Diagnostic performance of 18F-FDG PET/CT, ultrasonography and MRI. Detection of axillary lymph node metastasis in breast cancer patients

Peritumoral edema enhances MRI-based deep learning radiomic model for axillary lymph node metastasis burden prediction in breast cancer

To investigate whether peritumoral edema (PE) could enhance deep learning radiomic (DLR) model in predicting axillary lymph node metastasis (ALNM) burden in breast cancer. Invasive breast cancer patients with preoperative MRI were retrospectively enrolled and categorized into low (< 2 lymph nodes involved (LNs+)) and high (≥ 2 LNs+) burden groups based on surgical pathology. PE was evaluated on T2WI, and intra- and peri-tumoral radiomic features were extracted from MRI-visible tumors in DCE-MRI. Deep learning models were developed for LN burden prediction in the training cohort and validated in an independent cohort. The incremental value of PE was evaluated through receiver operating characteristic (ROC) analysis, confirming the improvement in the area under the curve (AUC) using the Delong test. This was complemented by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) metrics. The deep learning combined model, incorporating PE with selected radiomic features, demonstrated significantly higher AUC values compared to the MRI model and the DLR model in the training cohort (n = 177) (AUC: 0.953 vs. 0.849 and 0.867, p < 0.05) and the validation cohort (n = 111) (AUC: 0.963 vs. 0.883 and 0.882, p < 0.05). The complementary analysis demonstrated that PE significantly enhances the prediction performance of the DLR model (Categorical NRI: 0.551, p < 0.001; IDI = 0.343, p < 0.001). These findings were confirmed in the validation cohort (Categorical NRI: 0.539, p < 0.001; IDI = 0.387, p < 0.001). PE improved preoperative ALNM burden prediction of DLR model, facilitating personalized axillary management in breast cancer patients.

Reliability of predicting low-burden (≤ 2) positive axillary lymph nodes indicating sentinel lymph node biopsy in primary operable breast cancer - a retrospective comparative study with PET/CT and breast MRI

BACKGROUND

Sentinel lymph node biopsy (SLNB) is the standard of care for axillary staging in early breast cancer patients with low-burden axillary metastasis (≤ 2 positive nodes). This study aimed to determine the diagnostic performances of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and breast magnetic resonance imaging in detecting axillary lymph node (ALN) metastases and the reliability to predict ALN burden.

METHODS

A total of 275 patients with primary operable breast cancer receiving preoperative PET/CT and upfront surgery from January 2001 to December 2022 in a single institution were enrolled. A total of 244 (88.7%) of them also received breast MRI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PET/CT and breast MRI were assessed. The predictive values to determine ALN burden were evaluated using radio-histopathological concordance.

RESULTS

PET/CT demonstrated a sensitivity of 53.4%, specificity of 82.1%, PPV of 65.5%, NPV of 73.5%, and accuracy of 70.9% for detecting ALN metastasis, and the corresponding values for MRI were 71.8%, 67.8%, 56%, 80.8%, and 69.2%, respectively. Combining PET/CT and MRI showed a significantly higher PPV than MRI (72.7% vs 56% for MRI alone, p = 0.037) and a significantly higher NPV than PET/CT (84% vs 73.5% for PET/CT alone, p = 0.041). For predicting low-burden axillary metastasis (1-2 positive nodes), the PPVs were 35.9% for PET/CT, 36.7% for MRI, and 55% for combined PET/CT and MRI. Regarding patients with 0-2 positive ALNs in imaging, who were indicated for SLNB, the predictive correctness was 96.1% for combined PET/CT and MRI, 95.7% for MRI alone, and 88.6% for PET/CT alone.

CONCLUSIONS

PET/CT and breast MRI exhibit high predictive values for identifying low-burden axillary metastasis in patients with operable breast cancer with ≦ 2 positive ALNs on imaging.

Diagnostic accuracy of pre-operative breast magnetic resonance imaging (MRI) in predicting axillary lymph node metastasis: variations in intrinsic subtypes, and strategy to improve negative predictive value-an analysis of 2473 invasive breast cancer patients

BACKGROUND

The value and utility of axillary lymph node (ALN) evaluation with MRI in breast cancer were not clear for various intrinsic subtypes. The aim of the current study is to test the potential of combining breast MRI and clinicopathologic factors to identify low-risk groups of ALN metastasis and improve diagnostic performance.

MATERIAL AND METHODS

Patients with primary operable invasive breast cancer with pre-operative breast MRI and post-operative pathologic reports were retrospectively collected from January 2009 to December 2021 in a single institute. The concordance of MRI and pathology of ALN status were determined, and also analyzed in different intrinsic subtypes. A stepwise strategy was designed to improve MRI-negative predictive value (NPV) on ALN metastasis.

RESULTS

2473 patients were enrolled. The diagnostic performance of MRI in detecting metastatic ALN was significantly different between intrinsic subtypes (p = 0.007). Multivariate analysis identified tumor size and histologic type as independent predictive factors of ALN metastases. Patients with HER-2 (MRI tumor size ≤ 2 cm), or TNBC (MRI tumor size ≤ 2 cm) were found to have MRI-ALN-NPV higher than 90%, and these false cases were limited to low axillary tumor burden.

CONCLUSION

The diagnostic performance of MRI to predict ALN metastasis varied according to the intrinsic subtype. Combined pre-operative clinicopathologic factors and intrinsic subtypes may increase ALN MRI NPV, and further identify some groups of patients with low risks of ALN metastasis, high NPV, and low burdens of axillary disease even in false-negative cases.

Radiomics nomogram based on digital breast tomosynthesis: preoperative evaluation of axillary lymph node metastasis in breast carcinoma

PURPOSE

This study aimed to establish a radiomics nomogram model based on digital breast tomosynthesis (DBT) images, to predict the status of axillary lymph nodes (ALN) in patients with breast carcinoma.

METHODS

The data of 120 patients with confirmed breast carcinoma, including 49 cases with axillary lymph node metastasis (ALNM), were retrospectively analyzed in this study. The dataset was randomly divided into a training group consisting of 84 patients (37 with ALNM) and a validation group comprising 36 patients (12 with ALNM). Clinical information was collected for all cases, and radiomics features were extracted from DBT images. Feature selection was performed to develop the Radscore model. Univariate and multivariate logistic regression analysis were employed to identify independent risk factors for constructing both the clinical model and nomogram model. To evaluate the performance of these models, receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI) were conducted.

RESULTS

The clinical model identified tumor margin and DBT_reported_LNM as independent risk factors, while the Radscore model was constructed using 9 selected radiomics features. Incorporating tumor margin, DBT_reported_LNM, and Radscore, the radiomics nomogram model exhibited superior performance with AUC values of 0.933 and 0.920 in both datasets, respectively. The NRI and IDI showed a significant improvement, suggesting that the Radscore may serve as a useful biomarker for predicting ALN status.

CONCLUSION

The radiomics nomogram based on DBT demonstrated effective preoperative prediction performance for ALNM in patients with breast cancer.

Development and validation of a point-based scoring system for predicting axillary lymph node metastasis and disease outcome in breast cancer using clinicopathological and multiparametric MRI features

BACKGROUND

Axillary lymph node (ALN) metastasis is used to select treatment strategies and define the prognosis in breast cancer (BC) patients and is typically assessed using an invasive procedure. Noninvasive, simple, and reliable tools to accurately predict ALN status are desirable. We aimed to develop and validate a point-based scoring system (PSS) for stratifying the ALN metastasis risk of BC based on clinicopathological and quantitative MRI features and to explore its prognostic significance.

METHODS

A total of 219 BC patients were evaluated. The clinicopathological and quantitative MRI features of the tumors were collected. A multivariate logistic regression analysis was used to create the PSS. The performance of the models was evaluated using receiver operating characteristic curves, and the area under the curve (AUC) of the models was calculated. Kaplan-Meier curves were used to analyze the survival outcomes.

RESULTS

Clinical features, including the American Joint Committee on Cancer (AJCC) stage, T stage, human epidermal growth factor receptor-2, estrogen receptor, and quantitative MRI features, including maximum tumor diameter, K_ep, V_e, and TTP, were identified as risk factors for ALN metastasis and were assigned scores for the PSS. The PSS achieved an AUC of 0.799 in the primary cohort and 0.713 in the validation cohort. The recurrence-free survival (RFS) and overall survival (OS) of the high-risk (> 19.5 points) groups were significantly shorter than those of the low-risk (≤ 19.5 points) groups in the PSS.

CONCLUSION

PSS could predict the ALN metastasis risk of BC. A PSS greater than 19.5 was demonstrated to be a predictor of short RFS and OS.

Clinical applications of deep learning in breast MRI

Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in artificial intelligence (AI). It can automatically learn from raw data without manual feature selection. DL models have led to remarkable advances in data extraction and analysis for medical imaging. Magnetic resonance imaging (MRI) has proven useful in delineating the characteristics and extent of breast lesions and tumors. This review summarizes the current state-of-the-art applications of DL models in breast MRI. Many recent DL models were examined in this field, along with several advanced learning approaches and methods for data normalization and breast and lesion segmentation. For clinical applications, DL-based breast MRI models were proven useful in five aspects: diagnosis of breast cancer, classification of molecular types, classification of histopathological types, prediction of neoadjuvant chemotherapy response, and prediction of lymph node metastasis. For subsequent studies, further improvement in data acquisition and preprocessing is necessary, additional DL techniques in breast MRI should be investigated, and wider clinical applications need to be explored.

Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer

BACKGROUND

In breast cancer diagnosis and treatment, non-invasive prediction of axillary lymph node (ALN) metastasis can help avoid complications related to sentinel lymph node biopsy.

OBJECTIVE

This study aims to develop and evaluate machine learning models using radiomics features extracted from diffusion-weighted whole-body imaging with background signal suppression (DWIBS) examination for predicting the ALN status.

METHODS

A total of 100 patients with histologically proven, invasive, clinically N0 breast cancer who underwent DWIBS examination consisting of short tau inversion recovery (STIR) and DWIBS sequences before surgery were enrolled. Radiomic features were calculated using segmented primary lesions in DWIBS and STIR sequences and were divided into training (n = 75) and test (n = 25) datasets based on the examination date. Using the training dataset, optimal feature selection was performed using the least absolute shrinkage and selection operator algorithm, and the logistic regression model and support vector machine (SVM) classifier model were constructed with DWIBS, STIR, or a combination of DWIBS and STIR sequences to predict ALN status. Receiver operating characteristic curves were used to assess the prediction performance of radiomics models.

RESULTS

For the test dataset, the logistic regression model using DWIBS, STIR, and a combination of both sequences yielded an area under the curve (AUC) of 0.765 (95% confidence interval: 0.548-0.982), 0.801 (0.597-1.000), and 0.779 (0.567-0.992), respectively, whereas the SVM classifier model using DWIBS, STIR, and a combination of both sequences yielded an AUC of 0.765 (0.548-0.982), 0.757 (0.538-0.977), and 0.779 (0.567-0.992), respectively.

CONCLUSIONS

Use of machine learning models incorporating with the quantitative radiomic features derived from the DWIBS and STIR sequences can potentially predict ALN status.

Radiomics nomogram for predicting axillary lymph node metastasis in breast cancer based on DCE-MRI: A multicenter study

OBJECTIVES

This study aims to develop and validate a radiomics nomogram based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to noninvasively predict axillary lymph node (ALN) metastasis in breast cancer.

METHODS

This retrospective study included 263 patients with histologically proven invasive breast cancer and who underwent DCE-MRI examination before surgery in two hospitals. All patients had a defined ALN status based on pathological examination results. Regions of interest (ROIs) of the primary tumor and ipsilateral ALN were manually drawn. A total of 1,409 radiomics features were initially computed from each ROI. Next, the low variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) algorithms were used to extract the radiomics features. The selected radiomics features were used to establish the radiomics signature of the primary tumor and ALN. A radiomics nomogram model, including the radiomics signature and the independent clinical risk factors, was then constructed. The predictive performance was evaluated by the receiver operating characteristic (ROC) curves, calibration curve, and decision curve analysis (DCA) by using the training and testing sets.

RESULTS

ALNM rates of the training, internal testing, and external testing sets were 43.6%, 44.3% and 32.3%, respectively. The nomogram, including clinical risk factors (tumor diameter) and radiomics signature of the primary tumor and ALN, showed good calibration and discrimination with areas under the ROC curves of 0.884, 0.822, and 0.813 in the training, internal and external testing sets, respectively. DCA also showed that radiomics nomogram displayed better clinical predictive usefulness than the clinical or radiomics signature alone.

CONCLUSIONS

The radiomics nomogram combined with clinical risk factors and DCE-MRI-based radiomics signature may be used to predict ALN metastasis in a noninvasive manner.

Can computed tomography predict nodal metastasis in breast cancer patients?

Background

Axillary lymph node metastasis is considered one of the main prognostic factors clinically used for the evaluation of breast cancer patient. Also, an accurate diagnosis of axillary lymph node metastasis has a significant effect on the tumor staging and treatment planning. Ultrasonography is a noninvasive, available imaging modality that is capable of giving a real-time evaluation of axillary lymph nodes in breast cancer cases. On the other hand, multi-detector-row computed tomography is increasingly preferred by clinicians to preoperatively evaluate regional lymph node status in many cancers. The aim of this study was to compare the diagnostic performance of computed tomography against ultrasound in detecting axillary lymph node status in breast cancer patients.

Results

One hundred and fifty breast cancer patients were included in this prospective study. According to the final pathological results, 79/150 (52.7%) lymph nodes were metastatic, while 71/150 (47.3%) lymph nodes were benign with no evidence of metastases. Ultrasound examination has achieved a sensitivity of 76.4% and a specificity of 60.8% with overall diagnostic accuracy of 68.7%. Computed tomography (CT) examination has achieved a much higher sensitivity of 98.6%, a much lower specificity of 35.4%, and overall diagnostic accuracy of 65.3%. In our study, CT examination was superior on ultrasound in the determination of the level of lymph node affection, and this may be attributed to the dependency of ultrasound examination on the operator’s experience.

Conclusions

CT is not routinely used in the assessment of nodal stage. However, if used in proper clinical setting, it may increase our confidence in excluding nodal metastasis owing to its high sensitivity. Despite its low specificity, it may act as road map for the surgeon, providing the ability to assess all groups of lymph nodes as well as the number of the suspicious lymph nodes.

Detection of axillary lymph node metastasis in breast cancer using dual-layer spectral computed tomography

PURPOSE

To investigate the value of contrast-enhanced dual-layer spectral computed tomography (DLCT) in the detection of axillary lymph node (ALN) metastasis in breast cancer.

MATERIALS AND METHODS

In this prospective study, 31 females with breast cancer underwent contrast-enhanced DLCT from August 2019 to June 2020. All ALNs were confirmed by postoperative histology. Spectral quantitative parameters, including λ _HU (in Hounsfield units per kiloelectron-volt), nIC (normalized iodine concentration), and Z_eff (Z-effective value) in both arterial and delay phases, were calculated and contrasted between metastatic and nonmetastatic ALNs using the McNemar test. Discriminating performance from metastatic and nonmetastatic ALNs was analyzed using receiver operating characteristic curves.

RESULTS

In total, 132 ALNs (52 metastatic and 80 nonmetastatic) were successfully matched between surgical labels and preoperative labels on DLCT images. All spectral quantitative parameters (λ_Hu , nIC, and Z_eff) derived from both arterial and delayed phases were greater in metastatic ALNs than in nonmetastatic SLNs (all p < 0.001). Logistic regression analyses showed that λ_Hu in the delayed phase was the best single parameter for the detection of metastatic ALNs on a per-lymph node basis, with an area under the curve of 0.93, accuracy of 86.4% (114/132), sensitivity of 92.3% (48/52), and specificity of 87.5% (70/80).

CONCLUSION

The spectral quantitative parameters derived from contrast-enhanced DLCT, such as λ_Hu , can be applied for the preoperative detection of ALN metastasis in breast cancer.

Different Imaging Modalities for the Diagnosis of Axillary Lymph Node Metastases in Breast Cancer: A Systematic Review and Network Meta-Analysis of Diagnostic Test Accuracy

BACKGROUND

Accurate diagnosis of axillary lymph node metastasis (ALNM) of breast cancer patients is important to guide local and systemic treatment.

PURPOSE

To evaluate the diagnostic performance of different imaging modalities for ALNM in patients with breast cancer.

STUDY TYPE

Systematic review and network meta-analysis (NMA).

SUBJECTS

Sixty-one original articles with 8011 participants.

FIELD STRENGTH

1.5 T and 3.0 T.

ASSESSMENT

We used the QUADAS-2 and QUADAS-C tools to assess the risk of bias in eligible studies. The identified articles assessed ultrasonography (US), MRI, mammography, ultrasound elastography (UE), PET, CT, PET/CT, scintimammography, and PET/MRI.

STATISTICAL ANALYSIS

We used random-effects conventional meta-analyses and Bayesian network meta-analyses for data analyses. We used sensitivity and specificity, relative sensitivity and specificity, superiority index, and summary receiver operating characteristic curve (SROC) analysis to compare the diagnostic value of different imaging modalities.

RESULTS

Sixty-one studies evaluated nine imaging modalities. At patient level, sensitivities of the nine imaging modalities ranged from 0.27 to 0.84 and specificities ranged from 0.84 to 0.95. Patient-based NMA showed that UE had the highest superiority index (5.95) with the highest relative sensitivity of 1.13 (95% confidence interval [CI]: 0.93-1.29) among all imaging methods when compared to US. At lymph node level, MRI had the highest superiority index (6.91) with highest relative sensitivity of 1.13 (95% CI: 1.01-1.23) and highest relative specificity of 1.11 (95% CI: 0.95-1.23) among all imaging methods when compared to US. SROCs also showed that UE and MRI had the largest area under the curve (AUC) at patient level and lymph node level of 0.92 and 0.94, respectively.

DATA CONCLUSION

UE and MRI may be superior to other imaging modalities in the diagnosis of ALNM in breast cancer patients at the patient level and the lymph node level, respectively. Further studies are needed to provide high-quality evidence to validate our findings.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

ACR Appropriateness Criteria® Imaging of the Axilla

This publication reviews the current evidence supporting the imaging approach of the axilla in various scenarios with broad differential diagnosis ranging from inflammatory to malignant etiologies. Controversies on the management of axillary adenopathy results in disagreement on the appropriate axillary imaging tests. Ultrasound is often the appropriate initial imaging test in several clinical scenarios. Clinical information (such as age, physical examinations, risk factors) and concurrent complete breast evaluation with mammogram, tomosynthesis, or MRI impact the type of initial imaging test for the axilla. Several impactful clinical trials demonstrated that selected patient's population can received sentinel lymph node biopsy instead of axillary lymph node dissection with similar overall survival, and axillary lymph node dissection is a safe alternative as the nodal staging procedure for clinically node negative patients or even for some node positive patients with limited nodal tumor burden. This approach is not universally accepted, which adversely affect the type of imaging tests considered appropriate for axilla. This document is focused on the initial imaging of the axilla in various scenarios, with the understanding that concurrent or subsequent additional tests may also be performed for the breast. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

The impact of body mass index (BMI) on MRI diagnostic performance and surgical management for axillary lymph node in breast cancer

Background

We hypothesized that different BMI might have different impact on pre-operative MRI axillary lymph node (ALN) prediction accuracy and thereby subsequent surgical lymph node management. The aim of this study is to evaluate the effect of BMI on presentation, surgical treatment, and MRI performance characteristics of breast cancer with the main focus on ALN metastasis evaluation.

Methods

The medical records of patients with primary invasive breast cancer who had pre-operative breast MRI and underwent surgical resection were retrospectively reviewed. They were categorized into 3 groups in this study: underweight (BMI < 18.5), normal (BMI of 18.5 to 24), and overweight (BMI > 24). Patients’ characteristics, surgical management, and MRI performance for axillary evaluation between the 3 groups were compared.

Results

A total of 2084 invasive breast cancer patients with a mean age of 53.4 ± 11.2 years were included. Overweight women had a higher rate of breast conserving surgery (56.7% vs. 54.5% and 52.1%) and initial axillary lymph node dissection (15.9% vs. 12.2% and 8.5%) if compared to normal and underweight women. Although the post-operative ALN positive rates were similar between the 3 groups, overweight women were significantly found to have more axillary metastasis on MRI compared with normal and underweight women (50.2% vs 37.7% and 18.3%). There was lower accuracy in terms of MRI prediction in overweight women (65.1%) than in normal and underweight women (67.8% and 76.1%).

Conclusion

Our findings suggest that BMI may influence the diagnostic performance on MRI on ALN involvement and the surgical management of the axilla in overweight to obese women with breast cancer.

A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients

Background

Despite that machine learning (ML)-based MRI has been evaluated for diagnosis of axillary lymph node metastasis (ALNM) in breast cancer patients, diagnostic values they showed have been variable. In this study, we aimed to assess the use of ML to classify ALNM on MRI and to identify potential covariates that might influence the diagnostic performance of ML.

Methods

A systematic research of PubMed, Embase, Web of Science, and the Cochrane Library was conducted until 27 December 2020 to collect the included articles. Subgroup analysis was also performed.

Findings

Fourteen studies assessing a total of 2247 breast cancer patients were included in the analysis. The overall AUC for ML in the validation set was 0.80 (95% confidence interval [CI] 0.76–0.83) with a negative predictive value of 0.83. The pooled sensitivity and specificity were 0.79 (95% CI 0.74–0.84) and 0.77 (95% CI 0.73–0.81), respectively. In the subgroup analysis of the validation set, T1-weighted contrast-enhanced (T1CE) imaging with ML yielded a higher sensitivity (0.80 vs. 0.67 vs. 0.76) than the T2-weighted fat-suppressed (T2-FS) imaging and diffusion-weighted imaging (DWI). Support vector machines (SVMs) had a higher specificity than linear regression (LR) and linear discriminant analysis (LDA) (0.79 vs. 0.78 vs. 0.75), whereas LDA showed a higher sensitivity than LR and SVM (0.83 vs. 0.70 vs. 0.77).

Interpretation

MRI sequences and algorithms were the main factors that affect the diagnostic performance of ML. Although its results were encouraging with the pooled sensitivity of around 0.80, it meant that 1 in 5 women that would go with undetected metastases, which may have a detrimental effect on the overall survival for 20% of patients with positive SLN status. Despite that a high NPV of 0.83 meant that ML could potentially benefit those with negative SLN, it might also translate to 1 in 5 tests being false negative. We would like to suggest that ML may not be yet usable in clinical routine especially when patient survival is used as a primary measurement of its outcome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01034-1.

Nomogram Based on Breast MRI and Clinicopathologic Features for Predicting Axillary Lymph Node Metastasis in Patients with Early-Stage Invasive Breast Cancer: A Retrospective Study

INTRODUCTION

To establish a nomogram for predicting axillary lymph node (ALN) involvement in patients with early-stage invasive breast cancer (BC) based on magnetic resonance imaging (MRI) features and clinicopathological characteristics.

MATERIALS AND METHODS

Patients with confirmed early-stage invasive BC between 03/2016 and 05/2017 were retrospectively reviewed at the National Cancer Center/Cancer Hospital. Risk factors for ALN metastasis (ALNM) were identified by univariable and multivariable logistic regression analysis. The independent risk factors were used to create a nomogram.

RESULTS

This study included 214 early-stage invasive BC patients, including 57 (26.6%) with positive ALNs. Tumor location (OR = 4.019, 95% CI: 1.304 -12.383, P = .015), tumor size (OR = 3.702, 95%CI: 1.517 -9.034, P = .004), multifocality (OR = 3.534, 95%CI: 1.249 -9.995, P = .017), MR-reported suspicious ALN (OR = 9.829, 95%CI: 4.132 -23.384, P <0.001), apparent diffusion coefficient (ADC) value (OR = 0.367, 95%CI: 0.158 -0.852, P = .020), and lymphovascular invasion (LVI) (OR = 3.530, 95%CI: 1.483 -8.400, P = .004) were identified as independent risk factors associated with ALNM. A nomogram was created for predicting the probability of ALNM by using these risk factors. The calibration curve of the nomogram showed that the nomogram predictions are consistent with the actual ALNM rate. The area under the curve was 0.88 (95% CI: 0.83 -0.93). The nomogram had a bootstrapped-concordance index of 0.88 and was well-calibrated.

CONCLUSION

The nomogram based on MRI and clinicopathologic features might be a useful tool for predicting ALNM in early-stage invasive BC and could help clinical decision-making.

Diagnostic Values of 8 Different Imaging Modalities for Preoperative Detection of Axillary Lymph Node Metastasis of Breast Cancer: A Bayesian Network Meta-analysis

OBJECTIVE

This study aimed to compare diagnostic performances of 8 different imaging modalities for preoperative detection of axillary lymph node (LN) metastasis in patients with breast cancer by performing a network meta-analysis (NMA) using direct comparison studies with 2 or more imaging techniques.

MATERIALS AND METHODS

PubMed, Cochrane, and Embase were searched for the studies evaluating the performances of 8 different imaging modalities for preoperative axillary LN staging in patients with breast cancer. The NMA was performed in patient-based analyses. The consistency was evaluated by examining the agreement between direct and indirect treatment effects, and publication bias was assessed by funnel plot asymmetry tests. The surface under the cumulative ranking curve (SUCRA) values were obtained to calculate the probability of each imaging modality being the most effective diagnostic method.

RESULTS

A total of 2197 patients from 22 direct comparison studies using 8 different imaging modalities for preoperative detection of axillary LN metastasis in patients with breast cancer were included. For preoperative detection of axillary LN metastasis of breast cancer, elastography showed the highest SUCRA values of sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and diagnostic odds ratio. In addition, fluorine-18 fluorodeoxyglucose positron emission tomography (PET) or PET/computed tomography, fluorine-18 fluorodeoxyglucose PET/magnetic resonance, and contrast-enhanced computed tomography showed high SUCRA values.

CONCLUSION

Elastography showed the highest SUCRA values. Seven imaging modalities showed the complementary diagnostic roles for preoperative detection of axillary LN metastasis in patients with breast cancer, except mammography.

Early SUVmax is the best predictor of axillary lymph node metastasis in stage III breast cancers

Background

Although fluorine-18-labeled 2-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) positron emission/computed tomography (PET/CT) imaging has been investigated for its ability to evaluate lymph node metastasis of breast cancer, few comparative analyses have evaluated the preoperative and postoperative regional lymph node metastasis of breast cancer by dual-phase imaging, especially in patients with stage III (N2) disease.

Methods

The clinical, pathological, and imaging data of 40 patients with stage III (N2) breast cancer were retrospectively analyzed. All patients underwent dual-time point ¹⁸F-FDG imaging before surgery and postoperative pathology examinations were obtained. The short-axis lymph node diameter was measured, and the maximum standardized uptake value (SUV_max) and the percentage difference of SUV_max between dual-phase (ΔSUV_max) were calculated to compare metastatic and non-metastatic lymph nodes on dual-time point images.

Results

A total of 398 axillary lymph nodes were inspected, and 209 lymph nodes were matched with those on PET/CT images, including 97 metastatic and 112 non-metastatic lymph nodes. The SUV_max values were significantly different between metastatic and non-metastatic lymph nodes, in both the early and delayed scans (P<0.001). For metastatic lymph nodes, the SUV_max value on the delayed scan (6.17±2.62) was significantly higher compared with the early scan (5.45±1.35; ΔSUV_max =0.08±0.21, P<0.001). Moreover, the SUV_max values were not significantly different between the delayed (2.82±0.91) and early scans (2.79±0.72; ΔSUV_max=-0.00±0.11, P=0.77). The short diameters were not significantly different between metastatic and non-metastatic lymph nodes (P=0.12), and the SUV_max values of metastatic lymph nodes with short diameters of >4.00 and ≤6.00 mm were not significantly different between the early and delayed scans (P=0.06). However, the SUV_max values of metastatic lymph nodes with short diameters of >6.00 and ≤8.00 mm (7.11±0.19 vs. 5.96±0.08) and short diameters of >8.00 and ≤10.00 mm (10.76±0.35 vs. 6.82±0.50) were higher on the delayed scan versus the early scan, respectively (P<0.01 for each comparison). The difference between the ΔSUV_max values among the three subgroups was statistically significant (F=78.98, P<0.001).The receiver operating characteristic (ROC) curve analysis of the lymph nodes showed that the area under the curve (AUC) of the early and delayed PET/CT scans was 0.961 (0.925-0.983, P=0.013) and 0.897 (0.847-0.934, P=0.022), respectively. The ROC curves of the early and delayed scans were also significantly different (z=4.46, P<0.001). AUC of the ΔSUV_max for the early scan was significantly lower compared with delayed scans (z=8.95 vs. 9.13, respectively; P<0.001).

Conclusions

Dual-time point ¹⁸F-FDG PET imaging significantly improved the prediction and detection of axillary lymph node metastasis, compared with prediction based on size of lymph node alone, in patients with stage III breast cancer. We found that lymph nodes with continuously increased SUV_max values tended to show metastasis, and early SUV_max assessment offers the best capacity for prediction of axillary lymph node metastasis.

Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer

OBJECTIVE

To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer.

METHODS

216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort (n = 144) and validation cohort (n = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts.

RESULTS

95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591-0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 and 0.657 in the primary and validation cohorts) and radiomics signature (AUC 0.876 and 0.862 in the primary and validation cohorts).

CONCLUSION

The MG-based radiomics nomogram could be used as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to assist clinicians for pre-operative decision-making.

ADVANCES IN KNOWLEDGE

ALN status remains among the most important breast cancer prognostic factors and is essential for making treatment decisions. However, the value of detecting metastatic ALN by MG is very limited. The studies on pre-operative ALN metastasis prediction using the method of MG-based radiomics in breast cancer are very few. Therefore, we studied whether MG-based radiomics nomogram could be used as a predictive biomarker for the detection of metastatic ALN.

Evaluation of Diagnostic Accuracy and Impact of Preoperative Positron Emission Tomography/Computed Tomography in the Management of Early Operable Breast Cancers

AIM

Our aim of this study was to evaluate the diagnostic accuracy of staging positron emission tomography/computed tomography (PET/CT) in early breast cancers (EBCs) and to assess its impact on disease management.

PATIENTS AND METHODS

We retrospectively reviewed preoperative PET/CT scans of patients from January 2015 to December 2018 with Stage I/II, clinically T1-T2 N0-N1 breast cancers. The diagnostic performance of PET/CT for nodal (N) and distant metastases (M), its correlation with patient/tumor-specific factors, and its impact on disease management were analyzed using histopathology/clinical follow-up as standards of reference.

RESULTS

Of 158 patients evaluated, 14% of patients were Stage I (T1N0), 60% were Stage IIA (T1N1, T2N0), and 26% were Stage IIB (T2N1). Sensitivity, specificity, and the diagnostic accuracy of PET/CT for axillary staging were 76%, 97%, and 84% and for distant metastasis evaluation were 100%, 98%, and 99%, respectively. The diagnostic accuracy of PET/CT for axillary staging was lower for low-grade, T1 tumors, postmenopausal group, and luminal A pathological subtype (77%, 84%, 81%, and 73%, respectively) compared to high-grade, T2 tumors, premenopausal group, and nonluminal A subtype (88%, 88%, 94%, and 87%, respectively). Distant metastases were detected on PET/CT in overall 16% (n = 25) of the patients (9% in Stage IIA and 27% in Stage IIB). PET/CT also incidentally identified clinically occult internal mammary nodes in 5% (n = 8) and organ-confined synchronous second malignancies in 5% (n = 8) of the patients.

CONCLUSION

Preoperative PET/CT should be considered in all EBCs> 2 cm as it upstages the disease and alters management in about 24% of these patients. Given its high specificity for axillary staging PET/CT, patients with PET-positive axilla can be subjected to axillary dissection and those with PET-negative axilla to sentinel lymph node biopsy. The yield and diagnostic accuracy of PET/CT is less for low-grade tumors <2 cm and with luminal A subtype.

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset

The aim of this study is to evaluate the role of convolutional neural network (CNN) in predicting axillary lymph node metastasis, using a breast MRI dataset. An institutional review board (IRB)-approved retrospective review of our database from 1/2013 to 6/2016 identified 275 axillary lymph nodes for this study. Biopsy-proven 133 metastatic axillary lymph nodes and 142 negative control lymph nodes were identified based on benign biopsies (100) and from healthy MRI screening patients (42) with at least 3 years of negative follow-up. For each breast MRI, axillary lymph node was identified on first T1 post contrast dynamic images and underwent 3D segmentation using an open source software platform 3D Slicer. A 32 × 32 patch was then extracted from the center slice of the segmented tumor data. A CNN was designed for lymph node prediction based on each of these cropped images. The CNN consisted of seven convolutional layers and max-pooling layers with 50% dropout applied in the linear layer. In addition, data augmentation and L2 regularization were performed to limit overfitting. Training was implemented using the Adam optimizer, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Code for this study was written in Python using the TensorFlow module (1.0.0). Experiments and CNN training were done on a Linux workstation with NVIDIA GTX 1070 Pascal GPU. Two class axillary lymph node metastasis prediction models were evaluated. For each lymph node, a final softmax score threshold of 0.5 was used for classification. Based on this, CNN achieved a mean five-fold cross-validation accuracy of 84.3%. It is feasible for current deep CNN architectures to be trained to predict likelihood of axillary lymph node metastasis. Larger dataset will likely improve our prediction model and can potentially be a non-invasive alternative to core needle biopsy and even sentinel lymph node evaluation.

Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer

OBJECTIVE

To develop a radiomic nomogram for preoperative prediction of axillary lymph node (LN) metastasis in breast cancer patients.

METHODS

Preoperative magnetic resonance imaging data from 411 breast cancer patients was studied. Patients were assigned to either a training cohort (n = 279) or a validation cohort (n = 132). Eight hundred eight radiomic features were extracted from the first phase of T1-DCE images. A support vector machine was used to develop a radiomic signature, and logistic regression was used to develop a nomogram.

RESULTS

The radiomic signature based on 12 LN status-related features was constructed to predict LN metastasis, its prediction ability was moderate, with an area under the curve (AUC) of 0.76 and 0.78 in training and validation cohorts, respectively. Based on a radiomic signature and clinical features, a nomogram was developed and showed excellent predictive ability for LN metastasis (AUC 0.84 and 0.87 in training and validation sets, respectively). Another radiomic signature was constructed to distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes), which also showed moderate performance (AUC 0.79).

CONCLUSIONS

We developed a nomogram and a radiomic signature that can be used to identify LN metastasis and distinguish the number of metastatic LNs (less than 2 positive nodes/more than 2 positive nodes). Both nomogram and radiomic signature can be used as tools to assist clinicians in assessing LN metastasis in breast cancer patients.

KEY POINTS

• ALNM is an important factor affecting breast cancer patients' treatment and prognosis. • Traditional imaging examinations have limited value for evaluating axillary LNs status. • We developed a radiomic nomogram based on MR imagings to predict LN metastasis.

Can Preoperative Ultrasonography and MRI Replace Sentinel Lymph Node Biopsy in Management of Axilla in Early Breast Cancer-a Prospective Study from a Tertiary Cancer Center

Although SLNB is a less invasive procedure in detecting axillary lymph node metastases(ALNM) in early breast cancer; still, it carries some complications like lymphedema and in addition, performing SLNB requires surgical skills, technical knowledge, presence of facility like preoperative sentinel lymphoscintigraphy, and availability of hand-held gamma probe for intraoperative assessment. We calculated the relative diagnostic strength of preoperative axillary USG and MRI and compared with of SLNB for detection of ALNM in early breast cancer and assessed whether MRI and USG could accurately predict axillary LN status, potentially replacing SLNB. We evaluated 40 cases of clinically node-negative early breast cancer with preoperative axillary USG and MRI and subsequently subjected to SLNB. The sensitivity, specificity, PPV, NPV, and accuracy of axillary USG were 62.5%, 96.88%, 88.33%, 91.18%, and 90% respectively (p value < 0.001). The sensitivity, specificity, PPV, NPV, and accuracy of MRI in detection of ALNM were 75%, 93.75%, 75%, 93.75%, and 90% (p value < 0.001). The sensitivity, specificity, PPV, NPV, and accuracy of combined USG and MRI in detection of ALNM were 87.5%,90.63%, 70%, 96.67%, and 90% respectively (p value < 0.001), which are comparable to previous study series. The diagnostic performance of combined approach of axillary USG and MRI is promising, as the NPV of combined USG and MRI is approaching the NPV of the SLNB in detecting ALNM. Based on above findings, if axillary LNs are found nonsuspicious in preoperative axillary USG and MRI, further axillary dissection may be avoided, and if found suspicious, then ALND may be directly proceeded avoiding SLNB in between.

L’exploration axillaire : un standard du bilan préthérapeutique

L’exploration préthérapeutique axillaire est une étape majeure du bilan initial du cancer du sein. L’échographie associée à un prélèvement est actuellement recommandée en première intention. L’IRM et la TEP/TDM au 18FDG sont utiles dans l’évaluation ganglionnaire axillaire. Les sensibilités et spécificités des examens d’imagerie sont globalement identiques, et leur combinaison permet d’obtenir les meilleures performances. Actuellement, la technique du ganglion sentinelle est indispensable en cas de tumeurs mammaires T1-T2 N0 et en cas d’adénopathie suspecte échographiquement avec cytoponction ou microbiopsie négative.

Preoperative assessment of axillary lymph node status in breast cancer patients by ultrasonography combined with mammography: A STROBE compliant article

Preoperative assessment of nodal stage is of importance in breast cancer treatment decision-making. This study was done to determine the power of combined mammography and ultrasonography in differentiating N0-N1 from N2-N3 breast cancer.We retrospectively reviewed clinical data of 3944 female patients with invasive breast cancer by preoperative mammography and ultrasonography between January 2006 and December 2013 at our hospital. Pathological diagnosis was available for each patient. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of mammography alone, ultrasonography alone, and combination of them for assessment of axillary lymph node (ALN) status were calculated, using definitive histological results as the baseline.The sensitivity, specificity, PPV, NPV, and accuracy was 90.4%, 68.2%, 36.5%, 97.2%, and 71.9% for ultrasonography; was 66.9%, 80.8%, 41.3%, 92.3%, and 78.4% for mammography; and was 94.9%, 62.4%, 33.8%, and 98.4% for combined mammography and ultrasonography. For combination, accuracy and the area under the receiver operating characteristic curve was 67.9% and 0.85, respectively.In conclusion, combining ultrasonography and mammography improves the sensitivity in differentiating N0-N1 breast cancer from N2-N3 breast cancer, but leading to a reduced specificity. Addition of mammography to ultrasonography seems not to provide significant benefits in predicting ALN status in breast cancer patients.

Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review

Ultrasound imaging is a commonly used modality for breast cancer detection and diagnosis. In this review, we summarize ultrasound imaging technologies and their clinical applications for the management of breast cancer patients. The technologies include ultrasound elastography, contrast-enhanced ultrasound, 3-D ultrasound, automatic breast ultrasound and computer-aided detection of breast ultrasound. We summarize the study results seen in the literature and discuss their future directions. We also provide a review of ultrasound-guided, breast biopsy and the fusion of ultrasound with other imaging modalities, especially magnetic resonance imaging (MRI). For comparison, we also discuss the diagnostic performance of mammography, MRI, positron emission tomography and computed tomography for breast cancer diagnosis at the end of this review. New ultrasound imaging techniques, ultrasound-guided biopsy and the fusion of ultrasound with other modalities provide important tools for the management of breast patients.

Risk Factors for a False-Negative Result of Sentinel Node Biopsy in Patients with Clinically Node-Negative Breast Cancer

Purpose

Although sentinel lymph node biopsy (SLNB) can accurately represent the axillary lymph node (ALN) status, the false-negative rate (FNR) of SLNB is the main concern in the patients who receive SLNB alone instead of ALN dissection (ALND).

Materials and Methods

We analyzed 1,886 patientswho underwent ALND after negative results of SLNB,retrospectively. A logistic regression analysis was used to identify risk factors associated with a falsenegative (FN) result. Cox regression model was used to estimate the hazard ratio of factors affecting disease-free survival (DFS).

Results

Tumor located in the upper outer portion of the breast, lymphovascular invasion, suspicious node in imaging assessment and less than three sentinel lymph nodes (SLNs) were significant independent risk factors for FN in SLNB conferring an adjusted odds ratio of 2.10 (95% confidence interval [CI], 1.30 to 3.39), 2.69 (95% CI, 1.47 to 4.91), 2.59 (95% CI, 1.62 to 4.14), and 2.39 (95% CI, 1.45 to 3.95), respectively. The prognostic factors affecting DFS were tumor size larger than 2 cm (hazard ratio [HR], 1.86; 95% CI, 1.17 to 2.96) and FN of SLNB (HR, 2.51; 95% CI, 1.42 to 4.42) in SLN-negative group (FN and true-negative), but in ALN-positive group (FN and true-positive), FN of SLNB (HR, 0.64; 95% CI, 0.33 to 1.25) did not affect DFS.

Conclusion

In patients with risk factors for a FN such as suspicious node in imaging assessment, upper outer breast cancer, less than three harvested nodes, we need attention to find another metastatic focus in non-SLNs during the operation. It may contribute to provide an exact prognosis and optimizing adjuvant treatments.

An objective nodal staging system for breast cancer patients undergoing neoadjuvant systemic treatment

BACKGROUND

In this study, we aimed to develop an objective staging system to determine the degree of nodal metastasis in breast cancer patients undergoing neoadjuvant systemic treatment (NST).

METHODS

We reviewed the pretreatment computed tomography (CT) images of 392 breast cancer patients who received NST. The association between the patterns of the enlarged regional lymph nodes and treatment outcome was analyzed.

RESULTS

In the development cohort of 260 patients, 88 (33.8%) patients experienced tumor recurrence and had a significantly higher number of enlarged lymph nodes on the pretreatment CT compared to patients with no recurrence. When patients were classified according to the numbers and locations of enlarged lymph nodes on pretreatment CT, the number of lymph nodes larger than 1 cm was most significantly associated with tumor recurrence. The accuracy of the CT-based nodal staging system was validated in an independent cohort of 132 patients. The presence of the enlarged supraclavicular nodes was associated with worse outcome, but the effect seemed to originate from the accompanied extensive axillary nodal burden. The prognostic effect of the objectively measured axillary nodal metastasis was more pronounced in hormone receptor-negative tumors.

CONCLUSIONS

We have developed and validated an objective method of nodal staging in breast cancer patients who undergo NST based on the number of enlarged axillary lymph nodes. Our system can improve the current subjective approach, which uses physical examination alone.

Diagnostic Value of Diffusion Weighted Magnetic Resonance Imaging in Evaluation of Metastatic Axillary Lymph Nodes in a Sample of Iranian Women with Breast Cancer

Objective:

To evaluate the diagnostic value of diffusion weighted magnetic resonance imaging (DW-MRI) in assessment of metastases in axillary lymph nodes (ALNs) in a sample of Iranian women with breast cancer.

Methods:

A total of 50 axillary lymph nodes from 30 female patients with histologically verified breast cancer were assessed by 1.5 T MRI. DWI was implemented at b-values of 50, 400 and 800 s/mm². Short axis diameter, presence of fatty hilum and apparent diffusion coefficient (ADC) values (min, max and mean) of metastatic and non-metastatic ALNs was compared. Cutoff ADC values to discriminate between benign and malignant axillary lymph nodes were analyzed with receiver coefficient characteristic (ROC) curves.

Result:

The final histopathological examination revealed 46% (n=23) metastatic and 54% (n=27) non-metastatic ALNs. There was no statistically significant difference in short axis diameter between the two groups (p = 0.537). However there was significantly correlation between loss of fatty hilum and presence of metastases (p < 0.001) and ADC values (0.255 ± 0.19×10^-3 mm²/s vs 0.616 ±0.3×10^-3 mm²/s (ADC min), 1.088 ± 0.22×10^-3 mm²/s vs 1.497 ± 0.24×10^-3 mm²/s (ADC max) and 0.824 ± 0.103 ×10^-3 mm²/s vs 1.098 ± 0.23 ×10^-3 mm²/s (ADC mean)) of metastatic ALNs were significantly lower than those of non-metastatic ALNs (p < 0.001). The optimal mean ADC cut-off value for differentiation between metastatic and non-metastatic ALNs was 0.904×10^-3 mm²/s which had a higher specificity (88.9%) and accuracy (91.8%) as compared with ADC min and ADC max.

Conclusion:

DWI-MRI and ADC values are promising imaging methods which can assess metastatic ALNs in breast cancer with high sensitivity, specificity and accuracy.

Locoregional Recurrence Risk in Breast Cancer Patients with Estrogen Receptor Positive Tumors and Residual Nodal Disease following Neoadjuvant Chemotherapy and Mastectomy without Radiation Therapy

Among breast cancer patients treated with neoadjuvant chemotherapy (NAC) and mastectomy, locoregional recurrence (LRR) rates are unclear in women with ER+ tumors treated with adjuvant endocrine therapy without postmastectomy radiation (PMRT). To determine if PMRT is needed in these patients, we compared LRR rates of patients with ER+ tumors (treated with adjuvant endocrine therapy) with women who have non-ER+ tumors. 85 consecutive breast cancer patients (87 breast tumors) treated with NAC and mastectomy without PMRT were reviewed. Patients were divided by residual nodal disease (ypN) status (ypN+ versus ypN0) and then stratified by receptor subtype. Among ypN+ patients (n = 35), five-year LRR risk in patients with ER+, Her2+, and triple negative tumors was 5%, 33%, and 37%, respectively (p = 0.02). Among ypN+/ER+ patients, lymphovascular invasion and grade three disease increased the five-year LRR risk to 13% and 11%, respectively. Among ypN0 patients (n = 52), five-year LRR risk in patients with ER+, Her2+, and triple negative tumors was 7%, 22%, and 6%, respectively (p = 0.71). In women with ER+ tumors and residual nodal disease, endocrine therapy may be sufficient adjuvant treatment, except in patients with lymphovascular invasion or grade three tumors where PMRT may still be indicated.

The role of MRI in axillary lymph node imaging in breast cancer patients: a systematic review

Objectives

To assess whether MRI can exclude axillary lymph node metastasis, potentially replacing sentinel lymph node biopsy (SLNB), and consequently eliminating the risk of SLNB-associated morbidity.

Methods

PubMed, Cochrane, Medline and Embase databases were searched for relevant publications up to July 2014. Studies were selected based on predefined inclusion and exclusion criteria and independently assessed by two reviewers using a standardised extraction form.

Results

Sixteen eligible studies were selected from 1,372 publications identified by the search. A dedicated axillary protocol [sensitivity 84.7 %, negative predictive value (NPV) 95.0 %] was superior to a standard protocol covering both the breast and axilla simultaneously (sensitivity 82.0 %, NPV 82.6 %). Dynamic, contrast-enhanced MRI had a lower median sensitivity (60.0 %) and NPV (80.0 %) compared to non-enhanced T1w/T2w sequences (88.4, 94.7 %), diffusion-weighted imaging (84.2, 90.6 %) and ultrasmall superparamagnetic iron oxide (USPIO)- enhanced T2*w sequences (83.0, 95.9 %). The most promising results seem to be achievable when using non-enhanced T1w/T2w and USPIO-enhanced T2*w sequences in combination with a dedicated axillary protocol (sensitivity 84.7 % and NPV 95.0 %).

Conclusions

The diagnostic performance of some MRI protocols for excluding axillary lymph node metastases approaches the NPV needed to replace SLNB. However, current observations are based on studies with heterogeneous study designs and limited populations.

Main Messages

• Some axillary MRI protocols approach the NPV of an SLNB procedure.

• Dedicated axillary MRI is more accurate than protocols also covering the breast.

• T1w/T2w protocols combined with USPIO-enhanced sequences are the most promising sequences.

Prediction of macrometastasis in axillary lymph nodes of patients with invasive breast cancer and the utility of the SUV lymph node/tumor ratio using FDG-PET/CT

BACKGROUND

Axillary lymph node dissection (ALND) is important for improving the prognosis of patients with node-positive breast cancer. However, ALND can be avoided in select micrometastatic cases, preventing complications such as lymphedema or paresthesia of the upper limb. To appropriately omit ALND from treatment, evaluation of the axillary tumor burden is critical. The present study evaluated a method for preoperative quantification of axillary lymph node metastasis using positron emission tomography/computed tomography (PET/CT).

METHODS

The records of breast cancer patients who received radical surgery at the Gifu University Hospital (Gifu, Japan) between 2009 and 2014 were reviewed. The axillary lymph nodes were preoperatively evaluated by PET/CT. Lymph nodes were dissected by sentinel lymph node biopsy (SLNB) or ALND and were histologically diagnosed by experienced pathologists. The maximum standardized uptake value (SUVmax) was measured in both the axillary lymph node (SUV-LN) and primary tumor (SUV-T). The SUV-LN/T ratio (NT ratio) was calculated by dividing the SUV-LN by the SUV-T, and the efficacies of the NT ratio and SUV-LN were compared using receiver operating characteristic (ROC) curve analysis. The diagnostic performance was also compared between the techniques with the McNemar test.

RESULTS

A total of 171 operable invasive breast cancer patients were enrolled, comprising 69 node-positive patients (macrometastasis (Mac): n = 55; micrometastasis (Mic): n = 14) and 102 node-negative patients (Neg). The NT ratio for node-positive patients was significantly higher than in node-negative patients (0.5 vs. 0.316, respectively, P = 0.041). The NT ratio for Mac patients (0.571) was significantly higher than in Mic (0.227) and Neg (0.316) patients (P <0.01 and P = 0.021, respectively). The areas under the curves (AUCs) by ROC analysis for the NT ratio and SUV-LN were 0.647 and 0.811, respectively (P <0.01). In patients with an SUV-T ≥2.5, the modified AUCs for the NT ratio and SUV-LV were 0.757 and 0.797 (not significant).

CONCLUSION

The NT ratio and SUV-LN are significantly higher in patients with axillary macrometastasis than in those with micrometastasis or no metastasis. The NT ratio and SUV-LN can help quantify axillary lymph node metastasis and may assist in macrometastasis identification, particularly in patients with an SUV-T ≥2.5.