Diagnostic value of MRI for predicting axillary lymph nodes metastasis in newly diagnosed breast cancer patients: Diffusion-weighted MRI

Diagnostic accuracy of magnetic resonance imaging to evaluate axillary lymph node status in breast cancer patients receiving neoadjuvant chemotherapy

OBJECTIVES

Axillary lymph node status is an important prognostic factor for breast cancer patients. This study aimed to assess the accuracy of MRI in assessing the axillary nodal status in breast cancer patients receiving neo-adjuvant chemotherapy (NACT).

METHODS

Data were retrospectively collected for 88 patients between 2011 and 2016 from the hospital records. All patients had baseline MRI, followed by the end of neoadjuvant chemotherapy MRI. Patient demographics, cancer type, grade, stage, receptor status, and the number of positive lymph nodes identified on the baseline and preoperative MRI were recorded. The imaging results were compared to post-operative histopathological lymph node findings. The median patient age of the patients was 54 years (32-77 years).

RESULTS

There were 67 (76.1%) patients with histologically proven positive axillary lymph nodes on baseline MRI. The overall conversion from abnormal to normal axillary nodes following chemotherapy (ypN0) was 38.8% (26 patients out of 67). The sensitivity and specificity of MRI for predicting axillary status was 68.85 and 85.71%, respectively, with an overall MRI diagnostic accuracy of 73.07%.

CONCLUSION

MRI has low sensitivity and diagnostic accuracy in predicting axillary nodal status in breast cancer patients receiving neoadjuvant chemotherapy. Given the low NPV of MRI of the axilla, a negative MRI does not obviate the need for definitive axillary surgery.

ADVANCES IN KNOWLEDGE

In its current state, MRI cannot obviate the need for axillary surgery in patients receiving NACT. Long: short axis ratio (L: S) of the axillary lymph node is not a good predictor of axillary metastases.

The role of diffusion-weighted MR imaging in discrimination between benign and malignant axillary lymph nodes in breast cancer patients

Background

Noninvasive preoperative evaluation of axillary lymph nodes proved to have a significant role not only on the protocol of treatment of breast cancer but also impact the whole life of the patient. Complications of lymph node biopsy or axillary clearance increase the need for noninvasive reliable diagnostic tool. We aimed in the current study to evaluate the role of diffusion-weighted magnetic resonance imaging (DW-MRI) and apparent diffusion coefficient (ADC) in discrimination between benign and malignant axillary lymph nodes. We included 44 suspicious lymph nodes from 29 patients. Qualitative DW-MRI was analyzed into restricted or not; ADC maps and cut-off value were calculated, and they were correlated with histopathological results, which were the gold standard tool of the current study.

Results

The cut-off value of ADC-differentiated between malignant and benign lymph nodes was 0.89 × 10−3 mm2/s. The statistical indices including the sensitivity, specificity, PPV, NPV and accuracy were 89.66%, 86.67%, 93.9, 81.2% and 87.8%, respectively, with P value < 0.001, while DW-MRI results were classified into restricted or not restricted with qualitative statistical indices of 96.6%, 80%, 90.3%, 92.3% and 90.9% for sensitivity, specificity, PPV, NPV and accuracy, respectively, with P value < 0.001.

Conclusion

DW-MRI and ADC both have significant role in discrimination between benign and malignant axillary lymph nodes increasing the accuracy of MRI examination in breast cancer patients.

The additive role of diffusion-weighted magnetic resonance imaging to axillary nodal status evaluation in cases of newly diagnosed breast cancer

Background

Axillary nodal status is crucial for the management of cases with recently diagnosed breast cancer; usually addressed via axillary ultrasonography (US) along with tissue sampling in case of suspicion. Axillary nodal dissection and sentinel biopsy may be done, but are rather invasive, carrying a potential complication risk, which raises the need for non-invasive, reliable, pre-operative axillary imaging. We aimed at evaluating the performance of diffusion-weighted imaging (DWI) regarding preoperative axillary evaluation, using functional information derived from diffusion capacity differences between benign and malignant tissue. We included 77 axillary nodes from 77 patients (age range 20–78 years, mean 50 ± 12.6 SD) in our prospective study, presenting with variable clinical breast complaints, all scoring BIRADS 4/5 on sonomammography (SM). They underwent axillary evaluation by both US and DW-MRI where US classified nodes into benign, indeterminate, or malignant by evaluating nodal size, shape, cortical thickness, and hilar fat. Qualitative DWI classified them into either restricted or not and a cut-off apparent diffusion coefficient (ADC) value was calculated to differentiate benign and malignant nodal involvement. Results for each modality were correlated to those of final histopathology, which served as the standard of reference.

Results

The calculated sensitivity, specificity, accuracy, PPV, and NPV for US was 100%, 36.6%, 75.3%, 71.2%, and 100%, respectively. Statistical indices for qualitative DWI were 76.6%, 63.3%, 76.6%, 63.3%, and 71.4%, respectively (P value < 0.001). The calculated cut off value for ADC between infiltrated and non-infiltrated nodes was 0.95 × 10−3 mm2/s concluding statistical indices of 76.6%, 63.3%, 76.6%, 63.3%, and 71.4%, respectively (P value < 0.001).

Conclusion

Combining DW-MRI to conventional US improves diagnostic specificity and overall accuracy of preoperative axillary evaluation of patients with recently discovered breast cancer.

Spatiotemporal features of DCE-MRI for breast cancer diagnosis

BACKGROUND AND OBJECTIVE

Breast cancer is a major cause of mortality among women if not treated in early stages. Previous works developed non-invasive diagnosis methods using imaging data, focusing on specific sets of features that can be called spatial features or temporal features. However, limited set of features carry limited information, requiring complex classification methods to diagnose the disease. For non-invasive diagnosis, different imaging modalities can be used. DCE-MRI is one of the best imaging techniques that provides temporal information about the kinetics of the contrast agent in suspicious lesions along with acceptable spatial resolution.

METHODS

We have extracted and studied a comprehensive set of features from spatiotemporal space to obtain maximum available information from the DCE-MRI data. Then, we have applied a feature fusion technique to remove common information and extract a feature set with maximum information to be used by a simple classification method. We have also implemented conventional feature selection and classification methods and compared them with our proposed approach.

RESULTS

Experimental results obtained from DCE-MRI data of 26 biopsy or short-term follow-up proven patients illustrate that the proposed method outperforms alternative methods. The proposed method achieves a classification accuracy of 99% without missing any of the malignant cases.

CONCLUSIONS

The proposed method may help physicians determine the likelihood of malignancy in breast cancer using DCE-MRI without biopsy.