Diagnostic performance of image-guided vacuum-assisted breast biopsy after neoadjuvant therapy for breast cancer: prospective pilot study

Association of residual ductal carcinoma in situ with breast cancer treatment outcomes after neoadjuvant chemotherapy according to hormone receptor status

PURPOSE

This research aimed to clarify the impact of residual ductal carcinoma in situ(DCIS) in surgical specimens obtained after neoadjuvant chemotherapy(NAC) for breast cancer on the associated prognosis outcomes.

METHODS

This retrospective study was performed on a cohort of 1,009 patients who achieved pCR following NAC for breast cancer and underwent subsequent breast surgery at a single institution between January 2008 and December 2019. Overall survival, local recurrence-free survival, distant metastasis-free survival, and disease-free survival of the residual and non-residual DCIS groups were the outcomes compared, with further subgroup analysis performed according to hormone receptor status.

RESULTS

260 individuals (25.8%) presented with residual DCIS. Based on a median follow-up of 54.0 months, no significant differences in outcomes were observed between the two groups. Patients with residual DCIS and hormone receptor-negative (HR-) breast cancer demonstrated a significant decrease in distant metastasis-free survival (p = 0.030) compared to those without residual DCIS. In the HR + cohort, no significant difference was observed between the two groups. Multivariate analysis of the HR- cohort demonstrated a significant association between residual DCIS and an elevated risk for distant recurrence (hazard ratio = 2.3, 95% confidence interval = 1.01-5.20, p = 0.047).

CONCLUSIONS

Residual DCIS following NAC may impact breast cancer outcomes, particularly with respect to the occurrence of distant metastasis in HR- patients. Therefore, clinicians must vigilantly monitor patients with residual DCIS after NAC, and further research is needed to expand our understanding of the clinical implications of residual DCIS.

Omission of Breast Surgery in Exceptional Responders

Breast cancer management has transformed significantly over the last decades, primarily through the integration of neoadjuvant systemic therapy (NST) and the evolving understanding of tumor biology, enabling more tailored treatment strategies. The aim of this review is to critically present the historical context and contemporary evidence surrounding the potential of omission of surgery post-NST, focusing on exceptional responders who have achieved a pathologic complete response (pCR). Identifying these exceptional responders before surgery remains a challenge, however standardized image-guided biopsy may allow optimized patient selection. The safety and feasibility of omitting breast and axillary surgeries in these exceptional responders are explored in ongoing clinical trials and the reported preliminary results appear promising. Moreover, understanding patient and physician perspectives regarding the potential elimination of surgery post-NST is integral. While some patients express a preference to omit or minimize surgery, the majority of healthcare providers are intrigued by the prospect of avoiding surgical interventions and endorse further research in this field.

Nodal positivity in patients with clinically and radiologically node-negative breast cancer treated with neoadjuvant chemotherapy: multicentre collaborative study

BACKGROUND

The necessity of performing a sentinel lymph node biopsy in patients with clinically and radiologically node-negative breast cancer after neoadjuvant chemotherapy has been questioned. The aim of this study was to determine the rate of nodal positivity in these patients and to identify clinicopathological features associated with lymph node metastasis after neoadjuvant chemotherapy (ypN+).

METHODS

A retrospective multicentre study was performed. Patients with cT1-3 cN0 breast cancer who underwent sentinel lymph node biopsy after neoadjuvant chemotherapy between 2016 and 2021 were included. Negative nodal status was defined as the absence of palpable lymph nodes, and the absence of suspicious nodes on axillary ultrasonography, or the absence of tumour cells on axillary nodal fine needle aspiration or core biopsy.

RESULTS

A total of 371 patients were analysed. Overall, 47 patients (12.7%) had a positive sentinel lymph node biopsy. Nodal positivity was identified in 22 patients (29.0%) with hormone receptor+/human epidermal growth factor receptor 2- tumours, 12 patients (13.8%) with hormone receptor+/human epidermal growth factor receptor 2+ tumours, 3 patients (5.6%) with hormone receptor-/human epidermal growth factor receptor 2+ tumours, and 10 patients (6.5%) with triple-negative breast cancer. Multivariable logistic regression analysis showed that multicentric disease was associated with a higher likelihood of ypN+ (OR 2.66, 95% c.i. 1.18 to 6.01; P = 0.018), whilst a radiological complete response in the breast was associated with a reduced likelihood of ypN+ (OR 0.10, 95% c.i. 0.02 to 0.42; P = 0.002), regardless of molecular subtype. Only 3% of patients who had a radiological complete response in the breast were ypN+. The majority of patients (85%) with a positive sentinel node proceeded to axillary lymph node dissection and 93% had N1 disease.

CONCLUSION

The rate of sentinel lymph node positivity in patients who achieve a radiological complete response in the breast is exceptionally low for all molecular subtypes.

Is contrast-enhanced mammography (CEM) an alternative to MRI in assessing the response to primary systemic therapy of breast cancer?

PURPOSE

To evaluate the accuracy of contrast-enhanced mammography (CEM) and magnetic resonance imaging (MRI) in the assessing radiological response to primary systemic therapy (PST).

METHOD

Prospective study between February 2021 and October 2022. Women with breast cancer and indication of PST were enrolled. CEM and MRI were performed before and after PST, and the findings, including size and radiological response pattern, were compared with the size of the residual lesion measured in surgical specimens and its Miller-Payne classification (considered the gold standard). Two of four independent radiologists, with 2 years of CEM experience and 10 years of MRI experience, reviewed the images while being blinded to the results of the other technique. The agreement between measurements was evaluated using the Pearson correlation coefficient (r) and Lin's coefficient.

RESULTS

Forty-eight women with breast cancer who required PST were enrolled in the study, with a mean age of 57.21 ± 10.14 years. A total of thirty-three participants (68.75 %) completed the study. The correlation between CEM and MRI measurements was high before PST (r: 0.97), and local staging was identical for 45 out of 48 patients. MRI demonstrated better accuracy in predicting residual tumor size than CEM, with Lin's coefficient 0.91 and 0.73, respectively. However, no significant differences were observed in predicting response to therapy. Both methods tended to overestimate the size and degree of response in our study, with mean overestimations of 2.87 mm in CEM and 0.51 mm in MRI.

CONCLUSION

CEM was found to be as accurate as MRI in predicting response to PST, indicating its potential as an alternative imaging technique, but further research is necessary.

How Radiomics Can Improve Breast Cancer Diagnosis and Treatment

Recent technological advances in the field of artificial intelligence hold promise in addressing medical challenges in breast cancer care, such as early diagnosis, cancer subtype determination and molecular profiling, prediction of lymph node metastases, and prognostication of treatment response and probability of recurrence. Radiomics is a quantitative approach to medical imaging, which aims to enhance the existing data available to clinicians by means of advanced mathematical analysis using artificial intelligence. Various published studies from different fields in imaging have highlighted the potential of radiomics to enhance clinical decision making. In this review, we describe the evolution of AI in breast imaging and its frontiers, focusing on handcrafted and deep learning radiomics. We present a typical workflow of a radiomics analysis and a practical "how-to" guide. Finally, we summarize the methodology and implementation of radiomics in breast cancer, based on the most recent scientific literature to help researchers and clinicians gain fundamental knowledge of this emerging technology. Alongside this, we discuss the current limitations of radiomics and challenges of integration into clinical practice with conceptual consistency, data curation, technical reproducibility, adequate accuracy, and clinical translation. The incorporation of radiomics with clinical, histopathological, and genomic information will enable physicians to move forward to a higher level of personalized management of patients with breast cancer.