and pseudo-code for analysis using the apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) models. Following an overview of general principles, we provide details of optional steps, and steps for validation of results. Illustrative examples are provided, together with extensive notes discussing wider context of individual steps, and notes on potential pitfalls.This publication is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concepts and experimental procedure.

Renal Diffusion-Weighted Imaging (DWI) for Apparent Diffusion Coefficient (ADC), Intravoxel Incoherent Motion (IVIM), and Diffusion Tensor Imaging (DTI): Basic Concepts

The specialized function of the kidney is reflected in its unique structure, characterized by juxtaposition of disorganized and ordered elements, including renal glomerula, capillaries, and tubules. The key role of the kidney in blood filtration, and changes in filtration rate and blood flow associated with pathological conditions, make it possible to investigate kidney function using the motion of water molecules in renal tissue. Diffusion-weighted imaging (DWI) is a versatile modality that sensitizes observable signal to water motion, and can inform on the complexity of the tissue microstructure. Several DWI acquisition strategies are available, as are different analysis strategies, and models that attempt to capture not only simple diffusion effects, but also perfusion, compartmentalization, and anisotropy. This chapter introduces the basic concepts of DWI alongside common acquisition schemes and models, and gives an overview of specific DWI applications for animal models of renal disease.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This introduction chapter is complemented by two separate chapters describing the experimental procedure and data analysis.

Association between liver diffusion-weighted imaging apparent diffusion coefficient values and other measures of liver disease in pediatric autoimmune liver disease patients

BACKGROUND

Multiple quantitative magnetic resonance imaging (MRI) methods have been described to noninvasively detect and characterize liver fibrosis, including diffusion-weighted imaging (DWI).

PURPOSE

To evaluate associations between liver MRI DWI apparent diffusion coefficient (ADC) values and clinical factors and other quantitative liver MRI metrics in pediatric patients with autoimmune liver disease (AILD).

MATERIALS AND METHODS

Fifty-seven research liver MRI examinations performed from January 2017 to August 2018 for pediatric AILD registry participants were evaluated. Liver DWI ADC values, liver and spleen stiffness (kPa), and iron-corrected T1 (cT1; Perspectum Diagnostics) were measured at four anatomic levels. Participant age, sex, and laboratory data (alanine aminotransferase [ALT], total bilirubin, alkaline phosphatase, gamma-glutamyl transferase [GGT]) were recorded. Spearman's rank-order correlation (rho) and multiple linear regression were used to evaluate the associations between liver ADC values and predictor variables.

RESULTS

Mean (SD) participant age was 14.8 (4.0) years, 45.6% (26/57) were girls. Mean liver DWI ADC value was 1.34 (0.14 × 10^-3) mm²/s. Liver ADC values showed weak to moderate correlations with liver stiffness (r = - 0.42, p = 0.001), spleen stiffness (r = - 0.34; p = 0.015), whole-liver mean cT1 (r = - 0.39; p = 0.007), ALT (r = - 0.50; p = 0.0001), and GGT (r = - 0.48; p = 0.0004). Multiple linear regression showed liver stiffness (p = 0.0009) and sex (p = 0.023) to be independent predictors of liver ADC values.

CONCLUSION

Liver DWI ADC values are significantly associated with liver and spleen stiffnesses, liver cT1, ALT, GGT, and participant sex, with liver stiffness and sex remaining significant at multivariable regression. Liver ADC ultimately may play a role in multi-parametric prediction of chronic liver disease/fibrosis severity.

Differentiation between suture recurrence and suture granuloma after pulmonary resection for lung cancer by diffusion-weighted magnetic resonance imaging or FDG-PET / CT

There has been no publication which supports the usefulness of DWI differentiating for suture recurrence and suture granuloma after resection for lung cancer. We presented efficacy of DWI or FDG-PET/CT for an assessment of suture lesions after resection for lung cancer. Thirteen suture recurrences and 15 suture granulomas were examined. There were 24 adenocarcinomas and 4 squamous cell carcinomas, and 26 partial resections and 2 segmentectomies. The period of time (907±907 days) between surgery and suture recurrence was not significantly longer than that (546±547 days) between surgery and suture granuloma. Diffusion detectability scores (a 5-point scale) of suture recurrences was significantly higher than that of suture granulomas. The ADC value (1.35±0.24 × 10^-3mm²/sec) of suture recurrences was significantly lower than that (1.85±0.60 × 10^-3mm²/sec) of suture granulomas. The SUVmax (6.1 ± 5.0) of suture recurrences was not significantly higher than that (4.2 ± 2.5) of suture granulmas. The sensitivity of 85% (11/13) with DWI was not significantly higher than 69% (9/13) with FDG-PET/CT for suture recurrences. The specificity of 73% (11/15) with DWI was not significantly higher than the 60% (9/15) with FDG-PET/CT for suture granulomas. The accuracy of 79% (22/28) with DWI was not significantly higher than that of 64% (18/28) with FDG-PET/CT for suture recurrences and granulomas. DWI can differentiate suture granuloma from suture recurrence after resection of lung cancer. DWI is more useful than FDG-PET/CT for the differentiation between suture recurrence and suture granuloma after resection for lung cancer.

Longitudinal monitoring of apparent diffusion coefficient (ADC) in myeloma patients with lower M-gradient levels undergoing systemic treatment and whole-body MRI monitoring

OBJECTIVES

Longitudinal assessment of changes in apparent diffusion coefficient (ADC)-values in multiple myeloma (MM) patients and their potential role for classifying disease activity.

METHODS

Retrospective analysis of whole-body-MRI data in 73 stage III MM patients undergoing systemic treatment. Bone marrow involvement was evaluated using a standardized unenhanced 4-sequences whole-body-MRI protocol. We measured ADC-values in focal lesions (FL) and diffusely involved bone marrow (DIBM) areas. Response to treatment was based on the course of hematologic parameters. The time points of MRI-examinations were baseline, 1st (mean, 3 months), 2nd (mean, 10 months), and 3rd (mean, 18 months) follow up (FU).

RESULTS

Mean IgG and IgA serum values at baseline were 2.1 mg/dl and 1.8 mg/dl, respectively. Patients were classified into responders (n = 59) and non-responders (n = 34). Some patients were re-enrolled for new treatment regimens as they became therapy-refractory. Patterns of medullary involvement were focal (n = 44), diffuse (n = 61) and mixed (n = 30). In FL, a subgroup of myeloma patients undergoing short-term 1st FU experienced a significant increase in ADC in responders (p = 0.001), but not in non-responders (p = 0.9). In the further course of the study, ADC levels decreased continuously in responders (p = 0.02) and increased slightly in non-responders (p = 0.8). In patients with DIBM, ADC values decreased in the responders (p < 0.001) and in the non-responders (p = 0.78). An ADC cut-off value of 0.5-0.6 × 10^-3  mm²/s for diagnosing inactive disease at follow-up proved unreliable.

CONCLUSIONS

In myeloma-patients with lower tumor burden, the longitudinal course of ADC-values is predictable only for FL whereas for DIBM ADC-changes considerably overlap between responders and non-responders and are not indicative for assessment of the disease activity.

The predictive value of preoperative apparent diffusion coefficient (ADC) for facial nerve outcomes after vestibular schwannoma resection: clinical study

OBJECT

Diffusion MRI has been used to predict intraoperative consistency of tumors. Apparent diffusion coefficient (ADC) has shown predictive value as an imaging biomarker in many CNS tumors but has not been studied in a large cohort of patients with vestibular schwannoma. In this study, we examine the utility of ADC as a predictive biomarker for intraoperative tumor characteristics and postoperative facial nerve outcome.

METHODS

A retrospective review of patients who underwent vestibular schwannoma resection at our institution from 2008 to 2018 yielded 87 patients, of which 72 met inclusion criteria. Operative reports and clinical records were reviewed for clinical data; MRI data were interpreted in a blinded fashion for qualitative and quantitative biomarkers, including tumor ADC.

RESULTS

Mean tumor ADC values did not predict intraoperative consistency or adherence (p = 0.63). Adherent tumors were associated with worse facial nerve outcomes (p = 0.003). Regression tree analysis identified 3 ADC categories with statistically different facial nerve outcomes. The categories identified were ADC < 1006.04 × 10^-6 mm^2/s; ADC 1006.04-1563.93 × 10^-6 mm^2/s and ADC ≥ 1563.94 × 10^-6 mm^2/s. Postoperative and final House-Brackmann (HB) scores were significantly higher in the intermediate ADC group (2.3, p = 0.0038). HB outcomes were similar between the group with ADC < 1006.04 × 10^-6 mm^2/s and ≥ 1563.94 × 10^-6 mm^2/s (1.3 vs 1.3).

CONCLUSIONS

Middle-range preoperative ADC in vestibular schwannoma suggests a less favorable postoperative HB score. Preoperative measurement of ADC in vestibular schwannoma may provide additional information regarding prognostication of facial nerve outcomes.

Diffusion kurtosis imaging in the prediction of poor responses of locally advanced gastric cancer to neoadjuvant chemotherapy

PURPOSE

To assess the efficacy of diffusion kurtosis imaging (DKI) in the prediction of the treatment response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer (LAGC).

METHODS

A total of 31 LAGC patients were enrolled in this prospective study. All patients underwent diffusion-weighted MRI examination (with b = 0₁, 200₁, 500₁, 800₂, 1000₄, 1500₄, 2000₆ s/mm², the subscript denotes the number of signal averages) before and after chemotherapy. DKI and mono-exponential (b = 0, 800 s/mm²) models were built. Apparent diffusion coefficient (ADC), mean diffusivity (MD) and mean kurtosis (MK) of the LAGC tumors were measured. The absolute change values (ΔX) and percentage change values (%ΔX) of the above parameters post neoadjuvant chemotherapy (NACT) were calculated. The response was evaluated according to the pathological tumor regression grade scores (effective response group: TRG 0-2, poor response group: TRG 3). Mann-Whitney U test and receiver operating characteristic (ROC) curves were applicated for statistical analysis.

RESULTS

There were 17 patients in the effective response group (ERG), and 14 patients in the poor response group (PRG). The MK_pre and MK_post values in PRG were significantly higher than those in ERG [(0.671 ± 0.026) and (0.641 ± 0.019) vs. (0.584 ± 0.023) and (0.519 ± 0.018), p < 0.001]. ADC_post and MD_post in PRG were significantly lower than those in ERG (p = 0.005, p =0.001). Significant differences were also observed for % ΔMK, ΔMD and ΔMK between the two groups (p < 0.05). The area under the curve (AUC) for the prediction of PRG was highest for MK_post (AUC = 0.958, cutoff value = 0.614). The MK_pre and MK_post had the highest sensitivity (91.70 %) and specificity (93.80 %) in the prediction of PRG, respectively.

CONCLUSION

Both DKI and ADC values show potential for the prediction of the PRG in LAGC patients. The DKI parameters, especially MK_post displayed the best performance.

Advantage of high b value diffusion-weighted imaging for differentiation of common pediatric brain tumors in posterior fossa

PURPOSE

The pediatric posterior fossa (PF) brain tumors with higher frequencies are embryonal tumors (ET), ependymal tumors (EPN) and pilocytic astrocytomas (PA), however, it is often difficult to make a differential diagnosis among them with conventional MRI. The ADC calculated from DWI could be beneficial for diagnostic work up.

METHOD

We acquired DWI at b = 1000 and 4000(s/mm²). The relationship between ADC and the three types of brain tumors was evaluated with Mann-Whitney U test. We also performed simple linear regression analysis to evaluate the relationship between ADC and cellularity, and implemented receiver operating characteristic curve (ROC curve) to test the diagnostic performance among tumors.

RESULTS

The highest ADC (b1000/b4000 × 10^-3 mm²/s) was observed in PA (1.02-1.91/0.73-1.28), followed by PF-EPN (0.83-1.28/0.60-0.79) and the lowest was ET (0.41-0.75/0.29-0.47). There was significant difference among the groups in both ADC value (b-1000/b-4000: ET vs. PF-EPN p < 0.0001/0.0001, ET vs. PA p < 0.0001/0.0001, PF-EPN vs. PA p < 0.0001/0.0001). ROC analysis revealed that ADC in both b-values showed complete separation between ET and PF-EPN. And it also revealed that ADC at b-4000 could differentiate PF-EPN and PA (96.0%) better than ADC at b-1000 (90.1%). The stronger negative correlation was observed between the ADC and cellularity at b-4000 than at b-1000 (R²  = 0.7415 vs.0.7070) CONCLUSIONS: ADC of ET was significantly lower than the other two groups, and ADC of PA was significantly higher than the other two groups in both b-1000 and b-4000. Our results showed that ADC at b-4000 was more useful than ADC at b-1000 especially for differentiation between PF-EPN and PA.

Differentiating microcystic meningioma from atypical meningioma using diffusion-weighted imaging

PURPOSE

Microcystic meningioma (MCM) appears similar to atypical meningioma(AM) as per conventional diagnostic imaging. However, considering their different recurrence rate and prognosis, accurate differential diagnosis is essential for determine the appropriate treatment strategy. The aim of the study was to differentiate MCM from AM by diffusion-weighted imaging (DWI), in order to provide the basis for accurate preoperative diagnosis.

METHODS

The preoperative clinical data, conventional MRI and DWI data of 15 MCM and 30 AM cases were retrospectively analyzed. The average apparent diffusion coefficient (ADC_mean), minimum ADC (ADC_min) and normalized ADC (nADC) between MCM and AM were compared using two sample t-tests. The value of ADC_mean, ADC_min and nADC in the differential diagnosis of MCM and AM were calculated by the receiver operating curve (ROC) analysis.

RESULTS

The ADC_mean (1.06 ± 0.10 vs 0.80 ± 0.11 × 10^-3 mm²/s; P < 0.001), ADC_min (0.99 ± 0.10 vs 0.74 ± 0.12 × 10^-3 mm²/s; P < 0.001) and nADC (1.45 ± 0.17 vs 1.07 ± 0.17; P < .0001) were significantly higher in MCM compared to AM. ADC_mean of 0.91 × 10^-3 mm²/s showed an optimum area under the ROC curve of 0.967 ± 0.022, and distinguished between MCM and AM with 86.67% sensitivity, 100% specificity and 88.89% accuracy. In addition, its positive and negative predictive values were 96.29% and 77.78% respectively.

CONCLUSIONS

DWI can differentially diagnose MCM and AM, and ADC_mean is a potential quantitative tool that can improve preoperative diagnosis of both tumors.

Utility of diffusion weighted imaging with the quantitative apparent diffusion coefficient in diagnosing residual or recurrent hepatocellular carcinoma after transarterial chemoembolization: a meta-analysis

Background

Accurate and early diagnosis of residual tumors or intrahepatic recurrences after TACE is critically needed for determining the success of treatments and for guiding subsequent therapeutic planning. This meta-analysis was performed to assess the efficacy of diffusion weighted imaging (DWI) with the quantitative apparent diffusion coefficient (ADC) value in diagnosing residual or recurrent hepatocellular carcinoma after transarterial chemoembolization (TACE).

Materials and methods

A comprehensive literature search of PubMed, Embase, Web of Science, Scopus and the Cochrane Library database, from inception to July 2019, was conducted to select original studies on diagnosing residual or recurrent HCCs after TACE using DWI sequence with its ADC value. Two researchers independently chose study, extracted data, conducted meta-analysis, and evaluated methodological quality according to Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.

Results

Twelve studies comprising 624 patients and 712 tumors were finally included. The pooled sensitivity, specificity and AUC value of DWI in diagnosing residual or recurrent HCCs after TACE were 85% (95%CI: 74–92%), 83% (95%CI: 75–88%) and 0.90 (95%CI: 0.87–0.92), respectively. Residual or recurrent HCCs have significantly lower ADC value than necrotic tumors (MD = -0.48, 95%CI: − 0.69~ − 0.27, P < 0.01).

Conclusion

This study demonstrated that DWI performed better in diagnosing residual or recurrent HCCs after TACE, and ADC value may serve as alternatives for further evaluation of residual or recurrent leisions in HCC patients after TACE.

Sensitivity and specificity assessment of DWI and ADC for the diagnosis of osteoporosis in postmenopausal patients

OBJECTIVE

In this study, we prospectively investigated the diagnostic capability of diffusion-weighted magnetic resonance imaging (DWI) in assessing vertebral marrow changes in postmenopausal women with osteoporosis.

MATERIALS AND METHODS

Sixty postmenopausal women (mean age 60.2 ± 6.11 years) underwent both dual-energy X-ray absorptiometry (DEXA) of the spine and MRI. Results were acquired from each patient's L2 to L4, for a total of 180 lumbar vertebrae. Based on bone mineral density (BMD) measurements obtained from DEXA, the vertebrae were divided into three groups as follows: normal (n = 52), osteopenic (n = 92), and osteoporotic (n = 36). DWI of the vertebral body was performed to assess the apparent diffusion coefficient (ADC). The ADC outcomes were compared among the three groups and correlated with BMD.

RESULTS

ADC values (× 10^-6 mm²/s) were significantly lower in the osteoporotic group (135.67 ± 44.10) in comparison to the normal group (561.85 ± 190.37) (P = 0.0001). The results showed a positive correlation between ADC and BMD values (r = 0.748, P = 0.0001). In receiver operating characteristic (ROC) analysis, the area under the curve for DWI was 0.912 (P = 0.001). A cut-off value of 400 mm²/s for the diagnosis of osteoporosis; had sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 90.90%, 83.34%, 88.89%, 93.75%, and 76.93%, respectively.

CONCLUSION

ADC values correlated positively with BMD in women. DWI can allow quantitative evaluation of bone marrow changes and osteoporosis in postmenopausal women.

Diffusion tensor imaging for characterizing tumor microstructure and improving diagnostic performance on breast MRI: a prospective observational study

Background

Diffusion-weighted imaging (DWI) can increase breast MRI diagnostic specificity due to the tendency of malignancies to restrict diffusion. Diffusion tensor imaging (DTI) provides further information over conventional DWI regarding diffusion directionality and anisotropy. Our study evaluates DTI features of suspicious breast lesions detected on MRI to determine the added diagnostic value of DTI for breast imaging.

Methods

With IRB approval, we prospectively enrolled patients over a 3-year period who had suspicious (BI-RADS category 4 or 5) MRI-detected breast lesions with histopathological results. Patients underwent multiparametric 3 T MRI with dynamic contrast-enhanced (DCE) and DTI sequences. Clinical factors (age, menopausal status, breast density, clinical indication, background parenchymal enhancement) and DCE-MRI lesion parameters (size, type, presence of washout, BI-RADS category) were recorded prospectively by interpreting radiologists. DTI parameters (apparent diffusion coefficient [ADC], fractional anisotropy [FA], axial diffusivity [λ₁], radial diffusivity [(λ₂ + λ₃)/2], and empirical difference [λ₁ − λ₃]) were measured retrospectively. Generalized estimating equations (GEE) and least absolute shrinkage and selection operator (LASSO) methods were used for univariate and multivariate logistic regression, respectively. Diagnostic performance was internally validated using the area under the curve (AUC) with bootstrap adjustment.

Results

The study included 238 suspicious breast lesions (95 malignant, 143 benign) in 194 women. In univariate analysis, lower ADC, axial diffusivity, and radial diffusivity were associated with malignancy (OR = 0.37–0.42 per 1-SD increase, p < 0.001 for each), as was higher FA (OR = 1.45, p = 0.007). In multivariate analysis, LASSO selected only ADC (OR = 0.41) as a predictor for a DTI-only model, while both ADC (OR = 0.41) and FA (OR = 0.88) were selected for a model combining clinical and imaging parameters. Post-hoc analysis revealed varying association of FA with malignancy depending on the lesion type. The combined model (AUC = 0.81) had a significantly better performance than Clinical/DCE-MRI-only (AUC = 0.76, p < 0.001) and DTI-only (AUC = 0.75, p = 0.002) models.

Conclusions

DTI significantly improves diagnostic performance in multivariate modeling. ADC is the most important diffusion parameter for distinguishing benign and malignant breast lesions, while anisotropy measures may help further characterize tumor microstructure and microenvironment.

Volumetric quantitative histogram analysis using diffusion-weighted magnetic resonance imaging to differentiate HCC from other primary liver cancers

OBJECTIVE

To evaluate the ability of volumetric quantitative apparent diffusion coefficient (ADC) histogram parameters and LI-RADS categorization to distinguish hepatocellular carcinoma (HCC) from other primary liver cancers [intrahepatic cholangiocarcinoma (ICC) and combined HCC-ICC].

METHODS

Sixty-three consecutive patients (44 M/19F; mean age 62 years) with primary liver cancers and pre-treatment MRI including diffusion-weighted imaging (DWI) were included in this IRB-approved single-center retrospective study. Tumor type was categorized pathologically. Qualitative tumor features and LI-RADS categorization were assessed by 2 independent observers. Lesion volume of interest measurements (VOIs) were placed on ADC maps to extract first-order radiomics (histogram) features. ADC histogram metrics and qualitative findings were compared. Binary logistic regression and AUROC were used to assess performance for distinction of HCC from ICC and combined tumors.

RESULTS

Sixty-five lesions (HCC, n = 36; ICC, n = 17; and combined tumor, n = 12) were assessed. Only enhancement pattern (p < 0.015) and capsule were useful for tumor diagnosis (p < 0.014). ADC 5th/10th/95th percentiles were significant for discrimination between each tumor types (all p values < 0.05). Accuracy of LI-RADS for HCC diagnosis was 76.9% (p < 0.0001) and 69.2% (p = 0.001) for both observers. The combination of male gender, LI-RADS, and ADC 5th percentile yielded an AUROC/sensitivity/specificity/accuracy of 0.90/79.3%/88.9%/81.5% and 0.89/86.2%/77.8%/80.0% (all p values < 0.027) for the diagnosis of HCC compared to ICC and combined tumors for both observers, respectively.

CONCLUSION

The combination of quantitative ADC histogram parameters and LI-RADS categorization yielded the best prediction accuracy for distinction of HCC compared to ICC and combined HCC-ICC.

Application of diffusion-weighted MR imaging with ADC measurement for distinguishing between the histopathological types of sinonasal neoplasms

PURPOSE

To evaluate the potential contribution of quantitative DWI parameters including ADC_mean and ADC_ratio values to help in distinguishing the histopathological types of sinonasal neoplasms.

METHODS

This retrospective study included 83 patients (50 males, 33 females; mean age 61 years) with pathologically proven untreated sinonasal neoplasms who have undergone diffusion-weighted MRI imaging from February 2010 to August 2017. Diffusion-weighted MRI was performed on a 3 T unit with b factors of 0 and 1000 s/mm², and ADC maps were generated. Mean ADC values of sinonasal tumors and ADC ratios (ADC_mean of the tumor to ADC_mean of pterygoid muscles) were compared with the histopathological diagnosis by utilizing the Kruskal-Wallis non-parametric test.

RESULTS

Mean ADC_mean and ADC_ratio were 0.8 (SD, ±0.4) × (10^-3 mm²/s) and 1.2 (SD, ±0.5), respectively, and each parameter was significantly different between histopathological types (p < 0.05). Mean ADC_mean and ADC_ratio were higher in adenoid cystic carcinoma (ACC) than in SCC, lymphoma, neuroendocrine carcinoma and sinonasal undifferentiated carcinoma (SNUC) (p < 0.05). Optimized ADC_mean thresholds of 0.79, 0.81, 0.74 and 0.78 (10^-3 mm²/s) achieved maximal discriminatory accuracies of 100%, 79%, 100% and 89% for ACC/SNUC, ACC/SCC, ACC/neuroendocrine carcinoma, and ACC/lymphoma, respectively.

CONCLUSIONS

The optimized ADC_mean threshold of 0.80 (10^-3 mm²/s) could be used to differentiate ACC from non-ACC sinonasal neoplasms with maximal discriminatory accuracy (82%) and sensitivity of 100%. However, there is considerable overlapping of the ADC_mean and ADC_ratio values among non-ACC sinonasal neoplasms hence surgical biopsy is still needed.

Diffusion Tensor Imaging and Fiber Tractography Reveal Significant Microstructural Changes of Cervical Nerve Roots in Patients with Cervical Spondylotic Radiculopathy

OBJECTIVE

To delineate the fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of patients with cervical spondylotic radiculopathy by diffusion tensor imaging and fiber bundle tracing.

METHODS

Thirty patients with cervical spondylotic radiculopathy and 24 healthy volunteers were assessed using the International Standards for Neurological Classification of Spinal Cord Injury scale. All subjects underwent conventional sagittal T1- and T2-weighted imaging and horizontal 3-dimensional T2 driven equilibrium radiofrequency reset pulse and diffusion tensor imaging scan. The ADC and FA values were measured in the cervical nerve at most stenotic segment and heterolateral nonstenotic segment of patients.

RESULTS

Fiber tractography revealed thinned and sparse nerve roots and disruption of the fiber bundles in patients with cervical spondylotic radiculopathy. The FA values of C5-C8 in healthy volunteers or heterolateral nonstenotic nerve of patients with cervical spondylotic radiculopathy were significantly greater than those of the stenotic cervical segments of patients with cervical spondylotic radiculopathy (both P < 0.01). Furthermore, the ADC values of C5-C8 in healthy volunteers or heterolateral nonstenotic nerve of patients with cervical spondylotic radiculopathy were significantly lower than those of the stenotic cervical segments of cervical spondylotic radiculopathy patients (both P < 0.01).

CONCLUSIONS

Fiber tractography is capable of delineating microstructural changes of cervical nerve roots and cervical spondylotic radiculopathy exhibits significant changes in FA and ADC values.

Evaluation of ADCratio on liver MRI diffusion to discriminate benign versus malignant solid liver lesions

PURPOSE

The aim of this project is to investigate the usefulness of the absolute liver lesion ADC value and ratio of Apparent diffusion coefficient (ADC) values of a liver lesion and liver parenchyma to discriminate between a benign and malignant lesion.

METHODS

Liver MRI scans performed between January 2009 and June 2015 were retrospectively analysed. Scans were performed on either a 1.5 T or 3 T MRI unit. The type of liver lesion (benign or malignant) was determined by its radiological appearance, histology result and clinical management. Lesions with undetermined diagnosis or MRI studies degraded by artifacts were excluded. Liver cysts were also excluded from the analysis. ADC value of a lesion and liver parenchyma was measured and ADC_ratio was calculated. The values were analysed using independent samples t-test Results:Data set contained 39 benign lesions and 36 malignant lesions. Mean ADC value for benign lesions was 1678, and the mean value for malignant lesions was 1097 with a statistically significant difference of p < 0.001. All lesions with ADC value below 955 were malignant, while all lesions with ADC value above 1880 were benign. ADC value of 1260 was identified as the best available cut-off value for differentiating benign and malignant lesions, achieving sensitivity of 92%, specificity of 80% and an overall accuracy of 89%. The mean lesion to liver ADC_ratio for benign lesions was 1.3467 and for malignant lesions was 0.9038 with a statistically significant difference of p < 0.001. All lesions with ADC_ratio measuring <0.9 were malignant while lesions with ADC_ratio>1.5 were benign. ADC_ratio of 1.1 was identified statistically as the best available cut-off value for differentiating benign from malignant lesions, with sensitivity of 82%, specificity of 86% and an overall accuracy of 92%.

CONCLUSION

Our dataset indicates that lesion to background liver ADC_ratio is superior in discriminating between benign and malignant focal lesions compared to absolute ADC values of the hepatic lesions.

Apparent diffusion coefficient as an effective index for the therapeutic efficiency of brain chemoradiotherapy for brain metastases from lung cancer

BACKGROUND

The potential of apparent diffusion coefficient (ADC) value alteration before and after chemoradiotherapy as a potential monitor for therapeutic efficiency of treatment for brain metastases from lung cancer were discussed.

METHOD

Thirty lung cancer patients with brain metastases, conventional magnetic resonance imaging (MRI) examination and diffusion weighted imaging (DWI) were performed one week before chemoradiotherapy and after one treatment cycle and two treatment cycles. 43 tumor lesions were divided into effective group and invalid group according to the changes of the tumor size. The differences in ADC values at different time points before and after treatment in each treatment group were analyzed.

RESULT

The maximum diameter of the tumor was no difference after one treatment cycle, but decreased after two treatment cycles. ADC values significantly increased after both one and two treatment cycles. In effective group, the ADC values were significantly increased after one and two treatment cycles. While, there are no difference in invalid group after one treatment cycle but decreased after two treatment cycles. ΔADC values in effective group after one and two treatment cycles were both significantly higher than those in the invalid group. ROC curve analysis then revealed that the area under the curve (AUC) of ΔADC after one treatment was 0.872.

CONCLUSION

ADC values in brain metastases from lung cancer can help monitor and dynamically observe the therapeutic efficiency of whole brain chemoradiotherapy.

Interstitial ion homeostasis and acid-base balance are maintained in oedematous brain of mice with acute toxic liver failure

Acute toxic liver failure (ATLF) rapidly leads to brain oedema and neurological decline. We evaluated the ability of ATLF-affected brain to control the ionic composition and acid-base balance of the interstitial fluid. ATLF was induced in 10-12 weeks old male C57Bl mice by single intraperitoneal (i.p.) injection of 100 μg/g azoxymethane (AOM). Analyses were carried out in cerebral cortex of precomatous mice 20-24 h after AOM administration. Brain fluid status was evaluated by measuring apparent diffusion coefficient [ADC] using NMR spectroscopy, Evans Blue extravasation, and accumulation of an intracisternally-injected fluorescent tracer. Extracellular pH ([pH]_e) and ([K⁺]_e) were measured in situ with ion-sensitive microelectrodes. Cerebral cortical microdialysates were subjected to photometric analysis of extracellular potassium ([K⁺]_e), sodium ([Na⁺]_e) and luminometric assay of extracellular lactate ([Lac]_e). Potassium transport in cerebral cortical slices was measured ex vivo as ⁸⁶Rb uptake. Cerebral cortex of AOM-treated mice presented decreased ADC supporting the view that ATLF-induced brain oedema is primarily cytotoxic in nature. In addition, increased Evans blue extravasation indicated blood brain barrier leakage, and increased fluorescent tracer accumulation suggested impaired interstitial fluid passage. However, [K⁺]_e, [Na⁺]_e, [Lac]_e, [pH]_e and potassium transport in brain of AOM-treated mice was not different from control mice. We conclude that in spite of cytotoxic oedema and deregulated interstitial fluid passage, brain of mice with ATLF retains the ability to maintain interstitial ion homeostasis and acid-base balance. Tentatively, uncompromised brain ion homeostasis and acid-base balance may contribute to the relatively frequent brain function recovery and spontaneous survival rate in human patients with ATLF.

Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis

Background

The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter has been associated with treatment response in glioblastoma(GBM). Using pre-operative MRI techniques to predict MGMT promoter methylation status remains inconclusive. In this study, we investigated the value of features from structural and advanced imagings in predicting the methylation of MGMT promoter in primary glioblastoma patients.

Methods

Ninety-two pathologically confirmed primary glioblastoma patients underwent preoperative structural MR imagings and the efficacy of structural image features were qualitatively analyzed using Fisher’s exact test. In addition, 77 of the 92 patients underwent additional advanced MRI scans including diffusion-weighted (DWI) and 3-diminsional pseudo-continuous arterial spin labeling (3D pCASL) imaging. Apparent diffusion coefficient (ADC) and relative cerebral blood flow (rCBF) values within the manually drawn region-of-interest (ROI) were calculated and compared using independent sample t test for their efficacies in predicting MGMT promoter methylation. Receiver operating characteristic curve (ROC) analysis was used to investigate the predicting efficacy with the area under the curve (AUC) and cross validations. Multiple-variable logistic regression model was employed to evaluate the predicting performance of multiple variables.

Results

MGMT promoter methylation was associated with tumor location and necrosis (P <  0.05). Significantly increased ADC value (P <  0.001) and decreased rCBF (P <  0.001) were associated with MGMT promoter methylation in primary glioblastoma. The ADC achieved the better predicting efficacy than rCBF (ADC: AUC, 0.860; sensitivity, 81.1%; specificity, 82.5%; vs rCBF: AUC, 0.835; sensitivity, 75.0%; specificity, 78.4%; P = 0.032). The combination of tumor location, necrosis, ADC and rCBF resulted in the highest AUC of 0.914.

Conclusion

ADC and rCBF are promising imaging biomarkers in clinical routine to predict the MGMT promoter methylation in primary glioblastoma patients.

Diffusion-Weighted Imaging for the Discrimination of Benign and Malignant Breast Masses; Utility of ADC and Relative ADC

Purpose:

To determine the contribution of apparent diffusion coefficient (ADC), and relative ADC (rADC) values to differentiate between benign and malignant breast masses.

Materials and Methods:

Magnetic resonance imaging (MRI) of the breast with diffusion-weighted imaging (DWI) of patients with benign or malignant breast masses diagnosed either by histopathological findings or by follow-up imaging were evaluated retrospectively. Histopathological analyses were performed for 71 lesions (80.7%) while the remaining were followed up every six months for one year. DWI was performed using b-values of 0 and 1000 sec/mm², and ADC and rADC were calculated and compared. A receiver operating characteristic (ROC) curve and Youden index were used to evaluate the parameter’s optimal threshold and diagnostic value. Statistical significance was set as p < 0.05.

Results:

Eighty-eight lesions from a total of 81 patients, aged between 16 and 73 (mean age 42 ± 11.3) years were obtained and evaluated. Pathological results of 34 (38.6%) out of 71 lesions were malignant and 37 lesions (42%) were benign. Seventeen (19.3%) lesions remained stable at one-year follow-up and were accepted as benign breast masses. Mean ADC values of benign and malignant lesions were 1.584 × 10^–3mm²/sec and 0.884 × 10^–3mm²/sec (p < 0.05), respectively. Sensitivity and specificity of ADC were 88% and 87%, respectively at a cut-off value of 1.04 × 10^–3mm²/sec. Mean rADC was 0.931 for benign lesions and 0.557 for malignant lesions (p < 0.05). Sensitivity and specificity were 82% and 83% at a cut-off value of 0.639. No prominent superiority of rADC over ADC is identified in the differentiation of breast masses.

Conclusion:

ADC and rADC values derived from DWI can be equally useful in clinical setting to differentiate benign from malignant breast masses.

Normalization of ADC does not improve correlation with overall survival in patients with high-grade glioma (HGG)

Mixed reports leave uncertainty about whether normalization of apparent diffusion coefficient (ADC) to a within-subject white matter reference is necessary for assessment of tumor cellularity. We tested whether normalization improves the previously reported correlation of resection margin ADC with 15-month overall survival (OS) in HGG patients. Spin-echo echo-planar DWI was retrieved from 3 T MRI acquired between maximal resection and radiation in 37 adults with new-onset HGG (25 glioblastoma; 12 anaplastic astrocytoma). ADC maps were produced with the FSL DTIFIT tool (Oxford Centre for Functional MRI). 3 neuroradiologists manually selected regions of interest (ROI) in normal appearing white matter (NAWM) and in non-enhancing tumor (NT) < 2 cm from the margin of residual enhancing tumor or resection cavity. Normalized ADC (nADC) was computed as the ratio of absolute NT ADC to NAWM ADC. Reproducibility of nADC and absolute ADC among the readers' ROI was assessed using intra-class correlation coefficient (ICC) and within-subject coefficient of variation (wCV). Correlations of ADC and nADC with OS were compared using receiver operating characteristics (ROC) analysis. A p value 0.05 was considered statistically significant. Both mean ADC and nADC differed significantly between patients subgrouped by 15-month OS (p = 0.0014 and 0.0073 respectively). wCV and ICC among the readers were similar for absolute and normalized ADC. In ROC analysis of correlation with OS, nADC did not perform significantly better than absolute ADC. Normalization does not significantly improve the correlation of absolute ADC with OS in HGG, suggesting that normalization is not necessary for clinical or research ADC analysis in HGG patients.

Differentiation between hepatic alveolar echinococcosis and primary hepatic malignancy with diffusion-weighted magnetic resonance imaging

PURPOSE

To determine the value of diffusion-weighted magnetic resonance imaging (DW-MRI) in discriminating between hepatic alveolar echinococcosis (AE) and hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

METHODS

We included 49 patients (27 men, 22 women; mean age: 52.02±9.76 [SD] years; range: 25-72years) with 57 histopathologically confirmed hepatic AE lesions. Fifty patients (18 men, 32 women; mean age: 58.93±8.42 [SD] years; range: 42-71years) with 61 histopathologically confirmed hepatocellular carcinoma and 50 patients (24 men, 26 women; mean age: 50.11±7.70 [SD] years; range: 38-69years) with 54 histopathologically confirmed intrahepatic cholangiocarcinoma lesions were used as control groups. All patients had MRI examination of the liver that included conventional MRI sequences and DW-MRI using b values of 50, 400 and 800s/mm². Two radiologists evaluated conventional MRI and DW-MRI images and calculated ADC values of hepatic lesions.

RESULTS

The mean ADC value of solid components of hepatic AE lesions was 1.34±0.41×10^-3 mm²/s (range: 0.9-1.59×10^-3 mm²/s) and was significantly higher than that of the solid components of hepatocellular carcinoma lesions (mean ADC value, 0.99±0.29×10^-3 mm²/s; range: 0.7-1.15×10^-3 mm²/s) and of intrahepatic cholangiocarcinoma lesions (mean ADC value, 1.05±0.22×10^-3 mm²/s; range: 0.86-1.18×10^-3 mm²/s) (P<0.001).

CONCLUSION

In general ADC values can help discriminate between AE and hepatocellular carcinoma and intrahepatic cholangiocarcinoma. However, the use of ADC values cannot help differentiating Type 4 AE from hepatocellular carcinoma or intrahepatic cholangiocarcinoma.

Investigation of aquaporins and apparent diffusion coefficient from ultra-high b-values in a rat model of diabetic nephropathy

Background

To assess kidney damage in a rat model of type-2 diabetic nephropathy based on apparent diffusion coefficient (ADC) data obtained from ultra-high b-values and discuss its relationship to the expression of aquaporins (AQPs).

Methods

This study was approved by the institutional Animal Care and Use Committee. Thirty male Sprague-Dawley rats were randomised into two groups: (1) untreated controls and (2) diabetes mellitus (DM). All rats underwent diffusion-weighted imaging (DWI) with 18 b-values (0–4500 s/mm²). Maps of low ADC (ADC_low), standard ADC (ADC_st) and ultra-high ADC (ADC_uh) were calculated from low b-values (0–200 s/mm²), standard b-values (300–1500 s/mm²) and ultra-high b-values (1700–4500 s/mm²), respectively. The expression of AQPs in the kidneys was studied using immunohistochemistry. Laboratory parameters of diabetic and kidney functions, ADC_low, ADC_st, ADC_uh, and the optical density (OD) of AQP expression in the two groups were compared using an independent t test. Correlations between ADCs and the OD of AQP expression were evaluated by Pearson’s correlation analysis.

Results

ADC_uh were significantly higher in the cortex (CO), outer stripe of the outer medulla (OS) and inner stripe of the outer medulla (IS), and the OD values of AQ-2 were significantly higher in the OS, IS and inner medulla (IM) in DM animals compared with control animals. ADC_uh and OD values of AQP-2 expression were positively correlated in the OS, IS and IM of the kidney.

Conclusions

ADC_uh may work as useful metrics for early detection of kidney damage in diabetic nephropathy and may be associated with AQP-2 expression.

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

PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

Volumetric ADC histogram analysis of pre-CRT rectal cancer MRI appears promising for prediction of post-CRT complete response and disease progression.

Predicting Treatment Response of Colorectal Cancer Liver Metastases to Conventional Lipiodol-Based Transarterial Chemoembolization Using Diffusion-Weighted MR Imaging: Value of Pretreatment Apparent Diffusion Coefficients (ADC) and ADC Changes Under Therapy

PURPOSE

To use absolute pretreatment apparent diffusion coefficients (ADC) derived from diffusion-weighted MR imaging (DWI) to predict response to repetitive cTACE for unresectable liver metastases of colorectal carcinoma (CRLM) at 1 and 3 months after start of treatment.

MATERIALS AND METHODS

Fifty-five metastases in 34 patients were examined with DWI prior to treatment and 1 month after initial cTACE. Treatment was performed in 4-week intervals. Response was evaluated at 1 and 3 months after start of therapy. Metastases showing a decrease of ≥30% in axial diameter were classified as responding lesions.

RESULTS

One month after initial cTACE, seven lesions showed early response. There was no significant difference in absolute pretreatment ADC values between responding and non-responding lesions (p = 0.94). Three months after initial cTACE, 17 metastases showed response. There was a significant difference (p = 0.021) between absolute pretreatment ADC values of lesions showing response (median 1.08 × 10^-3 mm²/s) and no response (median 1.30 × 10^-3 mm²/s). Pretreatment ADC showed fair diagnostic value to predict response (AUC 0.7). Lesions showing response at 3 months also revealed a significant increase in ADC between measurements before treatment and at one month after initial cTACE (p < 0.001). Applying an increase in ADC of 12.17%, response at 3 months after initial cTACE could be predicted with a sensitivity and specificity of 77 and 74%, respectively (AUC 0.817). Furthermore, there was a strong and significant correlation (r = 0.651, p < 0.001) between percentage change in size after third cTACE and percentage change in ADC.

CONCLUSION

In patients with CRLM, ADC measurements are potential biomarkers for assessing response to cTACE.

Endometrial cancer: correlation of apparent diffusion coefficient (ADC) with tumor cellularity and tumor grade

BACKGROUND

Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) are widely used for detecting uterine endometrial cancer. The relationships between ADC values and pathological features of endometrial cancer have not yet been established.

PURPOSE

To investigate whether ADC values of endometrial cancer vary according to histologic tumor cellularity and tumor grade.

MATERIAL AND METHODS

We retrospectively reviewed 30 pathologically confirmed endometrial cancers. All patients underwent conventional non-enhanced magnetic resonance imaging (MRI) and DWI procedures, and ADC values were calculated. Tumor cellularity was evaluated by counting cancer cells in three high-power ( × 400) fields. The correlation between ADC values and tumor cellularity was assessed using Pearson's correlation coefficient test for statistical analysis.

RESULTS

The mean ± standard deviation (SD) ADC value ( ×10(-3) mm(2)/s) of endometrial cancer was 0.85 ± 0.22 (range, 0.55-1.71). The mean ± SD tumor cellularity was 528.36 ± 16.89 (range, 298.0-763.6). ADC values were significantly inversely correlated with tumor cellularity. No significant relationship was observed between ADC values and tumor grade (mean ADC values: G1, 0.88 ± 0.265 × 10(-3) mm(2)/s; G2, 0.80 ± 0.178 × 10(-3) mm(2)/s; G3, 0.81 ± 0.117 × 10(-3) mm(2)/s).

CONCLUSION

There is a significant inverse relationship between ADC values and tumor cellularity in endometrial cancer. No significant differences in average ADC value were observed between G1, G2, and G3 tumors. However, the lower the tumor grade, the wider the SD.

Complementary value of pre-treatment apparent diffusion coefficient in rectal cancer for predicting tumor recurrence

PURPOSE

To assess the complementary prognostic value of pre-treatment tumor apparent diffusion coefficient (ADC) for the prediction of tumor recurrence in patients with rectal cancer.

METHODS

From March 2012 to March 2013, a total of 128 patients with mid/lower rectal cancer who underwent pre-treatment rectal MRI were enrolled in this retrospective study. Two radiologists in consensus evaluated conventional imaging features (Cimg) in pre-treatment rectal MRI: tumor height from anal verge (≤5 cm vs. >5 cm), T stage (high vs. low), the presence or absence of lymph node metastasis, mesorectal fascia invasion, and extramural venous invasion. The mean tumor ADC values (TumorADC) based on high b-value (0, 1000 × 10(-3) mm(2)/s) diffusion weight images were extracted. A multivariate Cox proportional hazard (CPH) regression was performed to evaluate the association of Cimg and TumorADC with the 3-year local recurrence (LR) rate. Predictive performance of two multivariate CPH models (Cimg only vs. Cimg + TumorADC) was compared using Harrell's c index (HCI).

RESULTS

TumorADC (Adjusted HR, 7.830; 95% CI 3.937-15.571) and high T stage (Adjusted HR, 8.039; 95% CI 2.405-26.874) were independently associated with the 3-year LR rate. The CPH model generated with T stage + TumorADC (HCI, 0.820; 95% CI 0.708-0.932) showed significantly higher HCI than that with T stage only (HCI, 0.742; 95% CI 0.594-0.889) (P = 0.009).

CONCLUSIONS

In patients with mid/lower rectal cancer, integrating TumorADC to Cimg increases predictive performance of the CPH model than that with Cimg alone for the prediction of LR within 3 years after surgery.

Apparent diffusion coefficient value for estimating clinicohistological factors in bladder cancer including infiltration style and lymphatic invasion

Objective

To evaluate a role of apparent diffusion coefficient (ADC) values measured from diffusion-weighted imaging we investigated its association with clinicopathological tumor characteristics of bladder cancer.

Materials and methods

Diffusion-weighted MRI at 1.5 Tesla using b-values of 0, 1000 s/mm² was taken before transurethral resection by 114 bladder urothelial tumor patients. ADC value was measured and its relationship with pathological factors including T stage, tumor grade, infiltration style (INF) and lymphatic invasion (ly) was analyzed.

Results

Median ADC value was significantly lower in Grade 3 than in Grade 1 (P < 0.001) or in Grade 2 (P = 0.002), in INFb than in INFa (P = 0.004), in INFc than in INFa (P < 0.001), in ly1 than in ly0 (P < 0.001) and lower in T2≦ than in T1≧ (P < 0.001), respectively. Receiver operating curve demonstrated the accuracy of detecting muscle invasive bladder cancer or ly+ by using area under curve (AUC), showing 0.758 and 0.748.

Conclusion

ADC value is likely to serve as a useful biomarker showing clinicopathological characterictics of bladder cancer.

Diffusion-weighted imaging reflects variable cellularity and stromal density present in breast fibroadenomas

OBJECTIVE

To determine the underlying histopathologic features influencing apparent diffusion coefficient (ADC) values of breast fibroadenomas.

MATERIALS AND METHODS

Biopsy-proven fibroadenomas (n=26) initially identified as suspicious on breast magnetic resonance imaging (MRI) were retrospectively evaluated. Histopathologic assessments of lesion cellularity and stromal type were compared with ADC measures on diffusion-weighted MRI.

RESULTS

Presence of epithelial hyperplasia (increased cellularity) and dense collagenous stroma were both significantly associated with lower lesion ADC values (P=.02 and .004, respectively).

CONCLUSION

Variations in epithelial cellularity and stromal type influence breast lesion ADC values and may explain the wide range of ADC measures observed in benign fibroadenomas.

Diffusion tensor imaging of cervical spinal cord: A quantitative diagnostic tool in cervical spondylotic myelopathy

Background:

Diffusion tensor imaging (DTI) is a novel magnetic resonance imaging (MRI) technique potentially able to evaluate the microscopic structural organization of white matter fibers.

Aim:

This study aimed to compare fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values obtained by DTI in stenotic versus nonstenotic cervical spinal segments of patients with clinical and neurological evidence of cervical spondylotic myelopathy (CSM).

Materials and Methods:

This prospective study included 21 patients with CSM but without T2 changes on conventional MRI. Diffusion tensor (DT) images from the stenotic and nonstenotic segments of the subjects were obtained. FA and ADC values were estimated and compared with stenotic versus nonstenotic segments.

Statistical Analysis:

Paired t-test was used [Statistical Package for the Social Sciences (SPSS) 12.0].

Results:

In the most stenotic segments, the mean FA value was significantly lower (0.4228 ± 0.1090 vs 0.6884 ± 0.0075, P < 0.001) and the mean ADC value was significantly higher (1.312 ± 0.2405 vs 0.9183 ± 0.1477, P < 0.001) when compared to nonstenotic segments. In addition, there was a negative correlation between FA and ADC values (r = 0.63, P = 0.002).

Conclusions:

DTI of the cervical spine seems to be a promising novel imaging modality in patients with CSM.

Advances in Knowledge:

DTI may offer increased diagnostic sensitivity as compared to standard MRI and enables earlier detection of the disease.

Mean apparent diffusion coefficient values in defining radiotherapy planning target volumes in glioblastoma

BACKGROUND

To evaluate mean apparent diffusion coefficient (ADC) values on pre-radiotherapy magnetic resonance (MR) at sites that gave rise to glioblastoma (GBM) recurrence compared to similar surrounding background tissue that did not progress to tumor.

METHODS

Twenty out of 110 consecutive patients with pathology proven GBM treated at our institution from 1/1/2009 to 5/31/2012 had definitive recurrence 6 months following radiotherapy. In this single-center retrospective cohort study, pre- and post-radiotherapy MR brain exams were evaluated. Sites of tumor recurrence on post-therapy exams were co-localized to pre-therapy exams and the background tissue type which gave rise to tumor was noted (i.e., T2 hyperintensity, normal appearing white or gray matter). Similar surrounding background tissue not progressing to tumor was also selected. Two radiologists compared mean ADC values on pre-radiotherapy MR for sites which gave rise to future tumor recurrence and sites of similar background tissue.

RESULTS

Pre-radiotherapy mean ADC values were significantly lower in regions of future tumor recurrence than in regions of surrounding background tissue not progressing to tumor (P=0.003). There were no significant quantitative differences on T1-weighted pre contrast (P=0.50) or T2-weighted (P=0.10) sequences between sites. There was strong interobserver agreement with an intraclass correlation of 0.867 for ADC values at sites of future tumor recurrence and background tissue.

CONCLUSIONS

Mean ADC values may help predict sites of future gross tumor recurrence in GBM, which could be helpful in radiation therapy planning.

Evaluation of astrocytoma cell proliferation using diffusion-weighted imaging: correlation with expression of proliferating cell nuclear antigen

The purpose of this study was to analyze if there is a significant correlation between the results of diffusion-weighted imaging (DWI) and the expression of proliferating cell nuclear antigen (PCNA) in astrocytomas. The DWI scans of 19 different-grade astrocytomas were obtained on a 3 T magnetic resonance scanner. The average regional apparent diffusion coefficients (ADC) were measured. The positive expression of PCNA was determined immunohistochemically by using streptavidin-peroxidase complex staining, and was quantified by calculating its calibrated opacity density (COD) using an image analysis system. The average regional ADC and PCNA COD of low grade and high grade astrocytomas were compared. Correlations between regional ADC and PCNA COD were analyzed. The average regional ADC of high grade astrocytomas was significantly (t = 10.169, P = 0.000) less (0.687 ± 0.225 × 10⁻³ mm²/s) than that of low grade astrocytomas (1.572 ± 0.333 × 10⁻³ mm²/s). The PCNA COD (0.343 ± 0.052) of high grade astrocytomas was significantly (t=−7.858, P=0.000) greater than that (0.194 ± 0.012) of low grade astrocytomas. There were strong negative correlations between regional ADC and PCNA COD (r = −0.801, P = 0.000). The results demonstrated that DWI is helpful in evaluating cell proliferation and preoperatively grading astrocytomas by measuring regional ADC.

Hepatic angiomyolipoma with special attention to radiologic imaging

Background

Angiomyolipoma is a unique mesenchymal neoplasm composed of blood vessels as well as smooth muscle and adipose cells. The liver is a less common site of origin, and hepatic angiomyolipoma is often an incidental finding on diagnostic imaging or is identified on evaluation of nonspecific symptoms.

Case presentation

We experienced four patients who were diagnosed histologically with hepatic angiomyolipoma. The preoperative diagnoses were angiomyolipoma in two patients, hepatocellular carcinoma in one, and cavernous hemangioma in one. Three patients were treated with hepatectomy (one laparoscopic and two open approaches), and the diagnosis was completed by histological investigation of the resected specimen. The remaining one was diagnosed from tumor needle biopsy. Diffusion-weighted magnetic resonance imaging (MRI) with respiratory triggering using b values of 0 and 800 s/mm² was employed. An apparent diffusion coefficient map was generated from b values of 0 and 800 s/mm² for calculation of the apparent diffusion coefficient. The apparent diffusion coefficient values were calculated as 3.66, 1.21, 1.80, and 0.91 in patients 1 to 4, respectively. In MRI imaging, fat component was clearly demonstrated with chemical shift imaging in three patients. Early venous return was detected in three patients with computed tomography angiography.

Conclusion

Fat component and early venous return are important for a correct diagnosis of hepatic angiomyolipoma. Unfortunately, apparent diffusion coefficient values in hepatic angiomyolipoma were overlapping with those in other benign and malignant tumors.

Apparent diffusion coefficients distinguish amyotrophic lateral sclerosis from cervical spondylotic myelopathy

OBJECTIVE

Fifty percent of patients with amyotrophic lateral sclerosis (ALS) have cervical spondylotic myelopathy (CSM) as a complication. Because patients with ALS do not develop bulbar signs and symptoms at onset, differentiating them from patients with CSM is sometimes difficult. We aimed to determine whether the apparent diffusion coefficients (ADCs) of intracranial corticospinal tracts can be used to distinguish between patients with ALS and those with CSM.

METHODS

We evaluated 19 consecutive patients with ALS who did not have CSM by cervical MRI, 16 patients with CSM, and 11 healthy controls. We examined the mean ADCs in the precentral gyrus, the corona radiata, the posterior limbs of the internal capsule (PLIC), and the cerebral peduncle by 3T magnetic resonance imaging (MRI). The mean ADCs in the intracranial corticospinal tracts in patients with ALS were compared with those in patients with CSM.

RESULTS

The mean ADCs in the intracranial corticospinal tracts in patients with ALS were compared with those in patients with CSM (p<0.05). Additionally, the mean ADCs in the precentral gyrus, the PLIC, and the cerebral peduncle in the patients with ALS, including the patients who were initially diagnosed as having clinically possible ALS on the basis of the revised El Escorial criteria and did not develop bulbar symptoms at onset, were also higher than those in patients with CSM (p<0.05).

CONCLUSIONS

Elevated ADCs in the intracranial corticospinal tracts might be useful for distinguishing ALS from CSM in the early stage of the disease.

Infarct volume prediction using apparent diffusion coefficient maps during middle cerebral artery occlusion and soon after reperfusion in the rat

Middle cerebral artery occlusion (MCAO) in rodents causes brain infarctions of variable sizes that depend on multiple factors, particularly in models of ischemia/reperfusion. This is a major problem for infarct volume comparisons between different experimental groups since unavoidable variability can induce biases in the results and imposes the use of large number of subjects. MRI can help to minimize these difficulties by ensuring that the severity of ischemia is comparable between groups. Furthermore, several studies showed that infarct volumes can be predicted with MRI data obtained soon after ischemia onset. However, such predictive studies require multiparametric MRI acquisitions that cannot be routinely performed, and data processing using complex algorithms that are often not available. The aim here was to provide a simplified method for infarct volume prediction using apparent diffusion coefficient (ADC) data in a model of transient MCAO in rats. ADC images were obtained before, during MCAO and after 60 min of reperfusion. Probability histograms were generated using ADC data obtained either during MCAO, after reperfusion, or both combined. The results were compared to real infarct volumes, i.e.T2 maps obtained at day 7. Assessment of the performance of the estimations showed better results combining ADC data obtained during occlusion and at reperfusion. Therefore, ADC data alone can provide sufficient information for a reasonable prediction of infarct volume if the MRI information is obtained both during the occlusion and soon after reperfusion. This approach can be used to check whether drug administration after MRI acquisition can change infarct volume prediction.

Alveolar echinococcosis of the liver: diffusion-weighted MRI findings and potential role in lesion characterisation

PURPOSE

To report the diffusion-weighted MRI findings in alveolar echinococcosis (AE) of the liver and evaluate the potential role of apparent diffusion coefficients (ADCs) in the characterisation of lesions.

MATERIALS AND METHODS

We retrospectively included 22 patients with 63 AE liver lesions (≥1 cm), examined with 3-T liver MRI, including a free-breathing diffusion-weighted single-shot echo-planar imaging sequence (b-values=50, 300 and 600 s/mm(2)). Two radiologists jointly assessed the following lesion features: size, location, presence of cystic and/or solid components (according to Kodama's classification system), relative contrast enhancement, and calcifications (on CT). The ADC(total), ADC(min) and ADC(max) were measured in each lesion and the surrounding liver parenchyma.

RESULTS

Three type 1, 19 type 2, 17 type 3, three type 4 and 21 type 5 lesions were identified. The mean (±SD) ADC(total), ADC(min) and ADC(max) for all lesions were 1.73 ± 0.50, 0.76 ± 0.38 and 2.63 ± 0.76 × 10(-3)mm(2)/s, respectively. The mean ADC(total) for type 1, type 2, type 3, type 4 and type 5 lesions were 1.97 ± 1.01, 1.76 ± 0.53, 1.73 ± 0.41, 1.15 ± 0.42 and 1.76 ± 0.44 × 10(-3)mm(2)/s, respectively. No significant differences were found between the five lesion types, except for type 4 (p=0.0363). There was a significant correlation between the presence of a solid component and low ADCmin (r=0.39, p=0.0016), whereas an inverse correlation was found between the relative contrast enhancement and ADCtotal (r=-0.34, p=0.0072).

CONCLUSION

The ADCs of AE lesions are relatively low compared to other cystic liver lesions, which may help in the differential diagnosis. Although ADCs are of little use to distinguish between the five lesion types, their low value reflects the underlying solid component.