Differentiation Between Luminal-A and Luminal-B Breast Cancer Using Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Prediction of the Nottingham prognostic index and molecular subtypes of breast cancer through multimodal magnetic resonance imaging

PURPOSE

To explore the ability of intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) and background parenchyma enhancement (BPE) to predict the Nottingham prognostic index (NPI) and molecular subtypes of breast cancer (BC).

MATERIALS AND METHODS

In this study, 93 patients with BC were included, and they all underwent DKI, IVIM and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) examinations. The corresponding mean kurtosis value (MK), pure diffusion (MD), perfusion fraction (f), pseudo diffusion coefficient (D*), true diffusion coefficient (D), and BPE were measured. We used logistic regression analysis to investigate the relevance between the NPI, molecular subtypes and variables. The diagnostic efficacy was analyzed using receiver operating characteristic curves (ROC).

RESULTS

The MD and D values of the high-level NPI group were significantly lower than those of the low-level NPI group (p < 0.01), and the f value of the high-level NPI group was obviously higher than that of low-level NPI group (p < 0.001). The area under curve (AUC) of the combined model (f + D) was 0.824. Comparing with non-Luminal subtypes, the Luminal subtypes showed obviously lower MK, f and D*, and the AUC of the combined model (MK + f + D*) was 0.785. In comparison to other subtypes, the MK and D* values of triple-negative subtype were higher than other subtypes, and the combined model (MK + D*) represented an AUC of 0.865.

CONCLUSION

The quantitative parameters of DKI and IVIM have vital value in predicting the NPI and molecular subtypes of BC, while BPE could not provide additional information. Besides, these combined models can obviously improve the prediction performance.

Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis

PURPOSE

The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis.

MATERIAL AND METHODS

Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis.

RESULTS

A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively.

CONCLUSION

Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.

Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer

OBJECTIVE

To analyze the factors related to the efficacy of neoadjuvant therapy for breast cancer and find appropriate evaluation methods for evaluating the efficacy of neoadjuvant therapy METHODS: A total of 143 patients with breast cancer treated by neoadjuvant chemotherapy at Baotou Cancer Hospital were retrospectively analyzed. The chemotherapy regimen was mainly paclitaxel combined with carboplatin for 1 week, docetaxel combined with carboplatin for 3 weeks, and was replaced with epirubicin combined with cyclophosphamide after evaluation of disease progression. All HER2-positive patients were treated with simultaneous targeted therapy, including trastuzumab single-target therapy and trastuzumab combined with pertuzumab double-target therapy. Combined with physical examination, color Doppler ultrasound, and magnetic resonance imaging (MRI), a systematic evaluation system was initially established-the "triple evaluation method." A baseline evaluation was conducted before treatment. The efficacy was evaluated by physical examination and color Doppler every cycle, and the efficacy was evaluated by physical examination, color Doppler, and MRI every two cycles.

RESULTS

The increase in ultrasonic blood flow after treatment could affect the efficacy of monitoring. The presence of two preoperative time-signal intensity curves is a therapeutically effective protective factor for inflow. The triple evaluation determined by physical examination, color Doppler ultrasound, and MRI in determining clinical efficacy is consistent with the effectiveness of the pathological gold standard.

CONCLUSION

The therapeutic effect of neoadjuvant therapy can be better evaluated by combining clinical physical examination, color ultrasound, and nuclear magnetic resonance evaluation. The three methods complement each other to avoid the insufficient evaluation of a single method, which is convenient for most prefecty-level hospitals. Additionally, this method is simple, feasible, and suitable for promotion.

Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes

Objective

To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models.

Materials and Methods

A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-D, IVIM-D*, IVIM-f, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov-Smirnov test, Mann-Whitney U test, Spearman's rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations.

Results

The histogram metrics of Mono-ADC, IVIM-D, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive vs. ER-negative groups, progesterone receptor (PR)-positive vs. PR-negative groups, Luminal vs. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive vs. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) vs. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups.

Conclusions

Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of breast cancer.

Diffusion Breast MRI: Current Standard and Emerging Techniques

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Sigmoid model analysis of breast dynamic contrast-enhanced MRI: Distinguishing between benign and malignant breast masses and breast cancer subtype prediction

Dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is performed to distinguish between benign and malignant lesions by evaluating the changes in signal intensity of the acquired image (kinetic curve). This study aimed to verify whether the existing breast DCE‐MRI analyzed by the sigmoid model can accurately distinguish between benign and invasive ductal carcinoma (IDC) and predict the subtype.

A total of 154 patients who underwent breast MRI for detailed breast mass examinations were included in this study (38 with benign masses and 116 with IDC. The sigmoid model involved the acquisition of images at seven timepoints in 1‐min intervals to determine the change in signal intensity before and after contrast injection. From this curve, the magnitude of the increase in signal intensity in the early phase, the time to reach the maximum increase, and the slopes in the early and late phases were calculated. The Mann–Whitney U‐test was used for the statistical analysis.

The IDC group exhibited a significantly larger and faster signal increase in the early phase and a significantly smaller rate of increase in the late phase than the benign group (P < 0.001). The luminal A‐like group demonstrated a significantly longer time to reach the maximum signal increase rate than other IDC subtypes (P < 0.05).

The sigmoid model analysis of breast DCE‐MRI can distinguish between benign lesions and IDC and may also help in predicting luminal A‐like breast cancer.

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.

Diffusion-Weighted Imaging of Different Breast Cancer Molecular Subtypes: A Systematic Review and Meta-Analysis

Background

Magnetic resonance imaging can be used to diagnose breast cancer (BC). Diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure.

Objectives

This analysis aimed to compare ADC values between molecular subtypes of BC based on a large sample of patients.

Method

The MEDLINE library and Scopus database were screened for the associations between ADC and molecular subtypes of BC up to April 2020. The primary end point of the systematic review was the ADC value in different BC subtypes. Overall, 28 studies were included.

Results

The included studies comprised a total of 2,990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), luminal B in 899 (30.1%), human epidermal growth factor receptor (Her2)-enriched in 597 (20.0%), and triple-negative in 629 (21.0%). The mean ADC values of the subtypes were as follows: luminal A: 0.99 × 10^-3 mm²/s (95% CI 0.94-1.04), luminal B: 0.97 × 10^-3 mm²/s (95% CI 0.89-1.05), Her2-enriched: 1.02 × 10^-3 mm²/s (95% CI 0.95-1.08), and triple-negative: 0.99 × 10^-3 mm²/s (95% CI 0.91-1.07).

Conclusions

ADC values cannot be used to discriminate between molecular subtypes of BC.

Prediction of Prognostic Factors and Genotypes in Patients With Breast Cancer Using Multiple Mathematical Models of MR Diffusion Imaging

Purpose

To explore the clinical value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) based on diffusion-weighted MRI (DW-MRI) for predicting genotypes and prognostic factors of breast cancer.

Materials and Methods

A total of 227 patients with breast cancer confirmed by pathology were reviewed retrospectively. Diffusion-weighted imaging (DWI), IVIM, and DKI were performed in all patients. The corresponding ADC, true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), perfusion fraction (f), mean diffusion rate (MD), and mean kurtosis value (MK) were measured. Multivariate logistic regression analysis and receiver operating characteristic (ROC) curve were used to analyze the diagnostic efficacy in predicting the Nottingham prognostic index (NPI), the expression of antigen Ki-67, and the molecular subtypes of breast cancer. The nomogram of the combined genotype-prediction model was established based on the multivariate logistic regression model results.

Results

D* and MK values were significantly higher in the high-grade Nottingham group (NPI ≥ 3.4) than the low-grade Nottingham group (NPI < 3.4) (p < 0.01). When D* ≥ 30.95 × 10⁻³ mm²/s and MK ≥ 0.69, the NPI tended to be high grade (with areas under the curve (AUCs) of 0.712 and 0.647, respectively). The combination of D* and MK demonstrated the highest AUC of 0.734 in grading NPI with sensitivity and accuracy of 71.7% and 77.1%, respectively. Additionally, higher D*, f, and MK and lower ADC and D values were observed in the high Ki-67 than low Ki-67 expression groups (p < 0.05). The AUC of the combined model (D + D* + f + MK) was 0.755, being significantly higher than that of single parameters (Z = 2.770~3.244, p = 0.001~0.006) in distinguishing high from low Ki-67 expression. D* and f values in the Luminal A subtype were significantly lower than in other subtypes (p < 0.05). Luminal B showed decreased D value compared with other subtypes (p < 0.05). The HER-2-positive subtype demonstrated increased ADC values compared with the Luminal B subtype (p < 0.05). Luminal A/B showed significantly lower D, D*, MD, and MK than the non-Luminal subtypes (p < 0.05). The combined model (D + D* + MD + MK) showed an AUC of 0.830 in diagnosing the Luminal and non-Luminal subtypes, which is significantly higher than that of a single parameter (Z = 3.273~4.440, p < 0.01). f ≥ 54.30% [odds ratio (OR) = 1.038, p < 0.001] and MK ≥ 0.68 (OR = 24.745, p = 0.012) were found to be significant predictors of triple-negative subtypes. The combination of f and MK values demonstrated superior diagnostic performance with AUC, sensitivity, specificity, and accuracy of 0.756, 67.5%, 77.5%, and 82.4%, respectively. Moreover, as shown in the calibration curve, strong agreements were observed between nomogram prediction probability and actual findings in the prediction of genotypes (p = 0.22, 0.74).

Conclusion

DWI, IVIM, and DKI, as MR diffusion imaging techniques with different mathematical models showed potential to identify the prognosis and genotype of breast cancer. In addition, the combination of these three models can improve the diagnostic efficiency and thus may contribute to opting for an appropriate therapeutic approach in clinic treatment.

Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes

Objectives

To assess breast cancer receptor status and molecular subtypes by using the CAIPIRINHA-Dixon-TWIST-VIBE and readout-segmented echo-planar diffusion weighted imaging techniques.

Methods

A total of 165 breast cancer patients were retrospectively recruited. Patient age, estrogen receptor, progesterone receptor, human epidermal growth factorreceptor-2 (HER-2) status, and the Ki-67 proliferation index were collected for analysis. Quantitative parameters (K^trans, V_e, K_ep), semiquantitative parameters (W_-in, W_-out, TTP), and apparent diffusion coefficient (ADC) values were compared in relation to breast cancer receptor status and molecular subtypes. Statistical analysis were performed to compare the parameters in the receptor status and molecular subtype groups.Multivariate analysis was performed to explore confounder-adjusted associations, and receiver operating characteristic curve analysis was used to assess the classification performance and calculate thresholds.

Results

Younger age (<49.5 years, odds ratio (OR) =0.95, P=0.004), lower K_ep (<0.704,OR=0.14, P=0.044),and higher TTP (>0.629 min, OR=24.65, P=0.011) were independently associated with progesterone receptor positivity. A higher TTP (>0.585 min, OR=28.19, P=0.01) was independently associated with estrogen receptor positivity. Higher K_ep (>0.892, OR=11.6, P=0.047), lower TTP (<0.582 min, OR<0.001, P=0.004), and lower ADC (<0.719 ×10^-3 mm²/s, OR<0.001, P=0.048) had stronger independent associations with triple-negative breast cancer (TNBC) compared to luminal A, and those parameters could differentiate TNBC from luminal A with the highest AUC of 0.811.

Conclusions

K_ep and TTP were independently associated with hormone receptor status. In addition, the K_ep, TTP, and ADC values had stronger independent associations with TNBC than with luminal A and could be used as imaging biomarkers for differentiate TNBC from Luminal A.

Intravoxel incoherent motion magnetic resonance imaging for breast cancer: A comparison with molecular subtypes and histological grades

PURPOSE

The purpose of this paper is to investigate whether the IVIM parameters (D, D *, f) helps to determine the molecular subtypes and histological grades of breast cancer.

METHODS

Fifty-one patients with breast cancer were included in the study. All subjects were examined by 3 T Magnetic Resonance Imaging (MRI). Diffusion-weighted imaging (DWI) was undertaken with 16 b-values. IVIM parameters [D (true diffusion coefficient), D* (pseudo-diffusion coefficient), f (perfusion fraction)] were calculated. Histopathological reports were reviewed to histological grade, histological type, and immunohistochemistry. IVIM parameters of tumors with different histological grades and molecular subtypes were compared.

RESULTS

D* and f were significantly different between molecular subtypes (p = 0.019, p = 0.03 respectively). D* and f were higher in the HER-2 group and lower in Triple negative (-) group (D*:36.8 × 10^-3 ± 5.3 × 10^-3 mm²/s, f:29.5%, D*:29.8 × 10^-3 ± 5.6 × 10^-3 mm²/s, f:21.5% respectively). There was a significant difference in D* and f between HER-2 and Triple (-) subgroups (p = 0,028, p = 0.024, respectively). D* was also significantly different between the HER-2 group and the Luminal group (p = 0,041). While histological grades increase, D and f values tend to decrease, and D* tends to increase. While the Ki-67 index increases, D* and f values tend to increase, and D tend to decrease.

CONCLUSION

D* and f values measured with IVIM imaging were useful for assessing breast cancer molecular subtyping. IVIM imaging may be an alternative to breast biopsy for sub-typing of breast cancer with further research.

Correlation of prognostic factors with MRI findings in malignant breast lesions

Background

Magnetic resonance imaging (MRI) of the breast represents the most sensitive imaging modality in the detection of breast cancer, with a reported sensitivity between 94 and 100%. We aim to detect the correlation between MRI findings and pathologically detected prognostic factors in malignant breast lesions.

Breast parenchymal density distribution, background parenchymal enhancement pattern, lesion’s morphologic features, T2WI signal characteristics, contrast enhancement, time/signal intensity curves, and lesions localizations in breast were evaluated using dynamic MRI images. Histopathological diagnosis, maximum measurements of the lesion, histological grade, presence of estrogen and/or progesterone receptors, c-erb B2, and Ki-67 parameters were noted as prognostic factors.

Results

We cannot detect any relationship between the breast parenchymal density and prognostic factors. Mild background breast enhancement is related with ER presence, a good prognostic factor. Histopathological grade of the lesions augmented with the increase in the lesion diameters. ADC values are not related with prognostic factors.

Conclusion

A mild background enhancement, an intermediate signal intensity on T2WI, a high tpeak value, and absence of pathological axillary lymph node are found to be related with good prognostic factors. An irregular contour, a huge diameter, having a type III kinetic curve, a high slopei value, and presence of pathological axillary lymph node are found to be related with poor prognostic factors. MRI can be used to predict prognostic factors in breast cancer cases.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Quantitative Differentiation of Breast Tumors: A Meta-Analysis

Objectives: The diagnostic performance of intravoxel incoherent motion diffusion–weighted imaging (IVIM-DWI) in the differential diagnosis of breast tumors remains debatable among published studies. Therefore, this meta-analysis aimed to pool relevant evidence regarding the diagnostic performance of IVIM-DWI in the differential diagnosis of breast tumors.

Methods: Studies on the differential diagnosis of breast lesions using IVIM-DWI were systemically searched in the PubMed, Embase and Web of Science databases in recent 10 years. The standardized mean difference (SMD) and 95% confidence intervals of the apparent diffusion coefficient (ADC), tissue diffusivity (D), pseudodiffusivity (D^*), and perfusion fraction (f) were calculated using Review Manager 5.3, and Stata 12.0 was used to pool the sensitivity, specificity, and area under the curve (AUC), as well as assess publication bias and heterogeneity. Fagan's nomogram was used to predict the posttest probabilities.

Results: Sixteen studies comprising 1,355 malignant and 362 benign breast lesions were included. Most of these studies showed a low to unclear risk of bias and low concerns regarding applicability. Breast cancer had significant lower ADC (SMD = −1.38, P < 0.001) and D values (SMD = −1.50, P < 0.001), and higher f value (SMD = 0.89, P = 0.001) than benign lesions, except D^* value (SMD = −0.30, P = 0.20). Invasive ductal carcinoma showed lower ADC (SMD = 1.34, P = 0.01) and D values (SMD = 1.04, P = 0.001) than ductal carcinoma in situ. D value demonstrated the best diagnostic performance (sensitivity = 86%, specificity = 86%, AUC = 0.91) and highest post-test probability (61, 48, 46, and 34% for D, ADC, f, and D^* values) in the differential diagnosis of breast tumors, followed by ADC (sensitivity = 76%, specificity = 79%, AUC = 0.85), f (sensitivity = 80%, specificity = 76%, AUC = 0.85) and D^* values (sensitivity = 84%, specificity = 59%, AUC = 0.71).

Conclusion: IVIM-DWI parameters are adequate and superior to the ADC in the differentiation of breast tumors. ADC and D values can further differentiate invasive ductal carcinoma from ductal carcinoma in situ. IVIM-DWI is also superior in identifying lymph node metastasis, histologic grade, and hormone receptors, and HER2 and Ki-67 status.

Perfusion-driven Intravoxel Incoherent Motion (IVIM) MRI in Oncology: Applications, Challenges, and Future Trends

Recent developments in MR hardware and software have allowed a surge of interest in intravoxel incoherent motion (IVIM) MRI in oncology. Beyond diffusion-weighted imaging (and the standard apparent diffusion coefficient mapping most commonly used clinically), IVIM provides information on tissue microcirculation without the need for contrast agents. In oncology, perfusion-driven IVIM MRI has already shown its potential for the differential diagnosis of malignant and benign tumors, as well as for detecting prognostic biomarkers and treatment monitoring. Current developments in IVIM data processing, and its use as a method of scanning patients who cannot receive contrast agents, are expected to increase further utilization. This paper reviews the current applications, challenges, and future trends of perfusion-driven IVIM in oncology.

Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer

PURPOSE

The purpose of this study was to assess the clinical value of conventional magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) features for predicting the risk of axillary lymph node (ALN) metastasis in patients with breast cancer.

METHODS

This retrospective study involved 265 patients with breast cancer who underwent 3.0 T breast magnetic resonance imaging examinations prior to surgery and other treatment. Of these, 119 underwent IVIM examination. The features of MRI and IVIM and postoperative pathologic results were collected. The association of MRI features of breast cancer with ALN metastasis were determined by univariate and multivariate analyses. Comparison of IVIM parameters between breast cancer patients with and without ALN metastasis was performed using the Mann-Whitney U test.

RESULTS

Among the 265 patients, 144 (54.3%) had ALN metastasis, and 121 (45.7%) did not. The size and shape of the tumours, T2WI signal, inhomogeneous enhancement, washout intensity-time curves and the values of slow ADC, fast ADC and fraction of fast ADC parameters were significantly associated with ALN metastasis. The AUC of conventional MRI for diagnosing axillary lymph node metastasis was 0.722. The AUC of MRI combined with slow ADC, fast ADC and fraction of fast ADC parameters that were used to diagnose breast cancer with ALN metastasis were 0.814, 0.803 and 0.900, respectively.

CONCLUSIONS

The features of IVIM parameters and conventional MRI can be used to predict the ALN metastasis in patients with breast cancer. MRI combined with fraction of fast ADC showed higher diagnostic efficiency for ALN metastasis in breast cancer than MRI did.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Diffusion MRI of the breast: Current status and future directions

Diffusion-weighted imaging (DWI) is increasingly being incorporated into routine breast MRI protocols in many institutions worldwide, and there are abundant breast DWI indications ranging from lesion detection and distinguishing malignant from benign tumors to assessing prognostic biomarkers of breast cancer and predicting treatment response. DWI has the potential to serve as a noncontrast MR screening method. Beyond apparent diffusion coefficient (ADC) mapping, which is a commonly used quantitative DWI measure, advanced DWI models such as intravoxel incoherent motion (IVIM), non-Gaussian diffusion MRI, and diffusion tensor imaging (DTI) are extensively exploited in this field, allowing the characterization of tissue perfusion and architecture and improving diagnostic accuracy without the use of contrast agents. This review will give a summary of the clinical literature along with future directions. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;52:70-90.

Dynamic contrast-enhanced perfusion parameters in ovarian cancer: Good accuracy in identifying high HIF-1α expression

BACKGROUND

Hypoxia significantly influences treatment response and clinical outcome in solid tumors. A noninvasive marker for hypoxia will help physicians in treatment planning and encourage the efficient use of hypoxia targeted therapies. The purpose of this study was to investigate whether pharmacokinetic dynamic contrast-enhanced (DCE) perfusion parameters are associated with a specific marker of hypoxia, hypoxia-inducible factor 1 alpha (HIF-1α) in ovarian cancer (OC).

MATERIALS AND METHODS

Thirty-eight patients with primary OC were enrolled in this prospective study approved by the local ethical committee. Patients underwent dynamic gadolinium-enhanced 3.0 T MRI as part of their staging investigations. Pharmacokinetic perfusion parameters, including a rate constant for transfer of contrast agent from plasma to extravascular extracellular space (EES) (Ktrans) and a rate constant from EES to plasma (Kep), were measured by drawing two types of regions of interest (ROIs): a large solid lesion (L-ROI) and a solid, most enhancing small area (S-ROI) (NordicICE platform). Tissue samples for immunohistochemical analysis were collected during surgery. Kruskal-Wallis, Mann-Whitney U and Chi-square tests were used in statistical analyses. Receiver Operating Characteristic curve analyzes were done for DCE parameters to discriminate high HIF-1α expression.

RESULTS

Pharmacokinetic perfusion parameters Ktrans and Kep were inversely associated with HIF-1α expression (Ktrans L-ROI P = 0.021; Ktrans S-ROI P = 0.018 and Kep L-ROI P = 0.032; Kep S-ROI P = 0.033). Ktrans and Kep showed good accuracy in identifying high HIF-1α expression (AUC = 0.832 Ktrans L-ROI; 0.840 Ktrans S-ROI; 0.808 Kep L-ROI and 0.808 Kep L-ROI).

CONCLUSION

This preliminary study demonstrated that pharmacokinetic DCE-MRI perfusion parameters are associated with the hypoxia specific marker, HIF-1α in OC. DCE-MRI may be a useful supplementary tool in the characterization of OC tumors in a staging investigation.

DCE-MRI in Human Gliomas: A Surrogate for Assessment of Invasive Hypoxia Marker HIF-1Α Based on MRI-Neuronavigation Stereotactic Biopsies

RATIONALE AND OBJECTIVES

The purpose of this study was to correlate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters with data from a specific marker of hypoxia, hypoxia-inducible factor 1α (HIF-1α), in human gliomas on a point-to-point basis by using coregistered magnetic resonance imaging and frameless stereotactic biopsies.

MATERIALS AND METHODS

Thirty-four patients with treatment-naive gliomas underwent DCE, axial T1-weighted, T2-weighted, T2-weighted fluid acquisition of inversion recovery, and three-dimensional T1-weighted brain volume with gadolinium contrast enhancement sequences on a 3.0-T magnetic resonance scanner before stereotactic surgery. Quantitative perfusion indices such as endothelial transfer constant, fractional extravascular extracellular space volume, fractional plasma volume, and reflux rate were measured at corresponding stereotactic biopsy sites. Each sample was considered an independent measurement, and its histology grade was diagnosed. HIF-1α expression was quantified from the point-to-point biopsy tissues. Analyses of receiver operating characteristic curves were done for HIF-1α to discriminate different grades of glioma. To look for correlations between immunohistochemical parameters and DCE indices, Spearman's correlation coefficient was used.

RESULTS

Seventy biopsy samples from 34 subjects were included in the analysis. Mean immunoreactivity scores of HIF-1α were 2.75 ± 1.11 for grade II (n = 24), 6.20 ± 2.33 for grade III (n = 20), and 10.46 ± 2.42 for grade IV (n = 26). HIF-1α showed very good-to-excellent accuracy in discriminating grade II from III, III from IV, and II from IV (area under the curve = 0.838, 0.862, and 0.994, respectively). Endothelial transfer constant and fractional extravascular extracellular space volume showed a significantly positive correlation with HIF-1α expression (r = 0.686, P < .001; r = 0.549, P < .001, respectively).

CONCLUSION

Our study demonstrated HIF-1α to be a significant predictor of different grades of gliomas with high sensitivity and specificity. DCE-MRI is a useful, noninvasive imaging tool for quantitative evaluation of HIF-1α, and its parameters may be used as a surrogate for HIF-1α expression.

Diagnostic and prognostic values of contrast‑enhanced ultrasound combined with diffusion‑weighted magnetic resonance imaging in different subtypes of breast cancer

The present study aimed to investigate the diagnostic and prognostic values of contrast-enhanced ultrasound (CEUS) combined with diffusion-weighted magnetic resonance imaging (DW-MRI) in different subtypes of breast cancer (BC). CEUS and DW-MRI were conducted in 232 patients with BC prior to surgical treatment. Patients were categorized as having the luminal A subtype, the luminal B subtype, triple-negative subtype or the human epidermal growth factor receptor 2 (Her-2)-positive subtype according to their expression of the estrogen receptor (ER), progesterone receptor (PR) and Her-2, as detected by immunohistochemistry. The CEUS and DW-MRI parameters of patients with different subtypes of BC were obtained and analyzed. The risk factors for the prognosis of patients with different subtypes of BC were analyzed using Kaplan-Meier and COX regression analyses. The diagnostic accuracy rate of CEUS combined with DW-MRI (93.10%) was higher than that of CEUS (88.79%) or DW-MRI (82.33%) alone. The local recurrence rate and distant metastasis rate of the Her-2-positive subtype were the highest among all the subtypes. Furthermore, patients with Her-2-positive BC exhibited a higher proportion of lesions with indistinct margins and histological grade III. Lymph node metastasis and BC subtype were independent risk factors for the prognosis of BC. The overall survival and disease-free survival of patients with the luminal A subtype were higher than those of patients with the Her-2-positive subtype. The results of the current study therefore indicate that CEUS combined with DW-MRI is more effective at diagnosing the different subtypes of BC than either CEUS or DW-MRI alone.