Diagnostic performance of standard breast MRI compared to dedicated axillary MRI for assessment of node-negative and node-positive breast cancer

Application of Texture and Volume Model Analysis to Dedicated Axillary High-resolution 3D T2-weighted MR Imaging: A Novel Method for Diagnosing Lymph Node Metastasis in Patients with Clinically Node-negative Breast Cancer

PURPOSE

To evaluate the effectiveness of the texture analysis of axillary high-resolution 3D T2-weighted imaging (T2WI) in distinguishing positive and negative lymph node (LN) metastasis in patients with clinically node-negative breast cancer.

METHODS

Between December 2017 and May 2021, 242 consecutive patients underwent high-resolution 3D T2WI and were classified into the training (n = 160) and validation cohorts (n = 82). We performed manual 3D segmentation of all visible LNs in axillary level I to extract the texture features. As the additional parameters, the number of the LNs and the total volume of all LNs for each case were calculated. The least absolute shrinkage and selection operator algorithm and Random Forest were used to construct the models. We constructed the texture model using the features from the LN with the largest least axis length in the training cohort. Furthermore, we constructed the 3 models combining the selected texture features of the LN with the largest least axis length, the number of LNs, and the total volume of all LNs: texture-number model, texture-volume model, and texture-number-volume model. As a conventional method, we manually measured the largest cortical diameter. Moreover, we performed the receiver operating curve analysis in the validation cohort and compared area under the curves (AUCs) of the models.

RESULTS

The AUCs of the texture model, texture-number model, texture-volume model, texture-number-volume model, and conventional method in the validation cohort were 0.7677, 0.7403, 0.8129, 0.7448, and 0.6851, respectively. The AUC of the texture-volume model was higher than those of other models and conventional method. The sensitivity, specificity, positive predictive value, and negative predictive value of the texture-volume model were 90%, 69%, 49%, and 96%, respectively.

CONCLUSION

The texture-volume model of high-resolution 3D T2WI effectively distinguished positive and negative LN metastasis for patients with clinically node-negative breast cancer.

Deep Learning Radiomics of Preoperative Breast MRI for Prediction of Axillary Lymph Node Metastasis in Breast Cancer

The objective of this study is to develop a radiomic signature constructed from deep learning features and a nomogram for prediction of axillary lymph node metastasis (ALNM) in breast cancer patients. Preoperative magnetic resonance imaging data from 479 breast cancer patients with 488 lesions were studied. The included patients were divided into two cohorts by time (training/testing cohort, n = 366/122). Deep learning features were extracted from diffusion-weighted imaging-quantitatively measured apparent diffusion coefficient (DWI-ADC) imaging and dynamic contrast-enhanced MRI (DCE-MRI) by a pretrained neural network of DenseNet121. After the selection of both radiomic and clinicopathological features, deep learning signature and a nomogram were built for independent validation. Twenty-three deep learning features were automatically selected in the training cohort to establish the deep learning signature of ALNM. Three clinicopathological factors, including LN palpability (odds ratio (OR) = 6.04; 95% confidence interval (CI) = 3.06-12.54, P = 0.004), tumor size in MRI (OR = 1.45, 95% CI = 1.18-1.80, P = 0.104), and Ki-67 (OR = 1.01; 95% CI = 1.00-1.02, P = 0.099), were selected and combined with radiomic signature to build a combined nomogram. The nomogram showed excellent predictive ability for ALNM (AUC 0.80 and 0.71 in training and testing cohorts, respectively). The sensitivity, specificity, and accuracy were 65%, 80%, and 75%, respectively, in the testing cohort. MRI-based deep learning radiomics in patients with breast cancer could be used to predict ALNM, providing a noninvasive approach to structuring the treatment strategy.

High-accuracy prediction of axillary lymph node metastasis in invasive lobular carcinoma using focal cortical thickening on magnetic resonance imaging

BACKGROUND

Invasive lobular carcinoma (ILC) grows diffusely in a single-cell fashion, sometimes presenting only subtle changes in preoperative imaging; therefore, axillary lymph node (ALN) metastases of ILC are difficult to detect using magnetic resonance imaging (MRI). Preoperative underestimation of nodal burden occurs more frequently in ILC than in invasive ductal carcinoma (IDC), however, the morphological assessment for metastatic ALNs of ILC have not fully been investigated. We hypothesized that the high false-negative rate in ILC is caused by the discrepancy in the MRI findings of ALN metastases between ILC and IDC and aimed to identify the MRI finding with a strong correlation with ALN metastasis of ILC.

METHOD

This retrospective analysis included 120 female patients (mean ± standard deviation age, 57.2 ± 11.2 years) who underwent upfront surgery for ILC at a single center between April 2011 and June 2022. Of the 120 patients, 35 (29%) had ALN metastasis. Using logistic regression, we constructed prediction models based on MRI findings: primary tumor size, focal cortical thickening (FCT), cortical thickness, long-axis diameter (LAD), and loss of hilum (LOH).

RESULTS

The area under the curves were 0.917 (95% confidence interval [CI] 0.869-0.968), 0.827 (95% CI 0.758-0.896), 0.754 (95% CI 0.671-0.837), and 0.621 (95% CI 0.531-0.711) for the FCT, cortical thickness, LAD, and LOH models, respectively.

CONCLUSIONS

FCT may be the most relevant MRI finding for ALN metastasis of ILC, and although its prediction model may lead to less underestimation of the nodal burden, rigorous external validation is required.

A delta-radiomic lymph node model using dynamic contrast enhanced MRI for the early prediction of axillary response after neoadjuvant chemotherapy in breast cancer patients

BACKGROUND

The objective of this paper is to explore the value of a delta-radiomic model of the axillary lymph node (ALN) using dynamic contrast-enhanced (DCE) MRI for early prediction of the axillary pathological complete response (pCR) of breast cancer patients after neoadjuvant chemotherapy (NAC).

METHODS

A total of 120 patients with ALN-positive breast cancer who underwent breast MRI before and after the first cycle of NAC between October 2018 and May 2021 were prospectively included in this study. Patients were divided into a training (n = 84) and validation (n = 36) cohort based on the temporal order of their treatments. Radiomic features were extracted from the largest slice of targeted ALN on DCE-MRI at pretreatment and after one cycle of NAC, and their changes (delta-) were calculated and recorded. Logistic regression was then applied to build radiomic models using the pretreatment (pre-), first-cycle(1st-), and changes (delta-) radiomic features separately. A clinical model was also built and combined with the radiomic models. The models were evaluated by discrimination, calibration, and clinical application and compared using DeLong test.

RESULTS

Among the three radiomic models, the ALN delta-radiomic model performed the best with AUCs of 0.851 (95% CI: 0.770-0.932) and 0.822 (95% CI: 0.685-0.958) in the training and validation cohorts, respectively. The clinical model yielded moderate AUCs of 0.742 (95% CI: 0.637-0.846) and 0.723 (95% CI: 0.550-0.896), respectively. After combining clinical features to the delta-radiomics model, the efficacy of the combined model (AUC = 0.932) in the training cohort was significantly higher than that of both the delta-radiomic model (Delong p = 0.017) and the clinical model (Delong p < 0.001) individually. Additionally, in the validation cohort, the combined model had the highest AUC (0.859) of any of the models we tested although this was not statistically different from any other individual model's validation AUC. Calibration and decision curves showed a good agreement and a high clinical benefit for the combined model.

CONCLUSION

This preliminary study indicates that ALN-based delta-radiomic model combined with clinical features is a promising strategy for the early prediction of downstaging ALN status after NAC. Future axillary MRI applications need to be further explored.

Beyond N Staging in Breast Cancer: Importance of MRI and Ultrasound-based Imaging

The correct N-staging in breast cancer is crucial to tailor treatment and stratify the prognosis. N-staging is based on the number and the localization of suspicious regional nodes on physical examination and/or imaging. Since clinical examination of the axillary cavity is associated with a high false negative rate, imaging modalities play a central role. In the presence of a T1 or T2 tumor and 0–2 suspicious nodes, on imaging at the axillary level I or II, a patient should undergo sentinel lymph node biopsy (SLNB), whereas in the presence of three or more suspicious nodes at the axillary level I or II confirmed by biopsy, they should undergo axillary lymph node dissection (ALND) or neoadjuvant chemotherapy according to a multidisciplinary approach, as well as in the case of internal mammary, supraclavicular, or level III axillary involved lymph nodes. In this scenario, radiological assessment of lymph nodes at the time of diagnosis must be accurate. False positives may preclude a sentinel lymph node in an otherwise eligible woman; in contrast, false negatives may lead to an unnecessary SLNB and the need for a second surgical procedure. In this review, we aim to describe the anatomy of the axilla and breast regional lymph node, and their diagnostic features to discriminate between normal and pathological nodes at Ultrasound (US) and Magnetic Resonance Imaging (MRI). Moreover, the technical aspects, the advantage and limitations of MRI versus US, and the possible future perspectives are also analyzed, through the analysis of the recent literature.

Axillary lymph node response to neoadjuvant systemic therapy with dedicated axillary hybrid 18F-FDG PET/MRI in clinically node-positive breast cancer patients: a pilot study

AIM

To investigate the diagnostic performance of dedicated axillary hybrid ¹⁸F-2-[¹⁸F]-fluoro-2-deoxy-d-glucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI) in detecting axillary pathological complete response (pCR) following neoadjuvant systemic therapy (NST) in clinically node-positive breast cancer patients.

MATERIALS AND METHODS

Ten prospectively included clinically node-positive breast cancer patients underwent dedicated axillary hybrid ¹⁸F-FDG PET/MRI after completing NST followed by axillary surgery. PET images were reviewed by a nuclear medicine physician and coronal T1-weighted and T2-weighted MRI images by a radiologist. All axillary lymph nodes visible on PET/MRI were matched with those removed during axillary surgery. Diagnostic performance parameters were calculated based on patient-by-patient and node-by-node validation with histopathology of the axillary surgical specimen as the reference standard.

RESULTS

Six patients achieved axillary pCR at final histopathology. A total of 84 surgically harvested axillary lymph nodes were matched with axillary lymph nodes depicted on PET/MRI. Histopathological examination of the matched axillary lymph nodes resulted in 10 lymph nodes with residual axillary disease of which eight contained macrometastases and two micrometastases. The patient-by-patient analysis yielded a sensitivity, specificity, positive predictive value, and negative predictive value of 25%, 100%, 100%, and 67%, respectively. The diagnostic performance parameters of the node-by-node analysis were 0%, 96%, 0%, and 88%, respectively. Excluding micrometastases from the node-by-node analysis increased the negative predictive value to 90%.

CONCLUSION

This pilot study suggests that the negative predictive value and sensitivity of dedicated axillary ¹⁸F-FDG PET/MRI are insufficiently accurate to detect axillary pCR or exclude residual axillary disease following NST in clinically node-positive breast cancer patients.

Assessment of preoperative axillary nodal disease burden: breast MRI in locally advanced breast cancer before, during and after neoadjuvant endocrine therapy

Background

Axillary lymph node (LN) metastasis is one of the most important predictors of recurrence and survival in breast cancer, and accurate assessment of LN involvement is crucial. Determining extent of residual disease is key for surgical planning after neoadjuvant therapy. The aim of the study was to evaluate the diagnostic reliability of MRI for nodal disease in locally advanced breast cancer patients treated with neoadjuvant endocrine therapy (NET).

Methods

Thirty-three clinically node-positive locally advanced breast cancer patients who underwent NET and surgery were prospectively enrolled. Two radiologists reviewed the axillary nodes at 3 separate time points MRI examinations at baseline (before the first treatment regimen), interim (following at least 2 months after the first cycle and prior to crossing-over), and preoperative (after the final administration of therapy and immediately before surgery). According to LN status after surgery, imaging features and diagnostic performance were analyzed.

Results

All 33 patients had a target LN reduction, the greatest treatment benefit from week 8 to week 16.

There was a positive correlation between the maximal diameter of the most suspicious LN measured by MRI and pathology during and after NET, being highest at therapy completion (r = 0.6, P ≤ .001). Mean and median differences of maximal diameter of the most suspicious LN were higher with MRI than with pathology. Seven of 33 patients demonstrated normal posttreatment MRI nodal status (yrN0). Of these 7 yrN0, 3 exhibited no metastasis on final pathology (ypN0), 2 ypN1 and 2 ypN2. Reciprocally, MRI diagnosed 3 cases of ypN0 as yrN + . Diffusion -weighted imaging (DWI) was the only axillary node characteristic significant when associated with pathological node status (χ²(4) = 8.118, P = .072).

Conclusion

Performance characteristics of MRI were not completely sufficient to preclude surgical axillary staging. To our knowledge, this is the first study on MRI LN assessment following NET in locally advanced breast cancer, and further studies with larger sample sizes are required to consolidate the results of this preliminary study.

Trial Registration

Institutional Review Board approval was obtained (this current manuscript is from a prospective, open-label, randomized single-center cohort substudy of the NEOLETEXE trial). NEOLETEXE, a phase 2 clinical trial, was registered on March 23^rd, 2015 in the National trial database of Norway and approved by the Regional Ethical Committee of the South-Eastern Health Region in Norway; registration number: REK-SØ-84–2015.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09813-9.

Preoperative axillary nodal staging of invasive lobular breast cancer with ultrasound guided fine needle aspiration in patients with suspicious ultrasound findings versus aspiration in all patients - A retrospective single institutional analysis

INTRODUCTION

- At present, surgical strategies for breast cancer patients with >2 lymph nodes (LN) involved differ from those with no or lower degree of nodal involvement. Preoperative assessment of the axilla is less sensitive in patients with lobular carcinoma (ILC) than patients with other histological tumour types.

MATERIALS AND METHODS

- A retrospective analysis of axillary staging by palpation, axillary ultrasound (AXUS) and AXUS-guided fine-needle aspiration cytology (FNAC) of 153 patients with ILC diagnosed and operated on between January 2013 and December 2020 was performed. Patients had either sentinel node biopsy or axillary lymph node dissection according to current practice. In period 1, patients had FNAC only when AXUS suggested nodal involvement (n = 106), and in period 2, all ILC patients had axillary FNAC (n = 47).

RESULTS

- Of the factors associated with >2LNs involvement, logistic regression suggested only AXUS/FNAC based staging as independent variable for all patients. Patients with AXUS-guided FNAC had a significantly higher proportion of true negative and lower proportion of true positive cases in the P2 period (0 vs 55% and 72% vs 11% for >2 LNs involvement, respectively; both p < 0.0001).

CONCLUSIONS

- AXUS-guided FNAC of all ILC patients did not result in improved preoperative identification of patients with >2 metastatic LNs but increased the false-negative rate of the assessment by producing false-negative results in patients who would not have undergone a biopsy due to negative AXUS findings.

Dedicated Axillary MRI-Based Radiomics Analysis for the Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Radiomics features may contribute to increased diagnostic performance of MRI in the prediction of axillary lymph node metastasis. The objective of the study was to predict preoperative axillary lymph node metastasis in breast cancer using clinical models and radiomics models based on T2-weighted (T2W) dedicated axillary MRI features with node-by-node analysis. From August 2012 until October 2014, all women who had undergone dedicated axillary 3.0T T2W MRI, followed by axillary surgery, were retrospectively identified, and available clinical data were collected. All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions. Data were partitioned patient-wise to train 100 models using different splits for the training and validation cohorts to account for multiple lymph nodes per patient and class imbalance. Features were selected in the training cohorts using recursive feature elimination with repeated 5-fold cross-validation, followed by the development of random forest models. The performance of the models was assessed using the area under the curve (AUC). A total of 75 women (median age, 61 years; interquartile range, 51-68 years) with 511 axillary lymph nodes were included. On final pathology, 36 (7%) of the lymph nodes had metastasis. A total of 105 original radiomics features were extracted from the T2W MR images. Each cohort split resulted in a different number of lymph nodes in the training cohorts and a different set of selected features. Performance of the 100 clinical and radiomics models showed a wide range of AUC values between 0.41-0.74 and 0.48-0.89 in the training cohorts, respectively, and between 0.30-0.98 and 0.37-0.99 in the validation cohorts, respectively. With these results, it was not possible to obtain a final prediction model. Clinical characteristics and dedicated axillary MRI-based radiomics with node-by-node analysis did not contribute to the prediction of axillary lymph node metastasis in breast cancer based on data where variations in acquisition and reconstruction parameters were not addressed.

Assessing the accuracy of conventional gadolinium-enhanced breast MRI in measuring the nodal response to neoadjuvant chemotherapy (NAC) in breast cancer

Management of the axilla in the era of neoadjuvant chemotherapy for breast cancer is evolving. The aim of this study is to determine if conventional gadolinium-enhanced breast MRI can aid in evaluation of the response to neoadjuvant chemotherapy in the axilla. A retrospective review of a prospectively maintained database of patients undergoing neoadjuvant chemotherapy for breast cancer was performed. Pre and post-neoadjuvant chemotherapy MRI reports for node-positive patients were examined in conjunction with demographic data, treatment type, and final histopathology reports. One-hundred and fourteen patients with breast cancer undergoing neoadjuvant chemotherapy were included in the study. The sensitivity of magnetic resonance imaging in detecting nodal response post-neoadjuvant chemotherapy was 33.93% and the specificity was 82.76%. Magnetic resonance imaging had a positive predictive value of 65.52% and a negative predictive value of 56.47%. MRI was found to be most specific in the detection of triple-negative cancer response. Specificity was 100% in this group and sensitivity was 75%. Magnetic resonance imaging has a relatively high specificity in detecting nodal response post-neoadjuvant chemotherapy but has a low sensitivity. Alone it cannot be relied upon to identify active axillary malignancy post-neoadjuvant chemotherapy. However, given its increased specificity among certain subgroups, it may have a role in super-selecting patients suitable for sentinel lymph node biopsy post-neoadjuvant chemotherapy.