Effect of contrast agent on T2-weighted fat-suppressed imaging and diffusion-weighted imaging in the diagnosis of breast tumors

Background

Although previous studies have shown that the injection of contrast agents can improve image quality, the specific impact of this on T2-weighted fat-suppressed (T2 FS) and diffusion-weighted imaging (DWI) sequences in the diagnosis of breast cancer remains incompletely understood. In particular, there is insufficient research on how contrast agents affect the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values within these sequences, and how these changes influence the diagnosis of benign and malignant breast tumors.

Methods

Breast magnetic resonance images (MRI) were obtained from 178 consecutive patients on a 3T scanner. The SNR and CNR of lesions on T2 FS sequence were calculated before and after contrast agent injection and compared. Differences between pre- and post-contrast ADC in identifying different tumor types were compared using the Kruskal-Wallis H-test and the paired comparison test. The accuracy of ADC values between pre- and post-contrast in distinguishing benign and malignant breast masses was assessed using receiver operating characteristic (ROC) curves.

Results

The SNR and CNR of T2 FS sequence increased after contrast injection, and especially for invasive cancer and benign tumor, the increase was significant. For DWI, there was a slight increase or decrease of ADC values after contrast injection, but the ADC values before and after contrast had a similar effect in identifying different types of tumors. In the ROC curve analysis for assessing benign and malignant breast tumors, the area under the curve (AUC) before and after contrast showed similar results.

Conclusions

Contrast agent injection can improve the SNR and CNR of T2 FS sequence, thus providing higher quality images for the diagnosis of breast lesions. Furthermore, injection of contrast agent had little effect on the ability of ADC values to identify different types of lesions and both ADC values before and after the contrast agent were able to distinguish between benign and malignant tumors with almost the same accuracy.

Predicting long-term outcomes for acute ischemic stroke using multi-model MRI radiomics and clinical variables

Purpose

The objective of this study was to create and validate a novel prediction model that incorporated both multi-modal radiomics features and multi-clinical features, with the aim of accurately identifying acute ischemic stroke (AIS) patients who faced a higher risk of poor outcomes.

Methods

A cohort of 461 patients diagnosed with AIS from four centers was divided into a training cohort and a validation cohort. Radiomics features were extracted and selected from diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images to create a radiomic signature. Prediction models were developed using multi-clinical and selected radiomics features from DWI and ADC.

Results

A total of 49 radiomics features were selected from DWI and ADC images by the least absolute shrinkage and selection operator (LASSO). Additionally, 20 variables were collected as multi-clinical features. In terms of predicting poor outcomes in validation set, the area under the curve (AUC) was 0.727 for the DWI radiomics model, 0.821 for the ADC radiomics model, 0.825 for the DWI + ADC radiomics model, and 0.808 for the multi-clinical model. Furthermore, a prediction model was built using all selected features, the AUC for predicting poor outcomes increased to 0.86.

Conclusion

Radiomics features extracted from DWI and ADC images can serve as valuable biomarkers for predicting poor clinical outcomes in patients with AIS. Furthermore, when these radiomics features were combined with multi-clinical features, the predictive performance was enhanced. The prediction model has the potential to provide guidance for tailoring rehabilitation therapies based on individual patient risks for poor outcomes.

Histogram analysis of quantitative susceptibility mapping and apparent diffusion coefficient for identifying isocitrate dehydrogenase genotypes and tumor subtypes of adult-type diffuse gliomas

Background

Accurate preoperative identification of isocitrate dehydrogenase (IDH) genotypes and tumor subtypes is highly important for proper treatment planning and prognosis evaluation in patients with glioma. This study aimed to differentiate IDH genotypes and tumor subtypes of adult-type diffuse gliomas using histogram features of quantitative susceptibility mapping (QSM) and apparent diffusion coefficient (ADC).

Methods

This prospective study enrolled patients with suspected gliomas between March 2019 and January 2022 in a random series. Histogram features of QSM and ADC were extracted from the tumor parenchyma. The Mann-Whitney U test was used to compare the difference in histogram features between different IDH genotypes and among tumor subtypes. Receiver operating characteristic (ROC) curves were constructed to assess the corresponding diagnostic performance.

Results

This study included 47 patients with histopathologically confirmed adult-type diffuse gliomas. Totals of seven QSM features including 10th percentile (P10), 90th percentile (P90), interquartile range (IQR), maximum, mean absolute deviation (MAD), root mean squared (RMS), and variance, and five ADC features including P10, mean, median, RMS, and skewness exhibited significant differences between different IDH genotypes (P<0.05 for all), with the IQR of QSM demonstrating the highest area under curve (AUC) of 0.774 [95% confidence interval (CI): 0.635-0.913]. For separating tumor subtypes, the IQR of QSM also showed the highest AUC of 0.745 (95% CI: 0.566-0.924) for glioblastoma (GBM) versus astrocytoma and 0.848 (95% CI: 0.706-0.989) for GBM versus oligodendroglioma, but none of the features could discriminate astrocytoma from oligodendroglioma. The combination of the IQR of QSM, P10 of ADC, and age achieved the highest AUC of 0.910 (95% CI: 0.826-0.994) for IDH genotypes, and 0.939 (95% CI: 0.859-1.000) and 0.967 (95% CI: 0.904-1.000) for GBM versus astrocytoma and GBM versus oligodendroglioma, respectively.

Conclusions

QSM and ADC histogram features may serve as potential imaging markers for noninvasively assessing IDH genotypes and tumor subtypes of adult-type diffuse gliomas. Combining significant features may enhance the diagnostic performance substantially.

Restricted diffusion in benign intracranial neoplasms: a narrative review

Diffusion-weighted imaging (DWI) is a valuable diagnostic tool, which provides functional information by exploring the free diffusivity of water molecules into intra- and inter-cellular spaces that in tumours mainly depend on cellularity. It provides information regarding the tumour grade and helps with the diagnosis. Often high-grade tumours show restricted diffusion due to a high degree of cellularity, increased nuclear-to-cytoplasmic ratio, and reduced extracellular space. Benign central nervous system (CNS) tumours rarely show restricted diffusion on magnetic resonance imaging (MRI), and most of them have a characteristic imaging appearance. When benign CNS neoplasms reveal restricted diffusion on MRI, the radiologist is compelled to suggest a malignant neoplasm, making their diagnosis challenging. Knowledge of these exceptions helps to avoid possible errors in diagnosis. We present this integrated review with clinical, radiology-pathological correlation.

Quantitative Evaluation of Apparent Diffusion Coefficient Values, ISUP Grades and Prostate-Specific Antigen Density Values of Potentially Malignant PI-RADS Lesions

The aim of this study was to demonstrate the correlation between ADC values and the ADC/PSAD ratio for potentially malignant prostate lesions classified into ISUP grades and to determine threshold values to differentiate benign lesions (noPCa), clinically insignificant (nsPCa) and clinically significant prostate cancer (csPCa). We enrolled a total of 403 patients with 468 prostate lesions, of which 46 patients with 50 lesions were excluded for different reasons. Therefore, 357 patients with a total of 418 prostate lesions remained for the final evaluation. For all lesions, ADC values were measured; they demonstrated a negative correlation with ISUP grades (p < 0.001), with a significant difference between csPCa and a combined group of nsPCa and noPCa (ns-noPCa, p < 0.001). The same was true for the ADC/PSAD ratio, but only the ADC/PSAD ratio proved to be a significant discriminator between nsPCa and noPCa (p = 0.0051). Using the calculated threshold values, up to 31.6% of biopsies could have been avoided. Furthermore, the ADC/PSAD ratio, with the ability to distinguish between nsPCa and noPCa, offers possible active surveillance without prior biopsy.

Differentiation between pleomorphic adenoma and schwannoma in the parapharyngeal space: histogram analysis of apparent diffusion coefficient

OBJECTIVES

To elucidate the differences between pleomorphic adenomas and schwannomas occurring in the parapharyngeal space by histogram analyses of apparent diffusion coefficient (ADC) values measured with diffusion-weighted MRI.

METHODS

This retrospective study included 29 patients with pleomorphic adenoma and 22 patients with schwannoma arising in the parapharyngeal space or extending into the parapharyngeal space from the parotid region. Using pre-operative MR images, ADC values of tumor lesions showing the maximum diameter were measured. The regions of interest for ADC measurement were placed by contouring the tumor margin, and the histogram metrics of ADC values were compared between pleomorphic adenomas and schwannomas regarding the mean, skewness, and kurtosis by Wilcoxon's rank sum test. Subsequent to the primary analysis which included all lesions, we performed two subgroup analyses regarding b-values and magnetic field strength used for MRI.

RESULTS

The mean ADC values did not show significant differences between pleomorphic adenomas and schwannomas for the primary and subgroup analyses. Schwannomas showed higher skewness (p = 0.0001) and lower kurtosis (p = 0.003) of ADC histograms compared with pleomorphic adenomas in the primary analysis. Skewness was significantly higher in schwannomas in all the subgroup analyses. Kurtosis was consistently lower in schwannomas but did not reach statistical significance in one subgroup analysis.

CONCLUSIONS

Skewness and kurtosis showed significant differences between pleomorphic adenomas and schwannomas occupying the parapharyngeal space, but the mean ADC values did not. Our results suggest that the skewness and kurtosis of ADC histograms may be useful in differentiating these two parapharyngeal tumors.

Quantitative apparent diffusion coefficient metrics for MRI-only suspicious breast lesions: any added clinical value?

Background

Suspicious breast lesions [Breast Imaging Reporting and Data System (BI-RADS) category 4 or 5] detected only by magnetic resonance imaging (MRI) and invisible on other initial imaging modalities (MRI-only lesions) are usually small and poorly characterized in previous literature, thus making diagnosis and management difficult. This study aimed to investigate the clinical significance of quantitative apparent diffusion coefficient (ADC) metrics derived from conventional diffusion-weighted imaging (DWI) on evaluating MRI-only lesions.

Methods

A total of 90 suspicious MRI-only lesions were evaluated, including 51 malignant and 39 benign lesions. Morphological and kinetic characteristics of all lesions (termed BI-RADS parameters) were described according to the BI-RADS lexicon on dynamic contrast-enhanced (DCE) imaging. Minimum, maximum, and mean ADC values (ADCmin, ADCmax, ADCmean) were obtained by measuring the ADC map of DWI. ADCheterogeneity was then obtained by the following formula: ADCheterogeneity = (ADCmax - ADCmin)/ADCmean. Diagnostic performance of these parameters was assessed and compared using the receiver operating characteristic (ROC) curve.

Results

Of the 90 MRI-only lesions, there were 45 masses and 45 non-mass lesions. Among BI-RADS parameters, only two different kinetic patterns were significantly different between benign and malignant groups (P=0.005 and P<0.001, respectively). The area under the ROC curve (AUC) of combined significant ADC parameters (ADCmin, ADCmean, and ADCmax, all P≤0.001) was significantly higher than that of the two different kinetic patterns (P=0.006 for both). For MRI-only masses, only ADCmean and ADCmax, among all BI-RADS and ADC parameters, had diagnostic value (combined AUC =0.833). For non-mass lesions, size, distribution, ADCmin, and ADCmean were significantly different between benign and malignant groups (P=0.004, P<0.001, P=0.001, and P<0.001, respectively). In addition, ADCmean had the highest diagnostic performance among all ADC parameters, regardless of mass or non-mass (AUC =0.825 and 0.812, respectively). ADCheterogeneity showed no significant differences, no matter in mass or non-mass groups (P=0.62 and 0.43, respectively).

Conclusions

In differentiating MRI-only suspicious lesions, quantitative ADC metrics generally performed better than BI-RADS parameters, and ADCmean is still the best ADC parameter to distinguish MRI-only lesions.

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

Background

T stage is closely related to the treatment and prognosis of patients with bladder cancer (BC). However, preoperative T staging is still challenging. Multiparametric magnetic resonance imaging (mpMRI) may be valuable. This study was performed to explore the value of the Vesical Imaging-Reporting and Data System (VI-RADS) and the volumetric apparent diffusion coefficient (ADC) histogram parameters in detecting T2 stage and below stage (≤T2 stage) from T3 stage and above stage (≥T3 stage) BCs.

Methods

The study included 62 patients (mean age, males vs. females: 62.1±10.9 vs. 61.8±11.7 years) with BC pathologically confirmed by partial or radical cystectomy. All of the tumors were scored normatively by two radiologists using the VI-RADS scoring system by two radiologists. The volumetric ADC histogram of each lesion was obtained from the ADC maps. The Cochran-Armitage test was used to examine the relevance between VI-RADS scores and T stages. The Mann-Whitney U test was used to compare the histogram parameters between ≤T2 stage and ≥T3 stage BCs. A receiver operating characteristic (ROC) curve was used to assess the predictive power of each model.

Results

The minimum ADC; mean ADC; median ADC; maximum ADC; and 10th, 25th, 75th, and 90th percentile ADC of ≤T2 stage BCs were significantly higher than those of ≥T3 stage BCs, while skewness and kurtosis had opposite results. VI-RADS achieved the highest area under the curve (AUC) of 0.834 among all parameters. The combination of VI-RADS, skewness and kurtosis yield a significantly higher AUC than VI-RADS alone (0.915 vs. 0.834, P=0.0478).

Conclusions

VI-RADS and volume ADC histogram analysis can effectively discriminate between ≤T2 stage and ≥T3 stage BCs, and the volumetric ADC histogram can provide further information to supplement VI-RADS.

Golden-angle radial sparse parallel magnetic resonance imaging of rectal perfusion: utility in the diagnosis of poorly differentiated rectal cancer

Background

The objective of this retrospective investigation is to evaluate the diagnostic efficacy of a dual-parameter strategy that integrates either time-resolved angiography with stochastic trajectories (TWIST) or golden-angle radial sparse parallel (GRASP)-derived dynamic contrast agent-enhanced magnetic resonance imaging (DCE-MRI) with diffusion-weighted imaging (DWI) for the identification of poorly differentiated rectal cancer (RC). The purpose of this investigation is to contrast the aforementioned methodology with conventional single-factor assessments that rely solely on DWI, and ascertain its comparative efficacy.

Methods

This study was not registered on a clinical trial platform. Consecutive individuals diagnosed with non-mucinous rectal adenocarcinoma through endoscopy-guided biopsy between December 2020 and October 2022 were involved in our study. These patients had also undergone DCE-MRI and DWI. The perfusion metrics of influx forward volume transfer constant (Ktrans) and rate constant (Kep), along with the apparent diffusion coefficient (ADC), were quantified by a pair of investigators. The study compared the area under the curve (AUC) of the receiver operating characteristic (ROC) for both sequences to identify poorly differentiated RC. The investigation incorporated patients who fulfilled the specified criteria. The inclusion criteria for the investigation were as follows: (I) a diagnosis of RC proved through pathological examination, either via endoscopically-guided biopsy or surgical resection; (II) availability of complete MRI images; (III) absence of any prior history of neoadjuvant chemoradiotherapy during the MRI scan.

Results

Our investigation comprised a total of 179 participants. Compared to diffusion parameter alone, an integrated assessment of diffusion parameter (ADC) and perfusion parameters (Ktrans or Kep) obtained with GRASP leads to a superior diagnostic accuracy (AUC, 0.97±0.02 vs. 0.89±0.03, 0.97±0.02 vs. 0.89±0.03, P=0.005 and 0.003, respectively); however, there was no additional benefit from ADC with perfusion parameters obtained from TWIST (Ktrans or Kep) (AUC, 0.93±0.04 vs. 0.89±0.03, 0.93±0.03 vs. 0.89±0.03; P= 0.955 and 0.981, respectively, for the integration of ADC with Ktrans and Kep).

Conclusions

By integrating diffusion and perfusion features into a dual-parameter model, the GRASP method enhances the diagnostic efficacy of MRI in discriminating RCs with poor differentiation. Conversely, the TWIST approach did not yield the aforementioned outcome.

Nomogram based on quantitative dynamic contrast-enhanced magnetic resonance imaging, apparent diffusion coefficient, and clinicopathological features for early prediction of pathologic complete response in breast cancer patients receiving neoadjuvant chemotherapy

Background

The aim of this study was to develop two nomograms for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) for breast cancer based on quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC), and clinicopathological characteristics at two time-points: before and after two cycles of NACT, respectively.

Methods

3.0 T MRI scans were performed before and after 2 cycles of NACT in 215 patients. A total of 74 female patients with stage II-III breast cancer were included. According to univariate and multivariate logistic regression analysis, nomogram model 1 and nomogram model 2 were developed based on the independent predictors for pCR before and after 2 cycles of NACT, respectively. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope.

Results

The independent predictors of pCR were different at the two time points. Both nomograms were found to effectively predict pCR: nomogram model 2 based on Ki67, ΔK_trans%, and ΔADC% after 2 cycles of NACT showed better predictive discrimination [AUC =0.900 (0.829, 0.970) vs. 0.833 (0.736, 0.930)] and calibration ability (mean absolute error of the agreement: 0.017 vs. 0.051) compared to nomogram model 1 based on pre-NACT HER2, Ki67, and K_trans.

Conclusions

Nomograms based on quantitative DCE-MRI parameters, ADC, and clinicopathological characteristics can predict pCR in breast cancer and facilitate individualized decision-making for NACT.

Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps

Background

Preoperative differentiation between angiomatous meningioma (AM) and atypical meningioma (ATM) is related to treatment planning. In this study, we explored the utility of apparent diffusion coefficient (ADC) histogram analysis in differentiating AM and ATM, and further assess the correlations between these parameters and the Ki-67 proliferation index.

Methods

Thirty AM and 35 ATM patients were enrolled and their clinical and conventional magnetic resonance imaging (MRI) features were analyzed in this study. Nine ADC histogram parameters [mean, variance, skewness, and kurtosis, as well as the 1st (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentile of ADC] were selected and compared by independent t-test or Mann-Whitney U test. Diagnostic performance analysis was performed by receiver operating characteristic (ROC) curves. The relationship between ADC histogram parameters and the Ki-67 proliferation index was assessed by Spearman's correlation coefficient.

Results

AM group showed a significantly higher mean [median (interquartile range): 124.07 (22.66) vs. 112.12 (16.04), P<0.001], ADC1 [107.50 (17.00) vs. 82.00 (20.33), P<0.001], ADC10 (mean ± standard deviation: 115.80±12.09 vs. 96.86±9.86, P<0.001), and ADC50 [124.00 (21.13) vs. 109.00 (15.17), P<0.001], compared to the ATM group. Significant correlations were identified between the mean (r=-0.428, P<0.001), ADC1 (r=-0.549, P<0.001), ADC10 (r=-0.529, P<0.001), ADC50 (r=-0.483, P<0.001), and the Ki-67 proliferation index. ROC analysis showed that the best diagnostic performance was achieved by ADC1 (AUC =0.900). Whereas, no differences were found between variance, skewness, kurtosis, ADC90, and ADC99 (P=0.067-0.787).

Conclusions

AM and ATM exhibit overlapping conventional MRI features. ADC histogram analysis, especially ADC1, maybe a reliable quantitative imaging biomarker for differentiation between AM and ATM.

Diagnostic and prognostic performance of renal compartment volume and the apparent diffusion coefficient obtained from magnetic resonance imaging in mild, moderate and severe diabetic kidney disease

Background

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD). There are unmet needs for noninvasive diagnosis and prognosis prediction of DKD in clinical practice. This study examines the diagnostic and prognostic value of magnetic resonance (MR) markers of renal compartment volume and the apparent diffusion coefficient (ADC) for mild, moderate, and severe DKD.

Methods

This study was registered at the Chinese Clinical Trial Registry Center (registration number: ChiCTR-RRC-17012687). Sixty-seven DKD patients were prospectively randomly enrolled and underwent clinical examination and diffusion-weighted magnetic resonance imaging (DW-MRI). Patients with comorbidities that affected renal volumes or components were excluded. Ultimately, 52 DKD patients were included in the cross-sectional analysis. The ADC in the renal cortex (ADC_cortex)_, ADC in the renal medulla (ADC_medulla) and difference between ADC_cortex and ADC_medulla (ΔADC) were measured using a twelve-layer concentric objects (TLCO) approach. Renal compartment volumes of the parenchyma and pelvis were derived from T2-weighted MRI. Due to lost contact or ESRD diagnosed before follow-up (n=14), only 38 DKD patients remained for follow-up (median period =8.25 years) to investigate the correlations between MR markers and renal outcomes. The primary outcomes were the composite of doubling of the primary serum creatinine concentration or ESRD.

Results

ADC_cortex presented superior performance in discriminating DKD with normal and declined estimated glomerular filtration rate (eGFR) over ADC_medulla, ΔADC and renal compartment volumes with an AUC of 0.904 (sensitivity of 83% and specificity of 91%) and was moderately correlated with the clinical biomarkers eGFR and proteinuria (P<0.05). The Cox survival analysis demonstrated that ADC_cortex rather than ΔADC is a predictor of renal outcomes with a hazard ratio of 3.4 (95% CI: 1.1-10.2, P<0.05) independent of baseline eGFR and proteinuria.

Conclusions

ADC_cortex is a valuable imaging marker for the diagnosis and prediction of renal function decline in DKD.

[Evaluation of the Effectiveness of a Neck Fixation Device for Improving Image Quality in Diffusion-weighted Whole-body Imaging with Background Suppression]

The effectiveness of a neck fixation device to improve the image quality of DWIBS was investigated. Healthy volunteers were examined while chewing with and without a neck fixation device using a 3-T MRI system. Distance of mandibular movement was measured using true-fast imaging of steady-state precession (true FISP). Signal-to-noise ratio (SNR) and apparent diffusion coefficient (ADC) of DWIBS were measured. Image quality of DWIBS was scored by visual evaluation. These values were compared with and without a neck fixation device. Regarding results, the mandibular displacement and ADC were decreased, and the SNR and visual score were increased by the use of the fixation device. There is a significant difference between with and without a neck fixation device in each measurement. The technique using a neck fixation device helps improve image quality of DWIBS in the head and neck region.

Evaluating drug distribution in children and young adults with diffuse midline glioma of the pons (DIPG) treated with convection-enhanced drug delivery

AIMS

To determine an imaging protocol that can be used to assess the distribution of infusate in children with DIPG treated with CED.

METHODS

13 children diagnosed with DIPG received between 3.8 and 5.7 ml of infusate, through two pairs of catheters to encompass tumor volume on day 1 of cycle one of treatment. Volumetric T2-weighted (T2W) and diffusion-weighted MRI imaging (DWI) were performed before and after day 1 of CED. Apparent diffusion coefficient (ADC) maps were calculated. The tumor volume pre and post CED was automatically segmented on T2W and ADC on the basis of signal intensity. The ADC maps pre and post infusion were aligned and subtracted to visualize the infusate distribution.

RESULTS

There was a significant increase (p < 0.001) in mean ADC and T2W signal intensity (SI) ratio and a significant (p < 0.001) increase in mean tumor volume defined by ADC and T2W SI post infusion (mean ADC volume pre: 19.8 ml, post: 24.4 ml; mean T2W volume pre: 19.4 ml, post: 23.4 ml). A significant correlation (p < 0.001) between infusate volume and difference in ADC/T2W SI defined tumor volume was observed (ADC, r = 0.76; T2W, r = 0.70). Finally, pixel-by-pixel subtraction of the ADC maps pre and post infusion demonstrated a volume of high signal intensity, presumed infusate distribution.

CONCLUSIONS

ADC and T2W MRI are proposed as a combined parameter method for evaluation of CED infusate distribution in brainstem tumors in future clinical trials.

Comparison and combination of amide proton transfer magnetic resonance imaging and the apparent diffusion coefficient in differentiating the grades of prostate cancer

Background

More effective risk stratification of prostate cancer (PCa) than that possible with current methods can reduce undertreatment and guard against overtreatment. The aim of this study is to validate the differences and combined effects of amide proton transfer (APT) imaging and apparent diffusion coefficient (ADC) in discriminating the PCa grade group (GG) ≤2 from GG ≥3 PCa.

Methods

This is an ongoing prospective study conducted in the radiology department of Nanxishan Hospital of Guangxi Zhuang Autonomous Region. Patients pathologically diagnosed with PCa were enrolled consecutively according to the eligibility criteria. A total of 180 patients (age range, 42-92 years) were included in this study. Using histopathology as the reference standard, we placed 71 cases in GG ≤2 (mean age 67.03±8.696 years) and 109 cases in GG ≥3 (age 69.60±9.638 years). Magnetic resonance imaging (MRI) parameters, including APT and ADC values, were analyzed using an independent samples t-test and binary logistic regression analysis stratified with GG. Receiver operating characteristic curve was used to analyze the diagnostic performance for different parameters distinguishing GG ≤2 and GG ≥3.

Results

APT [odds ratio (OR) for the transitional zone (TZ) PCa: 3.20, 95% CI: 1.14-8.98, P=0.02; OR for the peripheral zone (PZ) PCa: 86.32, 95% CI: 13.24-562.88, P=0.003] and ADC values (OR for TZ PCa: 89.79; 95% CI: 2.85-2,827.99, P=0.01; OR for PZ PCa: 39.92; 95% CI: 3.22-494.18, P=0.004) were independent predictors that differentiated the GG of patients. The sensitivity and specificity of the APT values were 61.1% and 81.0%, respectively, while the sensitivity and specificity of the ADC values were 83.3% and 61.9%, respectively. The optimal cutoff value of APT was 3.35% and which of ADC was 1.25×10^-3 mm²/s in TZ origin PCa. At the optimal cutoff values of 3.31% (APT) and 0.79×10^-3 mm²/s (ADC) in PZ PCa, the sensitivity and specificity of the APT values were 74.0% and 83.6%, respectively, while the sensitivity and specificity of the ADC values were 94.0% and 53.4%, respectively. The area under the curve of the combination of APT and ADC was significantly higher than either of APT or ADC alone in Delong test (TZ: P=0.002 and P=0.020; PZ: P=0.033 and P<0.001).

Conclusions

APT and ADC have complementary effects on the sensitivity and specificity for identifying different PCa GGs. A combination model of APT and ADC could improve the diagnostic efficacy of PCa differentiation.

Diagnostic Usefulness of Diffusion-Weighted MRI for Axillary Lymph Node Evaluation in Patients with Breast Cancer

This study aimed to determine whether apparent diffusion coefficient (ADC) and morphological features on diffusion-weighted MRI (DW-MRI) can discriminate metastatic axillary lymph nodes (ALNs) from benign in patients with breast cancer. Two radiologists measured ADC, long and short diameters, long-to-short diameter ratio, and cortical thickness and assessed eccentric cortical thickening, loss of fatty hilum, irregular margin, asymmetry in shape or number, and rim sign of ALNs on DW-MRI and categorized them into benign or suspicious ALNs. Pathologic reports were used as a reference standard. Statistical analysis was performed using the Mann-Whitney U test and chi-square test. Overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of DW-MRI were calculated. The ADC of metastatic ALNs was 0.905 × 10^-3 mm²/s, and that of benign ALNs was 0.991 × 10^-3 mm²/s (p = 0.243). All morphologic features showed significant difference between the two groups. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the final categorization on DW-MRI were 77.1%, 93.3%, 79.4%, 92.5%, and 86.2%, respectively. Our results suggest that morphologic evaluation of ALNs on DWI can discriminate metastatic ALNs from benign. The ADC value of metastatic ALNs was lower than that of benign nodes, but the difference was not statistically significant.

The utility of diffusion-weighted imaging for differentiation of phyllodes tumor from fibroadenoma and breast cancer

Objective

To evaluate the utility of apparent diffusion coefficient (ADC) values for differentiating breast tumors.

Methods

The medical records of 17 patients with phyllodes tumor [PT; circular regions of interest (ROI-cs) n = 171], 74 patients with fibroadenomas (FAs; ROI-cs, n = 94), and 57 patients with breast cancers (BCs; ROI-cs, n = 104) confirmed by surgical pathology were retrospectively reviewed.

Results

There were significant differences between PTs, FAs, and BCs in ADCmean, ADCmax, and ADCmin values. The cutoff ADCmean for differentiating PTs from FAs was 1.435 × 10^-3 mm²/s, PTs from BCs was 1.100 × 10^-3 mm²/s, and FAs from BCs was 0.925 × 10^-3 mm²/s. There were significant differences between benign PTs, borderline PTs, and malignant PTs in ADCmean, ADCmax, and ADCmin values. The cutoff ADCmean for differentiating benign PTs from borderline PTs was 1.215 × 10^-3 mm²/s, and borderline PTs from malignant PTs was 1.665 × 10^-3 mm²/s.

Conclusion

DWI provides quantitative information that can help distinguish breast tumors.

Beyond Imaging and Genetic Signature in Glioblastoma: Radiogenomic Holistic Approach in Neuro-Oncology

Glioblastoma (GBM) is a malignant brain tumor exhibiting rapid and infiltrative growth, with less than 10% of patients surviving over 5 years, despite aggressive and multimodal treatments. The poor prognosis and the lack of effective pharmacological treatments are imputable to a remarkable histological and molecular heterogeneity of GBM, which has led, to date, to the failure of precision oncology and targeted therapies. Identification of molecular biomarkers is a paradigm for comprehensive and tailored treatments; nevertheless, biopsy sampling has proved to be invasive and limited. Radiogenomics is an emerging translational field of research aiming to study the correlation between radiographic signature and underlying gene expression. Although a research field still under development, not yet incorporated into routine clinical practice, it promises to be a useful non-invasive tool for future personalized/adaptive neuro-oncology. This review provides an up-to-date summary of the recent advancements in the use of magnetic resonance imaging (MRI) radiogenomics for the assessment of molecular markers of interest in GBM regarding prognosis and response to treatments, for monitoring recurrence, also providing insights into the potential efficacy of such an approach for survival prognostication. Despite a high sensitivity and specificity in almost all studies, accuracy, reproducibility and clinical value of radiomic features are the Achilles heel of this newborn tool. Looking into the future, investigators' efforts should be directed towards standardization and a disciplined approach to data collection, algorithms, and statistical analysis.

The Utility of Conventional CT, CT Perfusion and Quantitative Diffusion-Weighted Imaging in Predicting the Risk Level of Gastrointestinal Stromal Tumors of the Stomach: A Prospective Comparison of Classical CT Features, CT Perfusion Values, Apparent Diffusion Coefficient and Intravoxel Incoherent Motion-Derived Parameters

Background: The role of advanced functional imaging techniques in prediction of pathological risk categories of gastrointestinal stromal tumors (GIST) is still unknown. The purpose of this study was to evaluate classical CT features, CT-perfusion and magnetic-resonance-diffusion-weighted-imaging (MR-DWI)-related parameters in predicting the metastatic risk of gastric GIST. Patients and methods: Sixty-two patients with histologically proven GIST who underwent CT perfusion and MR-DWI using multiple b-values were prospectively included. Morphological CT characteristics and CT-perfusion parameters of tumor were comparatively analyzed in the high-risk (HR) and low-risk (LR) GIST groups. Apparent diffusion coefficient (ADC) and intravoxel-incoherent-motion (IVIM)-related parameters were also analyzed in 45 and 34 patients, respectively. Results: Binary logistic regression analysis revealed that greater tumor diameter (p < 0.001), cystic structure (p < 0.001), irregular margins (p = 0.007), irregular shape (p < 0.001), disrupted mucosa (p < 0.001) and visible EFDV (p < 0.001), as well as less ADC value (p = 0.001) and shorter time-to-peak (p = 0.006), were significant predictors of HR GIST. Multivariate analysis extracted irregular shape (p = 0.006) and enlarged feeding or draining vessels (EFDV) (p = 0.017) as independent predictors of HR GIST (area under curve (AUC) of predicting model 0.869). Conclusion: Although certain classical CT imaging features remain most valuable, some functional imaging parameters may add the diagnostic value in preoperative prediction of HR gastric GIST.

Correlation between apparent diffusion coefficient and tumor-stroma ratio in hybrid 18F-FDG PET/MRI: preliminary results of a rectal cancer cohort study

Background

To explore possible correlations between the tumor-stroma ratio (TSR) and different imaging features of fluorine-18-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (¹⁸F-FDG PET/MRI) in untreated rectal cancer patients.

Methods

A patients with rectal cancer were included in this study. All participants were examined preoperatively with whole-body ¹⁸F-FDG PET/MRI. Two pathologists evaluated the TSR of tumors together. Apparent diffusion coefficient (ADC) values and PET-related parameters of the primary lesions were measured and compared between the stroma-high and stroma-low groups. Pearson's correlation or Spearman's rank correlation were used to evaluate the correlation between the ADC values, PET-related parameters, and pathological indices.

Results

Our results showed that in the untreated rectal cancer patients, the ADC mean values correlated with the TSR (r=0.327; P=0.007), and stroma-high (low TSR) rectal cancer corresponded to relatively lower ADC mean values (813.54±88.68 vs. 879.92±133.18; P=0.018). The ADC mean and ADC minimum (ADCmin) values were found to be negatively correlated with the pathological T stages (r=-0.384, P=0.001; r=-0.416, P=0.001, respectively) as well as the largest tumor diameters (r=-0.340, P=0.005; r=-0.314, P=0.010, respectively) of rectal cancer. In addition, the pathological T stages correlated with all PET-related metabolic parameters, including mean standard uptake value (SUV), maximum SUV (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) (r=0.338, P=0.006; r=0.350, P=0.004; r=0.326, P=0.007; and r=0.472, P<0.001, respectively). Our results also identified associations between the ADCmin values and SUVmean, SUVmax, and TLG (r=-0.335, P=0.006; r=-0.343, P=0.005; and r=-0.343, P=0.005, respectively). However, there were no statistical correlations between the PET/MRI parameters and the immunohistochemical (IHC) results.

Conclusions

This study indicated that the intratumoral heterogeneity measured by PET/MRI may reflect characteristics of the tumor microenvironment. Hence, PET/MRI parameters might be helpful in predicting tumor aggressiveness and prognosis.

Radiomic Features From Diffusion-Weighted MRI of Retroperitoneal Soft-Tissue Sarcomas Are Repeatable and Exhibit Change After Radiotherapy

Background

Size-based assessments are inaccurate indicators of tumor response in soft-tissue sarcoma (STS), motivating the requirement for new response imaging biomarkers for this rare and heterogeneous disease. In this study, we assess the test-retest repeatability of radiomic features from MR diffusion-weighted imaging (DWI) and derived maps of apparent diffusion coefficient (ADC) in retroperitoneal STS and compare baseline repeatability with changes in radiomic features following radiotherapy (RT).

Materials and Methods

Thirty patients with retroperitoneal STS received an MR examination prior to treatment, of whom 23/30 were investigated in our repeatability analysis having received repeat baseline examinations and 14/30 patients were investigated in our post-treatment analysis having received an MR examination after completing pre-operative RT. One hundred and seven radiomic features were extracted from the full manually delineated tumor region using PyRadiomics. Test-retest repeatability was assessed using an intraclass correlation coefficient (baseline ICC), and post-radiotherapy variance analysis (post-RT-IMS) was used to compare the change in radiomic feature value to baseline repeatability.

Results

For the ADC maps and DWI images, 101 and 102 features demonstrated good baseline repeatability (baseline ICC > 0.85), respectively. Forty-three and 2 features demonstrated both good baseline repeatability and a high post-RT-IMS (>0.85), respectively. Pearson correlation between the baseline ICC and post-RT-IMS was weak (0.432 and 0.133, respectively).

Conclusions

The ADC-based radiomic analysis shows better test-retest repeatability compared with features derived from DWI images in STS, and some of these features are sensitive to post-treatment change. However, good repeatability at baseline does not imply sensitivity to post-treatment change.

Quantitative parameters of diffusion tensor imaging in the evaluation of carpal tunnel syndrome

BACKGROUND

To explore the value of diffusion tensor imaging (DTI)-derived metrics in quantitative evaluation of carpal tunnel syndrome (CTS).

METHODS

This prospective cross-sectional study included 39 wrists from 24 symptomatic CTS patients, who underwent clinical, electrophysiological, and magnetic resonance imaging (MRI) evaluations. In addition, 10 wrists of 6 healthy participants were included as controls. Clinical and nerve conduction study (NCS) findings were evaluated and graded according to the Boston Carpal Tunnel Questionnaire (BCTQ) and the American Association of Neuromuscular and Electrodiagnostic Medicine (AANEM), respectively. We performed MRI using a 1.5 Tesla scanner. Mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) of the median nerve at the distal radioulnar joint (DRUJ) (d), the inlet of the carpal tunnel (CT) at the pisiform level (i), the middle of the CT (m) and the outlet of the CT at the level of the hook of hamate (o), cross-sectional area at the inlet of the CT (iCSA), and the difference between MD and FA of the DRUJ and the outlet of CT (Delta MD and Delta FA) were measured.

RESULTS

The CTS patients had significantly lower FA [for example, oFA: mean difference 0.09, 95% confidence interval (CI): 0.05 to 0.12] and significantly higher MD than healthy participants (for example, iMD: mean difference 0.3, 95% CI: 0.03 to 0.57). There was a negative correlation between iCSA with iFA and between mFA and oFA (-0.5

CONCLUSIONS

The DTI-derived quantitative metrics add potential value to the evaluation of CTS. Alterations in the FA of the median nerve along the CT are the most significant features of CTS and reflect the degree of median nerve compression and clinical deficit. With a cutoff value of 0.45, FA at the carpal outlet has a sensitivity and specificity of 87.5% and 85.7% in the diagnosis of CTS, respectively.

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Key Words

• Boston Carpal Tunnel Questionnaire
• Carpal tunnel syndrome (CTS)
• apparent diffusion coefficient (ADC)
• diffusion tensor imaging (DTI)
• fraction anisotropy
• magnetic resonance imaging (MRI)
• nerve conduction study (NCS)

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Affiliation(s)

Nhu Quynh Vo Department of Radiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
Ngoc Thanh Hoang Department of Radiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
Duy Duan Nguyen Department of Internal Medicine, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
Thi Hieu Dung Nguyen Department of Physiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
Trong Binh Le Department of Radiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
Nghi Thanh Nhan Le Department of Surgery, University of Medicine and Pharmacy, Hue University, Hue, Vietnam
Thanh Thao Nguyen Department of Radiology, University of Medicine and Pharmacy, Hue University, Hue, Vietnam

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Predictive accuracy of an ADC map for hemorrhagic transformation in acute ischemic stroke patients after successful recanalization with endovascular therapy

Background

Hemorrhagic transformation (HT) of acute ischemic stroke (AIS) is associated with poor outcome. Previous studies only reported the association of mean ischemic severity or total infarct volume with HT after endovascular therapy (EVT). We aimed to investigate the predictive value of preoperative apparent diffusion coefficient (ADC) map for HT by combinated ischemic severity and corresponding volume in AIS after successful recanalization with EVT.

Methods

We retrospectively analyzed 119 consecutive cases of AIS with large vessel occlusion of anterior circulation within 24 hours after symptom onset and successful recanalization after EVT. All cases had baseline magnetic resonance imaging (MRI), follow-up computed tomography (CT), and magnetic resonance angiography (MRA) or computed tomography angiography (CTA). Volumes of ADC <0.6×10⁻³, 0.5×10⁻³, 0.4×10⁻³, and 0.3×10⁻³ mm²/s, baseline characteristics and outcomes of patients with and without HT identified by European Collaborative Acute Stroke Study (ECASS) were compared. The optimal ADC and volume threshold for predicting HT were analyzed using receiver operating characteristic (ROC) curve, and multivariate logistic regression analysis were performed with clinical characteristics and volumes of optimal ADC threshold to determine risk factors for HT.

Results

Among 119 patients, 42 patients had HT on follow-up CT, including 24 hemorrhagic infarct (HI) cases and 18 parenchymal hematoma (PH) cases. The optimal volumes were 6.46 mL with ADC <0.4×10⁻³ mm²/s for predicting both HT and PH, with a larger area under curve (AUC) of 83.3% for HT than that for PH of 80%. In logistic regression analysis, intravenous tissue plasminogen activator (IV tPA) treatment, atrial fibrillation, and volume of ADC <0.4×10⁻³ mm²/s were identified as independent predictors for HT and volume of ADC <0.4×10⁻³ mm²/s had the highest odds ratio (OR) value.

Conclusions

The combination of ischemic severity and corresponding volume in ADC map may predict HT after thrombectomy. In addition to the total infarct volume, volume with severe ischemia should be taken into consideration in preoperative patient selection.

Dynamic brain ADC variations over the cardiac cycle and their relation to tissue strain assessed with DENSE at high-field MRI

Purpose

The ADC of brain tissue slightly varies over the cardiac cycle. This variation could reflect physiology, including mixing of the interstitial fluid, relevant for brain waste clearance. However, it is known from cardiac diffusion imaging that tissue deformation by itself affects the magnitude of the MRI signal, leading to artificial ADC variations as well. This study investigates to what extent tissue deformation causes artificial ADC variations in the brain.

Theory and Methods

We implemented a high‐field MRI sequence with stimulated echo acquisition mode that simultaneously measures brain tissue deformation and ADC. Based on the measured tissue deformation, we simulated the artificial ADC variation by combining established theoretical frameworks and compared the results with the measured ADC variation. We acquired data in 8 healthy volunteers with diffusion weighting b = 300 and b = 1000 s/mm².

Results

Apparent diffusion coefficient variation was largest in the feet‐to‐head direction and showed the largest deviation from the mean ADC at peak systole. Artificial ADC variation estimated from tissue deformation was 1.3 ± 0.37·10⁻⁵ mm²/s in the feet‐to‐head direction for gray matter, and 0.75 ± 0.29·10⁻⁵ mm²/s for white matter. The measured ADC variation in the feet‐to‐head direction was 5.6·10⁻⁵ ± 1.5·10⁻⁵ mm²/s for gray matter and 3.2·10⁻⁵ ± 1.0·10⁻⁵ mm²/s for white matter, which was a factor of 3.5 ± 0.82 and 3.4 ± 0.57 larger than the artificial diffusion variations. The measured diffusion variations in the right‐to‐left/anterior‐to‐posterior direction were a factor of 1.5 ± 1.0/1.7 ± 1.4 and 2.0 ± 0.91/2.5 ± 0.94 larger than the artificial diffusion variations for gray matter and white matter, respectively.

Conclusion

Apparent diffusion coefficient variations in the brain likely largely reflect physiology.

Impact of Alternate b-Value Combinations and Metrics on the Predictive Performance and Repeatability of Diffusion-Weighted MRI in Breast Cancer Treatment: Results from the ECOG-ACRIN A6698 Trial

In diffusion-weighted MRI (DW-MRI), choice of b-value influences apparent diffusion coefficient (ADC) values by probing different aspects of the tissue microenvironment. As a secondary analysis of the multicenter ECOG-ACRIN A6698 trial, the purpose of this study was to investigate the impact of alternate b-value combinations on the performance and repeatability of tumor ADC as a predictive marker of breast cancer treatment response. The final analysis included 210 women who underwent standardized 4-b-value DW-MRI (b = 0/100/600/800 s/mm2) at multiple timepoints during neoadjuvant chemotherapy treatment and a subset (n = 71) who underwent test−retest scans. Centralized tumor ADC and perfusion fraction (fp) measures were performed using variable b-value combinations. Prediction of pathologic complete response (pCR) based on the mid-treatment/12-week percent change in each metric was estimated by area under the receiver operating characteristic curve (AUC). Repeatability was estimated by within-subject coefficient of variation (wCV). Results show that two-b-value ADC calculations provided non-inferior predictive value to four-b-value ADC calculations overall (AUCs = 0.60−0.61 versus AUC = 0.60) and for HR+/HER2− cancers where ADC was most predictive (AUCs = 0.75−0.78 versus AUC = 0.76), p < 0.05. Using two b-values (0/600 or 0/800 s/mm2) did not reduce ADC repeatability over the four-b-value calculation (wCVs = 4.9−5.2% versus 5.4%). The alternate metrics ADCfast (b ≤ 100 s/mm2), ADCslow (b ≥ 100 s/mm2), and fp did not improve predictive performance (AUCs = 0.54−0.60, p = 0.08−0.81), and ADCfast and fp demonstrated the lowest repeatability (wCVs = 6.71% and 12.4%, respectively). In conclusion, breast tumor ADC calculated using a simple two-b-value approach can provide comparable predictive value and repeatability to full four-b-value measurements as a marker of treatment response.

Complete Response Evaluation of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy Using Textural Features Obtained from T2 Weighted Imaging and ADC Maps

BACKGROUND

The prediction of pathological responses for locally advanced rectal cancer using magnetic resonance imaging (MRI) after neoadjuvant chemoradiotherapy (CRT) is a challenging task for radiologists, as residual tumor cells can be mistaken for fibrosis. Texture analysis of MR images has been proposed to understand the underlying pathology.

OBJECTIVE

This study aimed to assess the responses of lesions to CRT in patients with locally advanced rectal cancer using the first-order textural features of MRI T2-weighted imaging (T2-WI) and apparent diffusion coefficient (ADC) maps.

METHODS

Forty-four patients with locally advanced rectal cancer (median age: 57 years) who underwent MRI before and after CRT were enrolled in this retrospective study. The first-order textural parameters of tumors on T2-WI and ADC maps were extracted. The textural features of lesions in pathologic complete responders were compared to partial responders using Student's t- or Mann-Whitney U tests. A comparison of textural features before and after CRT for each group was performed using the Wilcoxon rank sum test. Receiver operating characteristic curves were calculated to detect the diagnostic performance of the ADC.

RESULTS

Of the 44 patients evaluated, 22 (50%) were placed in a partial response group and 50% were placed in a complete response group. The ADC changes of the complete responders were statistically more significant than those of the partial responders (P = 0.002). Pathologic total response was predicted with an ADC cut-off of 1310 x 10-6 mm2/s, with a sensitivity of 72%, a specificity of 77%, and an accuracy of 78.1% after neoadjuvant CRT. The skewness of the T2-WI before and after neoadjuvant CRT showed a significant difference in the complete response group compared to the partial response group (P = 0.001 for complete responders vs. P = 0.482 for partial responders). Also, relative T2-WI signal intensity in the complete response group was statistically lower than that of the partial response group after neoadjuvant CRT (P = 0.006).

CONCLUSION

As a result of the conversion of tumor cells to fibrosis, the skewness of the T2-WI before and after neoadjuvant CRT was statistically different in the complete response group compared to the partial response group, and the complete response group showed statistically lower relative T2-WI signal intensity than the partial response group after neoadjuvant CRT. Additionally, the ADC cut-off value of 1310 × 10-6 mm2/s could be used as a marker for complete response along with absolute ADC value changes within this dataset.

The role of "penumbra sign" and diffusion-weighted imaging in adnexal masses: do they provide a clue in differentiating tubo-ovarian abscess from ovarian malignancy?

Purpose

To evaluate the role of “penumbra sign”, diffusion-weighted imaging (DWI), and the apparent diffusion coefficient (ADC) value in differentiating tubo-ovarian abscess (TOA) from ovarian malignancy.

Material and methods

Thirty-six patients with 50 adnexal masses (tubo-ovarian abscess, n = 24; ovarian malignancy, n = 26), who underwent magnetic resonance imaging (MRI) with DWI, were retrospectively evaluated. “Penumbra sign” (hyperintense rim on T1W images), diffusion restriction, and mean apparent diffusion coefficient (ADC) values from cystic (c-ADC) and solid (s-ADC) components were evaluated for all the masses.

Results

“Penumbra sign” on T1W images was significantly more common in the TOA group (n = 21, 87.5%) than in the ovarian malignancy group (n = 2, 7.7%) (p < 0.001). Similarly, diffusion restriction in the cystic component was more frequent in the TOA group (n = 24, 100% vs. n = 2, 10.5%; p < 0.001). In contrast, diffusion restriction in the solid component was more common in the ovarian malignancy group (n = 5, 20.8% vs. n = 26, 100%; p < 0.001). The mean c-ADC value was significantly lower in TOAs (p < 0.001). A c-ADC value of 1.31 × 10^-3 mm²/s may be an optimal cut-off in distinguishing TOAs from ovarian malignancies. Conversely, the mean s-ADC value was significantly lower in the ovarian malignancy group (p < 0.001). An s-ADC value of 0.869 × 10^-3 mm²/s may be an optimal cut-off in differentiating ovarian malignancies from TOAs (p < 0.001). ROC curve analysis showed that c-ADC values had a higher diagnostic accuracy than s-ADC values.

Conclusions

“Penumbra sign” on T1W images, diffusion characteristics, and ADC values provide important clues in addition to conventional MR imaging features in differentiating TOA from ovarian malignancy.

Value of diffusion-weighted imaging in preoperative evaluation and prediction of postoperative supplementary therapy for patients with cervical cancer

Background

With the continuous progress of medical imaging technology, evaluation of cervical cancer is increasingly dependent on imaging methods. Diffusion-weighted imaging (DWI) plays an important role, and apparent diffusion coefficient (ADC) value is a unique quantitative parameter in the research of cervical cancer.

Methods

In this prospective study, a total of 273 patients diagnosed with stage IB1 to IIIC1 cervical cancer based on the International Federation of Gynecology and Obstetrics (FIGO) 2018 staging guidelines who underwent pelvic 3.0T magnetic resonance imaging (MRI), including MRI and DWI, were enrolled, and the diagnostic value of preoperative staging of cervical cancer was compared between the MRI and DWI groups. The DWI group was used to explore the potential association of mean ADC (ADC_mean) with different pathological characteristics and receiver operating characteristic (ROC) curves of ADC_mean generated to predict the appropriate postoperative supplementary therapy.

Results

The diagnostic coincidence rate of DWI was higher than that of MRI in preoperative staging of cervical cancer (χ², P<0.05) and determined as stages IB1 + IB2 + IIA1 (90.91%), IB3 + IIA2 (93.48%), and IIIC1p (95.16%). The DWI staging results were consistent with postoperative pathological staging (Kappa value =0.865, P<0.001). We observed significant differences in ADC_mean values in relation to pathological type, histological grade, depth of stromal infiltration, tumor diameter, lymphovascular invasion, and pelvic lymph node metastasis of cervical cancer (all P<0.05). The area under the ROC curve (AUC) was 0.815, with the best predictive value for postoperative supplementary therapy in cervical cancer (sensitivity 80.0%, specificity 74.0%) at ADC_mean of 0.910×10^-3 mm²/s.

Conclusions

The DWI is a useful tool for preoperative evaluation of cervical cancer. In local cervical lesions, ADC_mean varies in relation to different clinicopathological characteristics and a reference index of <0.910×10^-3 mm²/s can be effectively applied to predict the need for postoperative supplementary therapy.

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

BACKGROUND

To investigate the imaging findings and visibility of breast invasive lobular carcinoma (ILC) on diffusion-weighted imaging (DWI) and compare quantitative apparent diffusion coefficient (ADC) metrics of ILC and invasive carcinoma of no special type (NST) using a histogram analysis.

METHODS

We performed an observational retrospective study of 629 consecutive women with pathologically proven ILC and invasive ductal carcinoma of NST, who underwent 3-T MRI including DWI, between January 2017 and August 2020.

RESULTS

After propensity score matching, 71 women were allocated to each group. On DWI, 9 (12.7%) lesions of ILC and 4 (5.6%) invasive carcinomas of the NST were not visualized. For the tumor visibility on DWI, tumor size, tumor ADC value, and background diffusion grade were significantly associated with the visibility score in both groups (all P<0.05), whereas the mean background ADC value was not significant (P>0.05). The mean ADC (1.226×10^-3 vs. 1.052×10^-3 mm²/s, P<0.001), median ADC (1.222×10^-3 vs. 1.051×10^-3 mm²/s, P=0.002), maximum ADC (1.758×10^-3 vs. 1.504×10^-3 mm²/s, P<0.001), minimum ADC (0.717×10^-3 vs. 0.649×10^-3 mm²/s, P=0.003), 90th percentile ADC (1.506×10^-3 vs. 1.292×10^-3 mm²/s, P<0.001) and 10th percentile ADC (0.956×10^-3 vs. 0.818×10^-3 mm²/s, P=0.008) were higher in ILC than in invasive carcinoma of NST. Additionally, the ADC difference value of the ILC was higher than that of invasive carcinoma of NST (1.04×10^-3 vs. 0.855×10^-3 mm²/s, P=0.027).

CONCLUSIONS

On DWI, the visibility of ILC was lower compared to invasive carcinoma of NST. ILC showed higher quantitative ADC values and higher ADC difference values.

Texture and shape analysis of diffusion-weighted imaging for thyroid nodules classification using machine learning

PURPOSE

To assess whether the integration between (a) functional imaging features that will be extracted from diffusion-weighted imaging (DWI); and (b) shape and texture imaging features as well as volumetric features that will be extracted from T2-weighted magnetic resonance imaging (MRI) can noninvasively improve the diagnostic accuracy of thyroid nodules classification.

PATIENTS AND METHODS

In a retrospective study of 55 patients with pathologically proven thyroid nodules, T2-weighted and diffusion-weighted MRI scans of the thyroid gland were acquired. Spatial maps of the apparent diffusion coefficient (ADC) were reconstructed in all cases. To quantify the nodules' morphology, we used spherical harmonics as a new parametric shape descriptor to describe the complexity of the thyroid nodules in addition to traditional volumetric descriptors (e.g., tumor volume and cuboidal volume). To capture the inhomogeneity of the texture of the thyroid nodules, we used the histogram-based statistics (e.g., kurtosis, entropy, skewness, etc.) of the T2-weighted signal. To achieve the main goal of this paper, a fusion system using an artificial neural network (NN) is proposed to integrate both the functional imaging features (ADC) with the structural morphology and texture features. This framework has been tested on 55 patients (20 patients with malignant nodules and 35 patients with benign nodules), using leave-one-subject-out (LOSO) for training/testing validation tests.

RESULTS

The functionality, morphology, and texture imaging features were estimated for 55 patients. The accuracy of the computer-aided diagnosis (CAD) system steadily improved as we integrate the proposed imaging features. The fusion system combining all biomarkers achieved a sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and accuracy of 92.9 % (confidence interval [CI]: 78.9 % -- 99.5 % ), 95.8 % (CI: 87.4 % -- 99.7 % ), 93 % (CI: 80.7 % -- 99.5 % ), 96 % (CI: 88.8 % -- 99.7 % ), 92.8 % (CI: 83.5 % -- 98.5 % ), and 95.5 % (CI: 88.8 % -- 99.2 % ), respectively, using the LOSO cross-validation approach.

CONCLUSION

The results demonstrated in this paper show the promise that integrating the functional features with morphology as well as texture features by using the current state-of-the-art machine learning approaches will be extremely useful for identifying thyroid nodules as well as diagnosing their malignancy.

Neuroimaging Features of Optic Nerve Hemangioblastoma Identified by Conventional and Advanced Magnetic Resonance Techniques: A Case Report and Literature Review

Optic nerve hemangioblastoma is a very rare benign tumor with only 39 reported cases by now. It appears to be hyperintense on T2-weighted images with a significant enhancement on contrast scans, which are similar to glioma and meningioma. Due to the lack of specificity in MRI manifestations, optic nerve hemangioblastoma is often misdiagnosed. To provide new insights into differential diagnosis of optic nerve hemangioblastoma, we report for the first time an optic nerve hemangioblastoma case employing advanced magnetic resonance techniques including diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and magnetic resonance angiography (MRA). In addition, we have collected all reported optic nerve hemangioblastoma cases and reviewed their neuroimaging findings by MRI and angiography. Our results show that solid-type tumor is the dominant form of optic nerve hemangioblastoma and extensive edema is widely observed. These findings are surprisingly contrary to manifestations of cerebellar hemangioblastoma. Besides the structural features, quantitative indexes including ADC and relative cerebral blood volume (rCBV) ratio, which are significantly elevated in cerebellar hemangioblastoma, may also shed a light on the preoperative diagnosis of hemangioblastoma of optic nerve. Finally, we discuss the critical neuroimaging features in the differential diagnosis between optic nerve hemangioblastoma from optic pathway glioma and optic nerve sheath meningioma.

On the Additional Information Provided by 3T-MRI ADC in Predicting Tumor Cellularity and Microscopic Behavior

BACKGROUND

to evaluate whether Apparent Diffusion Coefficient (ADC) values of invasive breast cancer, provided by 3T Diffusion Weighted-Images (DWI), may represent a non-invasive predictor of pathophysiologic tumor aggressiveness.

METHODS

100 Patients with histologically proven invasive breast cancers who underwent a 3T-MRI examination were included in the study. All MRI examinations included dynamic contrast-enhanced and DWI/ADC sequences. ADC value were calculated for each lesion. Tumor grade was determined according to the Nottingham Grading System, and immuno-histochemical analysis was performed to assess molecular receptors, cellularity rate, on both biopsy and surgical specimens, and proliferation rate (Ki-67 index). Spearman's Rho test was used to correlate ADC values with histological (grading, Ki-67 index and cellularity) and MRI features. ADC values were compared among the different grading (G1, G2, G3), Ki-67 (<20% and >20%) and cellularity groups (<50%, 50-70% and >70%), using Mann-Whitney and Kruskal-Wallis tests. ROC curves were performed to demonstrate the accuracy of the ADC values in predicting the grading, Ki-67 index and cellularity groups.

RESULTS

ADC values correlated significantly with grading, ER receptor status, Ki-67 index and cellularity rates. ADC values were significantly higher for G1 compared with G2 and for G1 compared with G3 and for Ki-67 < 20% than Ki-67 > 20%. The Kruskal-Wallis test showed that ADC values were significantly different among the three grading groups, the three biopsy cellularity groups and the three surgical cellularity groups. The best ROC curves were obtained for the G3 group (AUC of 0.720), for G2 + G3 (AUC of 0.835), for Ki-67 > 20% (AUC of 0.679) and for surgical cellularity rate > 70% (AUC of 0.805).

CONCLUSIONS

3T-DWI ADC is a direct predictor of cellular aggressiveness and proliferation in invasive breast carcinoma, and can be used as a supporting non-invasive factor to characterize macroscopic lesion behavior especially before surgery.

Use of diffusion-weighted magnetic resonance imaging (DW-MRI) to predict early response to anti-tumor therapy in advanced non-small cell lung cancer (NSCLC): a comparison of intravoxel incoherent motion-derived parameters and apparent diffusion coefficient

Background

The intravoxel incoherent motion (IVIM) method of magnetic resonance imaging (MRI) analysis can provide information regarding many physiological and pathological processes. This study aimed to investigate whether IVIM-derived parameters and the apparent diffusion coefficient (ADC) can act as imaging biomarkers for predicting non-small cell lung cancer (NSCLC) response to anti-tumor therapy and compare their performances.

Methods

This prospective study included 45 patients with NSCLC treated with chemotherapy (29 men and 16 women, mean age 57.9±9.7 years). Diffusion-weighted imaging was performed with 13 b-values before and 2–4 weeks after treatment. The IVIM parameter pseudo-diffusion coefficient (D*), perfusion fraction (f), diffusion coefficient (D), and ADC from a mono-exponential model were obtained. Responses 2 months after chemotherapy were assessed. The diagnostic performance was evaluated, and optimal cut-off values were determined by receiver operating characteristic (ROC) curve analysis, and the differences of progression-free survival (PFS) in groups of responders and non-responders were tested by Cox regression and Kaplan-Meier survival analyses.

Results

Of 45 patients, 30 (66.7%) were categorized as responders, and 15 as non-responders. Differences in the diffusion coefficient D and ADC between responders and non-responders were statistically significant (all P<0.05). Conversely, differences in f and D* between responders and non-responders were both not statistically significance (all P>0.05). The ROC analyses showed the change in D value (ΔD) was the best predictor of early response to anti-tumor therapy [area under the ROC curve (AUC), 0.764]. The Cox-regression model showed that all ADC and D parameters were independent predictors of PFS, with a range of reduction in risk from 56.2% to 82.7%, and ΔD criteria responders had the highest reduction (82.7%).

Conclusions

ADC and D derived from IVIM are potentially useful for the prediction of NSCLC treatment response to anti-tumor therapy. Although ΔD is best at predicting response to treatment, ΔADC measurement may simplify manual efforts and reduce the workload.

An Integrated Radiomics Model Incorporating Diffusion-Weighted Imaging and 18F-FDG PET Imaging Improves the Performance of Differentiating Glioblastoma From Solitary Brain Metastases

BACKGROUND

The effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM.

METHODS

One hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set (n = 80) or validation set (n = 20). A total of 4,424 radiomic features were obtained from contrast-enhanced T1-weighted imaging (CE-T1WI) with the contrast-enhancing and peri-enhancing edema region, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images. The partial least squares (PLS) regression with fivefold cross-validation is used to analyze the correlation between different radiomic features and different modalities. The cross-validity analysis was performed to judge whether a new principal component or a new feature dimension can significantly improve the final prediction effect. The principal components with effective interpretation in all radiomic features were projected to a low-dimensional space (2D in this study). The effective features of the new projection mapping were then sent to the random forest classifier to predict the results. The performance of differentiating GBM from SBM was compared between the integrated radiomics model and other radiomics models or nonradiomics methods using the area under the receiver operating characteristics curve (AUC).

RESULTS

Through the cross-validity analysis of partial least squares, hundreds of radiomic features were projected into a new two-dimensional space to complete the construction of radiomics model. Compared with the combined radiomics model using DWI + 18F-FDG PET (AUC = 0.93, p = 0.014), cMRI + DWI (AUC = 0.89, p = 0.011), cMRI + 8F-FDG PET (AUC = 0.91, p = 0.015), and single radiomics model using cMRI (AUC = 0.85, p = 0.018), DWI (AUC = 0.84, p = 0.017), and 18F-FDG PET (AUC = 0.85, p = 0.421), the integrated radiomics model (AUC = 0.98) showed more efficient diagnostic performance. The integrated radiomics model (AUC = 0.98) also showed significantly better performance than any single ADC, SUV, or TBR parameter (AUC = 0.57-0.71, p < 0.05). The integrated radiomics model showed better performance in the training (AUC = 0.98) and validation (AUC = 0.93) sets than any other models and methods, demonstrating robustness.

CONCLUSIONS

We developed an integrated radiomics model incorporating DWI and 18F-FDG PET, which improved the performance of differentiating GBM from SBM greatly.

The correlation between magnetic resonance diffusion parameters and Ki-67 and PCNA in hepatic fibrosis and cirrhosis rats

BACKGROUND

The aim of this study was to assess the value of different 1.5 T MRI apparent diffusion coefficient (ADC) and exponential apparent diffusion coefficient (EADC) values in diagnosing the stages of liver cirrhosis. Sprague-Dawley (SD) rats were randomly divided into the experimental group and the control group.

METHODS

The experimental group was injected with thioacetamide intraperitoneally 3 times per week. After routine MR scanning, diffusion-weighted imaging (DWI) was processed by spin echo-echo planar imaging (SE-EPI) to generate the ADC value and EADC image. The liver ADC and EADC values of rats were measured in the control and experimental groups, followed by Masson staining and hematoxylin and eosin staining. Furthermore, immunohistochemistry was performed to detect Ki-67 and PCNA expression in liver tissues.

RESULTS

In the control group, the differences in ADC and EADC values between the liver fibrosis and cirrhosis group were different. The ADC values of the liver fibrosis stage I-II, III-IV, and cirrhosis rats in the experimental group were lower than the control group, while the EADC values were higher than the control group. The ADC values of the liver fibrosis stage III-IV group and cirrhosis nodules group were lower than the control group. There were significant differences in EADC values between the cirrhotic nodule groups and the control group.

CONCLUSIONS

DWI-ADC values showed a negative correlation between SD rat liver fibrosis and cirrhosis pathology classification.

Feasibility of diffusion weighting with a local inside-out nonlinear gradient coil for prostate MRI

PURPOSE

Prostate cancer remains the 2nd leading cancer killer of men, yet it is also a disease with a high rate of overtreatment. Diffusion weighted imaging (DWI) has shown promise as a reliable, grade-sensitive imaging method, but it is limited by low image quality. Currently, DWI quality image is directly related to low gradient amplitudes, since weak gradients must be compensated with long echo times.

METHODS

We propose a new type of MRI accessory, an "inside-out" and nonlinear gradient, whose sole purpose is to deliver diffusion encoding to a region of interest. Performance was simulated in OPERA and the resulting fields were used to simulate DWI with two compartment and kurtosis models. Experiments with a nonlinear head gradient prove the accuracy of DWI and ADC maps diffusion encoded with nonlinear gradients.

RESULTS

Simulations validated thermal and mechanical safety while showing a 5 to 10-fold increase in gradient strength over prostate. With these strengths, lesion CNR in ADC maps approximately doubled for a range of anatomical positions. Proof-of-principle experiments show that spatially varying b-values can be corrected for accurate DWI and ADC.

CONCLUSIONS

Dedicated nonlinear diffusion encoding hardware could improve prostate DWI.

How to Discriminate Lung Cancer From Benign Pulmonary Nodules and Masses? Usefulness of Diffusion-Weighted Magnetic Resonance Imaging With Apparent Diffusion Coefficient and Inside/Wall Apparent Diffusion Coefficient Ratio

Background:

Although diffusion-weighted imaging (DWI) is useful for differential diagnosis between lung cancers and benign pulmonary nodules and masses (BPNMs), it is difficult to differentiate pulmonary abscesses from lung cancers because pulmonary abscesses show restricted diffusion. With this research we will present how to assess the total apparent diffusion coefficient (ADC) and inside/wall ADC ratio for these pulmonary nodules and masses (PNMs).

Methods:

The pulmonary lesions were divided into next 3 groups. There were 40 lung cancers, 41 inflammatory benign PNMs (mycobacteria disease 13, pneumonia 12, pulmonary abscess 10, other 6) and 7 noninflammatory benign PNMs. Definitions were as follows: wall ADC = ADC value in outer one-third of the lesion; inside ADC = ADC value in central two-thirds of the lesion: inside/wall ADC ratio = ratio of inside ADC/wall ADC.

Results:

Mean total ADC (1.26 ± 0.32 × 10⁻³ mm²/s) of the lung cancers was remarkably lower than that (1.53 ± 0.53) of the BPNMs. The mean total ADC values were 1.26 ± 0.32 in lung cancer, 1.45 ± 0.47 in inflammatory BPNM and 2.04 ± 0.63 in noninflammatory BPNM, and there were significant differences among them. The mean inside ADC value (1.33 ± 0.32) of the lung cancers was remarkably higher than that (0.94 ± 0.42) of the pulmonary abscesses. The mean inside/wall ADC ratio (1.20 ± 0.28) of the lung cancers was remarkably higher than that (0.74 ± 0.14) of the pulmonary abscesses.

Conclusions:

Although ADC of DWI could differentiate lung cancer from BPNM, the inside/wall ADC ratio of DWI is efficient for differentiation between lung cancer and lung abscess. The inside/wall ADC ratio of DWI strengthens a weak point of DWI.

A Methodology to Compare Biomechanical Simulations With Clinical Brain Imaging Analysis Utilizing Two Blunt Impact Cases

According to the US Defense and Veterans Brain Injury Center (DVBIC) and Centers for Disease Control and Prevention (CDC), mild traumatic brain injury (mTBI) is a common form of head injury. Medical imaging data provides clinical insight into tissue damage/injury and injury severity, and helps medical diagnosis. Computational modeling and simulation can predict the biomechanical characteristics of such injury, and are useful for development of protective equipment. Integration of techniques from computational biomechanics with medical data assessment modalities (e.g., magnetic resonance imaging or MRI) has not yet been used to predict injury, support early medical diagnosis, or assess effectiveness of personal protective equipment. This paper presents a methodology to map computational simulations with clinical data for interpreting blunt impact TBI utilizing two clinically different head injury case studies. MRI modalities, such as T1, T2, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC), were used for simulation comparisons. The two clinical cases have been reconstructed using finite element analysis to predict head biomechanics based on medical reports documented by a clinician. The findings are mapped to simulation results using image-based clinical analyses of head impact injuries, and modalities that could capture simulation results have been identified. In case 1, the MRI results showed lesions in the brain with skull indentation, while case 2 had lesions in both coup and contrecoup sides with no skull deformation. Simulation data analyses show that different biomechanical measures and thresholds are needed to explain different blunt impact injury modalities; specifically, strain rate threshold corresponds well with brain injury with skull indentation, while minimum pressure threshold corresponds well with coup–contrecoup injury; and DWI has been found to be the most appropriate modality for MRI data interpretation. As the findings from these two cases are substantiated with additional clinical studies, this methodology can be broadly applied as a tool to support injury assessment in head trauma events and to improve countermeasures (e.g., diagnostics and protective equipment design) to mitigate these injuries.

Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection

Simple Summary

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules and masses. However, it is difficult to differentiate pulmonary abscesses and mycobacterium infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The purpose of this study was to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers and 19 PAMIs. Parameters more than 60% of AUC were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

Abstract

Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules. However, it is difficult to differentiate pulmonary abscesses and mycobacterial infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The study purpose is to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers (25 adenocarcinomas, 16 squamous cell carcinomas), and 19 PAMIs (9 pulmonary abscesses, 10 mycobacterial infections). Parameters more than 60% of the area under the ROC curve (AUC) were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. The ADC (1.19 ± 0.29 × 10⁻³ mm²/s) of lung cancer obtained from a single slice was significantly lower than that (1.44 ± 0.54) of PAMI (p = 0.0262). In contrast, mean, median, or most frequency ADC of lung cancer which was obtained in the ADC histogram was significantly higher than the value of each parameter of PAMI. ADC histogram could discriminate PAMIs from lung cancers by showing that AUCs of several parameters were more than 60%, and that several parameters of ADC of PAMI were significantly lower than those of lung cancer. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.

Combination Assessment of Diffusion-Weighted Imaging and T2-Weighted Imaging Is Acceptable for the Differential Diagnosis of Lung Cancer from Benign Pulmonary Nodules and Masses

Simple Summary

The purpose of this study is to determine whether the combination assessment of DWI and T2WI improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). As using the OCV (1.470 × 10⁻³ mm²/s) for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV (2.45) for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.

Abstract

The purpose of this study is to determine whether the combination assessment of DWI and T2-weighted imaging (T2WI) improves the diagnostic ability for differential diagnosis of lung cancer from benign pulmonary nodules and masses (BPNMs). The optimal cut-off value (OCV) for differential diagnosis was set at 1.470 × 10⁻³ mm²/s for apparent diffusion coefficient (ADC), and at 2.45 for T2 contrast ratio (T2 CR). The ADC (1.24 ± 0.29 × 10⁻³ mm²/s) of lung cancer was significantly lower than that (1.69 ± 0.58 × 10⁻³ mm²/s) of BPNM. The T2 CR (2.01 ± 0.52) of lung cancer was significantly lower than that (2.74 ± 1.02) of BPNM. As using the OCV for ADC, the sensitivity was 83.9% (220/262), the specificity 63.4% (33/52), and the accuracy 80.6% (253/314). As using the OCV for T2 CR, the sensitivity was 89.7% (235/262), the specificity 61.5% (32/52), and the accuracy 85.0% (267/314). In 212 PNMs which were judged to be malignant by both DWI and T2WI, 203 PNMs (95.8%) were lung cancers. In 33 PNMs which were judged to be benign by both DWI and T2WI, 23 PNMs (69.7%) were BPNMs. The combined assessment of DWI and T2WI could judge PNMs more precisely and would be acceptable for differential diagnosis of PNMs.

The Potential Role of Peritumoral Apparent Diffusion Coefficient Evaluation in Differentiating Glioblastoma and Solitary Metastatic Lesions of the Brain

OBJECTIVE

Differentiating glioblastoma (GBM) and solitary metastasis is not always possible using conventional magnetic resonance imaging (MRI) techniques. In conventional brain MRI, GBM and brain metastases are lesions with mostly similar imaging findings. In this study, we investigated whether apparent diffusion coefficient (ADC) ratios, ADC gradients, and minimum ADC values in the peritumoral edema tissue can be used to discriminate between these two tumors.

METHODS

This retrospective study was approved by the local institutional review board with a waiver of written informed consent. Prior to surgical and medical treatment, conventional brain MRI and diffusion-weighted MRI (b = 0 and b = 1000) images were taken from 43 patients (12 GBM and 31 solitary metastasis cases). Quantitative ADC measurements were performed on the peritumoral tissue from the nearest segment to the tumor (ADC1), the middle segment (ADC2), and the most distant segment (ADC3). The ratios of these three values were determined proportionally to calculate the peritumoral ADC ratios. In addition, these three values were subtracted from each other to obtain the peritumoral ADC gradients. Lastly, the minimum peritumoral and tumoral ADC values, and the quantitative ADC values from the normal appearing ipsilateral white matter, contralateral white matter and ADC values from cerebrospinal fluid (CSF) were recorded.

RESULTS

For the differentiation of GBM and solitary metastasis, ADC3 / ADC1 was the most powerful parameter with a sensitivity of 91.7% and specificity of 87.1% at the cut-off value of 1.105 (p < 0.001), followed by ADC3 / ADC2 with a cut-off value of 1.025 (p = 0.001), sensitivity of 91.7%, and specificity of 74.2%. The cut-off, sensitivity and specificity of ADC2 / ADC1 were 1.055 (p = 0.002), 83.3%, and 67.7%, respectively. For ADC3 - ADC1, the cut-off value, sensitivity and specificity were calculated as 150 (p < 0.001), 91.7% and 83.9%, respectively. ADC3 - ADC2 had a cut-off value of 55 (p = 0.001), sensitivity of 91.7%, and specificity of 77.4 whereas ADC2 - ADC1 had a cut-off value of 75 (p = 0.003), sensitivity of 91.7%, and specificity of 61.3%. Among the remaining parameters, only the ADC3 value successfully differentiated between GBM and metastasis (GBM 1802.50 ± 189.74 vs. metastasis 1634.52 ± 212.65, p = 0.022).

CONCLUSION

The integration of the evaluation of peritumoral ADC ratio and ADC gradient into conventional MR imaging may provide valuable information for differentiating GBM from solitary metastatic lesions.

[A Proposal for the Optimal Material for Evaluating Diffusion-weighted Image by Using Solid Phantom]

Recently, diffusion-weighted imaging (DWI) has become essential for diagnosing acute cerebral infractions and detecting lesions via magnetic resonance imaging (MRI). Investigations using phantoms have been performed to evaluate the optimizing parameters before clinical practice. However, there have been no studies on extracting appropriate phantom materials. It is known that the apparent diffusion coefficient (ADC) changes with temperature. To extract optimal materials from polyethylene glycol, sucrose, and dextrin in previous studies, evaluations were performed using ADC with temperature change and signal-to-noise ratio (SNR) . Results of comparison with difference between true and measured values depend on the Stokes-Einstein formula for ADC change with temperature change; the highest value was obtained for polyethylene glycol. In the SNR measurement, when the temperature increased, the rate of change of ADC decreased. Polyethylene glycol showed the highest value. According to these results, it can be concluded that polyethylene glycol can be extracted when nearest to true value and when there is a high SNR, thus making polyethylene glycol the most suitable material for diffusion-weighted image phantoms.

Factors Affecting Image Quality and Lesion Evaluability in Breast Diffusion-weighted MRI: Observations from the ECOG-ACRIN Cancer Research Group Multisite Trial (A6702)

OBJECTIVE

The A6702 multisite trial confirmed that apparent diffusion coefficient (ADC) measures can improve breast MRI accuracy and reduce unnecessary biopsies, but also found that technical issues rendered many lesions non-evaluable on diffusion-weighted imaging (DWI). This secondary analysis investigated factors affecting lesion evaluability and impact on diagnostic performance.

METHODS

The A6702 protocol was IRB-approved at 10 institutions; participants provided informed consent. In total, 103 women with 142 MRI-detected breast lesions (BI-RADS assessment category 3, 4, or 5) completed the study. DWI was acquired at 1.5T and 3T using a four b-value, echo-planar imaging sequence. Scans were reviewed for multiple quality factors (artifacts, signal-to-noise, misregistration, and fat suppression); lesions were considered non-evaluable if there was low confidence in ADC measurement. Associations of lesion evaluability with imaging and lesion characteristics were determined. Areas under the receiver operating characteristic curves (AUCs) were compared using bootstrapping.

RESULTS

Thirty percent (42/142) of lesions were non-evaluable on DWI; 23% (32/142) with image quality issues, 7% (10/142) with conspicuity and/or localization issues. Misregistration was the only factor associated with non-evaluability (P = 0.001). Smaller (≤10 mm) lesions were more commonly non-evaluable than larger lesions (p <0.03), though not significant after multiplicity correction. The AUC for differentiating benign and malignant lesions increased after excluding non-evaluable lesions, from 0.61 (95% CI: 0.50-0.71) to 0.75 (95% CI: 0.65-0.84).

CONCLUSION

Image quality remains a technical challenge in breast DWI, particularly for smaller lesions. Protocol optimization and advanced acquisition and post-processing techniques would help to improve clinical utility.

Relationships and Qualitative Evaluation Between Diffusion-Weighted Imaging and Pathologic Findings of Resected Lung Cancers

For detecting malignant tumors, diffusion-weighted magnetic resonance imaging (DWI) as well as fluoro-2-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT) are available. It is not definitive how DWI correlates the pathological findings of lung cancer. The aim of this study is to evaluate the relationships between DWI findings and pathologic findings. In this study, 226 patients with resected lung cancers were enrolled. DWI was performed on each patient before surgery. There were 167 patients with adenocarcinoma, 44 patients with squamous cell carcinoma, and 15 patients with other cell types. Relationships between the apparent diffusion coefficient (ADC) of DWI and the pathology were analyzed. When the optimal cutoff value (OCV) of ADC for diagnosing malignancy was 1.70 × 10⁻³ mm²/s, the sensitivity of DWI was 92.0% (208/226). The sensitivity was 33.3% (3/9) in mucinous adenocarcinoma. The ADC value (1.31 ± 0.32 × 10⁻³ mm²/s) of adenocarcinoma was significantly higher than that (1.17 ± 0.29 × 10⁻³ mm²/s) of squamous cell carcinoma (p = 0.012), or (0.93 ± 0.14 × 10⁻³ mm²/s) of small cell carcinoma (p = 0.0095). The ADC value (1.91 ± 0.36 × 10⁻³ mm²/s) of mucinous adenocarcinoma was significantly higher than that (1.25 ± 0.25 × 10⁻³ mm²/s) of adenocarcinoma with mucin and that (1.24 ± 0.30 × 10⁻³ mm²/s) of other cell types. The ADC (1.11 ± 0.26 × 10⁻³ mm²/s) of lung cancer with necrosis was significantly lower than that (1.32 ± 0.33 × 10⁻³ mm²/s) of lung cancer without necrosis. The ADC of mucinous adenocarcinoma was significantly higher than those of adenocarcinoma of other cell types. The ADC of lung cancer was likely to decrease according to cell differentiation decreasing. The sensitivity of DWI for lung cancer was 92% and this result shows that DWI is valuable for the evaluation of lung cancer. Lung cancer could be evaluated qualitatively using DWI.

Magnetic Resonance Imaging Derived Biomarkers of IDH Mutation Status and Overall Survival in Grade III Astrocytomas

The evaluation of the isocitrate dehydrogenase (IDH) mutation status in the glioma decision-making process has diagnostic, prognostic and therapeutic implications. The aim of this study was to evaluate whether conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) can noninvasively predict the most common IDH mutational status (R132H) in GIII-astrocytomas and the overall survival (OS). Hence, twenty-two patients (9-F, 13-M) with a histological diagnosis of GIII-astrocytoma and evaluation of IDH-mutation status (12-wild type, 10-mutant) were retrospectively evaluated. Imaging studies were reviewed for the morphological feature and mean ADC values (ADCm). Statistics included a Fisher’s exact test, Student’s t-test, Spearman’s Test and receiver operating characteristic analysis. A p ≤ 0.05 value was considered statistically significant for all the tests. A younger age and a frontal location were more likely related to mutational status. IDH-wild type (Wt) exhibited a slight enhancement (p = 0.039). The ADCm values in IDH-mutant (Mut) patients were higher than those of IDH-Wt patients (p < 0.0004). The value of ADC ≥ 0.99 × 10⁻³ mm²/s emerged as a “cut-off” to differentiate the mutation state. In the overall group, a positive relationship between the ADCm values and OS was detected (p = 0.003; r = 0.62). Adding quantitative measures of ADC values to conventional MR imaging could be used routinely as a noninvasive marker of specific molecular patterns.

Measurement of apparent diffusion coefficient in discrimination of benign and malignant axillary lymph nodes

Purpose

We aimed to determine the contribution of the apparent diffusion coefficient (ADC) value in the detection of axillary lymph node metastasis.

Material and methods

Breast magnetic resonance of 58 patients, performed in the radiology clinic of our hospital between 2015 and 2017 were examined retrospectively, and 43 lymph nodes in 43 patients were included in the study. They were evaluated morphologically on T1W and T2W sequences, and the lymph nodes showing rounded shape, focal or diffuse cortical thickness of more than 3 mm, and partial or total effacement of fatty hilum were included in the study. Subsequently, their ADC values were measured.

Results

There were 43 lymph nodes, 20 of which were malignant and 23 of which were benign. While the mean ADC value of malignant axillary lymph nodes was 0.749 10^-3 mm²/s (0.48-1.342), it was 0.982 10^-3 mm²/s (0.552-1.986) for benign lymph nodes. When the ADC cut-off value was taken as ≤ 0.753 × 10^-3 mm²/s, its discrimination power between benign and malignant axillary lymph nodes was as follows: sensitivity - 60%; specificity - 91.3%; accuracy - 76.7%; positive predictive value - 85.7%; and negative predictive value - 72.4%.

Conclusions

There was no significant difference between mean ADC value of 12 lymphadenopathies (LAP) associated with inflammatory breast diseases (granulomatous mastitis and acute suppurative mastitis) and mean ADC value of metastatic lymph nodes. However, the ADC value of lymph nodes showing thickened cortex due to systemic inflammatory diseases was over 1, and there was a statistically significant difference when compared with metastatic lymph nodes.

Evaluation of mediastinal lymphadenopathy by diffusion weighted MRI; correlation with histopathological results

INTRODUCTION

Diffusion weighted imaging (DWI) has shown its potential as a reliable noninvasive technique for tissue characterization. DWI reflects the tissue specific diffusion capacity which can be used for tissue characterization. Hypercellular tissue (e.g; malignant tumors) had restricted diffusion capacity with increased signals on DWI and low ADC values. Non-tumoral tissues show low cellularity, and diffusion capacity is not restricted resulting in signal loss on DWI and high apparent diffusion coefficient (ADC). Differential diagnosis of mediastinal lymphadenopathy is an issue of debate, especially in malignant benign differentiation. Diffusion weighted imaging with magnetic resonance could improve the diagnostic accuracy in differentiation between benign and malignant mediastinal nodes.

OBJECTIVES

to determine the efficacy of diffusion weighted MRI in evaluation of mediastinal lymphadenopathy with histopathological correlation to differentiate benign from malignant lymph nodes.

MATERIAL AND METHODS

30 patients with mediastinal lymphadenopathy underwent diffusion weighted MRI. ADCs of lymph nodes were derived and constructed from b = 0 and b = 1000 sec/mm2 values by drawing regions of interests (ROI). Consequently, mediastinal nodes were studied, biopsies and histopathological analysis were done after MRI examination.

RESULTS

The best cutoff point of ADC to differentiate benign from malignant lesions was 1.15 mm/sec (sensitivity 77%, specificity 92% and AUC 81.4%). Significant negative correlation of ADC by DW MRI and the size of the LNs. The mean ADC values in the lymphoma group was lower than in the sarcoidosis group, and the difference was statistically significant.

CONCLUSION

The study supports that MRI with diffusion weighted images can differentiate benign from malignant mediastinal lymphadenopathy and differentiate lymphoma from sarcoidosis non-invasively.

Amide proton transfer-weighted MRI for predicting histological grade of hepatocellular carcinoma: comparison with diffusion-weighted imaging

Background

Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver, preoperative grading of HCC is of great clinical significance. Amide proton transfer-weighted (APTw) imaging, as a novel contrast mechanism in the field of molecular imaging, provided new diagnostic ideas for the grading of HCC.

Methods

Between May 2017 and April 2018, 32 consecutive patients with pathologically confirmed HCC were enrolled, including 19 high-grade HCCs and 13 low-grade HCCs. DWI and APTw scanning was performed on a 3T MRI scanner. Two observers drew regions of interest independently by referring to the axial T₂-weighted imaging, and APTw and apparent diffusion coefficient (ADC) values were obtained. Inter- and intra-observer agreements were assessed with the intraclass correlation coefficients (ICCs). The independent sample t test was used to compare the APTw and ADC values between the high- and low-grade HCC tumor parenchyma. The receiver operating characteristic curve was used to analyze the diagnostic efficacy of high- from low-grade HCC tumors. Spearman correlation analysis was used to assess the relationship between APTw and ADC values and HCC histological grades.

Results

There were significant differences between the APTw or ADC values for the high- and low-grade HCCs (P=0.034 and 0.010). Both APTw and DWI had good diagnostic performance in differentiating the high- from the low-grade HCCs, with areas under the curves of 0.814 and 0.745, respectively. Moderate correlations existed between APTw values and histological grades (r=0.534; P=0.002), as well as ADC values and histological grades (r=-0.417; P=0.018).

Conclusions

The APTw imaging is a useful imaging biomarker that complements DWI for the more accurate and comprehensive HCC characterization.

Correlation between ADC values and Gleason score in evaluation of prostate cancer: multicentre experience and review of the literature

Prostate cancer (PCa) is one of the most common cancers in male population. Multiparametric prostate magnetic resonance imaging (mp-MRI) has assumed a primary role in the diagnosis of PCa, combining morphological and functional data. Among different sequences, functional diffusion weighted imaging (DWI) is a powerful clinical tool which provides information about tissue on a cellular level. However, there is a considerable overlap between either BPH (Benign Prostate Hypertrophy) and prostatic cancer condition, as a different DWI signal intensity could be shown in the normal architecture gland. Apparent diffusion coefficient (ADC) has shown an increasing accuracy in addition to the DWI analysis in detection and localization of PCa. Notably, ADC maps derived DWI sequences has shown an overall high correlation with Gleason score (GS), considering the importance of an accurate grading of focal lesion, as main predictor factor. Furthermore, beyond the comparative analysis with DWI, ADC values has proven to be an useful marker of tumor aggressiveness, providing quantitative information on tumor characteristics according with GS and Gleason pattern, even more strenuous data are needed in order to verify which ADC analysis is more accurate.