Differentiation between benign and malignant breast lesions using quantitative diffusion-weighted sequence on 3 T MRI. Clin Radiol. 2014;69:63-71. [PMID: 24156797 DOI: 10.1016/j.crad.2013.08.007]

Accuracy of high b-value diffusion-weighted imaging in identifying benign and malignant breast lesions: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic accuracy of high b-value diffusion-weighted imaging (DWI) in differentiating malignant from benign breast lesions.

METHODS

A comprehensive literature search was conducted across PubMed, Embase, Cochrane Library, Scopus, and Web of Science for English-language studies on high b-value DWI in breast lesions, from inception to June 2024. Study quality was assessed, and data were extracted. Heterogeneity analysis, forest plots, Deek's funnel plots, and summary receiver operating characteristic (SROC) curves were generated using Stata 16.0 and Meta-Disc 1.4 software. Meta-regression identified sources of heterogeneity.

RESULTS

The meta-analysis included 12 studies with 1,747 patients and 1,861 breast lesions (1,000 malignant, 861 benign). Pooled diagnostic metrics were: sensitivity, 0.91 (95% CI: 0.87-0.94); specificity, 0.93 (95% CI: 0.87-0.96); positive likelihood ratio, 12.21 (95% CI: 6.91-21.57); negative likelihood ratio, 0.09 (95% CI: 0.06-0.14); diagnostic odds ratio, 130.75 (95% CI: 56.95-300.21); and AUC, 0.97 (95% CI: 0.95-0.98).

CONCLUSION

High b-value DWI has high diagnostic accuracy in differentiating between benign and malignant breast lesions, demonstrating potential as a reliable imaging marker.

REGISTRATION

PROSPERO (CRD42024568777).

Dynamic contrast-enhanced magnetic resonance imaging parameters combined with diffusion-weighted imaging for discriminating malignant lesions, molecular subtypes, and pathological grades in invasive ductal carcinoma patients

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters or diffusion-weighted imaging (DWI) findings provide prognostic information on breast cancer. However, the accuracy of a single MRI technique is unsatisfactory. This study intended to explore the combination of DWI and DCE-MRI parameters in discriminating molecular subtypes in invasive ductal carcinoma (IDC) patients. Eighty-two IDC patients who underwent breast DWI and DCE-MRI examinations were retrospectively analyzed. Eighty-six patients with benign masses were retrieved as benign controls. The combination of ADC value, Ktrans, Kep, Ve, and iAUC had a good ability to discriminate IDC patients (vs. benign controls) with an area under the curve (AUC) [95% confidence interval (CI)] of 0.961 (0.935-0.987). A nomogram-based prediction model with the above combination showed a good predictive value for IDC probability. The combination of ADC value, Ktrans, Kep, and iAUC also had a certain ability to discriminate pathological grade III (vs. I or II) [AUC (95% CI): 0.698 (0.572-0.825)] in IDC patients. Notably, ADC value (P=0.010) and Kep (P=0.043) differed in IDC patients with different molecular subtypes. Besides, ADC value was increased (P<0.001), but Ktrans (P=0.037) and Kep (P=0.004) were decreased in IDC patients with Lumina A (vs. other molecular subtypes). The combination of ADC value, Ktrans, Kep, had an acceptable ability to discriminate Luminal A (vs. other molecular subtypes) [AUC (95% CI): 0.845 (0.748-0.941)] in IDC patients. DWI combined with DCE-MRI parameters discriminates IDC from benign masses; it also identifies Luminal A and pathological grade III in IDC patients.

Usefulness of Combined Advanced Dynamic Contrast-Enhanced and Diffusion-Weighted MRI Over Ultrasonography in Differentiating Cancer From Benign Lesions in Dense Breasts

Aim To evaluate the role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in characterizing suspicious lesions in dense breasts compared to ultrasonography (USG). Materials and methods Eighty-two consecutive female patients with suspicious lesions in dense breast parenchyma showing the American College of Radiology Breast Imaging Reporting And Data System (ACR BI-RADS) c/d composition on mammography underwent USG, where 63 lesions of 63 patients were suspicious. They underwent multiparametric MRI, followed by histopathological evaluation (HPE) of the lesions. Statistical analysis was done to calculate the sensitivity, specificity, and accuracy of USG and MRI in lesion characterization and the combined accuracy of DCE-MRI with DWI. The receiver operating characteristic (ROC) curve analysis provided the cut-off for the apparent diffusion coefficient (ADC) value. Results The sensitivity, specificity, and accuracy of USG were 91.7%, 63%, and 79.4%, respectively. Kinetic curve analysis on DCE-MRI showed a type I curve only in benign lesions. Malignant lesions predominantly showed a type III curve. The sensitivity, specificity, and accuracy of DCE-MRI were 95.8%, 78.5%, and 85.7%, respectively. The optimum cut-off ADC value was 1.05x10-3 mm2/s with sensitivity, specificity, and accuracy of 83.3 % each. The specificity and accuracy of combined DCE-MRI with DWI were 94.4% and 88.1%, respectively. Conclusion Advanced MRI, including a combination of DCE-MRI kinetics and DWI, would be more effective and accurate for lesion characterization in dense breasts and act as a superior problem-solving tool compared to USG in differentiating carcinoma from benign lesions.

Breast imaging with an ultra-low field MRI scanner: a pilot study

Breast cancer screening is necessary to reduce mortality due to undetected breast cancer. Current methods have limitations, and as a result many women forego regular screening. Magnetic resonance imaging (MRI) can overcome most of these limitations, but access to conventional MRI is not widely available for routine annual screening. Here, we used an MRI scanner operating at ultra-low field (ULF) to image the left breasts of 11 women (mean age, 35 years ±13 years) in the prone position. Three breast radiologists reviewed the imaging and were able to discern the breast outline and distinguish fibroglandular tissue (FGT) from intramammary adipose tissue. Additionally, the expert readers agreed on their assessment of the breast tissue pattern including fatty, scattered FGT, heterogeneous FGT, and extreme FGT. This preliminary work demonstrates that ULF breast MRI is feasible and may be a potential option for comfortable, widely deployable, and low-cost breast cancer diagnosis and screening.

Double Transformer Super-Resolution for Breast Cancer ADC Images

Diffusion-weighted imaging (DWI) has been extensively explored in guiding the clinic management of patients with breast cancer. However, due to the limited resolution, accurately characterizing tumors using DWI and the corresponding apparent diffusion coefficient (ADC) is still a challenging problem. In this paper, we aim to address the issue of super-resolution (SR) of ADC images and evaluate the clinical utility of SR-ADC images through radiomics analysis. To this end, we propose a novel double transformer-based network (DTformer) to enhance the resolution of ADC images. More specifically, we propose a symmetric U-shaped encoder-decoder network with two different types of transformer blocks, named as UTNet, to extract deep features for super-resolution. The basic backbone of UTNet is composed of a locally-enhanced Swin transformer block (LeSwin-T) and a convolutional transformer block (Conv-T), which are responsible for capturing long-range dependencies and local spatial information, respectively. Additionally, we introduce a residual upsampling network (RUpNet) to expand image resolution by leveraging initial residual information from the original low-resolution (LR) images. Extensive experiments show that DTformer achieves superior SR performance. Moreover, radiomics analysis reveals that improving the resolution of ADC images is beneficial for tumor characteristic prediction, such as histological grade and human epidermal growth factor receptor 2 (HER2) status.

Assessment of Apparent Diffusion Coefficient Parameters and Coefficient of Variance in Discrimination of Receptor Status and Molecular Subtypes of Breast Cancer

OBJECTIVE

The objective of this study was to investigate the diagnostic power of apparent diffusion coefficient/coefficient of variance (ADCcV) as well as ADC parameters formed based on magnetic resonance images (MRI) in the distinction of molecular breast cancer subtypes.

METHODS

The study involved 205 patients who had breast cancer at stages 1-3. Estrogen receptor (EsR), progesterone receptor (PrR), human epidermal growth factor receptor 2 (Her2), and proliferation index (Ki-67) were histologically analyzed in the tumor. The correlations between the immunohistochemistry and intrinsic subtypes were analyzed using ADC and ADCcV.

RESULTS

The maximum whole tumor (WTu) ADC (p=0.004), minimum WTu ADC (p<0.001), and mean WTu ADC (p<0.001) values were significantly smaller in the EsR-positive tumors than those in the EsR-negative tumors. Compared to the PrR-negative tumors, the PrR-positive tumors showed significantly smaller maximum, minimum, and mean WTu ADC values (p=0.005, p=0.001, and p<0.001, respectively). In the comparisons of the molecular subtypes in terms of ADCcV, the p-values indicated statistically significant differences between the luminal A (lumA) group and the triple negative (TN) group, between the luminal B (lumB) group and the TN group, and between the Her2-enriched and TN groups (p<0.001, p=0.011, and p=0.004, respectively). Considering the luminal and non-luminal groups, while a significant difference was observed between the groups considering their minimum, maximum, and mean WTu ADC values, their ADCcV values were similar (p<0.001, p=0.004, and p<0.001, respectively).

CONCLUSION

Using ADCcV in addition to ADC parameters increased the diagnostic power of diffusion weighted-MRI (DW-MRI) in the distinction of molecular subtypes of breast cancer.

Diagnostic ability of diffusion-weighted imaging using echo planar imaging with compressed SENSE (EPICS) for differentiating hepatic hemangioma and liver metastasis

PURPOSE

To assess the diagnostic abilities of diffusion-weighted imaging (DWI) with parallel imaging (PI-DWI) and that with Compressed SENSE (EPICS-DWI) for differentiating hepatic hemangiomas (HHs) and liver metastases (LMs).

METHOD

This prospective study included 30 participants with HH and/or LM who underwent PI-DWI and EPICS-DWI. Two radiologists assessed the DWI images and assigned confidence scores for hepatic lesions conspicuity using 4-point scale. One of the radiologists additionally calculated the contrast-to-noise ratio (CNR) and measured ADC value of the hepatic lesions. The conspicuity, CNR, and ADC values were compared between the two sequences. A receiver operating characteristic (ROC) analysis was performed to assess the diagnostic abilities of the two sequences for differentiating HHs and LMs.

RESULTS

The conspicuity of LMs was better in EPICS-DWI than in PI-DWI (P < .05 in both radiologists). The CNR of LMs was higher in EPICS-DWI than in PI-DWI (P = .008). No difference was found in the CNR of HHs (P = .52), ADC values for HHs (P = .79), and LMs (P = .29) between the two sequences. To differentiate between HHs and LMs, the cutoff ADC values were 1.38 × 10-3 mm2/s in PI-DWI and 1.37 × 10-3 mm2/s in EPICS-DWI. The area under the ROC curve (P = .86), sensitivity (P > .99), and specificity (P > .99) did not vary.

CONCLUSIONS

The LMs were more visible in EPICS-DWI than in PI-DWI. However, the cutoff ADC values and diagnostic abilities for differentiating HHs and LMs were almost comparable between the two sequences.

Diagnostic accuracy of intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI to differentiate benign from malignant breast lesions: A systematic review and meta-analysis

PURPOSE

Magnetic resonance imaging (MRI) can reduce the need for unnecessary invasive diagnostic tests by nearly half. In this meta-analysis, we investigated the diagnostic accuracy of intravoxel incoherent motion modeling (IVIM) and dynamic contrast-enhanced (DCE) MRI in differentiating benign from malignant breast lesions.

METHOD

We systematically searched PubMed, EMBASE, and Scopus. We included English articles reporting diagnostic accuracy for both sequences in differentiating benign from malignant breast lesions. Articles were assessed by quality assessment of diagnostic accuracy studies-2 (QUADAS-2) questionnaire. We used a bivariate effects model for standardized mean difference (SMD) analysis and diagnostic test accuracy analysis.

RESULTS

Ten studies with 537 patients and 707 (435 malignant and 272 benign) lesions were included. The D, f, Ktrans, and Kep mean values significantly differ between benign and malignant lesions. The pooled sensitivity (95 % confidence interval) and specificity were 86.2 % (77.9 %-91.7 %) and 70.3 % (56.5 %-81.1 %) for IVIM, and 93.8 % (85.3 %-97.5 %) and 68.1 % (52.7 %-80.4 %) for DCE, respectively. Combined IVIM and DCE depicted the highest area under the curve of 0.94, with a sensitivity and specificity of 91.8 % (82.8 %-96.3 %) and 87.6 % (73.8 %-94.7 %), respectively.

CONCLUSIONS

Combined IVIM and DCE had the highest diagnostic accuracy, and multiparametric MRI may help reduce unnecessary benign breast biopsy.

Preoperative staging by multimodal imaging in newly diagnosed breast cancer: Diagnostic performance of contrast-enhanced spectral mammography compared to conventional mammography, ultrasound, and MRI

PURPOSE

To compare contrast-enhanced spectral mammography (CESM) with mammography (Mx), ultrasound (US), and magnetic resonance imaging (MRI) regarding breast cancer detection rate and preoperative local staging.

MATERIAL AND METHODS

This prospective observational, single-centre study included 128 female patients (mean age 55.8 ± 11.5 years) with a newly diagnosed malignant breast tumour during routine US and Mx were prospectively enrolled. CESM and MRI examinations were performed within the study. Analysis included interreader agreement, tumour type and grade distribution, detection rates (DR), imaging morphology, contrast-enhancement and was performed by two independent readers blinded to patient history and histopathological diagnosis. Assessment of local disease extent was compared between modalities via Bland-Altman plots.

RESULTS

One-hundred-and-ten tumours were classified as NST (85.9%), 4 as ILC (3.1%) and 10 as DCIS (7.8%). DR was highest for MRI (128/128, 100.0%), followed by US (124/128, 96.9%) and CESM (123/128, 96.1%) and lowest for conventional Mx (106/128, 82.8%) (p = 0.0002). Higher breast density did not negatively affect DR of US, CESM or MRI. Local tumour extent measurements based on CESM (Bland-Altman bias 6.6, standard deviation 30.2) showed comparable estimation results to MRI, surpassing Mx (23.4/43.7) and US (35.4/40.5). Even though detection of multifocality and multicentricity was highest for CESM and MRI (p < 0.0001), second-look rates, i.e., targeted US examinations after MRI or CESM, were significantly lower for CESM (10.2% of cases) compared to MRI (16.2%) with a significantly higher true positive rate for CESM (72.0%) vs. MRI (42.5%).

CONCLUSION

CESM is a viable alternative to MRI for lesion detection and local staging in newly diagnosed malignant breast cancer and provides higher specificity in regard to second-look examinations.

MRI Breast: Current Imaging Trends, Clinical Applications, and Future Research Directions

Magnetic Resonance Imaging (MRI) is the most sensitive and advanced imaging technique in diagnosing breast cancer and is essential in improving cancer detection, lesion characterization, and determining therapy response. In addition to the dynamic contrast-enhanced (DCE) technique, functional techniques such as magnetic resonance spectroscopy (MRS), diffusion-weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) further characterize and differentiate benign and malignant lesions thus, improving diagnostic accuracy. There is now an increasing clinical usage of MRI breast, including screening in high risk and supplementary screening tools in average-risk patients. MRI is becoming imperative in assisting breast surgeons in planning breast-conserving surgery for preoperative local staging and evaluation of neoadjuvant chemotherapy response. Other clinical applications for MRI breast include occult breast cancer detection, investigation of nipple discharge, and breast implant assessment. There is now an abundance of research publications on MRI Breast with several areas that still remain to be explored. This review gives a comprehensive overview of the clinical trends of MRI breast with emphasis on imaging features and interpretation using conventional and advanced techniques. In addition, future research areas in MRI breast include developing techniques to make MRI more accessible and costeffective for screening. The abbreviated MRI breast procedure and an area of focused research in the enhancement of radiologists' work with artificial intelligence have high impact for the future in MRI Breast.

Value of apparent diffusion coefficient factor in correlation with the molecular subtypes, tumor grade, and expression of Ki-67 in breast cancer

Background

Breast cancer is known to be the most common cancer in women; in the last decade, contrast-enhanced magnetic resonance imaging has become an important tool in the diagnosis of cancer breast. Numerous studies have analyzed associations between imaging and histopathological features as well as the proliferation potential of breast cancer. The purpose of this study was to evaluate the relationship between the apparent diffusion coefficient (ADC) and expression of Ki-67 as well as tumor molecular subtype in breast cancer.

Results

No significant difference between the mean ADC value of tumors of grade I, II, and III was found. However, there was a significant difference between the mean ADC value of tumors of molecular type A and molecular type B (P = 0.000), HER2 overexpression (P = 0.018), and TN (P = 0.000), respectively. However, there was no significant difference between molecular type B, HER2 overexpression and TN. Also, no significant difference was found between the Ki-67 value of tumors of grade I, II, and III. Yet there was a significant difference between the mean ADC value of tumors of molecular type A and molecular type B (P = 0.000), HER2 overexpression (P = 0.014), and TN (P = 0.000), respectively. However, there was no significant difference between molecular type B, HER2 overexpression, and TN.

Conclusions

There is a significant inverse correlation between ADC values and Ki-67 expression. DWI and Ki-67 could be a good discriminator between tumors of molecular subtype A from other subtypes, yet it did not show a correlation with the tumor grade.

The role of MRI in comparison between benign and malignant chest wall masses in correlation with pathology

Background

Tumors that arise from the chest wall (including bone structures such as the sternum, clavicle, scapula, and ribs) or from adjacent soft tissues are less common than other parts of the body, and so the resulting unfamiliarity can make it difficult to limit the number of possible diagnoses. These tumors have a wide range of possibilities, including primary chest wall tumors arising from the bone or soft tissue, which are subdivided into malignant and benign tumors, and the secondary metastatic deposits. The aim of the study is to investigate the ability of MRI with diffusion sequence in differentiation between benign and malignant chest wall masses, which is subsequently reflected in the management of chest wall masses patients.

Main body

MRI has superior soft-tissue resolution and value for local assessment of primary tumors and accurate tissue characterization and plays a key role in preoperative staging to assess for multi-spatial and multi-compartment involvement. ADC values were obtained in 31 patients, and the mean ADC values of benign (13 patients) chest wall masses were 1.31 ± 0.50 × 10−3 mm2/s while the mean ADC values of the malignant (18 patients) chest wall masses were 0.98 ± 0.36 × 10−3 mm2/s. There was a statistically significant difference between the ADC values obtained from the malignant and benign chest wall masses (P < 0.001).

Conclusion

This study demonstrates that diffusion-weighted MR imaging is a growing imaging modality to predict the histopathological differentiation of malignant from benign chest wall masses.

Accuracy of multiparametric MRI in distinguishing the breast malignant lesions from benign lesions: a meta-analysis

BACKGROUND

The sensitivity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for detecting breast cancer was high and the specificity was relatively low. However, diffusion-weighted imaging (DWI) has a high specificity in the diagnosis of malignant lesions.

PURPOSE

To evaluate the accuracy of the multiparametric MRI (mp-MRI) in distinguishing the breast malignant lesions from the benign lesions.

MATERIAL AND METHODS

A comprehensive search of the PubMed, Embase, and Cochrane Library electronic databases was conducted up to March 2020. Data were analyzed for the following indexes: pooled sensitivity and specificity; positive likelihood ratio; negative likelihood ratio; diagnostic odds ratio; and the area under the curve.

RESULTS

A total of 2356 patients with 1604 malignant and 967 benign breast lesions were included from 22 studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve for mp-MRI were 0.93, 0.85, 6.3, 0.08, 81, and 0.96, respectively. The pooled sensitivity, specificity, and area under the curve for DCE-MRI alone were 0.95, 0.71, and 0.92, respectively. The pooled sensitivity, specificity, and area under the curve for DWI alone were 0.88, 0.84, and 0.93, respectively.

CONCLUSION

The mp-MRI did not improve the sensitivity but increased the specificity for the diagnosis of breast malignant lesions.

Role of exponential apparent diffusion coefficient in characterizing breast lesions by 3.0 Tesla diffusion-weighted magnetic resonance imaging

Objective:

To evaluate the role of exponential apparent diffusion coefficient (ADC) as a tool for differentiating benign and malignant breast lesions.

Patients and Methods:

This prospective observational study included 88 breast lesions in 77 patients (between 18 and 85 years of age) who underwent 3T breast magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI) using b-values of 0 and 800 s/mm² before biopsy. Mean exponential ADC and ADC of benign and malignant lesions obtained from DWI were compared. Receiver operating characteristics (ROC) curve analysis was undertaken to identify any cut-off for exponential ADC and ADC to predict malignancy. P value of <0.05 was considered statistically significant. Histopathology was taken as the gold standard.

Results:

According to histopathology, 65 lesions were malignant and 23 were benign. The mean ADC and exponential ADC values of malignant lesions were 0.9526 ± 0.203 × 10⁻³ mm²/s and 0.4774 ± 0.071, respectively, and for benign lesions were 1.48 ± 0.4903 × 10⁻³ mm²/s and 0.317 ± 0.1152, respectively. For both the parameters, differences were highly significant (P < 0.001). Cut-off value of ≤0.0011 mm²/s (P < 0.0001) for ADC provided 92.3% sensitivity and 73.9% specificity, whereas with an exponential ADC cut-off value of >0.4 (P < 0.0001) for malignant lesions, 93.9% sensitivity and 82.6% specificity was obtained. The performance of ADC and exponential ADC in distinguishing benign and malignant breast lesions based on respective cut-offs was comparable (P = 0.109).

Conclusion:

Exponential ADC can be used as a quantitative adjunct tool for characterizing breast lesions with comparable sensitivity and specificity as that of ADC.

Blood Oxygenation Level Dependent Magnetic Resonance Imaging (MRI), Dynamic Contrast Enhanced MRI, and Diffusion Weighted MRI for Benign and Malignant Breast Cancer Discrimination: A Preliminary Experience

Simple Summary

The aim of the study is to combine blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), dynamic contrast enhanced MRI (DCE-MRI), and diffusion weighted MRI (DW-MRI) in differentiation of benign and malignant breast lesions. The results suggest that the combined use of DCE-MRI, DW-MRI and/or BOLD-MRI does not provide a dramatic improvement compared to the use of DCE-MRI features alone, in the classification of breast lesions. However, an interesting result was the negative correlation between R₂* and D.

Abstract

Purpose. To combine blood oxygenation level dependent magnetic resonance imaging (BOLD-MRI), dynamic contrast enhanced MRI (DCE-MRI), and diffusion weighted MRI (DW-MRI) in differentiation of benign and malignant breast lesions. Methods. Thirty-seven breast lesions (11 benign and 21 malignant lesions) pathologically proven were included in this retrospective preliminary study. Pharmaco-kinetic parameters including K^trans, k^ep, v_e, and v_p were extracted by DCE-MRI; BOLD parameters were estimated by basal signal S0 and the relaxation rate R₂*; and diffusion and perfusion parameters were derived by DW-MRI (pseudo-diffusion coefficient (D_p), perfusion fraction (f_p), and tissue diffusivity (D_t)). The correlation coefficient, Wilcoxon-Mann-Whitney U-test, and receiver operating characteristic (ROC) analysis were calculated and area under the ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis and decision tree) with balancing technique and leave one out cross validation approach were considered. Results. R₂* and D had a significant negative correlation (−0.57). The mean value, standard deviation, Skewness and Kurtosis values of R₂* did not show a statistical significance between benign and malignant lesions (p > 0.05) confirmed by the ‘poor’ diagnostic value of ROC analysis. For DW-MRI derived parameters, the univariate analysis, standard deviation of D, Skewness and Kurtosis values of D* had a significant result to discriminate benign and malignant lesions and the best result at the univariate analysis in the discrimination of benign and malignant lesions was obtained by the Skewness of D* with an AUC of 82.9% (p-value = 0.02). Significant results for the mean value of K^trans, mean value, standard deviation value and Skewness of k_ep, mean value, Skewness and Kurtosis of v_e were obtained and the best AUC among DCE-MRI extracted parameters was reached by the mean value of k_ep and was equal to 80.0%. The best diagnostic performance in the discrimination of benign and malignant lesions was obtained at the multivariate analysis considering the DCE-MRI parameters alone with an AUC = 0.91 when the balancing technique was considered. Conclusions. Our results suggest that the combined use of DCE-MRI, DW-MRI and/or BOLD-MRI does not provide a dramatic improvement compared to the use of DCE-MRI features alone, in the classification of breast lesions. However, an interesting result was the negative correlation between R₂* and D.

Correlative study between ADC value and grading of invasive breast cancer

Background

Studying breast carcinoma is of great importance as it is the commonest female malignancy. Accurate preoperative assessment of disease characteristics and prognosis would be of great help in the diagnosis and treatment planning of breast cancer. The aim of this study was to evaluate the role of the apparent diffusion coefficient (ADC) value in detecting the grading of invasive breast carcinoma prior to management.

Results

There was a significant difference between the mean ADC value of tumors of grade I and III (p = 0.001) and between grade I and II (p = 0.002). However, there was no significant difference between grade II and III (p = 0.979). High ADC values were associated with low-grade tumors. The mean ADC value of 0.93 × 10–3 mm2/s showed sensitivity 98%, specificity 100%, PPV 100%, NPV 83.3%, accuracy 98.2%, AUC = 0.994, and 95% confidence interval of 0.978 to 1.000.

Conclusion

DWI is a contrast-free modality that allows for both morphological and quantitative analysis. ADC value may not be the proper modality to determine the prognosis of breast cancer due to overlap values, yet it could be a good discriminator between low- and high-grade tumors and hence predictor of breast cancer cells that would respond to chemotherapy.

Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination

PURPOSE

The purpose of this study is to assess Blood oxygenation level dependent Magnetic Resonance Imaging (BOLD-MRI) and Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) in the differentiation of benign and malignant breast lesions.

METHODS

Fifty-nine breast lesions (26 benign and 33 malignant lesions) pathologically proven in 59 patients were included in this retrospective study. As BOLD parameters were estimated basal signal S₀ and the relaxation rate R2*, diffusion and perfusion parameters were derived by DWI (pseudo-diffusion coefficient (Dp), perfusion fraction (fp) and tissue diffusivity (Dt)). Wilcoxon-Mann-Whitney U test and Receiver operating characteristic (ROC) analyses were calculated and area under ROC curve (AUC) was obtained. Moreover, pattern recognition approaches (linear discrimination analysis (LDA), support vector machine, k-nearest neighbours, decision tree) with least absolute shrinkage and selection operator (LASSO) method and leave one out cross validation approach were considered.

RESULTS

A significant discrimination was obtained by the standard deviation value of S0, as BOLD parameter, that reached an AUC of 0.76 with a sensitivity of 65%, a specificity of 85% and an accuracy of 76%. No significant discrimination was obtained considering diffusion and perfusion parameters. Considering LASSO results, the features to use as predictors were all extracted parameters except that the mean value of R2* and the best result was obtained by a LDA that obtained an AUC = 0.83, with a sensitivity of 88%, a specificity of 77% and an accuracy of 83%.

CONCLUSIONS

Good performance to discriminate benign and malignant lesions could be obtained using BOLD and DWI derived parameters with a LDA classification approach. However, these findings should be proven on larger and several dataset with different MR scanners.

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in the world. Early detection is paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is the most widely diagnosed cancer among women across the globe with a high percentage of total cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable when detected at an early stage. Hence, the use of state of the art computational approaches has been proposed as a potential alternative approach for the design and development of novel diagnostic imaging methods for breast cancer. Thus, this review provides a concise overview of past and present conventional diagnostics approaches in breast cancer detection. Further, we gave an account of several computational models (machine learning, deep learning, and robotics), which have been developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review will be helpful to academia, medical practitioners, and others for further study in this area to improve the biomedical breast cancer imaging diagnosis.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

A 32-channel receive array coil for bilateral breast imaging and spectroscopy at 7T

PURPOSE

This work describes the construction and evaluation of a bilateral 32-channel receive array for breast imaging at 7T.

METHODS

The receive array consisted of 32 receive coils, placed on two 3D-printed hemispherical formers. Each side of the receive array consisted of 16 receive loops, each loop having a corresponding detachable board with match/tune capacitors, active detuning circuitry, and a balun. Coil performance was evaluated on homogeneous canola oil phantoms using a Philips Achieva 7T system. Array coil performance was compared with a bilateral forced current excitation volume coil in transmit/receive mode and with a previously reported 16-channel unilateral coil with a similar design.

RESULTS

The 32-channel array had an increase in average SNR throughout both phantoms by a factor of five as compared with the volume coil, with SNR increases up to 10 times along the periphery and three times in the center. Noise measurements showed low interelement noise correlation (average: 5.4%; maximum: 16.8%). Geometry factor maps were acquired for various acceleration factors and showed mean geometry factors <1.2, for combined acceleration factors of up to six.

CONCLUSIONS

The improvements achieved demonstrate the clear potential for use in dynamic contrast-enhanced or diffusion-weighted MR studies, while maintaining diagnostically relevant spatial and temporal resolutions.

Generative adversarial network-based super-resolution of diffusion-weighted imaging: Application to tumour radiomics in breast cancer

Diffusion-weighted imaging (DWI) is increasingly used to guide the clinical management of patients with breast tumours. However, accurate tumour characterization with DWI and the corresponding apparent diffusion coefficient (ADC) maps are challenging due to their limited resolution. This study aimed to produce super-resolution (SR) ADC images and to assess the clinical utility of these SR images by performing a radiomic analysis for predicting the histologic grade and Ki-67 expression status of breast cancer. To this end, 322 samples of dynamic enhanced magnetic resonance imaging (DCE-MRI) and the corresponding DWI data were collected. A SR generative adversarial (SRGAN) and an enhanced deep SR (EDSR) network along with the bicubic interpolation were utilized to generate SR-ADC images from which radiomic features were extracted. The dataset was randomly separated into a development dataset (n = 222) to establish a deep SR model using DCE-MRI and a validation dataset (n = 100) to improve the resolution of ADC images. This random separation of datasets was performed 10 times, and the results were averaged. The EDSR method was significantly better than the SRGAN and bicubic methods in terms of objective quality criteria. Univariate and multivariate predictive models of radiomic features were established to determine the area under the receiver operating characteristic curve (AUC). Individual features from the tumour SR-ADC images showed a higher performance with the EDSR and SRGAN methods than with the bicubic method and the original images. Multivariate analysis of the collective radiomics showed that the EDSR- and SRGAN-based SR-ADC images performed better than the bicubic method and original images in predicting either Ki-67 expression levels (AUCs of 0.818 and 0.801, respectively) or the tumour grade (AUCs of 0.826 and 0.828, respectively). This work demonstrates that in addition to improving the resolution of ADC images, deep SR networks can also improve tumour image-based diagnosis in breast cancer.

Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis

BACKGROUND

Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC).

METHODS AND FINDINGS

In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95).

CONCLUSIONS

None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.

Added value of histogram analysis of apparent diffusion coefficient maps for differentiating triple-negative breast cancer from other subtypes of breast cancer on standard MRI

Background

Triple-negative breast cancers generally occur in young women with remarkable potential to be aggressive. It will be of great help to detect this subtype of tumor early. To retrospectively evaluate the performance of histogram analysis of apparent diffusion coefficient (ADC) maps in distinguishing triple-negative breast cancer (TNBC) from other subtypes of breast cancer (non-TNBC), when combined with magnetic resonance imaging (MRI) features.

Materials and methods

From February 2014 to December 2018, 192 patients were included in this study taking preoperative standard MRI (s-MRI) and DWI. Seventy-six of them were pathologically confirmed with TNBC and rest 116 with other subtypes. First, their clinical-pathological features and morphological characteristics on MRI were assessed, including tumor size, foci quantity, tumor shape, margin, internal enhancement, and time-signal intensity curve types, in addition to the signal intensity on T2-weighted images. Second, whole-lesion apparent diffusion coefficient (ADC) histogram analysis was executed. Finally, both univariate and multivariate regression analyses were applied to identify the most useful variables in separating TNBCs from non-TNBCs, and then their effects were evaluated following receiver operating characteristic curve analysis.

Result

Multivariate regression analysis indicated that circumscribed margin, rim enhancement, and ADC₉₀ were important predictors for TNBC. Increased area under curve (AUC) and improved specificity can be obtained when combined s-MRI and DWI (circumscribed margin+rim enhancement+ADC₉₀>1.47×10⁻³ mm²/s) is taken as the criterion, other than s-MRI (circumscribed margin+rim enhancement) alone (s-MRI+DWI vs s-MRI; AUC, 0.833 vs 0.797; specificity, 98.3% vs 89.7%; sensitivity, 68.4% vs 69.7%).

Conclusion

Circumscribed margin and rim enhancement on s-MRI and ADC₉₀ are three important elements in detecting TNBC, while ADC histogram analysis can provide additional value in this detection.

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.

A Clinical Approach to Diffusion-Weighted Magnetic Resonance Imaging in Evaluating Chest Wall Invasion of Breast Tumors

Objective:

The purpose of this study is to evaluate diffusion weighted magnetic rsonance imaging (MRI) acquisitions in delineating posterior extent of breast tumors and in predicting chest wall invasion prior to treatment. To our knowledge, there has not been any literature specifically evaluating the utility of diffusion-weighted acquisitions in chest wall invasion of breast tumors.

Materials and Methods:

A retrospective review of our breast imaging database for keywords “chest wall invasion” and “breast MRI” was performed over the last 14 years. Diffusion sequences, T1 sequences (pre and post contrast), and T2 sequences were evaluated. Apparent diffusion coefficient (ADC) values in tumor and chest wall were assessed. Imaging findings were correlated with surgical pathology.

Results:

23 patients met inclusion criteria. All 23 had loss of fat plane on T2 sequences. 22/23 had loss of fat plane on postcontrast T1 sequences. Pectoralis muscle enhancement was present in 19/23 (83%) tumors and chest wall enhancement was present 9/23 (39%) tumors. Qualitative restricted diffusion within the pectoralis muscle was present in 18/23 (71%) tumors and in the chest wall was present in 8/23 (35%) tumors. Mean ADC values were 1.15 s/mm² in the tumor and 1.29 s/mm² in the chest wall. Sensitivity, specificity, positive predictive value and negative predictive value were 100%, 36%, 63%, and 100% for chest wall enhancement respectively and 69%, 36%, 61%, and 80% for chest wall diffusion-weighted imaging restriction respectively.

Conclusion:

Diffusion weighted sequences can be helpful in characterizing chest wall invasion of breast tumors.

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.

Differentiation of breast tuberculosis and breast cancer using diffusion-weighted, T2-weighted and dynamic contrast-enhanced magnetic resonance imaging

Background

The use of multi-parametric magnetic resonance imaging (MRI) in the evaluation of breast tuberculosis (BTB).

Objectives

To evaluate the value of diffusion-weighted imaging (DWI), T2-weighted (T2W) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating breast cancer (BCA) from BTB.

Method

We retrospectively studied images of 17 patients with BCA who had undergone pre-operative MRI and 6 patients with pathologically proven BTB who underwent DCE-MRI during January 2014 to January 2015.

Results

All patients were female, with the age range of BTB patients being 23–43 years and the BCA patients being 31–74 years. Breast cancer patients had a statistically significant lower mean apparent diffusion coefficient (ADC) value (1072.10 ± 365.14), compared to the BTB group (1690.77 ± 624.05, p = 0.006). The mean T2-weighted signal intensity (T2SI) was lower for the BCA group (521.56 ± 233.73) than the BTB group (787.74 ± 196.04, p = 0.020). An ADC mean cut-off value of 1558.79 yielded 66% sensitivity and 94% specificity, whilst the T2SI cut-off value of 790.20 yielded 83% sensitivity and 83% specificity for differentiating between BTB and BCA. The homogeneous internal enhancement for focal mass was seen in BCA patients only.

Conclusion

Multi-parametric MRI incorporating the DWI, T2W and DCE-MRI may be a useful tool to differentiate BCA from BTB.

Prediction of tumor differentiation using sequential PET/CT and MRI in patients with breast cancer

OBJECTIVE

The aim of this study is to assess tumor differentiation using parameters from sequential positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) in patients with breast cancer.

METHODS

This retrospective study included 78 patients with breast cancer. All patients underwent sequential PET/CT and MRI. For fluorodeoxyglucose (FDG)-PET image analysis, the maximum standardized uptake value (SUV_max) of FDG was assessed at both 1 and 2 h and metabolic tumor volume (MTV) and total lesion glycolysis (TLG). The kinetic analysis of dynamic contrast-enhanced MRI parameters was performed using dynamic enhancement curves. We assessed diffusion-weighted imaging (DWI)-MRI parameters regarding apparent diffusion coefficient (ADC) values. Histologic grades 1 and 2 were classified as low-grade, and grade 3 as high-grade tumor.

RESULTS

Forty-five lesions of 78 patients were classified as histologic grade 3, while 26 and 7 lesions were grade 2 and grade 1, respectively. Patients with high-grade tumors showed significantly lower ADC-mean values than patients with low-grade tumors (0.99 ± 0.19 vs.1.12 ± 0.32, p = 0.007). With respect to SUV_max1, MTV2.5, and TLG2.5, patients with high-grade tumors showed higher values than patients with low-grade tumors: SUV_max1 (7.92 ± 4.5 vs.6.19 ± 3.05, p = 0.099), MTV2.5 (7.90 ± 9.32 vs.4.38 ± 5.10, p = 0.095), and TLG2.5 (40.83 ± 59.17 vs.19.66 ± 26.08, p = 0.082). However, other parameters did not reveal significant differences between low-grade and high-grade malignancies. In receiver-operating characteristic (ROC) curve analysis, ADC-mean values showed the highest area under the curve of 0.681 (95%CI 0.566-0.782) for assessing high-grade malignancy.

CONCLUSIONS

Lower ADC-mean values may predict the poor differentiation of breast cancer among diverse PET-MRI functional parameters.

Integrating dynamic contrast-enhanced magnetic resonance imaging and diffusion kurtosis imaging for neoadjuvant chemotherapy assessment of nasopharyngeal carcinoma

BACKGROUND

Since neoadjuvant chemotherapy (NAC) has proven a benefit for locally advanced nasopharyngeal carcinoma (NPC), early response evaluation after chemotherapy is important to implement individualized therapy for NPC in the era of precision medicine.

PURPOSE

To determine the combined and independent contribution between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion kurtosis imaging (DKI) in the early monitoring of NAC response for NPC.

STUDY TYPE

Prospective.

POPULATION

Fifty-three locally advanced NPC patients.

FIELD STRENGTH/SEQUENCE

Four examinations before and at 4, 20, and 40 days after NAC initiation were performed at 3T MRI including DCE-MRI and DKI (b values = 0, 500, 1000, 1500 s/mm² ).

ASSESSMENT

DCE-MRI parameters (K^trans [the volume transfer constant of Gd-DTPA], k_ep [rate constant], ν_e [the extracellular volume fraction of the imaged tissue], and ν_p [the blood volume fraction]) and DKI parameters (D_app [apparent diffusion for non-Gaussian distribution] and K_app [apparent kurtosis coefficient]) were analyzed using dedicated software.

STATISTICAL TESTS

MRI parameters and their corresponding changes were compared between responders and nonresponders after one or two NAC cycles treatment using independent-samples Student's t-test or Mann-Whitney U-test depending on the normality contribution test and then followed by logistic regression and receiver operating characteristic curve (ROC) analyses.

RESULTS

The responder group (RG) patients presented significantly higher mean K^trans and D_app values at baseline and larger Δ K ( 0 - 4 ) trans , Δv_p(0-4) , and ΔD_app(0-4) values after either one or two NAC cycles compared with the nonresponder group (NRG) patients (all P < 0.05). ROC analyses demonstrated the higher diagnostic accuracy of combined DCE-MRI and DKI model to distinguish nonresponders from responders after two NAC cycles than using DCE-MRI (0.987 vs. 0.872, P = 0.033) or DKI (0.987 vs. 0.898, P = 0.047) alone.

DATA CONCLUSION

Combined DCE-MRI and DKI models had higher diagnostic accuracy for NAC assessment compared with either model used independently.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1208-1216.

Apparent diffusion coefficient mapping using diffusion-weighted MRI: impact of background parenchymal enhancement, amount of fibroglandular tissue and menopausal status on breast cancer diagnosis

OBJECTIVES

To investigate the impact of background parenchymal enhancement (BPE), amount of fibroglandular tissue (FGT) and menopausal status on apparent diffusion coefficient (ADC) values in differentiation between malignant and benign lesions.

METHODS

In this HIPAA-compliant study, mean ADC values of 218 malignant and 130 benign lesions from 288 patients were retrospectively evaluated. The differences in mean ADC values between benign and malignant lesions were calculated within groups stratified by BPE level (high/low), amount of FGT (dense/non-dense) and menopausal status (premenopausal/postmenopausal). Sensitivities and specificities for distinguishing malignant from benign lesions within different groups were compared for statistical significance.

RESULTS

The mean ADC value for malignant lesions was significantly lower compared to that for benign lesions (1.07±0.21 x 10^-3 mm²/s vs. 1.53±0.26 x 10^-3 mm²/s) (p<0.0001). Using the optimal cut-off point of 1.30 x 10^-3 mm²/s, an area under the curve of 0.918 was obtained, with sensitivity and specificity both of 87 %. There was no statistically significant difference in sensitivities and specificities of ADC values between different groups stratified by BPE level, amount of FGT or menopausal status.

CONCLUSIONS

Differentiation between benign and malignant lesions on ADC values is not significantly affected by BPE level, amount of FGT or menopausal status.

KEY POINTS

• ADC allows differentiation between benign and malignant lesions. • ADC is useful for breast cancer diagnosis despite different patient characteristics. • BPE, FGT or menopause do not significantly affect sensitivity and specificity.

Advances in breast intervention: where are we now and where should we be?

In the past four decades, a variety of methods for minimal or non-invasive diagnosis and treatment of breast cancer have been introduced. Although breast imaging has become more and more specific for diagnosis, specimen biopsy with histopathological confirmation is still necessary. Core-needle biopsy under ultrasound guidance is the appropriate first choice for the diagnosis of most lesions. Fine-needle aspiration is of interest for identification of the presence of metastatic disease in abnormal lymph nodes. For microcalcifications, vacuum-assisted biopsy is recommended, especially with stereotactic guidance. In recent years different therapeutic techniques have been developed for the treatment of solid lesions, including breast cancer. Certainly, with the improvement of technology and medical scientific progress, it is becoming more common to use minimal- or non-invasive therapies. The proposed minimally invasive techniques may offer complete treatment of breast cancer, with better cosmetic results, less psychological stress, and shorter hospital stays. In this article, the strengths and weaknesses of the different diagnostic and therapeutic techniques are presented, and promising techniques for the future are discussed.

Magnetic Resonance Imaging in the Pre-Surgical Staging of Breast Cancer: Our Experience

PURPOSE

To investigate the clinical impact of magnetic resonance imaging (MRI) in the detection of multifocal-multicentric breast cancers, already identified by mammography and ultrasound, and analyzed histologically, to evaluate its role in preoperative staging.

MATERIALS AND METHODS

From January 2012 to February 2014, 188 patients, aged 28 to 74 years, newly diagnosed with breast cancer on conventional imaging (mammography and ultrasound) were enrolled. They underwent preoperative contrast-enhanced 3T MRI. Patients underwent surgery according to international guidelines. Results of all diagnostic procedures were compared.

RESULTS

Among the 188 patients, 163 (87%) had a unilateral and unifocal tumor at both conventional imaging; MRI diagnosed 22/22 (100%) of multifocal and multicentric tumors, the combination of mammography and ultrasound diagnosed 12/22 (54%), and mammography alone diagnosed 8/22 (36%) multifocal and multicentric tumors. MRI prompted a change in surgical strategy in 10/188 (5%) patients. This change comprised mastectomy instead of conservative surgery (n = 7) and more extensive conservative surgery (n = 3).

CONCLUSIONS

MRI was confirmed to show higher sensitivity than conventional imaging in detecting multifocal and multicentric breast cancers.

Accuracy of combined dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted imaging for breast cancer detection: a meta-analysis

BACKGROUND

Magnetic resonance imaging (MRI) is increasingly being used to examine patients with suspected breast cancer.

PURPOSE

To determine the diagnostic performance of combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) for breast cancer detection.

MATERIAL AND METHODS

A comprehensive search of the PUBMED, EMBASE, Web of Science, and Cochrane Library databases was performed up to September 2014. Statistical analysis included pooling of sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and diagnostic accuracy using the summary receiver operating characteristic (SROC). All analyses were conducted using STATA (version 12.0), RevMan (version 5.2), and Meta-Disc 1.4 software programs.

RESULTS

Fourteen studies were analyzed, which included a total of 1140 patients with 1276 breast lesions. The pooled sensitivity and specificity of combined DCE-MRI and DWI were 91.6% and 85.5%, respectively. The pooled sensitivity and specificity of DWI-MRI were 86.0% and 75.6%, respectively. The pooled sensitivity and specificity of DCE-MRI were 93.2% and 71.1%. The area under the SROC curve (AUC-SROC) of combined DCE-MRI and DWI was 0.94, the DCE-MRI of 0.85. Deeks testing confirmed no significant publication bias in all studies.

CONCLUSION

Combined DCE-MRI and DWI had superior diagnostic accuracy than either DCE-MRI or DWI alone for the diagnosis of breast cancer.

Potential of Diffusion-Weighted Imaging in the Characterization of Malignant, Benign, and Healthy Breast Tissues and Molecular Subtypes of Breast Cancer

The role of apparent diffusion coefficient (ADC) in the diagnosis of breast cancer and its association with molecular biomarkers was investigated in 259 patients with breast cancer, 67 with benign pathology, and 54 healthy volunteers using diffusion-weighted imaging (DWI) at 1.5 T. In 59 breast cancer patients, dynamic contrast-enhanced MRI (DCEMRI) was also acquired. Mean ADC of malignant lesions was significantly lower (1.02 ± 0.17 × 10⁻³ mm²/s) compared to benign (1.57 ± 0.26 × 10⁻³ mm²/s) and healthy (1.78 ± 0.13 × 10⁻³ mm²/s) breast tissues. A cutoff ADC value of 1.23 × 10⁻³ mm²/s (sensitivity 92.5%; specificity 91.1%; area under the curve 0.96) to differentiate malignant from benign diseases was arrived by receiver operating curve analysis. In 10/59 breast cancer patients, indeterminate DCE curve was seen, while their ADC value was indicative of malignancy, implying the potential of the addition of DWI in increasing the specificity of DCEMRI data. Further, the association of ADC with tumor volume, stage, hormonal receptors [estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor (HER2)], and menopausal status was investigated. A significant difference was seen in tumor volume between breast cancer patients of stages IIA and IIIA, IIB and IIIA, and IIB and III (B + C), respectively (P < 0.05). Patients with early breast cancer (n = 52) had significantly lower ADC and tumor volume than those with locally advanced breast cancer (n = 207). No association was found in ADC and tumor volume with the menopausal status. Breast cancers with ER−, PR−, and triple-negative (TN) status showed a significantly larger tumor volume compared to ER+, PR+, and non-triple-negative (nTN) cancers, respectively. Also, TN tumors showed a significantly higher ADC compared to ER+, PR+, and nTN cancers. Patients with ER− and TN cancers were younger than those with ER+ and nTN cancers. The present study demonstrated that ADC may increase the diagnostic specificity of DCEMRI and be useful for treatment management in clinical setting. Additionally, it provides an insight into characterization of molecular types of breast cancer and may serve as an indicator of metabolic reprograming underlying tumor proliferation.

Diagnostic Performance of Fused Diffusion-Weighted Imaging Using Unenhanced or Postcontrast T1-Weighted MR Imaging in Patients With Breast Cancer

To evaluate the diagnostic performance of fused diffusion-weighted imaging (DWI) using either unenhanced (UFMR) or early postcontrast T1-weighted imaging (PCFMR) to detect and characterize breast lesions in patients with breast cancer.This retrospective observational study was approved by institutional review board in our hospital and informed consents were waived. We retrospectively selected 87 consecutive patients who underwent preoperative breast magnetic resonance imaging, including DWI and definitive surgery. Both UFMR and PCFMR were reviewed by 5 radiologists for detection, lesion size, Breast Imaging Reporting and Data System final assessment, the probability of malignancy, lesion conspicuity, and apparent diffusion coefficients.A total of 129 lesions were identified by at least 2 readers on UFMR or PCFMR. Of 645 potentially detected lesions, there were 528 (82%) with UFMR and 554 (86%) with PCFMR. Malignant lesions or index cancers showed significantly higher detection rates than benign or additional lesions on both UFMR and PCFMR (P < 0.05). Area under the characteristic curves (AUCs) for predicting malignancy ranged 0.927 to 0.986 for UFMR, and 0.936 to 0.993 for PCFMR, which was not significantly different. Lesion conspicuity was significantly higher on PCFMR than UFMR (8.59 ± 1.67 vs 9.19 ± 1.36, respectively; P < 0.05) across 5 readers. Mean intraclass correlation coefficients for lesion size on UFMR and PCFMR were 0.89 and 0.92, respectively.Detection rates of index malignant lesions were similar for UFMR and PCFMR. Interobserver agreement for final assessments was reliable across 5 readers. Diagnostic accuracy for predicting malignancy with UFMR versus PCFMR was similar, although lesion conspicuity was significantly greater with the latter.

Association of Apparent Diffusion Coefficient with Disease Recurrence in Patients with Locally Advanced Cervical Cancer Treated with Radical Chemotherapy and Radiation Therapy

PURPOSE

To investigate whether volumetrically derived apparent diffusion coefficient (ADC) from pretreatment diffusion-weighted (DW) magnetic resonance (MR) imaging is associated with disease recurrence in women with locally advanced cervical cancer treated with chemotherapy and radiation therapy.

MATERIALS AND METHODS

An ethics board-approved, retrospective study was conducted in 85 women with stage IB-IVA cervical cancer treated with chemo- and radiation therapy in 2009-2013. All patients underwent MR imaging for staging, including T2-weighted and DW MR imaging series, by using a 1.5- or 3.0-T imager. The mean, median, 75th, 90th, and 95th percentile ADCs (ADCmean, ADC50, ADC75, ADC90, and ADC95, respectively) of all voxels that comprised each tumor were extracted and normalized to the mean urine ADC (nADCmean, nADC50, nADC75, nADC90, and nADC95, respectively) to reduce variability. The primary outcome was disease-free survival (DFS). Uni- and multivariable Cox regression analyses were used to evaluate the association of ADC parameters and relevant clinical variables with DFS.

RESULTS

Of the 85 women included, 62 were free of disease at last follow-up. Median follow-up was 37 months (range, 5-68 months). Significant variables at univariable analysis included T2-weighted derived tumor diameter, para-aortic nodal involvement, advanced stage, ADC90 and ADC95, nADC75, nADC90, and nADC95. Normalized parameters were more highly associated (hazard ratio per 0.01 increase in normalized ADC, 0.91-0.94; P < .04). Because nADC75, nADC90, and nADC95 were highly correlated, only nADC95 (which had the lowest P value) was included in multivariable analysis. At multivariable analysis, absolute and normalized ADC95 remained associated with DFS (hazard ratio, 0.90-0.98; P < .05).

CONCLUSION

The volumetric ADC95 may be a useful imaging metric to predict treatment failure in patients with locally advanced cervical cancer treated with chemo- and radiation therapy.

Diffusion-Weighted Imaging in 3.0 Tesla Breast MRI: Diagnostic Performance and Tumor Characterization Using Small Subregions vs. Whole Tumor Regions of Interest

Introduction

Apparent diffusion coefficient (ADC) values are increasingly reported in breast MRI. As there is no standardized method for ADC measurements, we evaluated the effect of the size of region of interest (ROI) to diagnostic utility and correlation to prognostic markers of breast cancer.

Methods

This prospective study was approved by the Institutional Ethics Board; the need for written informed consent for the retrospective analyses of the breast MRIs was waived by the Chair of the Hospital District. We compared diagnostic accuracy of ADC measurements from whole-lesion ROIs (WL-ROIs) to small subregions (S-ROIs) showing the most restricted diffusion and evaluated correlations with prognostic factors in 112 consecutive patients (mean age 56.2±11.6 years, 137 lesions) who underwent 3.0-T breast MRI.

Results

Intra- and interobserver reproducibility were substantial (κ = 0.616–0.784; Intra-Class Correlation 0.589–0.831). In receiver operating characteristics analysis, differentiation between malignant and benign lesions was excellent (area under curve 0.957–0.962, cut-off ADC values for WL-ROIs: 0.87×10⁻³ mm²s^-1; S-ROIs: 0.69×10⁻³ mm²s^-1, P<0.001). WL-ROIs/S-ROIs achieved sensitivities of 95.7%/91.3%, specificities of 89.5%/94.7%, and overall accuracies of 89.8%/94.2%. In S-ROIs, lower ADC values correlated with presence of axillary metastases (P = 0.03), high histological grade (P = 0.006), and worsened Nottingham Prognostic Index Score (P<0.05). In both ROIs, ADC values correlated with progesterone receptors and advanced stage (P<0.01), but not with HER2, estrogen receptors, or Ki-67.

Conclusions

ADC values assist in breast tumor characterization. Small ROIs were more accurate than whole-lesion ROIs and more frequently associated with prognostic factors. Cut-off values differed significantly depending on measurement procedure, which should be recognized when comparing results from the literature. Instead of using a whole lesion covering ROI, a small ROI could be advocated in diffusion-weighted imaging.

Interobserver variability of selective region-of-interest measurement protocols for quantitative diffusion weighted imaging in soft tissue masses: Comparison with whole tumor volume measurements

BACKGROUND

To assess the interobserver reliability of three selective region-of-interest (ROI) measurement protocols for apparent diffusion coefficient (ADC) quantifications in soft tissue masses (STMs) compared with whole tumor volume (WTV) ADC measurements.

METHODS

Institutional review board approval was obtained and informed consent was waived. Three observers independently measured minimum and mean ADCs of 73 benign and malignant musculoskeletal STMs using three selective methods (single-slice [SS], predefined three slices [PD], observer-based [OB]) and WTV measurements at 3.0 Tesla. Minimum and mean ADC values derived from each method were compared with WTV measurements, and inter-reader variation was assessed using the intraclass correlation coefficient (ICC). The time required for each method of ADC measurement was recorded.

RESULTS

For the SS, PD, OB, and WTV methods, minimum ADC values ((×10(-3) mm2 /s)) were 0.97, 0.78, 0.73, and 0.67, respectively, and mean ADC values ((×10(-3) mm2 /s)) were 1.49, 1.49, 1.51, and 1.49, respectively. Interobserver agreement was good to excellent for the minimum and mean ADC values for the three readers using the SS, PD, OB, and WTV (ICC range 0.78-0.90). The SS, PD and OB methods required the least amount of measurement time (14 ± 5, 40 ± 17, and 38 ± 15 s, respectively) while the reference WTV method required the longest measurement time (111 ± 54 s) (P < 0.01).

CONCLUSION

While all selective and WTV measurements offer good to excellent interobserver agreement, the selective OB method of ADC measurement results in the closest values to WTV measurements and requires significantly less measurement time than that required for the WTV method.

Breast magnetic resonance imaging performance: safety, techniques, and updates on diffusion-weighted imaging and magnetic resonance spectroscopy

Dynamic contrast-enhanced breast magnetic resonance imaging (MRI) is a well-established, highly sensitive technique for the detection and evaluation of breast cancer. Optimal performance of breast MRI continues to evolve. This article addresses breast MRI applications, covers emerging breast MRI safety concerns; outlines the technical aspects of breast MRI, including equipment and protocols at 3 T and 1.5 T; and describes current promising areas of research including diffusion-weighted imaging and magnetic resonance spectroscopy.

Quantitative radiology: applications to oncology

Oncologists, clinician-scientists, and basic scientists collect computed tomography, magnetic resonance, and positron emission tomography images in the process of caring for patients, managing clinical trials, and investigating cancer biology. As we have developed more sophisticated means for noninvasively delineating and characterizing neoplasms, these image data have come to play a central role in oncology. In parallel, the increasing complexity and volume of these data have necessitated the development of quantitative methods for assessing tumor burden, and by proxy, disease-free survival.

Assessment of axillary lymph nodes in patients with breast cancer with diffusion-weighted MR imaging in combination with routine and dynamic contrast MR imaging

PURPOSE

To assess axillary lymph nodes in patients with breast cancer with diffusion-weighted MR imaging in combination with routine and dynamic contrast MR imaging.

MATERIALS AND METHODS

Prospective study was conducted on 65 enlarged axillary lymph nodes in 34 consecutive female patients (28-64 years: mean 51 years) with breast cancer. They underwent T2-weighted, dynamic contrast-enhanced and diffusion-weighted MR imaging of the breast and axilla using a single-shot echo-planar imaging with a b factor of 0500 and 1000 s/mm². Morphologic and quantitative parameters included ADC value of the axillary lymph node which was calculated and correlated with surgical findings.

RESULTS

The mean ADC value of metastatic axillary lymph nodes was 1.08 ± 0.21 × 10⁻³ mm²/s and of benign lymph nodes was 1.58 ± 0.14 × 10⁻³ mm²s. There was statistically difference in mean ADC values between metastatic and of benign axillary lymph nodes (P = 0.001). Metastatic nodes were associated with low ADC ≤ 1.3 (OR = 8.0), short axis/long axis (TS/LS) > 0.6 (OR = 7.0) and absent hilum (OR = 6.21). When ADC of 1.3 × 10⁻³ mm²/s was used as a threshold value for differentiating metastatic from benign axillary lymph nodes, the best result was obtained with an accuracy of 95.6%, sensitivity of 93%, specificity of 100%, positive predictive value of 100 %, negative predictive value of 87.5 % and area under the curve of 0.974. Multivariate model involving combined ADC value and TS/LS improved the diagnostic performance of MR imaging with AUC of 1.00.

CONCLUSION

We concluded that combination of diffusion-weighted MR imaging with morphological and dynamic MR imaging findings helps for differentiation of metastatic from benign axillary lymph nodes.

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.