Apparent diffusion coefficient value in invasive ductal carcinoma at 3.0 Tesla: is it correlated with prognostic factors?

Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring

DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.

Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.

Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.

MRI as a biomarker for breast cancer diagnosis and prognosis

Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.

Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review

Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.

Quantitative Parameters of Diffusion Spectrum Imaging: HER2 Status Prediction in Patients With Breast Cancer

Objective

To explore the value of quantitative parameters derived from diffusion spectrum imaging (DSI) in preoperatively predicting human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer.

Methods

In this prospective study, 114 and 56 female patients with invasive ductal carcinoma were consecutively included in a derivation cohort and an independent validation cohort, respectively. Each patient was categorized into HER2-positive or HER2-negative groups based on the pathologic result. All patients underwent DSI and conventional MRI including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). The tumor size, type of the time-signal intensity curve (TIC) from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, and quantitative parameters derived from DSI, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) of primary tumors, were measured and compared between the HER2-positive and HER2-negative groups in the derivation cohort. Univariable and multivariable logistic regression analyses were used to determine the potential independent predictors of HER2 status. The discriminative ability of quantitative parameters was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort.

Results

In the derivation cohort, the tumor size, TIC type, and ADC values did not differ between the HER2-positive and HER2-negative groups (p = 0.126–0.961). DSI quantitative parameters including axial kurtosis of DKI (DKI_AK), non-Gaussianity (MAP_NG), axial non-Gaussianity (MAP_NG_Ax), radial non-Gaussianity (MAP_NG_Rad), return-to-origin probability (MAP_RTOP), return-to-axis probability of MAP (MAP_RTAP), and intracellular volume fraction of NODDI (NODDI_ICVF) were lower in the HER2-positive group than in the HER2-negative group (p ≤ 0.001–0.035). DSI quantitative parameters including radial diffusivity (DTI_RD), mean diffusivity of DTI (DTI_MD), mean squared diffusion (MAP_MSD), and q-space inverse variance of MAP (MAP_QIV) were higher in the HER2-positive group than in the HER2-negative group (p = 0.016–0.049). The ROC analysis showed that the area under the curve (AUC) of ADC was 0.632 and 0.568, respectively, in the derivation and validation cohorts. The AUC values of DSI quantitative parameters ranged from 0.628 to 0.700 and from 0.673 to 0.721, respectively, in the derivation and validation cohorts. Logistic regression analysis showed that only NODDI_ICVF was an independent predictor of HER2 status (p = 0.001), with an AUC of 0.700 and 0.721, respectively, in the derivation and validation cohorts.

Conclusions

DSI could be helpful for preoperative prediction of HER2, but DSI alone may not be sufficient in predicting HER2 status preoperatively in patients with breast cancer.

Nomogram Based on Breast MRI and Clinicopathologic Features for Predicting Axillary Lymph Node Metastasis in Patients with Early-Stage Invasive Breast Cancer: A Retrospective Study

INTRODUCTION

To establish a nomogram for predicting axillary lymph node (ALN) involvement in patients with early-stage invasive breast cancer (BC) based on magnetic resonance imaging (MRI) features and clinicopathological characteristics.

MATERIALS AND METHODS

Patients with confirmed early-stage invasive BC between 03/2016 and 05/2017 were retrospectively reviewed at the National Cancer Center/Cancer Hospital. Risk factors for ALN metastasis (ALNM) were identified by univariable and multivariable logistic regression analysis. The independent risk factors were used to create a nomogram.

RESULTS

This study included 214 early-stage invasive BC patients, including 57 (26.6%) with positive ALNs. Tumor location (OR = 4.019, 95% CI: 1.304 -12.383, P = .015), tumor size (OR = 3.702, 95%CI: 1.517 -9.034, P = .004), multifocality (OR = 3.534, 95%CI: 1.249 -9.995, P = .017), MR-reported suspicious ALN (OR = 9.829, 95%CI: 4.132 -23.384, P <0.001), apparent diffusion coefficient (ADC) value (OR = 0.367, 95%CI: 0.158 -0.852, P = .020), and lymphovascular invasion (LVI) (OR = 3.530, 95%CI: 1.483 -8.400, P = .004) were identified as independent risk factors associated with ALNM. A nomogram was created for predicting the probability of ALNM by using these risk factors. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual ALNM rate. The area under the curve was 0.88 (95% CI: 0.83 -0.93). The nomogram had a bootstrapped-concordance index of 0.88 and was well-calibrated.

CONCLUSION

The nomogram based on MRI and clinicopathologic features might be a useful tool for predicting ALNM in early-stage invasive BC and could help clinical decision-making.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Diffusion weighted imaging in breast cancer - Can it be a noninvasive predictor of nuclear grade?

BACKGROUND

DWI and ADC values are noninvasive MRI techniques, which provide quantitative information about tumor heterogeneity.

AIM

To determine the minimum and mean ADC values in breast carcinoma and to correlate ADC values with various prognostic factors.

SETTINGS AND DESIGN

Prospective observational study.

MATERIALS AND METHODS

Fifty-five patients with biopsy-proven breast carcinoma were included in this study. MRI with DWI was performed with Siemens 3T Skyra scanner. ADC values were measured by placing regions of interest (ROIs) within the targeted lesions on ADC maps manually. The histopathological and immunohistochemical analysis of surgical specimen was done to determine the prognostic factors.

STATISTICAL ANALYSIS

Students T test and ANOVA were used to study the difference in ADC between two groups. Pearson correlation coefficient was used to quantify the correlation between ADC values and prognostic factors.

RESULTS

Lower grade (grade I) breast carcinoma had a significantly high ADC value as compared to higher grade carcinoma (grade II and III). For differentiating Grade I tumors from grade II and III, a minimum ADC cut-off value was 0.79 × 10-3 mm2/sec (83% sensitivity and 84% specificity) and a mean ADC cut-off value was 0.82 × 10-3 mm2/sec (83% sensitivity and 71% specificity) was derived. There was no significant correlation between ADC and other prognostic factors.

CONCLUSION

ADC values can be used to differentiate lower grade breast carcinoma (grade I) from higher grades (grade II and III). Minimum ADC values are more accurate in predicting the grade of the breast tumor than mean ADC value.

Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers

Objective

To investigate if histogram analysis and visually assessed heterogeneity of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping can predict molecular subtypes of invasive breast cancers.

Materials and Methods

In this retrospective study, 91 patients with invasive breast carcinoma who underwent preoperative magnetic resonance imaging (MRI) with DWI at our institution were included. Two radiologists delineated a 2-D region of interest (ROI) on ADC maps in consensus. Tumors were also independently classified into low and high heterogeneity based on visual assessment of DWI. First-order statistics extracted through histogram analysis within the ROI of the ADC maps (mean, 10th percentile, 50th percentile, 90th percentile, standard deviation, kurtosis, and skewness) and visually assessed heterogeneity were evaluated for associations with tumor receptor status (ER, PR, and HER2 status) as well as molecular subtype.

Results

HER2-positive lesions demonstrated significantly higher mean (p=0.034), Perc50 (p=0.046), and Perc90 (p=0.040), with AUCs of 0.605, 0.592, and 0.652, respectively, than HER2-negative lesions. No significant differences were found in the histogram values for ER and PR statuses. Neither quantitative histogram analysis based on ADC maps nor qualitative visual heterogeneity assessment of DWI images was able to significantly differentiate between molecular subtypes, i.e., luminal A versus all other subtypes (luminal B, HER2-enriched, and triple negative) combined, luminal A and B combined versus HER2-enriched and triple negative combined, and triple negative versus all other types combined.

Conclusion

Histogram analysis and visual heterogeneity assessment cannot be used to differentiate molecular subtypes of invasive breast cancer.

Diffusion-Weighted Imaging of Breast Cancer: Correlation of the Apparent Diffusion Coefficient Value with Pathologic Prognostic Factors

Objective

The aim was to evaluate relationship between apparent diffusion coefficient (ADC) values with pathologic prognostic factors in breast carcinoma (BC).

Materials and Methods

83 patients were enrolled in this study. Prognostic factors included age, tumor size, expression of estrogen receptor (ER) and progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), nuclear grade (NG), lymph node involvement and histologic type. The relationship between ADC and prognostic factors was determined using Independent sample t-test, ANOVA, Pearson correlation and relative operating characteristics (ROC) analysis.

Results

There was no significant difference between ADC and prognostic factors, including age, tumor size, ER, HER2 and histologic type. The PR-positive tumors (p=0.03) and axillary lymph node involvement (p=0.000) showed a significant association with lower ADC values. The ADC values were significantly lower in high-grade tumors than low-grade tumors (p=0.000). ROC analysis showed an optimal ADC threshold of 0.66 (×10-3 mm2/s) for differentiating low-grade tumors from high-grade tumors (sensitivity, 85.5%; specificity, 81%; area under curve, 0.90).

Conclusion

The lower ADC values of BC were significantly associated with positive expression of PR, LN positivity and high-grade tumor. Especially, ADC values were valuable in predicting NG subgroups.

Predicting metastasis in clinically negative axillary lymph nodes with minimum apparent diffusion coefficient value in luminal A-like breast cancer

BACKGROUND

We investigated the usefulness of the minimum ADC value of primary breast lesions for predicting axillary lymph node (LN) status in luminal A-like breast cancers with clinically negative nodes in comparison with the mean ADC.

METHODS

Forty-four luminal A-like breast cancers without axillary LN metastasis at preoperative clinical evaluation, surgically resected with sentinel LN biopsy, were retrospectively studied. Mean and minimum ADC values of each lesion were measured and statistically compared between LN positive (n = 12) and LN negative (n = 32) groups. An ROC curve was drawn to determine the best cutoff value to differentiate LN status. Correlations between mean and minimum ADC values and the number of metastatic axillary LNs were investigated.

RESULTS

Mean and minimum ADC values of breast lesions with positive LN were significantly lower than those with negative LN (mean 839.9 ± 110.9 vs. 1022.2 ± 250.0 × 10^- 6 mm²/s, p = 0.027, minimum 696.7 ± 128.0 vs. 925.0 ± 257.6 × 10^- 6 mm²/s, p = 0.004). The sensitivity and NPV using the best cutoff value from ROC using both mean and minimum ADC were 100%. AUC of the minimum ADC (0.784) was higher than that of the mean ADC (0.719). Statistically significant negative correlations were observed between both mean and minimum ADCs and number of positive LNs, with stronger correlation to minimum ADC than mean ADC.

CONCLUSIONS

The minimum ADC value of primary breast lesions predicts axillary LN metastasis in luminal A-like breast cancer with clinically negative nodes, with high sensitivity and high NPV.

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.

Is there a correlation between 3T multiparametric MRI and molecular subtypes of breast cancer?

OBJECTIVES

To test whether 3 T multiparametric magnetic resonance imaging (mMRI) provides information related to molecular subtypes of breast cancer.

METHODS

Women with mammographic or US findings of breast lesions (BI-RADS 4-5) underwent 3 T mMRI (DCE, DWI and MR spectroscopy). The histological type of breast cancer was assessed. Estrogen-receptor (ER), progesterone-receptor (PgR), Ki-67 status and HER-2 expression, assessed by immunohistochemistry (IHC), defined four molecular subtypes: Luminal-A, Luminal-B, HER2-enriched and triple-negative. Non-parametric tests (Kruskal-Wallis, k-sample equality of medians, and Mann-Whitney), logistic regression or ANOVA, and a multivariate analysis were performed to investigate correlations between the four molecular subtypes and mMRI (lesion volume, margins or distribution, enhancement pattern, ADC, type of kinetic curve, and total choline (tCho) signal-to-noise-ratio (SNR)). A ROC analysis was finally performed to test the diagnostic power of a multivariate logistic regression model.

RESULTS

433 patients (453 lesions) were considered. Volume was smaller in Luminal-B and larger in triple-negative tumours compared to the other subtypes combined. Margins were significantly correlated to Luminal-A and Luminal-B. The type of curve was significantly correlated to Luminal-A. ADC values were higher in Luminal-A. tCho SNR was higher in triple-negative tumours. The ROC analysis showed that the area under the curve (AUC) significantly improved when multiple MRI features were used compared to individual parameters.

CONCLUSIONS

A significant correlation was found between some MRI features and molecular subtypes of breast tumours. A multiparametric approach improved the diagnostic power of MRI. However, further research is needed in order to predict the molecular subtype on the sole basis of mMRI.

Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging

OBJECTIVES

To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm²) and high (1500 s/mm²) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging.

METHODS

One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm², and corresponding ADC₈₀₀ and ADC₁₅₀₀ maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated.

RESULTS

Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC₁₅₀₀ entropy provided superior diagnostic performance over ADC₈₀₀ entropy (p = 0.04). Independent risk factors were ADC₁₅₀₀ entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC₁₅₀₀ entropy (p = 0.022) with luminal B (HER2 positive), mean ADC₁₅₀₀ (p = 0.018) with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative.

CONCLUSIONS

Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC₁₅₀₀ may facilitate better phenotype discrimination.

KEY POINTS

• ADC metrics are associated with molecular marker status in IDC. • ADC ₁₅₀₀ improves differentiation of histologic phenotypes compared with ADC ₈₀₀ . • ADC metrics add value to morphologic features in IDC phenotyping.

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.

Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.