Histologic Grade and Decrease in Tumor Dimensions Affect Axillary Lymph Node Status after Neoadjuvant Chemotherapy in Breast Cancer Patients

Enhanced preoperative prediction of breast lesion pathology, prognostic biomarkers, and molecular subtypes using multiple models diffusion-weighted MR imaging

This study aims to comprehensively evaluate the clinical utility of five diffusion models, including conventional mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential (SEM), and continuous-time random-walk (CTRW), for preoperatively predicting of breast lesion pathology, prognostic biomarkers, and molecular subtypes. We retrospectively analyzed 132 patients with pathologically verified breast lesions (41 benign and 91 malignant) who underwent a full protocol preoperative breast MRI protocol, including a diffusion-weighted imaging (DWI) sequence with nine b values (0 to 2000 s/mm2) on a 3.0T MR scanner. The diffusion parameters from each model-Mono (ADC), IVIM (D, D*, f), DKI (MD, MK), SEM (DDC, α) and CTRW (Dm, α, β)-were quantitatively calculated and compared between benign and malignant breast lesions, as well as across different prognostic biomarker statuses in breast cancer, using Mann-Whitney U-tests. For molecular subtypes comparisons, we employed the Kruskal-Wallis test followed by Bonferroni. All parameters, except IVIM-D*, significantly differentiated benign from malignant lesions. Notably, IVIM-D and DKI-MK values were significantly different between estrogen receptor (ER)-positive and ER-negative tumors. Progesterone receptor (PR)-positive cancers exhibited lower Mono-ADC, IVIM-D, DKI-MD, SEM-DDC, CTRW-Dm, and CTRW-α values, alongside higher DKI-MK value compared to PR-negative cancers (p < 0.05). Significant differences in IVIM-D, IVIM-D*, and DKI-MK values were observed between human epidermal growth factor receptor 2 (HER2)-negative and HER2-positive tumors. Furthermore, higher SEM-α and CTRW-β values, along with lower DKI-MD and SEM-DDC values, were noted in the high Ki-67 expression group compared to the low Ki-67 group (p < 0.05). All five diffusion models proved valuable for breast cancer diagnosis, with the CTRW model exhibiting the highest diagnostic performance, although the difference was not statistically significant. The diffusion parameters derived from these models can effectively assist in distinguishing prognostic factors and molecular subtypes of breast cancer.

Development of an Ultrasound-based Nomogram for Predicting Pathologic Complete Response and Axillary Response in Node-Positive Patients with Triple- Negative Breast Cancer

BACKGROUND

The accurate prediction of pathological complete response (pCR) in the breast and axillary lymph nodes (ALN) before neoadjuvant chemotherapy (NAC) is of utmost importance for the development of treatment strategies. We aim to construct a nomogram on ultrasound (US) and clinical-pathologic factors to predict breast and ALN pCR in node-positive triple-negative breast cancers (TNBCs).

METHODS

Patients identified with TNBCs from institution 1 (n = 328) were used for training cohort and those from institution 2 (n = 192) were for validation cohort. US was conducted before and after NAC, and characteristics were obtained from medical records. Univariate and multivariate regression analysis were performed to identify US and clinical-pathologic factors associated with breast and ALN pCR in the training cohort. The assessment of predictive performance was conducted using the receiving operating characteristic curve (ROC), discrimination, and calibration.

RESULTS

Overall, 34.6% of patients achieved breast pCR and 48.1% of patients achieved ALN pCR. The nomogram 1 used for predicting pCR in the breast (AUC, 0.84; 95% CI: 0.79, 0.88) outperformed the clinical (AUC, 0.73; 95% CI: 0.68, 0.78) and US models (AUC, 0.79; 95% CI: 0.74, 0.83). The nomogram 2 used for predicting pCR in the axllia (AUC, 0.83; 95% CI: 0.78, 0.87) also outperformed the clinical (AUC, 0.64; 95% CI: 0.58, 0.69) and US models (AUC, 0.80; 95% CI: 0.75, 0.84). The calibration curve and discrimination curve indicate that the nomogram has good calibration performance and clinical applicability.

CONCLUSION

The nomogram showed promising predictive performance for predicting breast and ALN pCR in patients with TNBCs.

Diagnostic performance of MRI for assessing axillary lymph node status after neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis

OBJECTIVE

This systematic review examined the diagnostic performance of magnetic resonance imaging (MRI) for assessing axillary lymph node status (ALNS) after neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

We searched PubMed, Embase, Cochrane Library, and Web of Science to identify relevant studies and used the QUADAS-2 tool to assess methodological quality of eligible studies. We used STATA version 12.0 to perform data pooling, heterogeneity testing, subgroup analysis, and sensitivity analysis.

RESULTS

For the 21 enrolled studies, including 2875 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were respectively 0.63 (95% CI: 0.53-0.72), 0.75 (95% CI: 0.68-0.81), 2.52 (95% CI: 1.98-3.19), 0.50 (95% CI: 0.39-0.63), and 5.08 (95% CI: 3.38-7.63). The AUC was 0.76 (95% CI: 0.72-0.79). I2 values of sensitivity (I2 = 94.41%) and specificity (I2 = 88.97%) were both > 50%. For the initial positive ALN patients, the pooled sensitivity and specificity were 0.64 (95% CI: 0.53-0.75) and 0.74 (95% CI: 0.64-0.82), respectively. Sensitivity analyses by focusing on studies with MRI performed post-NAC, studies using DCE-MRI, or studies with low risk of bias showed similar results to the primary analyses.

CONCLUSION

MRI may have suboptimal diagnostic value in assessing ALNS after NAC for breast cancer patients. Due to the inconsistency of NAC regimens, the variability of axillary surgery, and the lack of time interval between MRI and surgery, further studies are needed to confirm our findings.

CLINICAL RELEVANCE STATEMENT

Our study provided the diagnostic value of MRI in assessing axillary lymph node status after neoadjuvant chemotherapy for breast cancer patients.

KEY POINTS

• MRI may have suboptimal diagnostic value in assessing axillary lymph node status after NAC for general breast cancer patients. • The initial axillary lymph node status has little impact on the diagnostic efficacy of MRI. • The substantial heterogeneity among studies highlights the need for further studies to provide more high-quality evidence in this field.

Prediction of pathological complete response of breast cancer patients who received neoadjuvant chemotherapy with a nomogram based on clinicopathologic variables, ultrasound, and MRI

OBJECTIVE

To establish a nomogram for predicting the pathologic complete response (pCR) in breast cancer (BC) patients after NAC by applying magnetic resonance imaging (MRI) and ultrasound (US).

METHODS

A total of 607 LABC women who underwent NAC before surgery between January 2016 and June 2022 were retrospectively enrolled, and then were randomly divided into the training (n = 425) and test set (n = 182) with the ratio of 7:3. MRI and US variables were collected before and after NAC, as well as the clinicopathologic features. Univariate and multivariate logistic regression analyses were applied to confirm the potentially associated predictors of pCR. Finally, a nomogram was developed in the training set with its performance evaluated by the area under the receiver operating characteristics curve (ROC) and validated in the test set.

RESULTS

Of the 607 patients, 108 (25.4%) achieved pCR. Hormone receptor negativity (odds ratio [OR], 0.3; P < .001), human epidermal growth factor receptor 2 positivity (OR, 2.7; P = .001), small tumour size at post-NAC US (OR, 1.0; P = .031), tumour size reduction ≥50% at MRI (OR, 9.8; P < .001), absence of enhancement in the tumour bed at post-NAC MRI (OR, 8.1; P = .003), and the increase of ADC value after NAC (OR, 0.3; P = .035) were all significantly associated with pCR. Incorporating the above variables, the nomogram showed a satisfactory performance with an AUC of 0.884.

CONCLUSION

A nomogram including clinicopathologic variables and MRI and US characteristics shows preferable performance in predicting pCR.

ADVANCES IN KNOWLEDGE

A nomogram incorporating MRI and US with clinicopathologic variables was developed to provide a brief and concise approach in predicting pCR to assist clinicians in making treatment decisions early.

Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous-Time Random-Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis

BACKGROUND

The continuous-time random-walk (CTRW) diffusion model to evaluate breast cancer prognosis is rarely reported.

PURPOSE

To investigate the correlations between apparent diffusion coefficient (ADC) and CTRW-specific parameters with prognostic factors and molecular subtypes of breast cancer.

STUDY TYPE

Retrospective.

POPULATION

One hundred fifty-seven women (median age, 50 years; range, 26-81 years) with histopathology-confirmed breast cancer.

FIELD STRENGTH/SEQUENCE

Simultaneous multi-slice readout-segmented echo-planar imaging at 3.0T.

ASSESSMENT

The histogram metrics of ADC, anomalous diffusion coefficient (D), temporal diffusion heterogeneity (α), and spatial diffusion heterogeneity (β) were calculated for whole-tumor volume. Associations between histogram metrics and prognostic factors (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67 proliferation index), axillary lymph node metastasis (ALNM), and tumor grade were assessed. The performance of histogram metrics, both alone and in combination, for differentiating molecular subtypes (HER2-positive, Luminal or triple negative) was also assessed.

STATISTICAL TESTS

Comparisons were made using Mann-Whitney test between different prognostic factor statuses and molecular subtypes. Receiver operating characteristic curve analysis was used to assess the performance of mean and median histogram metrics in differentiating the molecular subtypes. A P value <0.05 was considered statistically significant.

RESULTS

The histogram metrics of ADC, D, and α differed significantly between ER-positive and ER-negative status, and between PR-positive and PR-negative status. The histogram metrics of ADC, D, α, and β were also significantly different between the HER2-positive and HER2-negative subgroups, and between ALNM-positive and ALNM-negative subgroups. The histogram metrics of α and β significantly differed between high and low Ki-67 proliferation subgroups, and between histological grade subgroups. The combination of αmean and βmean achieved the highest performance (AUC = 0.702) to discriminate the Luminal and HER2-positive subtypes.

DATA CONCLUSION

Whole-tumor histogram analysis of the CTRW model has potential to provide additional information on the prognosis and intrinsic subtyping classification of breast cancer.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography

OBJECTIVE

To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer.

MATERIALS AND METHODS

This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance.

RESULTS

Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively.

CONCLUSION

CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

Intraoperative Lymph Node Assessment (Touch Preparation Only) for Metastatic Breast Carcinoma in Neoadjuvant and Non-neoadjuvant Settings

CONTEXT.—

Touch preparation (TP) alone is discouraged for intraoperative lymph node (LN) assessment in the neoadjuvant setting (NAS) owing to overall low sensitivity in detecting metastatic breast cancer.

OBJECTIVE.—

To compare the sensitivity, specificity, and negative predictive value of intraoperative LN assessment via TP and examine potential causes of discrepancies along with the clinical, radiologic, and pathologic parameters in the NAS and non-neoadjuvant setting (NNAS).

DESIGN.—

A total of 99 LNs from 47 neoadjuvant patients and 108 LNs from 56 non-neoadjuvant patients were identified. Discordant cases were reviewed retrospectively to reveal the discrepancy reasons. Clinical, radiologic, and pathologic data were obtained from chart review and the pathology CoPath database.

RESULTS.—

The sensitivity, specificity, and negative predictive value of TP in NAS and NNAS were 34.2% versus 37.5%, 100% versus 100%, and 70.9% versus 90.2%, respectively. In NAS, discrepancy reasons were interpretation challenge due to lobular histotype, poor TP quality secondary to therapy-induced histomorphologic changes, and undersampling due to small tumor deposits (≤2 mm); the latter was the major reason in NNAS. More cases with macrometastasis were missed in NAS compared to NNAS (14 of 25 versus 1 of 10). The parameters associated with discrepancy were lobular histotype, histologic grade 2, estrogen receptor positivity, HER2 human epidermal growth factor receptor 2 negativity, multifocality, and pathologic tumor size greater than 10 mm in NAS; and lymphovascular space involvement and pathologic tumor size greater than 20 mm in NNAS.

CONCLUSIONS.—

In NAS, intraoperative TP alone should be used very cautiously owing to a high false-negative rate of macrometastasis, especially for patients with invasive lobular carcinoma and known axillary LN metastasis before neoadjuvant therapy.

Predicting Axillary Response to Neoadjuvant Chemotherapy: Breast MRI and US in Patients with Node-Positive Breast Cancer

Background In patients who are expected to achieve axillary pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC), omission of axillary lymph node (LN) dissection could prevent morbidity and complications. Purpose To develop a clinical model to predict residual axillary LN metastasis in patients with clinically node-positive breast cancer after NAC by using MRI and US. Materials and Methods In this retrospective study, women with clinically node-positive breast cancer who were treated with NAC following surgery between January 2015 and September 2017 were included. The patients were randomly assigned to a test and validation set (7:3 ratio). Univariable and multivariable logistic regression analyses were performed to evaluate the independent predictors of residual axillary LN metastasis in the test set. A prediction risk score was developed based on the odds ratios from the multivariable analysis and validated in both sets. Results A total of 408 women were included (mean age ± standard deviation, 47.9 years ± 9.6). The axillary pCR rate was 56.6% (231 of 408). Independent predictors of residual axillary LN metastasis were clinical stage N2 or N3, presence of axillary lymphadenopathy at US after NAC, tumor size reduction less than 50% at MRI, Ki-67 negativity, hormone receptor positivity, and human epidermal growth factor receptor 2 negativity (all, P < .05). In a model using these predictors, the area under the receiver operating characteristic curve in the test and validation sets was 0.84 (95% confidence interval: 0.79, 0.88) and 0.78 (95% confidence interval: 0.70, 0.87), respectively. When the patients had a simplified risk score of 1, the false-negative rates ranged between 5%-10%. Conclusion A prediction model incorporating nodal status stage, US finding, MRI response, and molecular receptor status shows good diagnostic performance for residual axillary lymph node metastasis after neoadjuvant chemotherapy in patients with clinically node-positive breast cancer. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Whitman in this issue.

Is it always necessary to perform an axillary lymph node dissection after neoadjuvant chemotherapy for breast cancer?

INTRODUCTION

Recent prospective studies support the feasibility of performing sentinel lymph node biopsy following neoadjuvant chemotherapy in initially fine-needle aspiration cytology or ultrasound-guided biopsy-proven node-positive breast cancer. The main aid is to identify preoperative features that help us predict a complete axillary response to neoadjuvant chemotherapy in these patients and thus select the candidates for sentinel lymph node biopsy post-neoadjuvant chemotherapy to avoid unnecessary axillary lymphadenectomy.

MATERIALS AND METHODS

A retrospective observational study with a total of 150 patients, biopsy-proven node-positive breast cancer who underwent neoadjuvant chemotherapy followed by breast surgery and axillary lymphadenectomy were included and retrospectively analysed. A predictive model was generated by a multivariate logistic regression analysis for pathological complete response-dependent variable.

RESULTS

The response of the primary lesion to neoadjuvant chemotherapy according to post-treatment magnetic resonance imaging, Her2/neu overexpression and a low estrogen receptor expression are associated with a higher rate of nodal pathologically complete response. The multivariant model generated a receiver operating characteristic curve with an area under the curve of 0.79 and a confidence interval of 0.72-0.87 at a 95% level of significance.

CONCLUSIONS

This model could be a helpful tool for the surgeon to help in predicting which cases have a higher likelihood of achieving a pathologically complete response and therefore selecting those who may benefit from a post-neoadjuvant chemotherapy sentinel lymph node biopsy and avoid unnecessary axillary lymphadenectomy.

Correlation between histopathological grading and shear-wave elastography in evaluating invasive carcinoma of no special type

The present study aimed to investigate the correlation between histologic grade and shear-wave elastography in evaluating invasive breast carcinoma of no special type (NST). A total of 84 breast lesions in 80 patients (age range, 32-64 years) were retrospectively evaluated. The patients underwent B-mode ultrasound and virtual touch tissue quantification diagnosis. A semi-quantitative method was then applied in order to determine the histologic grade of the invasive carcinoma of NST according to the grading system published by the World Health Organization. Among the 84 breast lesions, 14 (16.7%) were classified as Grade I, while 29 (34.5%) and 41 (48.8%) were determined to be of Grade II and Grade III, respectively. The size of the breast lesions increased with the increase in histological grade (P<0.001). In addition, 29.3% of the Grade-III breast lesions exhibited acoustic enhancement, while the majority of the Grade-I breast lesions (78.6%) exhibited acoustic shadowing (P=0.002). Furthermore, a higher histopathological grade was closely correlated with a higher mean, minimum and maximum shear-wave velocity value (P<0.05). In conclusion, regarding the evaluation of invasive breast carcinoma of NST, the histologic grade was strongly correlated with tissue stiffness; a higher histologic grade was associated with a harder lesion. Therefore, shear-wave elastography may provide important clinical reference values.