Can imaging kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging be valuable in predicting clinicopathological prognostic factors of invasive breast cancer?

MR Imaging Features Predictive of Pathologic Complete Response and Survival Outcomes for Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

PURPOSE

This study aims to evaluate the predictive value of MRI features for pathologic complete response (pCR) and survival outcomes in patients with breast cancer (BC) undergoing neoadjuvant chemotherapy (NAC).

METHODS

A retrospective analysis was conducted on 168 BC patients treated with NAC between 2018 and 2022. Pre-NAC breast MRI scans were evaluated for enhancement patterns, time-intensity curve (TIC), peritumoral edema, and background enhancement. Both pre- and post-NAC MRIs were assessed for Epeak %, mean apparent diffusion coefficient (ADC) value, and ADC ratio (mean ADC of lesion/contralateral normal breast parenchyma). Survival outcomes were analyzed using Kaplan-Meier and Cox regression models.

RESULTS

pCR was achieved in 34% of patients. MRI demonstrated a sensitivity of 74% and a specificity of 86% in predicting pCR, with an overall accuracy of 82%. The post-NAC percentage of initial peak enhancement (Epeak) was significantly lower in the pCR group (P < 0.001). Multivariate analysis identified a pre-NAC Epeak ≤ 96 (hazard ratio [HR]: 6.26, P < 0.001) and a post-NAC Epeak > 188 (HR: 18.40, P < 0.001) as independent risk factors for disease-free survival. Additionally, a lower pre-NAC ADC ratio (≤0.65) was associated with poorer overall survival (HR: 2.8, P: 0.041). Pre-NAC peritumoral edema, background enhancement, and TIC were not significant predictors of survival outcomes.

CONCLUSION

MRI features, including Epeak % and ADC ratio, are important predictors of pCR and survival outcomes in BC patients undergoing NAC. Incorporating these biomarkers into clinical practice may improve treatment planning and optimize patient outcomes.

MRI biomarkers and their correlation with the Oncotype DX test

Breast cancer (BC) has high rates of incidence and prevalence, causing significant impact in our society. Magnetic resonance imaging (MRI) plays a crucial role in its detection and staging. The Oncotype DX Breast Recurrence Score (ODXRS) test can be used to guide decision making regarding adjuvant chemotherapy (CT) in early-stage luminal BC to allow for more tailored cancer treatment. The aim of this article is to review knowledge regarding MRI biomarkers to date according to the BI-RADS® classification and the use of artificial intelligence (AI) in this imaging technique to establish its correlation with the ODXRS test. The latest studies published on AI and MRI present promising findings, and their standardisation could mark a turning point in breast radiology.

The utility of ultrafast MRI and conventional DCE-MRI for predicting histologic aggressiveness in patients with breast cancer

BACKGROUND

Prediction of histologic prognostic markers is important for determining management strategy and predicting prognosis.

PURPOSE

To identify important features of ultrafast and conventional dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) that can predict histopathologic prognostic markers in patients with breast cancer.

MATERIAL AND METHODS

Preoperative MRI scans of 158 consecutive women (mean age = 54.0 years; age range = 29-86 years) with 163 breast cancers between February 2021 and August 2022 were retrospectively reviewed. Inter-observer agreements for ultrafast MRI parameters were analyzed by two radiologists. The qualitative and quantitative MRI parameters were correlated with histopathologic prognostic markers including molecular subtypes and histologic invasiveness.

RESULTS

Inter-observer agreements for ultrafast MRI parameters were excellent (intraclass correlation coefficients of area under the kinetic curve [AUC], maximum slope [MS], maximum enhancement [ME], and slope = 0.987, 0.844, 0.822, and 0.760, respectively). Triple-negative breast cancers (TNBC) were significantly associated with rim enhancement (odds ratio [OR] = 9.4, P = 0.003) and peritumoral edema (OR = 17.9, P = 0.002), compared to luminal cancers. Invasive cancers were associated with lesion type-mass, increased delayed washout, angiovolume, ME, slope, MS, and AUC, compared to in situ cancers. In regression analysis, the combination of MS (>46.2%/s) (OR = 5.7, P = 0.046) and delayed washout (>17.5%) (OR = 17.6, P = 0.01), and that of AUC (>27,410.3) (OR = 9.6, P = 0.04), delayed washout (>17.5%) (OR = 8.9, P = 0.009), and lesion-type mass (OR = 4.6, P = 0.04) were predictive of histologic invasiveness.

CONCLUSION

Conventional DCE-MRI with ultrafast imaging can provide useful information for predicting histologic underestimation and aggressive molecular subtype. MS and AUC on ultrafast MRI can be potential imaging markers for predicting histologic upgrade from DCIS to invasive cancer with high reliability.

Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging

Background. Breast cancer is the uncontrolled proliferation of breast epithelial cells under the action of various carcinogenic factors. The evaluation of early efficacy of neoadjuvant chemotherapy for breast cancer is helpful to change the treatment plan in time. On this basis, dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) was used to evaluate the effects of neoadjuvant chemotherapy on angiogenesis and cell proliferation in breast cancer. Objective. To evaluate the effect of neoadjuvant chemotherapy on angiogenesis and cell proliferation of breast cancer by dynamic enhanced DCE-MRI. Method. 80 breast cancer patients were divided into the routine chemotherapy group (3 cycles) and neoadjuvant chemotherapy groups (3 cycles) of 40 cases each from January 2018 to June 2021. Based on conventional imaging, DCE-MRI was performed with Intera Achieva 3.0 TMR superconducting MR scanner before and after treatment. The quantitative indexes, MRI parameters, cell proliferation expression, and DCE-MRI angiogenesis were compared between the two groups. Result. The inhibition rate, Vepost, Ktranspre, ADC, Bax, Alexi, and Aurora in the neoadjuvant chemotherapy group were significantly higher than those in the conventional chemotherapy group ( $P<0.05$ ), while Kep, Ktrans, and Nek2 were significantly lower than those in the conventional chemotherapy group ( $P<0.05$ ). Vepre (cm3), Ktranspre (ml/min/100 cm3), and Ve had no significant difference ( $P>0.05$ ). Conclusion. The quantitative parameters, MRI parameters, proliferation, and expression of DCE-MRI in breast cancer patients with different chemotherapy regimens are quite different. They can be applied to the diagnosis of neoadjuvant chemotherapy in breast cancer patients with angiogenesis and cell proliferation.

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.

Texture Analysis Using Semiquantitative Kinetic Parameter Maps from DCE-MRI: Preoperative Prediction of HER2 Status in Breast Cancer

Objective

To evaluate whether texture features derived from semiquantitative kinetic parameter maps based on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can determine human epidermal growth factor receptor 2 (HER2) status of patients with breast cancer.

Materials and Methods

This study included 102 patients with histologically confirmed breast cancer, all of whom underwent preoperative breast DCE-MRI and were enrolled retrospectively. This cohort included 48 HER2-positive cases and 54 HER2-negative cases. Seven semiquantitative kinetic parameter maps were calculated on the lesion area. A total of 55 texture features were extracted from each kinetic parameter map. Patients were randomly divided into training (n = 72) and test (n = 30) sets. The least absolute shrinkage and selection operator (LASSO) was used to select features in the training set, and then, multivariate logistic regression analysis was conducted to establish the prediction models. The classification performance was evaluated by receiver operating characteristic (ROC) analysis.

Results

Among the seven prediction models, the model with features extracted from the early signal enhancement ratio (ESER) map yielded an area under the ROC curve (AUC) of 0.83 in the training set (sensitivity of 70.59%, specificity of 92.11%, and accuracy of 81.94%), and the highest AUC of 0.83 in the test set (sensitivity of 57.14%, specificity of 100.00%, and accuracy of 80.00%). The model with features extracted from the slope of signal intensity (SI_slope) map yielded the highest AUC of 0.92 in the training set (sensitivity of 82.35%, specificity of 97.37%, and accuracy of 90.28%), and an AUC of 0.79 in the test set (sensitivity of 92.86%, specificity of 68.75%, and accuracy of 80.00%).

Conclusions

Texture features derived from kinetic parameter maps, calculated based on breast DCE-MRI, have the potential to be used as imaging biomarkers to distinguish HER2-positive and HER2-negative breast cancer.

Association between Oncotype DX recurrence score and dynamic contrast-enhanced MRI features in patients with estrogen receptor-positive HER2-negative invasive breast cancer

BACKGROUND

Oncotype DX is a multigene assay used in breast cancer, and the result provided as a 'recurrence score (RS)' corresponds to the risk of a cancer recurrence and the chemotherapeutic benefit in estrogen receptor (ER)-positive human epidermal growth factor receptor (HER)2-negative invasive breast cancer. However, its accessibility is limited.

PURPOSE

To evaluate whether magnetic resonance imaging (MRI) could be used to predict Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.

MATERIAL AND METHODS

We enrolled 473 patients with ER-positive HER2-negative invasive breast cancer who underwent a preoperative MRI and Oncotype DX assay between January 2015 and December 2018. The MRI was reviewed and associations between Oncotype DX RS values were evaluated. Logistic regression analysis was used to identify independent predictors of high and low RS.

RESULTS

Of the 485 cancers, 288 (59.4%) had low (<18), 155 (31.9%) had intermediate (18-30), and 42 (8.7%) had high (≥31) RS. Multiple logistic regression analysis revealed that a round shape (odds ratio [OR] = 2.554, P = 0.089) and low proportion of washout component (OR = 1.011, P = 0.014) were associated with low RS and that heterogeneously dense (OR = 3.205, P = 0.007) or scattered fibroglandular (OR = 3.776, P = 0.005) breast tissue, a non-spiculated margin (OR = 5.435, P = 0.007), and low proportion of persistent component (OR = 1.012, P = 0.036) were associated with high RS.

CONCLUSION

MRI features showed the potential for the discrimination of Oncotype DX RS in patients with ER-positive HER2-negative invasive breast cancer.

Maximum slope of ultrafast dynamic contrast-enhanced MRI of the breast: Comparisons with prognostic factors of breast cancer

PURPOSE

To determine the relationship between the maximum slope (MS) of ultrafast dynamic contrast-enhanced (DCE)-MRI and prognostic factors of breast cancer.

METHODS

One hundred thirteen patients with 118 breast cancers were included in this study. The ultrafast DCE sequence was acquired using a higher parallel imaging factor. Its spatial resolution was 0.9 × 0.9 × 2.5 mm and its temporal resolution was 8.3 s/phase. Each lesion was automatically segmented, and the ROI of highest enhancement in the lesion was identified. In this ROI, the MS was calculated. The MS of each lesion was compared with various prognostic factors of breast cancer.

RESULTS

The MS of invasive cancer (median: 9.81%/sec) was significantly higher than that of ductal carcinoma in situ (median: 7.26%/sec) (p = 0.001). In the ROC analysis, the area under the ROC curve (AUC) was 0.7295. The MS of invasive cancer with axillary lymph node (LN) metastasis (median: 11.97%/sec) was significantly higher than that without axillary LN metastasis (median: 9.425%/sec) (p = 0.0024). In the ROC analysis, the AUC was 0.7177. In addition, the MS became significantly higher as the level of the proliferation marker ki-67 increased (correlation coefficient: 0.3317) (p = 0.0009).

CONCLUSIONS

MS of ultrafast DCE-MRI is useful for predicting the prognostic factors of breast cancer. Higher maximum slope (MS) is significantly associated with an invasive breast cancer component. Higher MS is significantly associated with an axillary lymph node metastasis. MS becomes significantly higher with increasing ki-67 (a proliferation marker). Ultrafast MRI is useful for predicting the prognostic factors of breast cancer.

Optoacoustic Imaging and Gray-Scale US Features of Breast Cancers: Correlation with Molecular Subtypes

Background Optoacoustic imaging can assess tumor hypoxia coregistered with US gray-scale images. The combination of optoacoustic imaging and US may have a role in distinguishing breast cancer molecular subtypes. Purpose To investigate whether optoacoustic US feature scores correlate with breast cancer molecular subtypes. Materials and Methods A total of 1972 women (with a total of 2055 breast masses) underwent prebiopsy optoacoustic US in a prospective multi-institutional study between December 2012 and September 2015. Seven readers blinded to pathologic diagnosis scored gray-scale US and optoacoustic US features of the known cancers. Optoacoustic US features within (internal) and outside of the tumor boundary (external) were scored. Immunohistochemistry findings were obtained from pathology reports. Multinomial logistic regression analysis was used to fit the US scores, adding optoacoustic US features to the model to investigate the incremental benefit of each feature. Kruskal-Wallis tests were used to analyze the relationship between molecular subtypes and feature scores. Results Among 653 invasive cancers identified in 629 women, a total of 532 cancers in 519 women, all of which had molecular markers available, were included in the analysis. Mean age ± standard deviation was 57.9 years ± 12.6. Mean total external optoacoustic US feature scores of luminal (A and B) breast cancers were higher (9.9 vs 8.8; P < .05) and total internal scores were lower (6.8 vs 7.7; P < .001) than those of triple-negative and human epidermal growth factor receptor 2-positive (HER2+) cancers. A multinomial logistic regression model showed that optoacoustic internal vessel (odds ratio [OR], 0.6; 95% confidence interval [CI]: 0.5, 0.8; P = .002), optoacoustic internal blush (OR, 0.7; 95% CI: 0.5, 0.9; P = .02), and optoacoustic internal hemoglobin (OR, 0.6; 95% CI: 0.5, 0.8; P = .001) were associated with classification of luminal versus triple-negative and HER2+ cancer subtypes. Conclusion Combined optoacoustic US imaging and gray-scale US features may help distinguish luminal breast cancers from triple-negative and human epidermal growth factor receptor 2-positive cancers. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Mann in this issue.