Diffusion-weighted Imaging of Breast Cancer with the Sensitivity Encoding Technique: Analysis of the Apparent Diffusion Coefficient Value

Intraobserver and Interobserver Reproducibility of Breast Diffusion-Weighted Imaging Quantitative Parameters: Readout-Segmented vs. Single-Shot Echo-Planar Imaging

BACKGROUND

The recommended technique for breast diffusion-weighted imaging (DWI) acquisitions is not sufficiently standardized in clinical practice.

PURPOSE

To investigate the intraobserver and interobserver reproducibility of DWI measurements, diffusion-kurtosis imaging (DKI) parameters, and image quality evaluation in breast lesions between single-shot echo-planar imaging (ss-EPI) and readout-segmented echo-planar imaging (rs-EPI).

STUDY TYPE

Prospective.

POPULATION

A total of 295 women with 209 malignant and 86 benign breast lesions.

FIELD STRENGTH/SEQUENCE

A 3-T; fat-saturated T2-weighted MR imaging (T2WI); multi-b-value DWI with both ss-EPI and rs-EPI readouts; T1-weighted dynamic contrast-enhanced MRI (DCE-MRI).

ASSESSMENT

Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were measured for each lesion on ss-EPI and rs-EPI, respectively. Image quality was visually evaluated regarding image sharpness, geometric distortion, lesion conspicuity, visualization of anatomic structures, and overall quality. Quantitative and qualitative analyses were performed twice with a time interval of 2 weeks.

STATISTICAL TESTS

Intraobserver and interobserver reproducibility were evaluated using intra-class correlation coefficients (ICC), within-subject coefficient of variation (wCV), and Bland-Altman plots.

RESULTS

MK, MD, and ADC quantitative parameters for breast lesions showed excellent intraobserver and interobserver reproducibility, with ICCs >0.75 and wCV values ranging from 2.51% to 7.08% for both sequences. The wCV values in both intraobserver and interobserver measurements were higher in the ss-EPI sequence (3.63%-7.08%) than that of the rs-EPI sequence (2.51%-3.62%). The wCV values differed in subgroups with different histopathological types of lesions, breast density, lesion morphology, and lesion sizes, respectively. Furthermore, rs-EPI (ICCs, 0.76-0.97; wCV values, 2.41%-6.04%) had better intraobserver and interobserver reproducibility than ss-EPI (ICCs, 0.54-0.90; wCV values, 6.18%-13.69%) with regard to image quality.

DATA CONCLUSION

Compared to the ss-EPI, the rs-EPI sequence showed higher intraobserver and interobserver reproducibility for quantitative diffusion-related parameters and image quality assessments measured in breast DWI and DKI.

EVIDENCE LEVEL

2.

TECHNICAL EFFICACY

Stage 2.

Computed diffusion-weighted imaging with a low-apparent diffusion coefficient-pixel cut-off technique for breast cancer detection

OBJECTIVE

This study aimed to compare the image quality and diagnostic performance of computed diffusion-weighted imaging (DWI) with low-apparent diffusion coefficient (ADC)-pixel cut-off technique (cDWI cut-off) and actual measured DWI (mDWI).

METHODS

Eighty-seven consecutive patients with malignant breast lesions and 72 with negative breast lesions who underwent breast MRI were retrospectively evaluated. Computed DWI with high b-values of 800, 1200, and 1500 s/mm2 and ADC cut-off thresholds of none, 0, 0.3, and 0.6 (×10-3 mm2/s) were generated from DWI with two b-values (0 and 800 s/mm2). To identify the optimal conditions, two radiologists evaluated the fat suppression and lesion reduction failure using a cut-off technique. The contrast between breast cancer and glandular tissue was evaluated using region of interest analysis. Three other board-certified radiologists independently assessed the optimised cDWI cut-off and mDWI data sets. Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis.

RESULTS

When an ADC cut-off threshold of 0.3 or 0.6 (× 10-3 mm2/s) was applied, fat suppression improved significantly (p < .05). The contrast of the cDWI cut-off with a b-value of 1200 or 1500 s/mm2 was better than the mDWI (p < .01). The ROC area under the curve for breast cancer detection was 0.837 for the mDWI and 0.909 for the cDWI cut-off (p < .01).

CONCLUSION

The cDWI cut-off provided better diagnostic performance than mDWI for breast cancer detection.

ADVANCES IN KNOWLEDGE

Using the low-ADC-pixel cut-off technique, computed DWI can improve diagnostic performance by increasing contrast and eliminating un-suppressed fat signals.

The outstanding diagnostic value of DKI in multimodal magnetic resonance imaging for benign and malignant breast tumors: A diagnostic accuracy study

To explore the value of applying different magnetic resonance imaging MRI sequences in the differential diagnosis of benign and malignant breast tumors. Routine breast magnetic resonance scans (T1-weighted image, T1WI; T2-weighted image, T2WI), dynamically enhanced scans, diffusion-weighted Imaging, and diffusion kurtosis imaging (DKI) scans were performed on 63 female patients with breast-occupying lesions. The benign and malignant lesions were confirmed by biopsy, excision-histopathology reports. There are 70 lesions, of which 46 are benign and 24 are malignant. Analyze the primary conditions, such as the shape, size, and boundary of the lesion, and determine the apparent diffusion coefficient (ADC), mean kurtosis (MK), and mean diffusion (MD) values. The receiver operating characteristic curve was used to evaluate the value and difference in differentiating benign and malignant lesions. In this study, the results of the 2 testers both showed that the MK of malignant lesions was significantly higher than that of benign lesions (P < .001), and the MD of benign lesions was higher than that of malignant lesions (P < .05). The ADC of benign lesions was higher than that of malignant lesions (P < .05). For MK, the area under the curve of the 2 testers was 0.855/0.869, respectively. When the best cutoff value of MK for tester 1 was 0.515, the sensitivity and specificity of MK for diagnosing malignant tumors were 83.3%/87.0%, respectively. For the 2 testers MD, and ADC, the area under the curve was < 0.5, and the diagnostic value was low. The MK value obtained by DKI has a specific value in the differential diagnosis of benign and malignant breast lesions. DKI is helpful in the identification of benign and malignant breast tumors. The diagnostic value is outstanding, and its importance to the changes in the microstructure of the organization needs to be further explored.

Synthetic MRI, multiplexed sensitivity encoding, and BI-RADS for benign and malignant breast cancer discrimination

Objective

To assess the diagnostic value of predictive models based on synthetic magnetic resonance imaging (syMRI), multiplexed sensitivity encoding (MUSE) sequences, and Breast Imaging Reporting and Data System (BI-RADS) in the differentiation of benign and malignant breast lesions.

Methods

Clinical and MRI data of 158 patients with breast lesions who underwent dynamic contrast-enhanced MRI (DCE-MRI), syMRI, and MUSE sequences between September 2019 and December 2020 were retrospectively collected. The apparent diffusion coefficient (ADC) values of MUSE and quantitative relaxation parameters (longitudinal and transverse relaxation times [T1, T2], and proton density [PD] values) of syMRI were measured, and the parameter variation values and change in their ratios were calculated. The patients were randomly divided into training (n = 111) and validation (n = 47) groups at a ratio of 7:3. A nomogram was built based on univariate and multivariate logistic regression analyses in the training group and was verified in the validation group. The discriminatory and predictive capacities of the nomogram were assessed by the receiver operating characteristic curve and area under the curve (AUC). The AUC was compared by DeLong test.

Results

In the training group, univariate analysis showed that age, lesion diameter, menopausal status, ADC, T2_pre, PD_pre, PD_Gd, T2_Delta, and T2_ratio were significantly different between benign and malignant breast lesions (P < 0.05). Multivariate logistic regression analysis showed that ADC and T2_pre were significant variables (all P < 0.05) in breast cancer diagnosis. The quantitative model (model A: ADC, T2_pre), BI-RADS model (model B), and multi-parameter model (model C: ADC, T2_pre, BI-RADS) were established by combining the above independent variables, among which model C had the highest diagnostic performance, with AUC of 0.965 and 0.986 in the training and validation groups, respectively.

Conclusions

The prediction model established based on syMRI, MUSE sequence, and BI-RADS is helpful for clinical differentiation of breast tumors and provides more accurate information for individualized diagnosis.

Evaluation of Different Types of Breast Lesions With Apparent Diffusion Coefficient and Shear Wave Elastography Values: Comparison of Shear Wave Elastography and Apparent Diffusion Coefficient in Breast Lesions

Objective:

The aim of this study was to compare the stiffness of different histological types of breast lesions by obtaining shear wave elastography (SWE) and apparent diffusion coefficient (ADC) values, and to determine the contribution of these two methods to the diagnosis.

Materials and Methods:

In total, 70 patients with biopsy-proven breast lesions were included in the study. The mean SWE values of breast lesions were recorded and ADC values of these lesions were calculated. Receiver operating characteristic (ROC) curve analyses and the diagnostic accuracies of SWE-ADC values were determined.

Results:

The mean SWE values were 45.47 ± 25.11 kPa and 3.51 ± 1.04 m/s in benign group, and 161.11 ± 219.34 kPa and 5.96 ± 1.06 m/s in malignant group, respectively. The mean ADC values were 1.38 ± 0.32 (×10–3 mm2/s) in benign group and 0.96 ± 0.22 (×10–3 mm2/s) in malignant group, respectively. When the diagnostic performances of both imaging modalities on mass stiffness are evaluated, statistically significant negative correlations were found between SWE lesion values and ADC lesion values.

Conclusion:

Evaluation of tissue elasticity has recently been used frequently in the diagnosis of breast diseases. SWE-ADC values, which are negatively correlated in the diagnosis of breast masses, may prove to be a powerful alternative diagnostic tool that can be used interchangeably, as appropriate.

Toward Computer-Assisted Triaging of Magnetic Resonance Imaging-Guided Biopsy in Preoperative Breast Cancer Patients

OBJECTIVES

Incidental MR-detected breast lesions (ie, additional lesions to the index cancer) pose challenges in the preoperative workup of patients with early breast cancer. We pursue computer-assisted triaging of magnetic resonance imaging (MRI)-guided breast biopsy of additional lesions at high specificity.

MATERIALS AND METHODS

We investigated 316 consecutive female patients (aged 26 to 76 years; mean, 54 years) with early breast cancer who received preoperative multiparametric breast MRI between 2013 and 2016. In total, 82 (26%) of 316 patients had additional breast lesions on MRI. These 82 patients had 101 additional lesions in total, 51 were benign and 50 were malignant. We collected 4 clinical features and 46 MRI radiomic features from T1-weighted dynamic contrast-enhanced imaging, high-temporal-resolution dynamic contrast-enhanced imaging, T2-weighted imaging, and diffusion-weighted imaging. A multiparametric computer-aided diagnosis (CAD) model using 10-fold cross-validated ridge regression was constructed. The sensitivities were calculated at operating points corresponding to 98%, 95%, and 90% specificity. The model calibration performance was evaluated by calibration plot analysis and goodness-of-fit tests. The model was tested in an independent testing cohort of 187 consecutive patients from 2017 and 2018 (aged 35 to 76 years; mean, 59 years). In this testing cohort, 45 (24%) of 187 patients had 55 additional breast lesions in total, 23 were benign and 32 were malignant.

RESULTS

The multiparametric CAD model correctly identified 48% of the malignant additional lesions with a specificity of 98%. At specificity 95% and 90%, the sensitivity was 62% and 72%, respectively. Calibration plot analysis and goodness-of-fit tests indicated that the model was well fitted.In the independent testing cohort, the specificity was 96% and the sensitivity 44% at the 98% specificity operating point of the training set. At operating points 95% and 90%, the specificity was 83% at 69% sensitivity and the specificity was 78% at 81% sensitivity, respectively.

CONCLUSIONS

The multiparametric CAD model showed potential to identify malignant disease extension with near-perfect specificity in approximately half the population of preoperative patients originally indicated for a breast biopsy. In the other half, patients would still proceed to MRI-guided biopsy to confirm absence of malignant disease. These findings demonstrate the potential to triage MRI-guided breast biopsy.

Simultaneous Multislice Readout-Segmented Echo Planar Imaging for Diffusion-Weighted MRI in Patients With Invasive Breast Cancers

BACKGROUND

In diffusion-weighted imaging (DWI) of breast MRI, simultaneous multislice acceleration techniques can be used for readout-segmented echo planar imaging (rs-EPI) to shorten the scan time.

PURPOSE

To compare the image quality, apparent diffusion coefficient (ADC) value, and scan time of rs-EPI and simultaneous multislice rs-EPI (SMS rs-EPI) sequences.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 134 consecutive women (mean age: 55.3 years) with invasive breast cancer who underwent preoperative MRI.

FIELD STRENGTH/ SEQUENCES

3.0T; rs-EPI sequence, prototypic SMS rs-EPI sequence and dynamic contrast-enhanced MRI (DCE-MRI) sequence ASSESSMENT: For quantitative comparison, two radiologists independently measured the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), lesion contrast, and apparent diffusion coefficient (ADC). For qualitative comparison, image quality, lesion conspicuity, and reader preference were assessed with a reference of DCE-MRI.

STATISTICAL TESTS

Paired t-tests and Mann-Whitney tests were used.

RESULTS

For SNR and CNR, there were no differences between the sequences (P = 0.342 and 0.665 for reader 1; P = 0.606 and P = 0.116 for reader 2). Lesion contrast of SMS rs-EPI was higher than that of rs-EPI (P < 0.05 for both reader 1 and reader 2). Mean tumor ADC was similar in rs-EPI and SMS rs-EPI sequences (0.98 ± 0.22 vs. 1.00 ± 0.22; P = 0.291 for reader 1, 0.98 ± 0.21 vs. 1.00 ± 0.22; P = 0.418 for reader 2). Regarding qualitative comparison, image quality and lesion conspicuity were higher in SMS rs-EPI than in rs-EPI (both P < 0.05 for both readers). The two readers regarded SMS rs-EPI as superior or equal to rs-EPI in over 90% of cases. The acquisition time was 4:30 minutes for rs-EPI and 2:31 minutes for SMS rs-EPI.

DATA CONCLUSION

The SMS rs-EPI sequence resulted in a similar ADC value and better image quality than the rs-EPI sequence in a 44.1% reduced scan time.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: 3.

Accelerating acquisition of readout-segmented echo planar imaging with a simultaneous multi-slice (SMS) technique for diagnosing breast lesions

OBJECTIVES

To investigate the feasibility and effectiveness of SMS rs-EPI for evaluating breast lesions.

METHODS

This prospective study was approved by IRB. Ninety-six patients had 102 histopathologically verified lesions (80 malignant and 22 benign) that were evaluated. Conventional rs-EPI and SMS rs-EPI data were acquired on a 3T scanner. Mean kurtosis (MK), mean diffusion (MD), and apparent diffusion coefficient (ADC) values were quantitatively calculated for each lesion on both sequences. Images were qualitatively and quantitatively analyzed with respect to image sharpness, geometric distortion, lesion conspicuity, anatomic structure, overall image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Student's t test, Pearson correlation, receiver operating characteristic curve, Wilcoxon rank sum test, and paired-sample t tests were used for statistical analysis.

RESULTS

Compared to conventional rs-EPI, the acquisition time of SMS rs-EPI was markedly reduced (2:17 min vs 4:27 min). Pearson's correlations showed excellent linear relationships for each parameter between conventional rs-EPI and SMS rs-EPI (MK, r = 0.908; MD, r = 0.938; and ADC, r = 0.975; p < 0.01 for all). Furthermore, SMS rs-EPI had similar diagnostic performance compared with conventional rs-EPI. SMS rs-EPI had comparable visual image quality as conventional rs-EPI, with excellent inter-reader reliability (ICC = 0.851-0.940). No differences existed between conventional rs-EPI and SMS rs-EPI for either SNR or CNR (p > 0.05).

CONCLUSIONS

Applying the SMS technique can significantly reduce the acquisition time and produce similar diagnostic accuracy while generating comparable image quality as the conventional rs-EPI.

KEY POINTS

• SMS rs-EPI reduces scan time from 4:27 min to 2:17 min compared with conventional rs-EPI. • SMS rs-EPI has a comparable diagnostic performance to conventional rs-EPI in the differentiation between malignant and benign breast lesions. • SMS rs-EPI demonstrates comparable image quality to conventional rs-EPI with shorter scan time.

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

BACKGROUND

Although whole-lesion apparent diffusion coefficient (ADC) histogram has been increasingly used for breast lesions, it has not been routinely used in clinical practice as an emergent promising imaging tool.

PURPOSE

To evaluate the performance of whole-lesion ADC histogram analysis metrics for differentiating benign and malignant breast lesions.

MATERIAL AND METHODS

A systematic PubMed/EMBASE/Cochrane electronic database search was performed for original diagnostic studies from 1 January 1970 to 2 January 2019. Summary estimates of diagnostic accuracy were generated and meta-regression was performed to explore sources of heterogeneity according to study and magnetic resonance imaging characteristics.

RESULTS

Five original articles involving 493 patients were included in the meta-analysis. The pooled sensitivity and specificity of whole-lesion ADC histogram analysis were 0.85 (95% confidence interval [CI] = 0.81-0.89) and 0.79 (95% CI = 0.72-0.84) for distinguishing benign and malignant breast lesions, respectively. The area under the curve (AUC) was 0.9178. No publication bias was detected (P = 0.51). In subgroup analysis, the summary sensitivity and specificity of 50th percentile ADC value were 0.81 (95% CI = 0.71-0.88) and 0.86 (95% CI = 0.74-0.94), respectively. Meta-regression analysis indicated no covariates were sources of heterogeneity (P > 0.05).

CONCLUSION

Whole-lesion ADC histogram analysis demonstrated good diagnostic performance for differentiating between benign and malignant breast lesions, with 50th percentile ADC value showing higher diagnostic accuracy than other histogram parameters. Given the limited number of studies included in the analysis, the findings from our meta-analysis will need further confirmation in future research.

Turbo Spin-echo Diffusion-weighted Imaging Compared with Single-shot Echo-planar Diffusion-weighted Imaging: Image Quality and Diagnostic Performance When Differentiating between Ductal Carcinoma in situ and Invasive Ductal Carcinoma

Purpose:

To compare the image quality between turbo spin-echo (TSE)-diffusion weighted imaging (DWI) and single-shot echo-planar imaging (EPI)-DWI, and to verify the diagnostic performance of the apparent diffusion coefficient (ADC) parameters of the two techniques by using histogram analysis in terms of differentiation between ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) lesions.

Methods:

Ninety-four women with 94 lesions diagnosed as breast cancer by surgery underwent IRB-approved preoperative magnetic resonance imaging, including TSE and EPI-DWI with b-values of 50 and 850 s/mm². Twenty lesions were identified as DCIS and 74 as IDC. Image quality [signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and geometric distortion] was evaluated quantitatively and compared between the TSE and EPI-DWI. A histogram analysis of the entire tumor voxel-based ADC data was performed, and the 10th, 25th, 50th, 75th, and 90th percentile values of each technique were compared between DCIS and IDC lesions.

Results:

The SNR and CNR of TSE-DWI were significantly higher than those of EPI-DWI (P < 0.0001 and < 0.0001). The geometric distortion of TSE-DWI was significantly lower than that of EPI-DWI (P < 0.0001). In TSE-DWI, the 10th, 25th, 50th, and 75th percentile values were significantly different between the DCIS and IDC lesions (P = 0.0010, 0.0004, 0.0008, and 0.0044, respectively). In EPI-DWI, the 50th and 75th percentile values were significantly different between the two groups (P = 0.0009 and 0.0093). There was no significant difference in the area under the curve of the receiver operating characteristic analysis of the 10th, 25th, 50th, and 75th percentile values of TSE-DWI, and the 50th and 75th percentile values of EPI-DWI (P = 0.29).

Conclusion:

The image quality of TSE-DWI was better than that of EPI-DWI. DCIS lesions were distinguished from IDC lesions with a wider range of percentile values in TSE-DWI than in EPI-DWI, although diagnostic performance was not significantly different between the techniques.

Nomogram for predicting pathological complete response and tumor downstaging in patients with locally advanced rectal cancer on the basis of a randomized clinical trial

Background

Preoperative fluoropyrimidine with radiotherapy was regarded as the standard of care for locally advanced rectal cancer (LARC). The model for predicting pCR in LARC patients was based on standard treatment only. This study aimed to establish a nomogram with pretherapeutic parameters and different neoadjuvant regimens for predicting pathologic complete response (pCR) and tumor downstaging or good response (ypT0-2N0M0) after receiving neoadjuvant treatment in patients with LARC based on a randomized clinical trial.

Methods

Between January 2011 and February 2015, 309 patients with rectal cancer were enrolled from a prospective randomized study (NCT01211210). All pretreatment clinical parameters were collected to build a nomogram for predicting pCR and tumor downstaging. The model was subjected to bootstrap internal validation. The predictive performance of the model was assessed with concordance index (C-index) and calibration plots.

Results

Of the 309 patients, 53 (17.2%) achieved pCR and 132 (42.7%) patients were classified as tumor downstaging with ypT0-2N0M0. Based on the logistic-regression analysis and clinical consideration, tumor length (P = 0.005), tumor circumferential extent (P = 0.036), distance from the anal verge (P = 0.019), and neoadjuvant treatment regimen (P < 0.001) showed independent association with pCR following neoadjuvant treatment. The tumor length (P = 0.015), tumor circumferential extent (P = 0.001), distance from the anal verge (P = 0.032), clinical T category (P = 0.012), and neoadjuvant treatment regimen (P = 0.001) were significantly associated with good tumor downstaging (ypT0-2N0M0). Nomograms were developed to predict the probability of pCR and tumor downstaging with a C-index of 0.802 (95% confidential interval [CI], 0.736–0.867) and 0.730 (95% CI, 0.672–0.784). Internal validation revealed good performance of the calibration plots.

Conclusions

The nomogram provided individual prediction responses to different preoperative treatment for patients with rectal cancer. This model might help physicians in selecting an optimized treatment, but warrants further external validation.

Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions

BACKGROUND

The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.

METHODS

MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions.

RESULTS

The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10^- 3 mm²/s and the mean value of the benign lesions was 1.5 × 10^- 3 mm²/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10^- 3 mm²/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area).

CONCLUSION

An ADC threshold of 1.00 × 10^- 3 mm²/s can be recommended for distinguishing breast cancers from benign lesions.

[The Influence of the Decrease in Data Acquisition Ratio Resulted from Readout Partial Fourier during Readout Segmented EPI on ADC Map: A Phantom Study]

Readout segmented echo planar imaging (readout segmentation of long variable echo-trains, RESOLVE) is a method of dividing the k-space in the readout direction and sampling signals from multiple shots. Compared to the conventional single-shot echo planar imaging, echo space is shortened by dividing, and distortion of images is reduced, but there is a disadvantage that the imaging time is increased. To shorten the imaging time, readout partial Fourier (RPF) method was developed. In this study, it is evaluated how the apparent diffusion coefficient (ADC) value and uniformity of images are affected by RPF. In addition, signal intensity, noise, and signal-to-noise ratio of diffusion-weighted imaging were evaluated as factors influencing the ADC value and uniformity of images, and the artifacts of images were observed. When the data acquisition ratio decreased due to the RPF, the ADC value increased and the uniformity of images decreased. We had better to find special indices for the ADC map when we use RPF.

Combining multiparametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: preliminary results

Pathologic complete response following neoadjuvant therapy (NAT) is used as a short-term surrogate marker of eventual outcome in patients with breast cancer. Analyzing voxel-level heterogeneity in MRI-derived parametric maps, obtained before and after the first cycle of NAT ([Formula: see text]), in conjunction with receptor status, may improve the predictive accuracy of tumor response to NAT. Toward that end, we incorporated two MRI-derived parameters, the apparent diffusion coefficient and efflux rate constant, with receptor status in a logistic ridge-regression model. The area under the curve (AUC) and Brier score of the model computed via 10-fold cross validation were 0.94 (95% CI: 0.85, 0.99) and 0.11 (95% CI: 0.06, 0.16), respectively. These two statistics strongly support the hypothesis that our proposed model outperforms the other models that we investigated (namely, models without either receptor information or voxel-level information). The contribution of the receptor information was manifested by an 8% to 15% increase in AUC and a 14% to 21% decrease in Brier score. These data indicate that combining multiparametric MRI with hormone receptor status has a high likelihood of improved prediction of pathologic response to NAT in breast cancer.

Value of Minimum Apparent Diffusion Coefficient on Magnetic Resonance Imaging as a Biomarker for Predicting Progression of Disease Following Surgery and Radiotherapy in Glial Tumors from a Tertiary Care Center in Northern India

Purpose:

Studies have shown that cellularity of glial tumors are inversely correlated to minimum apparent diffusion coefficient (ADC) values derived on diffusion-weighted imaging (DWI). The purpose of this prospective exploratory study was to evaluate whether temporal change in “minimum ADC” values during follow-up predict progressive disease in glial tumors post radiotherapy and surgery.

Materials and Methods:

Adult patients of glial tumors, subjected to surgery followed by Radiotherapy (RT), were included in the study. Serial conventional magnetic resonance imaging with DWI at the following time points – presurgery, pre-RT, post-RT imaging at 3, 7, and 15 months were done. For “minimum ADC” values, multiple regions of interest (ROI) were identified on ADC maps derived from DWI. A mean of 5 minimum ADC values was chosen as “minimum ADC” value. The correlation was drawn between histology and minimum ADC values and time trends were studied.

Results:

Fourteen patients were included in this study. Histologies were low-grade glioma (LGG) – 5, anaplastic oligodendroglioma (ODG) -5, and glioblastoma multiforme (GBM) – 4. Minimum ADC values were significantly higher in LGG and GBM than ODG. Presurgery, the values were 0.812, 0.633, and 0.787 × 10⁻³ mm²/s for LGG, ODG, and GBM, respectively. DWI done at the time of RT planning showed values of 0.786, 0.636, 0.869 × 10⁻³ mm²/s, respectively. During follow-up, the increasing trend of minimum ADC was observed in LGG (P = 0.02). All these patients were clinically and radiologically stable. Anaplastic ODGs, however, showed an initial increase followed by the fall of minimum ADC in all the 5 cases (P = 0.00). Four of the five cases developed progressive disease subsequently. In all the 4 GBM cases, a consistent fall of minimum ADC values was observed (P = 0.00), and they all progressed in spite of RT.

Conclusions:

The DWI-derived minimum ADC values are an important yet simple quantitative tool to assess the treatment response and disease progression before they are evident on conventional imaging during the follow-up of glial tumors.

Optimization of intra-voxel incoherent motion measurement in diffusion-weighted imaging of breast cancer

Purpose

The purpose of this study was to optimize intra‐voxel incoherent motion (IVIM) measurement in diffusion‐weighted imaging (DWI) of breast cancer by separating perfusion and diffusion effects through the determination of an optimal threshold b‐value, thus benign and cancerous breast tissues can be accurately differentiated using IVIM‐derived diffusion and perfusion parameters.

Materials and Methods

Twenty‐eight patients, with biopsy‐confirmed breast cancers, were studied with a 3T MRI scanner, using T1‐weighted dynamic contrast‐enhanced MRI images, and diffusion‐weighted images with nine b‐values, ranging from 0 to 1000 s/mm². IVIM‐derived parameter maps for tissue diffusion coefficients D, perfusion fraction f, and pseudo‐diffusion coefficients D* were computed using the segmented fitting method with optimized threshold b‐value, and the sum of squared residuals (SSR) were calculated for IVIM‐derived parameters in different breast lesions.

Results

The IVIM analysis method developed in this work can separate perfusion and diffusion effects with the optimal threshold b‐value of 300 s/mm², and the results of diffusion and perfusion parameters from IVIM analysis can be used to differentiate pathological changes in breast tissues. It was found that the averages and standard deviations of the diffusion and perfusion parameters, D, f, D*, are the following, for malignant, benign and normal breast tissues respectively: D (0.813 ± 0.225 × 10⁻³ mm²/s, 1.437 ± 0.538 × 10⁻³ mm²/s, 1.838 ± 0.213 × 10⁻³ mm²/s), f (10.73 ± 3.44%, 7.86 ± 3.70%, 8.92 ± 3.72%), D* (15.23 ± 12.17×10⁻³ mm²/s, 12.02 ± 3.19 × 10⁻³ mm²/s, 12.03 ± 7.21 × 10⁻³ mm²/s).

Conclusion

IVIM‐derived diffusion and perfusion parameter maps depend highly on the choice of threshold b‐value. Using the methodology developed in this work, and with the optimized threshold b‐value, the diffusion and perfusion parameters of breast tissues can be accurately assessed, making IVIM MRI a technique of choice for differential diagnosis of breast cancer.

Contribution of diffusion weighted MRI to diagnosis and staging in gastric tumors and comparison with multi-detector computed tomography

Background

Diagnostic performance of Diffusion-Weighted magnetic resonance Imaging (DWI) and Multi-Detector Computed Tomography (MDCT) for TNM (Tumor, Lymph node, Metastasis) staging of gastric cancer was compared.

Patients and methods

We used axial T2-weighted images and DWI (b-0,400 and b-800 s/mm2) protocol on 51 pre-operative patients who had been diagnosed with gastric cancer. We also conducted MDCT examinations on them. We looked for a signal increase in the series of DWI images. The depth of tumor invasion in the stomach wall (tumor (T) staging), the involvement of lymph nodes (nodal (N) staging), and the presence or absence of metastases (metastatic staging) in DWI and CT images according to the TNM staging system were evaluated. In each diagnosis of the tumors, sensitivity, specificity, positive and negative accuracy rates of DWI and MDCT examinations were found through a comparison with the results of the surgical pathology, which is the gold standard method. In addition to the compatibilities of each examination with surgical pathology, kappa statistics were used.

Results

Sensitivity and specificity of DWI and MDCT in lymph node staging were as follows: N1: DWI: 75.0%, 84.6%; MDCT: 66.7%, 82%;N2: DWI: 79.3%, 77.3%; MDCT: 69.0%, 68.2%; N3: DWI: 60.0%, 97.6%; MDCT: 50.0%, 90.2%. The diagnostic tool DWI seemed more compatible with the gold standard method (surgical pathology), especially in the staging of lymph node, when compared to MDCT. On the other hand, in T staging, the results of DWI and MDCT were better than the gold standard when the T stage increased. However, DWI did not demonstrate superiority to MDCT. The sensitivity and specificity of both imaging techniques for detecting distant metastasis were 100%.

Conclusions

The diagnostic accuracy of DWI for TNM staging in gastric cancer before surgery is at a comparable level with MDCT and adding DWI to routine protocol of evaluating lymph nodes metastasis might increase diagnostic accuracy.

Diagnostic Performance of Diffusion Tensor Imaging with Readout-segmented Echo-planar Imaging for Invasive Breast Cancer: Correlation of ADC and FA with Pathological Prognostic Markers

Purpose:

To assess the diagnostic performance of readout-segmented echo-planar diffusion tensor imaging (DTI based on rs-EPI) for breast cancer and to determine the correlation between the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values obtained from DTI based on rs-EPI with prognostic markers of invasive breast cancer.

Materials and Methods:

This retrospective study examined 80 pathologically proven breast lesions (22 benign and 58 malignant lesions) of 80 patients who underwent both diffusion-weighted imaging based on single-shot echo-planar imaging (DWI based on ss-EPI) and DTI based on rs-EPI with b-values of 0 and 1000. We identified and compared the diagnostic performances of the DWI based on ss-EPI and the DTI based on rs-EPI using ADCs by conducting a receiver-operating-characteristics (ROC) analysis. We determined the correlations between the ADCs and the prognostic markers and those of the FA values and the same markers.

Results:

The median ADCs of the benign and malignant lesions based on the ss-EPI were 1.57 and 1.2 × 10⁻³ mm²/sec, and those based on the rs-EPI were 1.53 and 1.09 × 10⁻³ mm²/sec, respectively. The area under the curve on the ROC analysis based on rs-EPI (0.924) was greater than that based on ss-EPI (0.897). There were no significant correlations between the ADCs and the prognostic markers, but there were significant correlations between the FA values and the estrogen receptor status, a proliferative marker, the nuclear grade and the intrinsic subtype.

Conclusion:

For breast cancer, DTI based on rs-EPI had superior diagnostic performance compared to DWI based on ss-EPI. Compared with the ADCs, the FA values were more closely correlated with prognostic markers of invasive breast cancer.

Nomogram basing pre-treatment parameters predicting early response for locally advanced rectal cancer with neoadjuvant chemotherapy alone: a subgroup efficacy analysis of FOWARC study

Objective

To develop an accurate model with pre-treatment parameters to predict tumor regression and down-staging in locally advanced rectal cancer patients, basing the cohort of preoperative chemotherapy alone in FOWARC study.

Patients and Methods

From Jan 2011 to Feb 2015, complete data was available for 137 out of 165 patients who received preoperative chemotherapy alone. All pre-treatment clinical parameters were collected. Tumor regression grade (TRG) 0-1 was defined as good regression, and pathological TNM stage (ypTNM) 0-I after neoadjuvant treatment was defined as good down-staging. Nomogram was established to predict tumor regression and down-staging. The predictive performance of the model was assessed with concordance index and calibration plots.

Results

Of the 137 patients, 10 had TRG 0 (complete regression); 32 patients, TRG 1; and 95 patients, TRG 2 and 3 (poor regression); 56 (40.9%) patients were classified as good down-staging with ypTNM stage 0-I. The predictive nomograms were developed to predict the probability of TRG 0-1 and good down-staging with a C-index of 0.72 (95% CI: 0.604-0.797) and 0.76 (95% CI: 0.681-0.844). Calibration plots showed good statistical performance on internal validation. Predictive factors in the models included tumor length, tumor circumferential extent, age, and ApoA1.

Conclusions

The model based on available clinical parameters could accurately predict early efficacy with neoadjuvant mFOLFOX6 chemotherapy alone, which might help in patient selection for optimized treatment.

Potential Clinical Applications of 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Mammography in Breast Cancer

The whole-body positron emission tomography (PET)/magnetic resonance (MR) scan is a cutting edge technology providing comprehensive structural information from MR imaging and functional features from PET in a single session. Recent research findings and clinical experience have shown that ¹⁸F-fluorodeoxyglucose (FDG) whole-body PET/MR imaging has a diagnostic performance comparable with or superior to that of PET/CT in the field of oncology, including for breast cancer. In particular, FDG PET/MR mammography in the prone position with the breast hanging in a pendant manner can provide more comprehensive information about the metabolism, anatomy, and functional features of a breast lesion than a whole-body PET/MR scan. This article reports on current state-of-the-art PET/MR mammography in patients with breast cancer and the prospects for potential application in the future.

Analysis of factors influencing the degree of detectability on diffusion-weighted MRI and diffusion background signals in patients with invasive breast cancer

To determine the factors influencing the degree of detectability of lesions and diffusion background signals on magnetic resonance diffusion-weighted imaging (DWI) in invasive breast cancer.Institutional review board approval was obtained and patient consent was waived. Patients with newly diagnosed invasive ductal carcinoma, who underwent preoperative breast magnetic resonance imaging with DWI were included in this study (n = 167). Lesion detectability on DWI and contrast-enhanced subtracted T1-weighted images, the degree of background parenchymal enhancement (BPE), and diffusion background signal were qualitatively rated. Detectability of lesions on DWI was compared with clinicopathological findings including menopausal status, mammographic density, and molecular subtype of breast cancer. Multivariate linear regression analysis was performed to determine variables independently associated with detectability of lesions on DWI and diffusion background signals.Univariate analysis showed that the detectability of lesions on DWI was significantly associated with lesion size (P = 0.001), diffuse background signal (P < 0.0001), and higher detectability scores for contrast-enhanced T1-weighted subtraction images (P = 0.000). The degree of diffusion background signal was significantly affected by age (P < 0.0001), BPE (P < 0.0001), mammographic density (P = 0.002), and menopausal status (P < 0.0001). On multivariate analysis, the diffusion background signal (P < 0.0001) and histologic grade (P < 0.0001) were correlated with the detectability on DWI of invasive breast cancer. Only BPE was correlated with the amount of diffusion background signal on DWI (P < 0.0001).For invasive breast cancers, detectability on DWI was significantly affected by the diffusion background signal. BPE, menopausal status, menstrual cycle, or mammographic density did not show statistically significant correlation with the diffusion detectability of lesions on DWI.

Diffusion Weighted MR Imaging of Breast and Correlation of Prognostic Factors in Breast Cancer

BACKGROUND

Through Diffusion Weighted Imaging (DWI), information related to early molecular changes, changes in the permeability of cell membranes, and early morphologic and physiologic changes such as cell swelling can be obtained.

AIMS

We investigated the correlation between the prognostic factors of breast cancer and apparent diffusion coefficient (ADC) in DWI sequences of malignant lesions.

STUDY DESIGN

Retrospective cross-sectional study.

METHODS

Patients who were referred to our clinic between September 2012 and September 2013, who underwent dynamic breast MRI before or after biopsy and whose biopsy results were determined as malignant, were included in our study. Before the dynamic analysis, DWI sequences were taken. ADC relationship with all prognostic factors was investigated. Pearson correlation test was used to compare the numerical data, while Spearman correlation and Fisher exact tests were used to compare the categorical data. The advanced relationships were evaluated with linear regression analysis and univariate analysis. The efficiency of the parameters was evaluated using ROC analysis. The significance level (P) was accepted as 0.05.

RESULTS

In total, 41 female patients with an average age of 49.4 years (age interval 21-77) and 44 lesions were included into the study. In the Pearson correlation test, no statistically significant difference was determined between ADC and the patient's age and tumor size. In the Spearman correlation test, a statistically significant difference was determined between nuclear grade (NG) and ADC (r=-0.424, p=0.04); no statistically significant correlation was observed between the other prognostic factors with each other and ADC values. In the linear regression analysis, the relationship of NG with ADC was found to be more significant alone than when comparing all parameters (corrected r2=0.196, p=0.005). Further evaluations between the NG and ADC correlation were carried out with ROC analysis. A statistically significant difference was determined when NG 1 separately was compared with NG 2 and 3 (p=0.03). A statistically significant difference was also determined (p=0.05) in the comparison of NG 1 with only NG 3. No statistically significant difference was determined when NG 2 separately was compared with NG 1 and NG 3 and when NG 3 separately was compared with NG 1 and 2 (p=0.431, p=0.097).

CONCLUSION

We found that ADC values obtained by breast DWI showed a higher correlation with the NG of breast cancer, which is an important factor in the patient's treatment. Predictions can be made about NG by analyzing the ADC values. Additional studies are needed, however, and the ADC value of the lesion can be used as a prognostic factor proving the aggressiveness.

Significance of the ADC ratio in the differential diagnosis of breast lesions

BACKGROUND

Magnetic resonance imaging (MRI) has high sensitivity but low specificity for breast cancer, and consequently, new techniques to improve the specificity of breast MRI in diagnosing breast cancer are under development.

PURPOSE

To assess the ability of the apparent diffusion coefficient (ADC) compared with the ADC ratio (ADCr) to differentially diagnose benign compared with malignant breast lesions.

MATERIAL AND METHODS

Forty-eight women with breast lesions (average age, 45 years) underwent MRI scanning including T1-weighted dynamic contrast-enhanced (DCE) scanning and diffusion-weighted imaging (DWI). The average ADC and ADCr values for both lesions and pectoralis major muscles (ADCrmuscle and ADCrmuscle) were measured in patients with malignant (n = 25) and benign (n = 23) breast lesions. The ADCr of the contralateral breast (ADCr contralateral) was also evaluated. All histology was confirmed by pathological analysis of biopsied tissue. ADC and ADCr values were analyzed using receiver-operating characteristic (ROC) curves.

RESULTS

For benign lesions compared with malignant lesions, lesion-side ADC was 1.45 vs. 1.05, respectively (P < 0.001), normal-side ADC was 1.82 vs.1.64 (P = 0.002), ADCrmuscle was 1.35 vs. 0.9 (P < 0.001), and ADCrcontralateral was 0.79 vs. 0.64 (P = 0.001). ADCrmuscle showed higher sensitivity (82.61%) and specificity (96.00%) than ADCrcontralateral (60.87% and 92.00%, respectively) and ADC (69.57% and 96.00%) for discriminating malignant from benign lesions. The AUC using ADCrmuscle had higher discriminatory power (0.92, P < 0.001) for malignant versus benign breast lesions compared with either ADC (0.82, P < 0.001) or ADCrcontralateral (0.78, P = 0.001).

CONCLUSION

The ADCrmuscle value showed higher sensitivity and specificity and improved diagnostic accuracy compared with either ADC or ADCrcontralateral in differentiating benign from malignant breast lesions.

Comparison of image quality and application values on different field-of-view diffusion-weighted imaging of breast cancer

BACKGROUND

Single-shot echo planar imaging (SS-EPI) diffusion-weighted magnetic resonance imaging (DW-MRI) is the most-widely sequence in breast MRI. MRI artifacts and magnetic susceptibility are sometimes severe when this sequence is utilized at 3T.

PURPOSE

To compare the imaging quality, ADC values between SS-EPI DWI sequence and two reduced field-of-view (rFOV) DWI sequences of breast cancer.

MATERIAL AND METHODS

Twelve cases with breast cancer were scanned using SS-EPI DWI (FOV, 360 × 360 mm), rFOV DWI1 (FOV, 360 × 180 mm), and rFOV DWI2 (FOV, 280 × 140 mm), respectively. Image quality (scores 1 to 5) and ADC values of breast imaging were compared among three groups by different DWI sequences. SNR were compared between rFOV DWI1 and rFOV DWI2.

RESULTS

The imaging quality score of 12 cases was 5.00 in rFOV DWI1, 3.60 in SS-EPI DWI, and 3.75 in rFOV DWI2. The mean ADC value of 12 cases was 1.211 × 10(-3 )mm(2)/s in SS-EPI DWI, 1.107 × 10(-3 )mm(2)/s in rFOV DWI1, and 1.038 × 10(-3 )mm(2)/s in rFOV DWI2. SNR of rFOV DWI1 images was much higher than that of rFOV DWI2.

CONCLUSION

rFOV DWI1 is the optimal DWI sequence in our study. Comparing with SS EPI DWI, suitable rFOV DWI has an obvious advantage, which can present higher image resolution and less distortion. It may be helpful in the diagnosis of breast cancer.

Simultaneous multi-slice readout-segmented echo planar imaging for accelerated diffusion-weighted imaging of the breast

OBJECTIVES

Readout-segmented echo planar imaging (rs-EPI) significantly reduces susceptibility artifacts in diffusion-weighted imaging (DWI) of the breast compared to single-shot EPI but is limited by longer scan times. To compensate for this, we tested a new simultaneous multi-slice (SMS) acquisition for accelerated rs-EPI.

MATERIALS AND METHODS

After approval by the local ethics committee, eight healthy female volunteers (age, 38.9 ± 13.1 years) underwent breast MRI at 3T. Conventional as well as two-fold (2× SMS) and three-fold (3× SMS) slice-accelerated rs-EPI sequences were acquired at b-values of 50 and 800 s/mm(2). Two independent readers analyzed the apparent diffusion coefficient (ADC) in fibroglandular breast parenchyma. The signal-to-noise ratio (SNR) was estimated based on the subtraction method. ADC and SNR were compared between sequences by using the Friedman test.

RESULTS

The acquisition time was 4:21 min for conventional rs-EPI, 2:35 min for 2× SMS rs-EPI and 1:44 min for 3× SMS rs-EPI. ADC values were similar in all sequences (mean values 1.62 × 10(-3)mm(2)/s, p=0.99). Mean SNR was 27.7-29.6, and no significant differences were found among the sequences (p=0.83).

CONCLUSION

SMS rs-EPI yields similar ADC values and SNR compared to conventional rs-EPI at markedly reduced scan time. Thus, SMS excitation increases the clinical applicability of rs-EPI for DWI of the breast.

Diffusion-weighted imaging of breast tumours at 3 Tesla and 7 Tesla: a comparison

OBJECTIVES

To compare bilateral diffusion-weighted MR imaging (DWI) at 3 T and 7 T in the same breast tumour patients.

METHODS

Twenty-eight patients were included in this IRB-approved study (mean age 56 ± 16 years). Before contrast-enhanced imaging, bilateral DWI with b = 0 and 850 s/mm(2) was performed in 2:56 min (3 T) and 3:48 min (7 T), using readout-segmented echo planar imaging (rs-EPI) with a 1.4 × 1.4 mm(2) (3 T)/0.9 × 0.9 mm(2) (7 T) in-plane resolution. Apparent diffusion coefficients (ADC), signal-to-noise (SNR) and contrast-to-noise ratios (CNR) were assessed.

RESULTS

Twenty-eight lesions were detected (18 malignant, 10 benign). CNR and SNR were comparable at both field strengths (p > 0.3). Mean ADC values at 7 T were 4-22% lower than at 3 T (p ≤ 0.03). An ADC threshold of 1.275 × 10(-3) mm(2)/s resulted in a diagnostic specificity of 90% at both field strengths. The sensitivity was 94% and 100% at 3 T and 7 T, respectively.

CONCLUSION

7-T DWI of the breast can be performed with 2.4-fold higher spatial resolution than 3 T, without significant differences in SNR if compared to 3 T.

KEY POINTS

• 7 T provides a 2.4-fold higher resolution in breast DWI than 3 T • 7 T DWI has a high diagnostic accuracy comparable to that at 3 T • At 7 T malignant lesions had 22 % lower ADC than at 3 T (p < 0.001).

MR mammography using diffusion-weighted imaging in evaluating breast cancer: a correlation with proliferation index

PURPOSE

To evaluate whether the variation of the apparent diffusion coefficient (ADC) values obtained with DWI can predict elevated levels of Ki67 proliferation index and aggressive subtypes in patients with breast cancer.

MATERIALS AND METHODS

Breast MRI studies of 115 patients (mean age 57.3 years, range 36-81 years) with a biopsy-proven breast cancers were evaluated in this retrospective IRB-approved study. Examinations were performed on a 1.5 T magnet and included a Single-Shot Echoplanar DWI sequence with b values of 0 and 1000 s/mm(2). For each target lesion, ADC was measured. ADC values were compared considering Ki67 status (≥20 % or <20 %), histology, grade (G1, G2 or G3) and clinical-pathological classification (Luminal A, Luminal B HER2-positive, Luminal B HER-2 negative, HER-2 enriched and Triple Negative). Mann-Whitney U test and Kruskal-Wallis test were used for comparisons and receiver operating characteristic (ROC) curves were obtained. Inter- and intra-reader variability was evaluated in a subset of 40 patients, using interclass correlation coefficient (ICC) and Bland-Altman plots.

RESULTS

Of 115 lesions, 53 (46.1 %) were assessed as Ki67 positive and 62 (53.9 %) as Ki67 negative. ADC values were significantly (p < 0.0001) lower in Ki67-positive patients (median 0.86 × 10(-3) mm(2)/s; interquartile range 0.75-0.92) compared to Ki67-negative (median 1.03 × 10(-3) mm(2)/s; interquartile range 0.92-1.13). Median ADC was also lower in G2 and G3 cancer and in the Luminal B Her2-negative subtype (p = 0.0015). No differences were found when evaluating histology. ROC curve showed a sensitivity and specificity of 83.0 and 66.1 %, respectively, when using a cutoff of 0.95 s/mm(2) to differentiate Ki67-positive from Ki67-negative cancers. Inter- and intra-reader variability was moderate (ICC = 0.623; ICC = 0.548, respectively). No systematic differences were identified with Bland-Altman plots.

CONCLUSIONS

Lower ADC values are associated with elevated Ki67 proliferation index and more aggressive pathologic features. Moderate agreement in ADC measurement could be a limitation.

Breast ultrasound elastography and magnetic resonance imaging of fibrotic changes of breast disease: correlations between elastography findings and pathologic and short Tau inversion recovery imaging results, including the enhancement ratio and apparent diffusion coefficient

PURPOSE

Ultrasound (US) elastography provides information regarding tissue hardness and is expected to become a novel diagnostic tool for breast disease. In contrast, magnetic resonance (MR) images reflect the tissue characteristics. Fibrosis of the stroma of breast diseases may affect their hardness. We investigated the correlation among elasticity score (ES) and signal intensity of short Tau inversion recovery MR images, enhancement ratio, apparent diffusion coefficient (ADC), and the fibrosis in the breast lesions.

MATERIALS AND METHODS

We reviewed the findings of US elastography and MR imaging from 41 consecutive patients with breast lesions (25 invasive ductal carcinoma, 3 fibroadenoma, 1 phyllodes tumor, 2 ductal hyperplasia, 2 primary malignant lymphoma, 3 mastopathy, 1 metastasis, 1 tubular adenoma, 1 ductal carcinoma in situ, 1 diabetic mastopathy, and 1 intraductal papilloma). In each patient, elastography images were classified based on Tsukuba ES. We calculated the ratio of signal intensity of the lesion to the muscle on short Tau inversion recovery images (L/M ratio), enhancement ratio of early to precontrast and early to delayed images, and ADC for each lesion. The ES and MR findings were correlated with the degree of fibrosis (based on Masson trichrome stain).

RESULTS

The ES significantly correlated with the L/M ratio (P = 0.0306) and the ADC (P = 0.0256). The stromal fibrosis also correlated with ES (P = 0.0023), the L/M ratio (P = 0.0344), and enhancement ratio of the early-to-delayed images (P = 0.049).

CONCLUSIONS

The ES and L/M ratio are correlated significantly with each other, and they are correlated with the fibrosis. These results suggest that they will provide the information on the fibrosis and may help the diagnosis of breast lesions.

Diffusion weighted images of metastatic as compared with nonmetastatic axillary lymph nodes in patients with newly diagnosed breast cancer

BACKGROUND

To investigate the ability of diffusion weighted images (DWI) to differentiate between metastatic and nonmetastatic axillary lymph nodes (LNs) in patients with newly diagnosed breast cancer.

METHODS

From January 2010 to February 2012, DWI was performed at b values of 0 and 800 for 16 metastatic LNs from 16 patients with breast cancer, and 20 nonmetastatic LNs from 20 women without breast cancer. The metastatic LNs were proven by ultrasound (US) guided core biopsy and the same LNs were identified on MRI by comparing the US images with MR images. Nonmetastatic LNs were verified by the stability in size and shape for at least 2 years on MRI. The apparent diffusion coefficient (ADC) value of the metastatic and nonmetastatic axillary LNs was compared. Receiver-operating-characteristics (ROC) analysis was performed to evaluate the diagnostic performance of the ADC value in differentiating between metastatic and nonmetastatic axillary LNs.

RESULTS

The mean ADC value was 0.746 × 10(-3) for metastatic LNs and 1.033 × 10(-3) for nonmetastatic LNs (P < 0.001). The area under the ROC curve was 0.884. The sensitivity and specificity for differentiating metastatic from nonmetastatic axillary LNs using a cutoff ADC value of 0.852 were 85% and 81%, respectively.

CONCLUSION

There is a statistically significant difference between the ADC values of pathologically proven metastatic LNs and nonmetastatic LNs. DWI and ADC values are a useful tool for differentiating metastatic from nonmetastatic axillary LNs.

Dynamic properties of water in breast pathology depend on the histological compounds: distinguishing tissue malignancy by water diffusion coefficients

Background

The parameters that characterize the intricate water diffusion in tumors may also reveal their distinct pathology. Specifically, characterization of breast cancer could be aided by diffusion magnetic resonance.

The present in vitro study aimed to discover connections between the NMR biexponential diffusion parameters [fast diffusion phase (D_FDP ), slow diffusion phase (D_SDP ), and spin population of fast diffusion phase (P₁)] and the histological constituents of nonmalignant (control) and malignant human breast tissue. It also investigates whether the diffusion coefficients indicate tissue status.

Methods

Post-surgical specimens of control (mastopathy and peritumoral tissues) and malignant human breast tissue were placed in an NMR spectrometer and diffusion sequences were applied. The resulting decay curves were analyzed by a biexponential model, and slow and fast diffusion parameters as well as percentage signal were identified. The same samples were also histologically examined and their percentage composition of several tissue constituents were measured: parenchyma (P), stroma (St), adipose tissue (AT), vessels (V) , pericellular edema (PCE), and perivascular edema (PVE). Correlations between the biexponential model parameters and tissue types were evaluated for different specimens. The effects of tissue composition on the biexponential model parameters, and the effects of histological and model parameters on cancer probability, were determined by non-linear regression.

Results

Meaningful relationships were found among the in vitro data. The dynamic parameters of water in breast tissue are stipulated by the histological constituents of the tissues (P, St, AT, PCE, and V). High coefficients of determination (R²) were obtained in the non-linear regression analysis: D_FDP (R² = 0.92), D_SDP (R² = 0.81), and P₁(R² = 0.93).

In the cancer probability analysis, the informative value (R²) of the obtained equations of cancer probability in distinguishing tissue malignancy depended on the parameters input to the model. In order of increasing value, these equations were: cancer probability (P, St, AT, PCE, V) (R² = 0.66), cancer probability (D_FDP, D_SDP)(R² = 0.69), cancer probability (D_FDP, D_SDP, P₁) (R² = 0.85).

Conclusion

Histological tissue components are related to the diffusion biexponential model parameters. From these parameters, the relative probability of cancer in a given specimen can be determined with some certainty.

Overcoming limitations in diffusion-weighted MRI of breast by spatio-temporal encoding

PURPOSE

Evaluating the usefulness of diffusion-weighted spatio-temporal encoding (SPEN) methods to provide quantitative apparent diffusion coefficient (ADC)-based characterizations of healthy and malignant human breast tissues, in comparison with results obtained using techniques based on spin-echo echo planar imaging (SE-EPI).

METHODS

Twelve healthy volunteers and six breast cancer patients were scanned at 3T using scanner-supplied diffusion-weighted imaging EPI sequences, as well as two fully refocused SPEN variants programmed in-house. Suitable codes were written to process the data, including calculations of the actual b-values and retrieval of the ADC maps.

RESULTS

Systematically better images were afforded by the SPEN scans, with negligible geometrical distortions and markedly weaker ghosting artifacts arising from either fat tissues or from strongly emitting areas such as cysts. SPEN-derived images provided improved characterizations of the fibroglandular tissues and of the lesions' contours. When translated into the calculation of the ADC maps, there were no significant differences between the mean ADCs derived from SPEN and SE-EPI: if reliable images were available, both techniques showed that ADCs decreased by nearly two-fold in the malignant lesion areas.

CONCLUSION

SPEN-based sequences yielded diffusion-weighted breast images with minimal artifacts and distortions, enabling the calculation of improved ADC maps and the identification of decreased ADCs in malignant regions.

Investigation of the optimal b-value to detect breast tumors with diffusion weighted imaging by 1.5-T MRI

Background

Previous studies have reported that the signal attenuation of diffusion-weighted magnetic resonance imaging (DWI) for normal breast tissue and tumor were well fitted by a monoexponential and a biexponential function, respectively. The aim of this study was to investigate the optimal b-value to detect breast tumors from DWI signal attenuations.

Methods

Sixty-four subjects with breast cancer underwent DWI using six b-values up to 3500 s/mm². The signal attenuations of normal breast and tumor were fitted by mono- and biexponential functions, respectively. The maximum contrast b-values were estimated and compared in terms of frequency.

Results

In almost all cases, the contrast increased with a b-value from 0 to approximately 1500 s/mm². For b > 1500 s/mm², the contrast decreased. The highest contrast b-value in the range of 0 to 2500 s/mm² most frequently was b = 1500 and the next most frequent was 1400 s/mm². Comparing sensitivity and specificity between b = 700 and b = 1400 s/mm², b =1400 s/mm² was slightly superior.

Conclusion

Based on these results, DWI with a b-value of approximately 1400-1500 s/mm² is recommended for optimizing breast tumor detectability.

Diffusion-weighted magnetic resonance imaging combined with T2-weighted images in the detection of small breast cancer: a single-center multi-observer study

BACKGROUND

Breast cancer is the most common cancer in women worldwide. However, it remains a difficult diagnosis problem to differentiate between benign and malignant breast lesions, especially in small early breast lesions.

PURPOSE

To assess the diagnostic value of diffusion-weighted imaging (DWI) combined with T2-weighted imaging (T2WI) for small breast cancer characterization.

MATERIAL AND METHODS

Fifty-eight patients (65 lesions) with a lesion <2 cm in diameter underwent 3.0 Tesla breast magnetic resonance imaging (MRI) including DWI and histological analysis. Three observers with varying experience levels reviewed MRI. The probability of breast cancer in each lesion on MR images was recorded with a 5-point scale. Areas under the receiver-operating characteristic curve (AUCs) were compared by using the Z test; sensitivity and specificity were determined with the Z test after adjusting for data clustering.

RESULTS

AUC of T2WI and DWI (Observer 1, 0.95; Observer 2, 0.91; Observer 3, 0.83) was greater than that of T2WI (Observer 1, 0.80; Observer 2, 0.74; Observer 3, 0.70) for all observers (P < 0.0001 in all comparisons). Sensitivity of T2WI and DWI (Observer 1, 90%; Observer 2, 93%; and Observer 3, 86%) was greater than that of T2WI alone (Observer 1, 76%; Observer 2, 83%; Observer 3, 79%) for all observers (P < 0.0001 in all comparisons). Specificity of T2WI and DWI was greater than that of T2WI alone for observer 1 (89% vs. 72%, P < 0.01) and observer 2 (94% vs. 78%, P < 0.001).

CONCLUSION

DWI combined with T2WI can improve the diagnostic performance of MRI in small breast cancer characterization. It should be considered selectively in the preoperative evaluation of patients with small lesions of the breast.

Role of diffusion-weighted imaging as an adjunct to contrast-enhanced breast MRI in evaluating residual breast cancer following neoadjuvant chemotherapy

OBJECTIVE

To investigate whether the addition of diffusion-weighted imaging (DWI) to dynamic contrast-enhanced MRI (DCE-MRI) improves diagnostic performance in predicting pathologic response and residual breast cancer size following neoadjuvant chemotherapy.

MATERIALS AND METHODS

A total of 78 consecutive patients who underwent preoperative breast MRI with DWI following neoadjuvant chemotherapy were enrolled. DWI was performed on a 1.5 T system with b values of 0 and 750 s/mm. or on a 3T system with b values of 0 and 800 or 0 and 1,000 s/mm. The images on DCE-MRI alone, DWI alone, and DCE-MRI plus DWI were retrospectively reviewed. We evaluated the diagnostic performances of the three MRI protocols for the detection of residual cancer. The tumor size as predicted by MRI was compared with histopathologic findings. Apparent diffusion coefficient (ADC) values were also compared between the groups with and without residual cancer.

RESULTS

Of the 78 patients, 59 (75.6%) had residual cancer. For detection of residual cancer, DCE-MRI plus DWI had higher specificity (80.0%), accuracy (91.0%), and PPV (93.2%) than DCE-MRI or DWI alone (P=0.004, P=0.007, and P=0.034, respectively). The ICC values for residual cancer size between MRI and histopathology were 0.891 for DCE-MRI plus DWI, 0.792 for DCE-MRI, and 0.773 for DWI. ADC values showed no significant differences between residual cancer and chemotherapeutic changes (P=0.130).

CONCLUSIONS

The addition of DWI to DCE-MRI significantly improved diagnostic performance in predicting pathologic response and residual breast cancer size after neoadjuvant chemotherapy.