Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC)

Predicting axillary residual disease after neoadjuvant therapy in breast cancer using baseline MRI and ultrasound

OBJECTIVES

To predict axillary node residual disease in women treated for node-positive breast cancer (BC) by neoadjuvant therapy (NAT), using breast BI-RADS MRI features and axillary ultrasound at baseline.

MATERIAL AND METHODS

In this single-center, retrospective study, women with node-positive BC who underwent NAT between 2016 and 2021 were included. Pre-treatment axillary US and breast MRIs were evaluated using the BI-RADS lexicon and T2 features, including Breast Edema Score. Univariate and multivariate logistic regression analyses were conducted for the prediction of axillary residual disease (ARD). A multivariable model based on logistic regression was trained and evaluated on randomly split train and test sets (7:3 ratio).

RESULTS

Out of the 141 women, 41% had post-NAT ARD. Axillary metastasis was independently associated with luminal subtype (odds ratio (OR), 25.5; p < 0.001), anterior tumor location (OR, 14.1; p = 0.008), and cortical thickening ≥ 7 mm (OR, 6.09; p = 0.002). Intratumoral T2 high signal intensity was protective (OR, 0.16; p = 0.006), while Ki67 had a marginal association (p = 0.064). In the training and test sets, the model, which is available online, achieved AUCs of 0.860 (95% CI: 0.783-0.936) and 0.843 (95% CI: 0.714-0.971), respectively. Anterior depth location and cortical thickening greater than 7 mm were also independently associated with post-NAT axillary burden.

CONCLUSION

Adjusting for BC subtype and KI-67 index, the anterior third location of BC, a cortical thickness greater than 7 mm, and the absence of intratumoral T2 hyperintensity is predictive of ARD after NAT.

KEY POINTS

Question What baseline imaging-based predictive models can identify patients at risk of persistent nodal disease after neoadjuvant therapy? Findings Baseline US cortical thickness superior to 7 mm, anterior tumor location, and absence of an intratumoral high signal on T2-weighted MRI predict residual axillary disease. Clinical relevance Our predictive model, available online at: litoic.shinyapps.io/LNPred_Apps , including breast cancer subtype, Ki-67 index level, breast cancer location, intratumoral signal intensity on T2WI, and initial lymph node thickness, could guide post-NAT axillary management.

Preoperative MRI Features Associated With Axillary Nodal Burden and Disease-Free Survival in Patients With Early-Stage Breast Cancer

OBJECTIVE

To investigate the potential association among preoperative breast MRI features, axillary nodal burden (ANB), and disease-free survival (DFS) in patients with early-stage breast cancer.

MATERIALS AND METHODS

We retrospectively reviewed 297 patients with early-stage breast cancer (cT1-2N0M0) who underwent preoperative MRI between December 2016 and December 2018. Based on the number of positive axillary lymph nodes (LNs) determined by postoperative pathology, the patients were divided into high nodal burden (HNB; ≥3 positive LNs) and non-HNB (<3 positive LNs) groups. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors associated with ANB. Predictive efficacy was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Univariable and multivariable Cox proportional hazards regression analyses were performed to determine preoperative features associated with DFS.

RESULTS

We included 47 and 250 patients in the HNB and non-HNB groups, respectively. Multivariable logistic regression analysis revealed that multifocality/multicentricity (adjusted odds ratio [OR] = 3.905, 95% confidence interval [CI]: 1.685-9.051, P = 0.001) and peritumoral edema (adjusted OR = 3.734, 95% CI: 1.644-8.479, P = 0.002) were independent risk factors for HNB. Combined peritumoral edema and multifocality/multicentricity achieved an AUC of 0.760 (95% CI: 0.707-0.807) for predicting HNB, with a sensitivity and specificity of 83.0% and 63.2%, respectively. During the median follow-up period of 45 months (range, 5-61 months), 26 cases (8.75%) of breast cancer recurrence were observed. Multivariable Cox proportional hazards regression analysis indicated that younger age (adjusted hazard ratio [HR] = 3.166, 95% CI: 1.200-8.352, P = 0.021), larger tumor size (adjusted HR = 4.370, 95% CI: 1.671-11.428, P = 0.002), and multifocality/multicentricity (adjusted HR = 5.059, 95% CI: 2.166-11.818, P < 0.001) were independently associated with DFS.

CONCLUSION

Preoperative breast MRI features may be associated with ANB and DFS in patients with early-stage breast cancer.

Diagnostic Accuracy of Combined 3.0T Magnetic Resonance Imaging and Molybdenum Target X-Ray in Triple-Negative Breast Cancer: Correlation with Prognosis in Patients Undergoing Sentinel Lymph Node Biopsy

Objective

This study assessed the diagnostic efficacy of combining 3.0T MRI and molybdenum target X-ray in triple-negative breast carcinoma (TNBC) and its association with the prognosis of sentinel lymph node biopsy (SLNB).

Methods

The retrospective analysis included 128 patients suspected of having TNBC, who underwent 3.0T MRI and molybdenum target X-ray. Sensitivity and specificity were calculated for each imaging technique, and their combined diagnosis was evaluated using the four-table method. Consistency between the imaging techniques and pathological examination was assessed using the consistency checking method. Additionally, changes in imaging indicators were compared among patients with different prognostic indicators.

Results

Among the 128 patients, 86 were diagnosed with TNBC through pathological examination. The sensitivity and specificity of 3.0T MRI for TNBC were 82.56% and 76.19%, respectively. Molybdenum target X-ray exhibited a sensitivity of 77.91% and specificity of 78.57%. The combined diagnosis of the two techniques showed a sensitivity of 90.70% and specificity of 86.36%. There was good agreement between both imaging techniques and pathological examination results. Significant differences were observed in imaging indicators based on tumor diameter, histological grade, and lymph node metastasis.

Conclusion

Both 3.0T MRI and molybdenum target X-ray are valuable in diagnosing TNBC. Additionally, these imaging techniques provide prognostic information and can aid in treatment decision-making. The findings highlight the importance of 3.0T MRI and molybdenum target X-ray in improving the outcomes of patients with TNBC.

Correlation of Perfusion Metrics with Ki-67 Proliferation Index and Axillary Involvement as a Prognostic Marker in Breast Carcinoma Cases: A Dynamic Contrast-Enhanced Perfusion MRI Study

Our study aims to reveal clinically helpful prognostic markers using quantitative radiologic data from perfusion magnetic resonance imaging for patients with locally advanced carcinoma, using the Ki-67 index as a surrogate. Patients who received a breast cancer diagnosis and had undergone dynamic contrast-enhanced magnetic resonance imaging of the breast for pretreatment evaluation and follow-up were searched retrospectively. We evaluated the MRI studies for perfusion parameters and various categories and compared them to the Ki-67 index. Axillary involvement was categorized as low (N0-N1) or high (N2-N3) according to clinical stage. A total sum of 60 patients' data was included in this study. Perfusion parameters and Ki-67 showed a significant correlation with the transfer constant (Ktrans) (ρ = 0.554 p = 0.00), reverse transfer constant (Kep) (ρ = 0.454 p = 0.00), and initial area under the gadolinium curve (IAUGC) (ρ = 0.619 p = 0.00). The IAUGC was also significantly different between axillary stage groups (Z = 2.478 p = 0.013). Outside of our primary hypothesis, associations between axillary stage and contrast enhancement (x2 = 8.023 p = 0.046) and filling patterns (x2 = 8.751 p = 0.013) were detected. In conclusion, these parameters are potential prognostic markers in patients with moderate Ki-67 indices, such as those in our study group. The relationship between axillary status and perfusion parameters also has the potential to determine patients who would benefit from limited axillary dissection.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

Triple-Negative Breast Cancer: Multimodality Appearance

Purpose of Review

Triple-negative breast cancer (TNBC) represents about 15–20% of all breast cancers and often presents as an aggressive cancer with poor prognosis compared to other forms of breast cancer. This article will review the clinical manifestations, imaging features, pathology correlation, treatment and management, and prognosis of TNBC.

Recent Findings

While mammography and ultrasound can be used to diagnose TNBC, MRI is the most accurate and sensitive modality to detect TNBC at nearly 100% sensitivity. Contrast-enhanced breast MRI is the optimal imaging study for assessing response to neoadjuvant chemotherapy and can be used to tailor systemic therapy.

Summary

Understanding the imaging appearance of TNBC is imperative to diagnose TNBC accurately and to help guide management.

Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting

Breast cancer (BC) is the most common cancer among women worldwide. Neoadjuvant chemotherapy (NACT) indications have expanded from inoperable locally advanced to early-stage breast cancer. Achieving a pathological complete response (pCR) has been proven to be an excellent prognostic marker leading to better disease-free survival (DFS) and overall survival (OS). Although diagnostic accuracy of MRI has been shown repeatedly to be superior to conventional methods in assessing the extent of breast disease there are still controversies regarding the indication of MRI in this setting. We intended to review the complex literature concerning the tumor size in staging, response and surgical planning in patients with early breast cancer receiving NACT, in order to clarify the role of MRI. Morphological and functional MRI techniques are making headway in the assessment of the tumor size in the staging, residual tumor assessment and prediction of response. Radiomics and radiogenomics MRI applications in the setting of the prediction of response to NACT in breast cancer are continuously increasing. Tailored therapy strategies allow considerations of treatment de-escalation in excellent responders and avoiding or at least postponing breast surgery in selected patients.

Deep learning-based automatic segmentation for size and volumetric measurement of breast cancer on magnetic resonance imaging

Purpose

In clinical work, accurately measuring the volume and the size of breast cancer is significant to develop a treatment plan. However, it is time-consuming, and inter- and intra-observer variations among radiologists exist. The purpose of this study was to assess the performance of a Res-UNet convolutional neural network based on automatic segmentation for size and volumetric measurement of mass enhancement breast cancer on magnetic resonance imaging (MRI).

Materials and methods

A total of 1,000 female breast cancer patients who underwent preoperative 1.5-T dynamic contrast-enhanced MRI prior to treatment were selected from January 2015 to October 2021 and randomly divided into a training cohort (n = 800) and a testing cohort (n = 200). Compared with the masks named ground truth delineated manually by radiologists, the model performance on segmentation was evaluated with dice similarity coefficient (DSC) and intraclass correlation coefficient (ICC). The performance of tumor (T) stage classification was evaluated with accuracy, sensitivity, and specificity.

Results

In the test cohort, the DSC of automatic segmentation reached 0.89. Excellent concordance (ICC > 0.95) of the maximal and minimal diameter and good concordance (ICC > 0.80) of volumetric measurement were shown between the model and the radiologists. The trained model took approximately 10–15 s to provide automatic segmentation and classified the T stage with an overall accuracy of 0.93, sensitivity of 0.94, 0.94, and 0.75, and specificity of 0.95, 0.92, and 0.99, respectively, in T1, T2, and T3.

Conclusions

Our model demonstrated good performance and reliability for automatic segmentation for size and volumetric measurement of breast cancer, which can be time-saving and effective in clinical decision-making.

Habitat Analysis of Breast Cancer-Enhanced MRI Reflects BRCA1 Mutation Determined by Immunohistochemistry

Objective

To use habitat analysis (also termed habitat imaging) for classifying untreated breast cancer-enhanced magnetic resonance imaging (MRI) in women. Moreover, we intended to obtain clustering parameters to predict the BReast CAncer gene 1 (BRCA1) gene mutation and to determine the use of MRI as a noninvasive examination tool.

Methods

We obtained enhanced MRI data of patients with breast cancer before treatment and selected some sequences as the source of habitat imaging. We used the k-means clustering to classify these images. According to the formed subregions, we calculated several parameters to evaluate the clustering. We used immunohistochemistry to detect BRCA1 mutations. Moreover, we separately determined the ability of these parameters through independent modeling or multiple parameter joint modeling to predict these mutations.

Results

Of all extracted values, separation (SP) demonstrated the best prediction performance for a single parameter (area under the receiver operating characteristic curve (AUC), 0.647; 95% confidence interval (CI), 0.557–0.731). Simultaneously, models based on the Calinski-Harabasz Index and sum of square error performed better in the training (AUC, 0.903; 95% CI, 0.831–0.96) and verification (AUC, 0.845; 95% CI, 0.723–0.942) sets for multiparameter joint modeling.

Conclusion

Based on the enhanced MRI of breast tumors and the subregions generated according to the habitat imaging theory, the parameters extracted to describe the clustering effect could reflect the BRCA1 status. Differences between clusters, including the general differences of cluster centers and clusters and the similarity of samples within clusters, were the embodiment of this mutation. We propose an algorithm to predict the BRCA1 mutation of a patient according to the enhanced MRI of the breast tumor.

Development and validation of a nomogram based on pretreatment dynamic contrast-enhanced MRI for the prediction of pathologic response after neoadjuvant chemotherapy for triple-negative breast cancer

OBJECTIVES

To develop a nomogram based on pretreatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with triple-negative breast cancer (TNBC).

METHODS

A total of 108 female patients with TNBC treated with neoadjuvant chemotherapy followed by surgery between January 2017 and October 2020 were enrolled. The patients were randomly divided into the primary cohort (n = 87) and validation cohort (n = 21) at a ratio of 4:1. The pretreatment DCE-MRI and clinicopathological features were reviewed and recorded. Univariate analysis and multivariate logistic regression analyses were used to determine the independent predictors of pCR in the primary cohort. A nomogram was developed based on the predictors, and the predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC). The validation cohort was used to test the predictive model.

RESULTS

Tumor volume measured on DCE-MRI, time to peak (TTP), and androgen receptor (AR) status were identified as independent predictors of pCR. The AUCs of the nomogram were 0.84 (95% CI: 0.75-0.93) and 0.79 (95% CI: 0.59-0.99) in the primary cohort and validation cohort, respectively.

CONCLUSIONS

Pretreatment DCE-MRI could predict pCR after NAC in patients with TNBC. The nomogram can be used to predict the probability of pCR and may help individualize treatment.

KEY POINTS

• Pretreatment DCE-MRI findings can predict pathologic complete response (pCR) after neoadjuvant chemotherapy in patients with triple-negative breast cancer. • A nomogram based on the independent predictors of tumor volume measured on DCE-MRI, time to peak, and androgen receptor status could help personalized cancer treatment in TNBC patients.