Diffusion-weighted imaging of breast lesions: Region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values

Diffusion-Weighted MRI of the Breast in Women with a History of Mantle Radiation: Does Radiation Alter Apparent Diffusion Coefficient?

Objective

Fibrosis from chest irradiation could lower the apparent diffusion coefficient (ADC) of breast tissue. ADC values of normal breast tissue in high-risk women who underwent mantle radiation before age 30 years were compared with a screening control group matched for breast fibroglandular tissue (FGT).

Methods

In this retrospective study, we reviewed 21 women with a history of mantle radiation who underwent breast MRI examinations between 2008 and 2013, and 20 nonirradiated patients (control group) imaged during the same period with matching FGT and similar age. The women were dichotomized into low FGT (10/20, 50%) and high-FGT (10/20, 50%) groups, based on BI-RADS descriptors. All MRI examinations included diffusion-weighted imaging (DWI) (b = 0, 1000); ADC maps were generated and evaluated on PACS workstations by two radiologists in agreement. Region of interest markers were placed on ADC maps in visualized breast tissue in the retroareolar region of each breast. The ADC value was averaged for the right and left breast in each patient included in the study. The Wilcoxon signed-rank test was used to compare the ADC values in the irradiated patients and the matched control patients.

Results

The median breast ADC was lower in the irradiated group (1.32 × 10-3mm2/sec) than in the control group (1.62 × 10-3mm2/sec; P = 0.0089). Low FGT in the irradiated group had a lower median ADC (1.25 × 10-3mm2/sec) than it did in the control group (1.53 × 10-3mm2/sec). Irradiated high-FGT breasts had a median ADC (1.52 × 10-3mm2/sec), as compared with nonirradiated control patients with high FGT (1.82 × 10-3mm2/sec).

Conclusion

Previously irradiated breasts have lower ADC values than do nonirradiated breasts.

q-Space Imaging Yields a Higher Effect Gradient to Assess Cellularity than Conventional Diffusion-weighted Imaging Methods at 3.0 T: A Pilot Study with Freshly Excised Whole-Breast Tumors

Purpose

To determine whether q-space imaging (QSI), an advanced diffusion-weighted MRI method, provides a higher effect gradient to assess tumor cellularity than existing diffusion imaging methods, and fidelity to cellularity obtained from histologic analysis.

Materials and Methods

In this prospective study, diffusion-weighted images were acquired from 20 whole-breast tumors freshly excised from participants (age range, 35-78 years) by using a clinical 3.0-T MRI unit. Median and skewness values were extracted from the histogram distributions obtained from QSI, monoexponential model, diffusion kurtosis imaging (DKI), and stretched exponential model (SEM). The skewness from QSI and other diffusion models was compared by using paired t tests and relative effect gradient obtained from correlating skewness values.

Results

The skewness obtained from QSI (mean, 1.34 ± 0.77 [standard deviation]) was significantly higher than the skewness from monoexponential fitting approach (mean, 1.09 ± 0.67; P = .015), SEM (mean, 1.07 ± 0.70; P = .014), and DKI (mean, 0.97 ± 0.63; P = .004). QSI yielded a higher effect gradient in skewness (percentage increase) compared with monoexponential fitting approach (0.26 of 0.74; 35.1%), SEM (0.26 of 0.74; 35.1%), and DKI (0.37 of 0.63; 58.7%). The skewness and median from QSI were significantly correlated with the skewness (ρ = -0.468; P = .038) and median (ρ = -0.513; P = .021) of cellularity from histologic analysis.

Conclusion

QSI yields a higher effect gradient in assessing breast tumor cellularity than existing diffusion methods, and fidelity to underlying histologic structure.Keywords: Breast, MR-Diffusion Weighted Imaging, MR-Imaging, Pathology, Tissue Characterization, Tumor ResponseOnline supplemental material is available for this article.Published under a CC BY 4.0 license.

[Contrast agent-free breast MRI : Advantages and potential disadvantages]

BACKGROUND

Contrast-enhanced breast magnetic resonance imaging (MRI) is the most sensitive method for detection of breast cancer. The further spread of breast MRI is limited by the complicated examination procedure and the need for intravenously administered contrast media.

OBJECTIVES

Can diffusion-weighted imaging (DWI) replace contrast-enhanced sequences to achieve an unenhanced breast MRI examination?

MATERIALS AND METHODS

Narrative review and meta-analytic assessment of previously published studies.

RESULTS

DWI can visualize breast lesions and distinguish benign from malignant findings. It is thus a valid alternative to contrast-enhanced sequences. As an additional technique, the use of DWI can reduce the numbers of unnecessary breast biopsies. The lack of robustness leading to variable sensitivity that is currently lower than that of contrast-enhanced breast MRI is a disadvantage of DWI.

CONCLUSIONS

Presently, DWI can be recommended as an integral part of clinical breast MRI protocols. The application as a stand-alone technique within unenhanced protocols is still under evaluation.

Breast MRI in the era of diffusion weighted imaging: do we still need signal-intensity time curves?

Objective

Dynamic contrast-enhanced imaging of the initial (IP) and delayed phase (DP) is an integral part of any clinical breast MRI protocol. Furthermore, DWI is increasingly used as an add-on sequence by the breast-imaging community. We investigated whether DWI could be used as a substitute DP.

Material and methods

One hundred thirty-two consecutive patients with equivocal or suspicious findings at ultrasound and/or mammography received a full diagnostic breast MRI according to international recommendations. Histopathological verification served as reference standard. We evaluated three sections of the MRI protocol: IP, DP, and apparent diffusion coefficient (ADC) maps derived from DWI. Circular ROIs (regions of interest, mean size 5–10 mm²) were drawn into the enhancing parts of the lesion (first postcontrast). ROIs were transferred to the corresponding location on ADC maps and IP and DP images. Mean ROI values were investigated signal intensity (SI): (1) Initial-phase enhancement = (SI(IP) − SI(precontrast))/SI(precontrast); (2) Delayed-phase enhancement = (SI(DP) − SI(IP))/SI(IP); (3) ADC. Multiparametric combinations were computed using logistic regression analysis: (1) IP+: Initial-phase enhancement and ADC; (2) Curve: Initial-phase enhancement and delayed-phase enhancement; (3) Curve+: Curve and ADC. The diagnostic performances of these feature combinations to diagnose malignancy were compared by the area under the receiver-operating characteristics curve (AUC).

Results

One hundred thirty-two patients (age: mean = 57.1 years, range 23–83 years) with 145 lesions were included (malignant/benign 101/44). IP+ (AUC = 0.877) outperformed Curve (AUC = 0.788, p = 0.03). Curve+ was not superior to IP+ (p = 1).

Conclusion

DWI could substitute DP. Because DWI is typically used as an add-on to IP and DP, our results might help to abbreviate and to simplify current practice of breast MRI.

Key Points

• DWI provides similar but superior diagnostic information for diagnosis of malignancy in enhancing breast lesions compared to DP.

• Adding DP to DWI does not provide incremental information to distinguish benign from malignant lesions.

• DWI could substitute DP. As DWI is typically used as an add-on to IP and DP, our findings might help to abbreviate and to simplify current breast MRI practice.

The role of diffusion weighted imaging as supplement to dynamic contrast enhanced breast MRI: Can it help predict malignancy, histologic grade and recurrence?

RATIONALE AND OBJECTIVES

To evaluate the value of adding Diffusion Weighted Imaging (DWI) with Apparent Diffusion Coefficient (ADC) mapping to dynamic contrast enhanced (DCE-MRI) to distinguish benign from malignant pathology subtypes and tumor recurrence.

METHOD AND MATERIALS

In this retrospective IRB approved study, 956 consecutive patients underwent bilateral breast MRI between 1/2015 and 12/2015, with 156 BIRADS 4, 5, or 6 lesions detected in 111 patients. DWI imaging at B0, B100, B600, B1000 was performed with DCE-MRI. Values for diffusion and ADC images were recorded by two fellowship-trained breast radiologists. Mean ADC and signal intensity (SI) values were correlated with histology, tumor grade, hormone receptors (ER, PR, and HER-2)and Oncotype DX scores, when available. p ≤ 0.05 was considered significant.

RESULTS

Of 156 lesions, there were 59 (38%) benign lesions, 24 (15%) Ductal Carcinoma In-Situ, 47 (30%) Invasive Ductal Carcinoma (IDC), 15 (10%) Invasive Lobular Carcinoma (ILC) and 2 (2%) Mucinous carcinoma (MC), five (5%) mixed IDC and ILC, and four (4%) other, including tubular and rare types of malignancy. Mean ADC values for malignancy were significantly lower than for benign lesions (1085 ± 343 × 10-6 vs 1481 ± 276 × 10-6 mm2/s), which is highly predictive (area under curve = 0.82). In addition, tumors with PR negativity and Oncotype score ≥18 (intermediate to high risk for recurrence) demonstrated significantly lower ADC values. SI at B100 and B600 was helpful in distinguishing benign versus IDC. There was no significant correlation between ADC values and tumor grade or ER/HER2 status.

CONCLUSION

ADC value is important factor in distinguishing malignancy, differentiating tumors with higher Oncotype score, and PR negativity. Therefore, it can be used as an important tool to assist appropriate treatment selection.

A multiparametric [18F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis

Purpose

To develop a multiparametric [¹⁸F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue.

Methods

In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [¹⁸F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [¹⁸F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC).

Results

There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5–10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P < 0.001), and BPE of the contralateral healthy breast (P = 0.0019) as independent predictors for breast cancer diagnosis. Other biomarkers did not reach significance. Combination of the three significant biomarkers achieved an AUC value of 0.98 for breast cancer diagnosis.

Conclusion

A multiparametric [¹⁸F]FDG PET/MRI diagnostic model incorporating both qualitative and quantitative parameters of the tumor and the healthy contralateral tissue aids breast cancer diagnosis.

Diffusion-Weighted Imaging With Apparent Diffusion Coefficient Mapping for Breast Cancer Detection as a Stand-Alone Parameter: Comparison With Dynamic Contrast-Enhanced and Multiparametric Magnetic Resonance Imaging

PURPOSE

The aims of this study were to compare dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) with apparent diffusion coefficient mapping as a stand-alone parameter without any other supportive sequence for breast cancer detection and to assess its combination as multiparametric MRI (mpMRI) of the breast.

MATERIALS AND METHODS

In this institutional review board-approved single-center study, prospectively acquired data of 106 patients who underwent breast MRI from 12/2010 to 09/2014 for an imaging abnormality (Breast Imaging Reporting and Data System 0, 4/5) were retrospectively analyzed. Four readers independently assessed DWI and DCE as well as combined as mpMRI. Breast Imaging Reporting and Data System categories, lesion size, and mean apparent diffusion coefficient values were recorded. Histopathology was used as the gold standard. Appropriate statistical tests were used to compare diagnostic values.

RESULTS

There were 69 malignant and 41 benign tumors in 106 patients. Four patients presented with bilateral lesions. Dynamic contrast-enhanced MRI was the most sensitive test for breast cancer detection, with an average sensitivity of 100%. Diffusion-weighted imaging alone was less sensitive (82%; P < 0.001) but more specific than DCE-MRI (86.8% vs 76.6%; P = 0.002). Diagnostic accuracy was 83.7% for DWI and 90.6% for DCE-MRI. Multiparametric MRI achieved a sensitivity of 96.8%, not statistically different from DCE-MRI (P = 0.12) and with a similar specificity as DWI (83.8%; P = 0.195), maximizing diagnostic accuracy to 91.9%. There was almost perfect interreader agreement for DWI (κ = 0.864) and DCE-MRI (κ = 0.875) for differentiation of benign and malignant lesions.

CONCLUSION

Dynamic contrast-enhanced MRI is most sensitive for breast cancer detection and thus still indispensable. Multiparametric MRI using DCE-MRI and DWI maintains a high sensitivity, increases specificity, and maximizes diagnostic accuracy, often preventing unnecessary breast biopsies. Diffusion-weighted imaging should not be used as a stand-alone parameter because it detects significantly fewer cancers in comparison with DCE-MRI and mpMRI.

Role of diffusion weighted imaging and magnetic resonance spectroscopy in breast cancer patients with indeterminate dynamic contrast enhanced magnetic resonance imaging findings

PURPOSE

Dynamic contrast enhanced MRI (DCEMRI), diffusion weighted imaging (DWI) and in vivo proton (¹H) magnetic resonance spectroscopy (MRS) provides functional and molecular nature of breast cancer. This study evaluates the potential of the combination of three MR parameters [curve kinetics, apparent diffusion coefficient (ADC) and total choline (tCho) concentration] determined from these techniques in increasing the sensitivity of breast cancer detection.

METHODS

MR investigations were carried out at 1.5 T on 56 patients with cytologically/histologically confirmed breast carcinoma. Single-voxel MRS was used to determine the tCho concentration. 3D FLASH was used for DCEMRI while single shot EPI based DWI was used for ADC determination.

RESULTS

On DCEMRI, one patient showed type I curve, while 8 showed type II and 47 showed type III curve thus giving a sensitivity of 83.9% as detection rate of malignancy. tCho concentration was above cut-off value (2.54 mmol/kg) for 50/56 cases giving a sensitivity of 89.3%. Among 9 indeterminate DCEMRI cases, tCho showed malignancy in 6 cases with type II curve. DWI detected malignancy in 54/56 cases that included 9 cases that were false negative on DCEMRI, yielding a sensitivity of 96.4%. A total of 54 cases showed malignancy when any two of the three MR parameters was positive for malignancy yielding a sensitivity of 96.4% while it increased to 100% when any one parameters showed positive result.

CONCLUSION

DWI showed highest sensitivity of detection compared to DCEMRI and MRS. Multi-parametric approach yielded 96.4% and 100% sensitivity when any two or one of the three parameters was taken as positive for malignancy, respectively. Also the results demonstrated that addition of DWI and MRS play a significant role in establishing the final diagnosis of malignancy, especially in cases where DCEMRI is indeterminate.

Diffusion-weighted imaging of the breast: current status as an imaging biomarker and future role

Diffusion-weighted imaging (DWI) of the breast is a MRI sequence that shows several advantages when compared to the dynamic contrast-enhanced sequence: it does not need intravenous contrast, it is relatively quick and easy to implement (artifacts notwithstanding). In this review, the current applications of DWI for lesion characterization and prognosis as well as for response evaluation are analyzed from the point of view of the necessary steps to become a useful surrogate of underlying biological processes (tissue architecture and cellularity): from the proof of concept, to the proof of mechanism, the proof of principle and finally the proof of effectiveness. Future applications of DWI in screening, DWI modeling and radiomics are also discussed.

Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping as a quantitative imaging biomarker for prediction of immunohistochemical receptor status, proliferation rate, and molecular subtypes of breast cancer

BACKGROUND

Diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping is one of the most useful additional MRI parameters to improve diagnostic accuracy and is now often used in a multiparameric imaging setting for breast tumor detection and characterization.

PURPOSE

To evaluate whether different ADC metrics can also be used for prediction of receptor status, proliferation rate, and molecular subtype in invasive breast cancer.

STUDY TYPE

Retrospective.

SUBJECTS

In all, 107 patients with invasive breast cancer met the inclusion criteria (mean age 57 years, range 32-87) and underwent multiparametric breast MRI.

FIELD STRENGTH/SEQUENCE

3 T, readout-segmented echo planar imaging (rsEPI) with IR fat suppression, dynamic contrast-enhanced (DCE) T₁ -weighted imaging, T₂ -weighted turbo-spin echo (TSE) with fatsat.

ASSESSMENT

Two readers independently drew a region of interest on ADC maps on the whole tumor (WTu), and on its darkest part (DpTu). Minimum, mean, and maximum ADC values of both WTu and DpTu were compared for receptor status, proliferation rate, and molecular subtypes.

STATISTICAL TESTS

Wilcoxon rank sum, Mann-Whitney U-tests for associations between radiologic features and histopathology; histogram and q-q plots, Shapiro-Wilk's test to assess normality, concordance correlation coefficient for precision and accuracy; receiver operating characteristics curve analysis.

RESULTS

Estrogen receptor (ER) and progesterone receptor (PR) status had significantly different ADC values for both readers. Maximum WTu (P = 0.0004 and 0.0005) and mean WTu (P = 0.0101 and 0.0136) were significantly lower for ER-positive tumors, while PR-positive tumors had significantly lower maximum WTu values (P = 0.0089 and 0.0047). Maximum WTu ADC was the only metric that was significantly different for molecular subtypes for both readers (P = 0.0100 and 0.0132) and enabled differentiation of luminal tumors from nonluminal (P = 0.0068 and 0.0069) with an area under the curve of 0.685 for both readers.

DATA CONCLUSION

Maximum WTu ADC values may be used to differentiate luminal from other molecular subtypes of breast cancer.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:836-846.

The MRI characteristics of non-mass enhancement lesions of the breast: associations with malignancy

OBJECTIVE:

The American College of Radiology updated the terms used for expressing the imaging characteristics of non-mass enhancement (NME) lesions in the fifth edition of the breast imaging-reporting data system (BI-RADS) lexicon. Both the distribution and internal enhancement descriptors were revised for NME lesions. Our aim was to determine the MRI characteristics of NME lesions and to investigate their association with malignancy.

METHODS:

The MRI results of 129 NME lesions were retrospectively evaluated. The medical files, biopsy results and follow-up findings of lesions were recorded. Patients who had benign biopsy and those who had stable or regressed lesions during follow-up were classified as benign. All MRI results had been obtained with a 1.5 Tesla Signa HDx MR system (GE Healthcare).

RESULTS:

Segmental and diffuse distribution along with clustered-ring internal enhancement were significantly associated with malignancy, while linear distribution and homogeneous enhancement pattern were associated with benignancy. Additionally, the plateau type (Type II) curve was significantly more frequent in malignant lesions. There was no association between the presence of cystic structures and the benign/malignant nature of the lesion. However, multivariate logistic regression showed that only segmental distribution and diffusion restriction were associated with malignancy.

CONCLUSION:

In the current study, segmental distribution, clustered-ring enhancement, Type II dynamic curve and the presence of diffusion restriction were found to be associated with malignancy. There is a requirement for multicenter studies which include higher numbers of patients in order to better evaluate lesions with rarer characteristics for distribution and enhancement pattern.

ADVANCES IN KNOWLEDGE:

Our aim in this study was to investigate the MRI characteristics of NME lesions. We have reported the MRI findings of NME lesions and have found that segmental distribution and clustered-ring enhancement patterns are significantly more frequent in malignant lesions.

Utility of Diffusion-weighted Imaging to Decrease Unnecessary Biopsies Prompted by Breast MRI: A Trial of the ECOG-ACRIN Cancer Research Group (A6702)

PURPOSE

Conventional breast MRI is highly sensitive for cancer detection but prompts some false positives. We performed a prospective, multicenter study to determine whether apparent diffusion coefficients (ADCs) from diffusion-weighted imaging (DWI) can decrease MRI false positives.Experimental Design: A total of 107 women with MRI-detected BI-RADS 3, 4, or 5 lesions were enrolled from March 2014 to April 2015. ADCs were measured both centrally and at participating sites. ROC analysis was employed to assess diagnostic performance of centrally measured ADCs and identify optimal ADC thresholds to reduce unnecessary biopsies. Lesion reference standard was based on either definitive biopsy result or at least 337 days of follow-up after the initial MRI procedure.

RESULTS

Of 107 women enrolled, 67 patients (median age 49, range 24-75 years) with 81 lesions with confirmed reference standard (28 malignant, 53 benign) and evaluable DWI were analyzed. Sixty-seven of 81 lesions were BI-RADS 4 (n = 63) or 5 (n = 4) and recommended for biopsy. Malignancies exhibited lower mean in centrally measured ADCs (mm²/s) than benign lesions [1.21 × 10^-3 vs.1.47 × 10^-3; P < 0.0001; area under ROC curve = 0.75; 95% confidence interval (CI) 0.65-0.84]. In centralized analysis, application of an ADC threshold (1.53 × 10^-3 mm²/s) lowered the biopsy rate by 20.9% (14/67; 95% CI, 11.2%-31.2%) without affecting sensitivity. Application of a more conservative threshold (1.68 × 10^-3 mm²/s) to site-measured ADCs reduced the biopsy rate by 26.2% (16/61) but missed three cancers.

CONCLUSIONS

DWI can reclassify a substantial fraction of suspicious breast MRI findings as benign and thereby decrease unnecessary biopsies. ADC thresholds identified in this trial should be validated in future phase III studies.

Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial

BACKGROUND

Quantitative diffusion-weighted imaging (DWI) MRI is a promising technique for cancer characterization and treatment monitoring. Knowledge of the reproducibility of DWI metrics in breast tumors is necessary to apply DWI as a clinical biomarker.

PURPOSE

To evaluate the repeatability and reproducibility of breast tumor apparent diffusion coefficient (ADC) in a multi-institution clinical trial setting, using standardized DWI protocols and quality assurance (QA) procedures.

STUDY TYPE

Prospective.

SUBJECTS

In all, 89 women from nine institutions undergoing neoadjuvant chemotherapy for invasive breast cancer.

FIELD STRENGTH/SEQUENCE

DWI was acquired before and after patient repositioning using a four b-value, single-shot echo-planar sequence at 1.5T or 3.0T.

ASSESSMENT

A QA procedure by trained operators assessed artifacts, fat suppression, and signal-to-noise ratio, and determine study analyzability. Mean tumor ADC was measured via manual segmentation of the multislice tumor region referencing DWI and contrast-enhanced images. Twenty cases were evaluated multiple times to assess intra- and interoperator variability. Segmentation similarity was assessed via the Sørenson-Dice similarity coefficient.

STATISTICAL TESTS

Repeatability and reproducibility were evaluated using within-subject coefficient of variation (wCV), intraclass correlation coefficient (ICC), agreement index (AI), and repeatability coefficient (RC). Correlations were measured by Pearson's correlation coefficients.

RESULTS

In all, 71 cases (80%) passed QA evaluation: 44 at 1.5T, 27 at 3.0T; 60 pretreatment, 11 after 3 weeks of taxane-based treatment. ADC repeatability was excellent: wCV = 4.8% (95% confidence interval [CI] 4.0, 5.7%), ICC = 0.97 (95% CI 0.95, 0.98), AI = 0.83 (95% CI 0.76, 0.87), and RC = 0.16 * 10^-3 mm² /sec (95% CI 0.13, 0.19). The results were similar across field strengths and timepoint subgroups. Reproducibility was excellent: interreader ICC = 0.92 (95% CI 0.80, 0.97) and intrareader ICC = 0.91 (95% CI 0.78, 0.96).

DATA CONCLUSION

Breast tumor ADC can be measured with excellent repeatability and reproducibility in a multi-institution setting using a standardized protocol and QA procedure. Improvements to DWI image quality could reduce loss of data in clinical trials.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;49:1617-1628.

Risk stratification of ductal carcinoma in situ using whole-lesion histogram analysis of the apparent diffusion coefficient

OBJECTIVES

To investigate the value of the whole-lesion histogram apparent diffusion coefficient (ADC) metrics for differentiating low-risk from non-low-risk ductal carcinoma in situ (DCIS).

METHODS

The authors identified 93 women with pure DCIS who had undergone preoperative MR imaging and diffusion-weighted imaging from 2013 to 2016. Histogram analysis of pixel-based ADC data of the whole tumour volume was performed by two radiologists using a software tool. The results were compared between low-risk and non-low-risk DCIS. Associations between quantitative ADC metrics and low-risk DCIS were evaluated by receiver operating characteristics (ROC) curve and logistic regression analyses.

RESULTS

In whole-lesion histogram analysis, mean ADC and 5th, 50th and 95th percentiles of ADC were significantly different between low-risk and non-low-risk DCIS (1.522, 1.207, 1.536 and 1.854 × 10^-3 mm²/s versus 1.270, 0.917, 1.261 and 1.657 × 10^-3 mm²/s, respectively; p = .004, p = .003, p = .004 and p = .024, respectively). ROC curve analysis for differentiating low-risk DCIS revealed that 5th percentile ADC yielded the largest area under the curve (0.786) among the metrics of whole-lesion histogram, and the optimal cut-off point was 1.078 × 10^-3 mm²/s (sensitivity 80%, specificity 75.9%, p = .001). Multivariate regression analysis revealed that a high 5th percentile of ADC (> 1.078× 10^-3 mm²/s; odds ratio [OR] = 10.494, p = .016), small tumour size (≤ 2 cm; OR = 12.692, p = .008) and low Ki-67 status (< 14%; OR = 10.879, p = .046) were significantly associated with low-risk DCIS.

CONCLUSIONS

Assessment with whole-lesion histogram analysis of the ADC could be helpful for identifying patients with low-risk DCIS.

KEY POINTS

• Whole-lesion histogram ADC metrics could be helpful for differentiating low-risk from non-low-risk DCIS. • A high 5th percentile ADC was a significant factor associated with low-risk DCIS. • Risk stratification of DCIS is important for their management.

MR diffusion kurtosis imaging for cancer diagnosis: A meta-analysis of the diagnostic accuracy of quantitative kurtosis value and diffusion coefficient

PURPOSE

To perform a meta-analysis for assessing the accuracy of diffusion kurtosis imaging (DKI)-derived quantitative parameters (kurtosis values, K; and corrected diffusion coefficients non-Gaussian bias, D) in separating malignant cancers from benign lesions.

METHODS

Relevant studies were searched in PubMed and Cochrane Library databases and were analyzed by Meta-DiSc software.

RESULTS

Fourteen eligible studies involving 1847 lesions in 1107 patients (895 were benign and 952 were malignant) were included. Pooled analysis showed the sensitivity, specificity, positive likelihood ratio (LR), and negative LR were respectively 0.83 (95% CI, 0.79-0.85), 0.83 (95% CI, 0.80-0.86), 4.61 (95% CI, 2.98-7.14), and 0.22 (95% CI, 0.18-0.28) for K, with the overall area under curve (AUC) of 0.89. The sensitivity, specificity, positive LR, and negative LR were 0.85 (95% CI, 0.80-0.88), 0.85 (95% CI, 0.79-0.89), 6.39 (95% CI, 3.14-12.99), and 0.18 (95% CI, 0.14-0.23) for D, with the overall AUC of 0.92. The sensitivity, specificity, positive LR, and negative LR for apparent diffusion coefficient (ADC) derived from standard diffusion-weighted imaging (DWI) were 0.82 (95% CI, 0.79-0.84), 0.85 (95% CI, 0.82-0.88), 4.75 (95% CI, 3.38-6.68), and 0.24 (95% CI, 0.19-0.29), with the overall AUC of 0.89. The superiority of D to K and ADC was also confirmed by the subgroup analysis of prostate cancer.

CONCLUSION

Our findings suggest that DKI should be added to the routine imaging protocol for screening cancer, with the highest diagnostic accuracy of diffusion coefficients.

How to use the Kaiser score as a clinical decision rule for diagnosis in multiparametric breast MRI: a pictorial essay

Due to its superior sensitivity, breast MRI (bMRI) has been established as an important additional diagnostic tool in the breast clinic and is used for screening in patients with an elevated risk for breast cancer. Breast MRI, however, is a complex tool, providing multiple images containing several contrasts. Thus, reading bMRI requires a structured approach. A lack of structure will increase the rate of false-positive findings and sacrifice most of the advantages of bMRI as additional work-up will be required. While the BI-RADS (Breast Imaging Reporting And Data System) lexicon is a major step toward standardised and structured reporting, it does not provide a clinical decision rule with which to guide diagnostic decisions. Such a clinical decision rule, however, is provided by the Kaiser score, which combines five independent diagnostic BI-RADS lexicon criteria (margins, SI-time curve type, internal enhancement and presence of oedema) in an intuitive flowchart. The resulting score provides probabilities of malignancy that can be used for evidence-based decision-making in the breast clinic. Notably, considerable benefits have been demonstrated for radiologists with initial and intermediate experience in bMRI. This pictorial essay is a practical guide to the application of the Kaiser score in the interpretation of breast MRI examinations.

TEACHING POINTS

• bMRI requires standardisation of patient-management, protocols, and reading set-up. • Reading bMRI includes the assessment of breast parenchyma, associated findings, and lesions. • Diagnostic decisions should be made according to evidence-based clinical decision rules. • The evidence-based Kaiser score is applicable independent of bMRI protocol and scanner. • The Kaiser score provides high diagnostic accuracy with low inter-observer variability.

ADC at 3.0 T as a noninvasive biomarker for preoperative prediction of Ki67 expression in invasive ductal carcinoma of breast

PURPOSE

To investigate the role of apparent diffusion coefficient (ADC) as an imaging biomarker for invasive ductal carcinoma (IDC) in the breast.

METHODS

Seventy-one patients undergoing 3.0 Tesla DWI were retrospectively enrolled. Correlations between the ADC values and prognostic factors were evaluated.

RESULTS

Multivariate regression analyses showed that Ki67 expression and molecular subtype were independently associated with the ADC. Discriminant analysis excluded the ADC as a good biomarker for subtype, but the mean ADC significantly distinguished Ki67-positive (low ADC) from Ki67-negative (high ADC) lesions, as observed in the in ROC curves, with a diagnostic sensitivity of 1.00 and a cut-off value of 0.97 × 10^-3 mm²/s.

CONCLUSION

The ADC may be helpful for predicting Ki67 expression in IDC preoperatively.

DWI in the Assessment of Breast Lesions

Diffusion-weighted imaging (DWI) holds promise to address some of the shortcomings of routine clinical breast magnetic resonance imaging (MRI) and to expand the capabilities of imaging in breast cancer management. DWI reflects tissue microstructure, and provides unique information to aid in characterization of breast lesions. Potential benefits under investigation include improving diagnostic accuracy and guiding treatment decisions. As a result, DWI is increasingly being incorporated into breast MRI protocols and multicenter trials are underway to validate single-institution findings and to establish clinical guidelines. Advancements in DWI acquisition and modeling approaches are helping to improve image quality and extract additional biologic information from breast DWI scans, which may extend diagnostic and prognostic value.