Prospects of perfusion contrast-enhanced ultrasound (CE-US) in diagnosing axillary lymph node metastases in breast cancer: a comparison with lymphatic CE-US

Accurate diagnosis of lymph node (LN) metastasis is vital for prognosis and treatment in patients with breast cancer. Imaging 1modalities such as ultrasound (US), MRI, CT, and 18F-FDG PET/CT are used for preoperative assessment. While conventional US is commonly recommended due to its resolution and sensitivity, it has limitations such as operator subjectivity and difficulty detecting small metastases. This review shows the microanatomy of axillary LNs to enhance accurate diagnosis and the characteristics of contrast-enhanced US (CE-US), which utilizes intravascular microbubble contrast agents, making it ideal for vascular imaging. A significant focus of this review is on distinguishing between two types of CE-US techniques for axillary LN evaluation: perfusion CE-US and lymphatic CE-US. Perfusion CE-US is used to assess LN metastasis via transvenous contrast agent administration, while lymphatic CE-US is used to identify sentinel LNs and diagnose LN metastasis through percutaneous contrast agent administration. This review also highlights the need for future research to clarify the distinction between studies involving "apparently enlarged LNs" and "clinical node-negative" cases in perfusion CE-US research. Such research standardization is essential to ensure accurate diagnostic performance in various clinical studies. Future studies should aim to standardize CE-US methods for improved LN metastasis diagnosis, not only in breast cancer but also across various malignancies.

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.

A Radiogenomic multimodal and whole-transcriptome sequencing for preoperative prediction of axillary lymph node metastasis and drug therapeutic response in breast cancer: a retrospective, machine learning And international multi-cohort study

BACKGROUND

Axillary lymph nodes (ALN) status serves as a crucial prognostic indicator in breast cancer (BC). The aim of this study was to construct a radiogenomic multimodal model, based on machine learning (ML) and whole-transcriptome sequencing (WTS), to accurately preoperative evaluate the risk of ALN metastasis (ALNM), drug therapeutic response and avoid unnecessary axillary surgery in BC patients.

METHODS

In this study, we conducted a retrospective analysis of 1078 BC patients from The Cancer Genome Atlas (TCGA), The Cancer Imaging Archive (TCIA), and Foshan cohort. These patients were divided into the TCIA cohort(N=103), TCIA validation cohort(N=51), Duke cohort(N=138), Foshan cohort(N=106), and TCGA cohort(N=680). Radiological features were extracted from BC radiological images and differentially expressed gene expression was calibrated using WTS technology. A support vector machine (SVM) model was employed to screen radiological and genetic features, and a multimodal model was established based on radiogenomic and clinical pathological features to predict ALNM and stratify. The accuracy of the model predictions was assessed using the area under the curve (AUC) and the clinical benefit was measured using decision curve analysis (DCA). Risk stratification analysis of BC patients was performed by gene set enrichment analysis (GSEA), differential comparison of immune checkpoint gene expression, and drug sensitivity testing.

RESULTS

For the prediction of ALNM, rad-score was able to significantly differentiate between ALN- and ALN+ patients in both the Duke and Foshan cohorts (P<0.05). Similarly, the gene-score was able to significantly differentiate between ALN- and ALN+ patients in the TCGA cohort (P<0.05). The radiogenomic multimodal nomogram demonstrated satisfactory performance in the TCIA cohort (AUC 0.82, 95% CI: 0.74-0.91) and TCIA validation cohort (AUC 0.77, 95% CI: 0.63-0.91). In the risk sub-stratification analysis, there were significant differences in gene pathway enrichment between high and low-risk groups (P<0.05). Additionally, different risk groups may exhibit varying treatment responses to chemotherapy (including Doxorubicin, Methotrexate and Lapatinib) (P<0.05).

CONCLUSION

Overall, the radiogenomic multimodal model employs multimodal data, including radiological images, genetic and clinicopathological typing. The radiogenomic multimodal nomogram can precisely predict ALNM and drug therapeutic response in BC patients.

A review of quantitative diffusion-weighted MR imaging for breast cancer: Towards noninvasive biomarker

Quantitative diffusion-weighted imaging (DWI) is an important adjunct to conventional breast MRI and shows promise as a noninvasive biomarker of breast cancer in multiple clinical scenarios, from the discrimination of benign and malignant lesions, prediction, and evaluation of treatment response to a prognostic assessment of breast cancer. Various quantitative parameters are derived from different DWI models based on special prior knowledge and assumptions, have different meanings, and are easy to confuse. In this review, we describe the quantitative parameters derived from conventional and advanced DWI models commonly used in breast cancer and summarize the promising clinical applications of these quantitative parameters. Although promising, it is still challenging for these quantitative parameters to become clinically useful noninvasive biomarkers in breast cancer, as multiple factors may result in variations in quantitative parameter measurements. Finally, we briefly describe some considerations regarding the factors that cause variations.

Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model

Purpose

To develop a multiparametric MRI model for predicting axillary lymph node metastasis in invasive breast cancer.

Methods

Clinical data and T2WI, DWI, and DCE-MRI images of 252 patients with invasive breast cancer were retrospectively analyzed and divided into the axillary lymph node metastasis (ALNM) group and non-ALNM group using biopsy results as a reference standard. The regions of interest (ROI) in T2WI, DWI, and DCE-MRI images were segmented using MATLAB software, and the ROI was unified into 224 × 224 sizes, followed by image normalization as input to T2WI, DWI, and DCE-MRI models, all of which were based on ResNet 50 networks. The idea of a weighted voting method in ensemble learning was employed, and then T2WI, DWI, and DCE-MRI models were used as the base models to construct a multiparametric MRI model. The entire dataset was randomly divided into training sets and testing sets (the training set 202 cases, including 78 ALNM, 124 non-ALNM; the testing set 50 cases, including 20 ALNM, 30 non-ALNM). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of models were calculated. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic performance of each model for axillary lymph node metastasis, and the DeLong test was performed, P< 0.05 statistically significant.

Results

For the assessment of axillary lymph node status in invasive breast cancer on the test set, multiparametric MRI models yielded an AUC of 0.913 (95% CI, 0.799-0.974); T2WI-based model yielded an AUC of 0.908 (95% CI, 0.792-0.971); DWI-based model achieved an AUC of 0.702 (95% CI, 0.556-0.823); and the AUC of the DCE-MRI-based model was 0.572 (95% CI, 0.424-0.711). The improvement in the diagnostic performance of the multiparametric MRI model compared with the DWI and DCE-MRI-based models were significant (P< 0.01 for both). However, the increase was not meaningful compared with the T2WI-based model (P = 0.917).

Conclusion

Multiparametric MRI image analysis based on an ensemble CNN model with deep learning is of practical application and extension for preoperative prediction of axillary lymph node metastasis in invasive breast cancer.

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.

A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer

PURPOSE

To develop a nomogram based on radiomics signature and deep-learning signature for predicting the axillary lymph node (ALN) metastasis in breast cancer.

METHODS

A total of 151 patients were assigned to a training cohort (n = 106) and a test cohort (n = 45) in this study. Radiomics features were extracted from DCE-MRI images, and deep-learning features were extracted by VGG-16 algorithm. Seven machine learning models were built using the selected features to evaluate the predictive value of radiomics or deep-learning features for the ALN metastasis in breast cancer. A nomogram was then constructed based on the multivariate logistic regression model incorporating radiomics signature, deep-learning signature, and clinical risk factors.

RESULTS

Five radiomics features and two deep-learning features were selected for machine learning model construction. In the test cohort, the AUC was above 0.80 for most of the radiomics models except DecisionTree and ExtraTrees. In addition, the K-nearest neighbor (KNN), XGBoost, and LightGBM models using deep-learning features had AUCs above 0.80 in the test cohort. The nomogram, which incorporated the radiomics signature, deep-learning signature, and MRI-reported LN status, showed good calibration and performance with the AUC of 0.90 (0.85-0.96) in the training cohort and 0.90 (0.80-0.99) in the test cohort. The DCA showed that the nomogram could offer more net benefit than radiomics signature or deep-learning signature.

CONCLUSIONS

Both radiomics and deep-learning features are diagnostic for predicting ALN metastasis in breast cancer. The nomogram incorporating radiomics and deep-learning signatures can achieve better prediction performance than every signature used alone.

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.

Diagnostic performance of T2-weighted imaging and intravoxel incoherent motion diffusion-weighted MRI for predicting metastatic axillary lymph nodes in T1 and T2 stage breast cancer

BACKGROUND

Non-invasive modalities for assessing axillary lymph node (ALN) are needed in clinical practice.

PURPOSE

To investigate the suspicious ALN on unenhanced T2-weighted (T2W) imaging and intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) for predicting ALN metastases (ALNM) in patients with T1-T2 stage breast cancer and clinically negative ALN.

MATERIAL AND METHODS

Two radiologists identified the most suspicious ALN or the largest ALN in negative axilla by T2W imaging features, including short axis (Size-S), long axis (Size-L)/S ratio, fatty hilum, margin, and signal intensity on T2W imaging. The IVIM parameters of these selected ALNs were also obtained. The Mann-Whitney U test or t-test was used to compare the metastatic and non-metastatic ALN groups. Finally, logistic regression analysis with T2W imaging and IVIM features for predicting ALNM was conducted.

RESULTS

This study included 49 patients with metastatic ALNs and 50 patients with non-metastatic ALNs. Using the above conventional features on T2W imaging, the sensitivity and specificity in predicting ALNM were not high. Compared with non-metastatic ALNs, metastatic ALNs had lower pseudo-diffusion coefficient (D*) (P = 0.043). Logistic regression analysis showed that the most useful features for predicting ALNM were signal intensity and D*. The sensitivity and specificity predicting ALNM that satisfied abnormal signal intensity and lower D* were 73.5% and 84%, respectively.

CONCLUSIONS

The abnormal signal intensity on T2W imaging and one IVIM feature (D*) were significantly associated with ALNM, with sensitivity of 73.5% and specificity of 84%.

The NILS Study Protocol: A Retrospective Validation Study of an Artificial Neural Network Based Preoperative Decision-Making Tool for Noninvasive Lymph Node Staging in Women with Primary Breast Cancer (ISRCTN14341750)

Newly diagnosed breast cancer (BC) patients with clinical T1–T2 N0 disease undergo sentinel-lymph-node (SLN) biopsy, although most of them have a benign SLN. The pilot noninvasive lymph node staging (NILS) artificial neural network (ANN) model to predict nodal status was published in 2019, showing the potential to identify patients with a low risk of SLN metastasis. The aim of this study is to assess the performance measures of the model after a web-based implementation for the prediction of a healthy SLN in clinically N0 BC patients. This retrospective study was designed to validate the NILS prediction model for SLN status using preoperatively available clinicopathological and radiological data. The model results in an estimated probability of a healthy SLN for each study participant. Our primary endpoint is to report on the performance of the NILS prediction model to distinguish between healthy and metastatic SLNs (N0 vs. N+) and compare the observed and predicted event rates of benign SLNs. After validation, the prediction model may assist medical professionals and BC patients in shared decision making on omitting SLN biopsies in patients predicted to be node-negative by the NILS model. This study was prospectively registered in the ISRCTN registry (identification number: 14341750).

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using CNN Based on Multiparametric MRI

BACKGROUND

Multiparametric magnetic resonance imaging (MRI) is widely used in breast cancer screening. Accurate prediction of the axillary lymph nodes metastasis (ALNM) is essential for breast cancer surgery and treatment. However, there is no mature and effective discerning method for ALNM based on multiparametric MRI.

PURPOSE

To evaluate the ALNM using T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) sequences, respectively, and construct a quantitative ALNM discerning model of integrated multiparametric MRI.

STUDY TYPE

Retrospective.

POPULATION

Three-hundred forty-eight breast cancer patients, 163 with ALNM (99.39% females), and 185 without ALNM (100% females). The dataset was randomly divided into the training set (315 cases) and the testing set (33 cases).

FIELD STRENGTH/SEQUENCE

1.5 T; T1WI (VIBRANT), T2WI (FSE), and DWI (echo planar imaging [EPI]).

ASSESSMENT

The lesion region of interest images were cropped and sent to a pretrained ResNet50 network. Then, the results of different sequences were sent to a classifier for ensemble learning to construct the ALNM model of multiparametric MRI.

STATISTICAL TESTS

Performance indicators such as accuracy, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) were calculated. Student's t-test, chi-square test, Fisher's exact test, and Delong test were performed, and P < 0.05 was considered statistically significant.

RESULTS

T2WI performed the best among the three sequences, and achieved the accuracy and AUC of 0.933/0.989 in the testing set. Compared to T1WI with the accuracy and AUC of 0.691/0.806, the increase is significant. While compared to DWI with the accuracy and AUC of 0.800/0.910, the improvement is not significant (P = 0.126). After integrating three sequences, the accuracy and AUC improved to 0.970 and 0.996.

DATA CONCLUSION

T2WI performed better than DWI and T1WI in discerning ALNM in this breast cancer dataset. The proposed quantitative model of integrated multiparametric MRI could effectively help the ALNM diagnosis.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY STAGE: 2.

Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy

BACKGROUND

Accurate assessment of the axillary lymph nodes (aLNs) in breast cancer patients is essential for prognosis and treatment planning. Current radiological staging of nodal metastasis has poor accuracy. This study aimed to investigate the machine learning convolutional neural networks (CNNs) on multiparametric MRI to detect nodal metastasis with 18FDG-PET as ground truths.

MATERIALS AND METHODS

Data were obtained via a retrospective search. Inclusion criteria were patients with bilateral breast MRI and 18FDG-PETand/or CT scans obtained before neoadjuvant chemotherapy. In total, 238 aLNs were obtained from 56 breast cancer patients with 18FDG-PET and/or CT and breast MRI data. Radiologists scored each node based on all MRI as diseased and non-diseased nodes. Five models were built using T1-W MRI, T2-W MRI, DCE MRI, T1-W + T2-W MRI, and DCE + T2-W MRI model. Performance was evaluated using receiver operating curve (ROC) analysis, including area under the curve (AUC).

RESULTS

All CNN models yielded similar performance with an accuracy ranging from 86.08% to 88.50% and AUC ranging from 0.804 to 0.882. The CNN model using T1-W MRI performed better than that using T2-W MRI in detecting nodal metastasis. CNN model using combined T1- and T2-W MRI performed the best compared to all other models (accuracy = 88.50%, AUC = 0.882), but similar in AUC to the DCE + T2-W MRI model (accuracy = 88.02%, AUC = 0.880). All CNN models performed better than radiologists in detecting nodal metastasis (accuracy = 65.8%).

CONCLUSION

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Added Value of MRI for Invasive Breast Cancer including the Entire Axilla for Evaluation of High-Level or Advanced Axillary Lymph Node Metastasis in the Post-ACOSOG Z0011 Trial Era

Background In the post-American College of Surgeons Oncology Group Z0011 trial era, radiologists have increasingly focused on excluding high-level or advanced axillary lymph node metastasis (ALNM) by using an additional MRI scan positioned higher than lower axillae; however, the value of these additional scans remains undetermined. Purpose To evaluate whether a standard MRI protocol is sufficient to exclude high-level or advanced ALNM in breast cancer or additional MRI of entire axilla is needed. Materials and Methods This retrospective study evaluated women with invasive breast cancer who underwent breast MRI from April 2015 to December 2016. Some underwent neoadjuvant chemotherapy (NAC) and others underwent upfront surgery. Standard (routine axial scans including the lower axillae) and combined (routine axial scans plus additional scans including the entire axilla) MRI protocols were compared for high-level or advanced ALNM detection. Clinical-pathologic characteristics were analyzed. Uni- and multivariable logistic regression was performed to identify predictors of high-level or advanced ALNM. Results A total of 435 women (mean age ± standard deviation, 52 years ± 11) were evaluated (65 in the NAC group, 370 in the non-NAC group). With the standard MRI protocol, predictors of high-level ALNM were peritumoral edema (odds ratio [OR], 12.3; 95% CI: 3.9, 39.4; P < .001) and positive axilla (OR, 5.9; 95% CI: 2.0, 15.2; P < .001). Only three of 289 women with negative axillae without peritumoral edema had high-level ALNM. Predictors of advanced ALNM were positive axillae (OR, 8.9; 95% CI: 3.7, 21.5; P < .001) and peritumoral edema (OR, 2.8; 95% CI: 1.1, 6.9; P = .03). Only six of 310 women who had negative axillae without peritumoral edema had advanced ALNM. Conclusion The performance of standard MRI was satisfactory in excluding high-level and advanced axillary lymph node metastasis in most patients with breast cancer. However, the presence of peritumoral edema or positive axillae in the MRI findings emphasizes the benefits of a combined MRI protocol. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Abe in this issue.

Pretherapeutic Imaging for Axillary Staging in Breast Cancer: A Systematic Review and Meta-Analysis of Ultrasound, MRI and FDG PET

BACKGROUND

This systematic review aimed at comparing performances of ultrasonography (US), magnetic resonance imaging (MRI), and fluorodeoxyglucose positron emission tomography (PET) for axillary staging, with a focus on micro- or micrometastases.

METHODS

A search for relevant studies published between January 2002 and March 2018 was conducted in MEDLINE database. Study quality was assessed using the QUality Assessment of Diagnostic Accuracy Studies checklist. Sensitivity and specificity were meta-analyzed using a bivariate random effects approach; Results: Across 62 studies (n = 10,374 patients), sensitivity and specificity to detect metastatic ALN were, respectively, 51% (95% CI: 43-59%) and 100% (95% CI: 99-100%) for US, 83% (95% CI: 72-91%) and 85% (95% CI: 72-92%) for MRI, and 49% (95% CI: 39-59%) and 94% (95% CI: 91-96%) for PET. Interestingly, US detects a significant proportion of macrometastases (false negative rate was 0.28 (0.22, 0.34) for more than 2 metastatic ALN and 0.96 (0.86, 0.99) for micrometastases). In contrast, PET tends to detect a significant proportion of micrometastases (true positive rate = 0.41 (0.29, 0.54)). Data are not available for MRI.

CONCLUSIONS

In comparison with MRI and PET Fluorodeoxyglucose (FDG), US is an effective technique for axillary triage, especially to detect high metastatic burden without upstaging majority of micrometastases.

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.

Preoperative prediction of axillary sentinel lymph node burden with multiparametric MRI-based radiomics nomogram in early-stage breast cancer

OBJECTIVES

To develop and validate a multiparametric MRI-based radiomics nomogram for pretreatment predicting the axillary sentinel lymph node (SLN) burden in early-stage breast cancer.

METHODS

A total of 230 women with early-stage invasive breast cancer were retrospectively analyzed. A radiomics signature was constructed based on preoperative multiparametric MRI from the training dataset (n = 126) of center 1, then tested in the validation cohort (n = 42) from center 1 and an external test cohort (n = 62) from center 2. Multivariable logistic regression was applied to develop a radiomics nomogram incorporating radiomics signature and predictive clinical and radiological features. The radiomics nomogram's performance was evaluated by its discrimination, calibration, and clinical use and was compared with MRI-based descriptors of primary breast tumor.

RESULTS

The constructed radiomics nomogram incorporating radiomics signature and MRI-determined axillary lymph node (ALN) burden showed a good calibration and outperformed the MRI-determined ALN burden alone for predicting SLN burden (area under the curve [AUC]: 0.82 vs. 0.68 [p < 0.001] in training cohort; 0.81 vs. 0.68 in validation cohort [p = 0.04]; and 0.81 vs. 0.58 [p = 0.001] in test cohort). Compared with the MRI-based breast tumor combined descriptors, the radiomics nomogram achieved a higher AUC in test cohort (0.81 vs. 0.58, p = 0.005) and a comparable AUC in training (0.82 vs. 0.73, p = 0.15) and validation (0.81 vs. 0.65, p = 0.31) cohorts.

CONCLUSION

A multiparametric MRI-based radiomics nomogram can be used for preoperative prediction of the SLN burden in early-stage breast cancer.

KEY POINTS

• Radiomics nomogram incorporating radiomics signature and MRI-determined ALN burden outperforms the MRI-determined ALN burden alone for predicting SLN burden in early-stage breast cancer. • Radiomics nomogram might have a better predictive ability than the MRI-based breast tumor combined descriptors. • Multiparametric MRI-based radiomics nomogram can be used as a non-invasive tool for preoperative predicting of SLN burden in patients with early-stage breast cancer.

Apparent diffusion coefficient magnetic resonance imaging (ADC-MRI) in the axillary breast cancer lymph node metastasis detection: a narrative review

The presence of axillary lymph nodes metastases in breast cancer is the most significant prognostic factor, with a great impact on morbidity, disease-related survival and management of oncological therapies; for this reason, adequate imaging evaluation is strictly necessary. Physical examination is not enough sensitive to assess breast cancer nodal status; axillary ultrasonography (US) is commonly used to detect suspected or occult nodal metastasis, providing exclusively morphological evaluation, with low sensitivity and positive predictive value. Currently, sentinel lymph node biopsy (SLNB) and/or axillary dissection are the milestone for the diagnostic assessment of axillary lymph node metastases, although its related morbidity. The impact of magnetic resonance imaging (MRI) in the detection of nodal metastases has been widely investigated, as it continues to represent the most promising imaging modality in the breast cancer management. In particular, diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values represent additional reliable non-contrast sequences, able to improve the diagnostic accuracy of breast cancer MRI evaluation. Several studies largely demonstrated the usefulness of implementing DWI/ADC MRI in the characterization of breast lesions. Herein, in the light of our clinical experience, we perform a review of the literature regarding the diagnostic performance and accuracy of ADC value as potential pre-operative tool to define metastatic involvement of nodal structures in breast cancer patients. For the purpose of this review, PubMed, Web of Science, and SCOPUS electronic databases were searched with different combinations of "axillary lymph node", "breast cancer", "MRI/ADC", "breast MRI" keywords. All original articles, reviews and metanalyses were included.

Fat fractions from high-resolution 3D radial Dixon MRI for predicting metastatic axillary lymph nodes in breast cancer patients

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High-Resolution 3D radial Dixon MRI allows for the creation of quantitative fat fraction images.

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Lymph node fat fractions improves diagnostic performance of MRI to detect axillary lymph node metastases.

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Lymph node fat fractions are a promising quantitative indicator of metastases in axillary lymph nodes.

Purpose

To assess diagnostic performance of fat fractions (FF) from high-resolution 3D radial Dixon MRI for differentiating metastatic and non-metastatic axillary lymph nodes in breast cancer patients.

Method

High-resolution 3D radial Dixon MRI was prospectively performed on 1.5 T in 70 biopsy-verified breast cancer patients. 35 patients were available for analysis with histopathologic and imaging data. FF images were calculated as fat / in-phase. Two radiologists measured lymph node FF and assessed morphological features in one ipsilateral and one contralateral lymph node in consensus. Diagnostic performance of lymph node FF and morphological criteria were compared using histopathology as reference.

Results

22 patients had metastatic axillary lymph nodes. Mean lymph node FF were 0.20 ± 0.073, 0.31 ± 0.079, and 0.34 ± 0.15 (metastatic, non-metastatic ipsi- and non-metastatic contralateral lymph nodes, respectively). Metastatic lymph node FF were significantly lower than non-metastatic ipsi- (p <  0.001) and contralateral lymph nodes (p <  0.001). Area under the receiver operating characteristics curve for lymph node FF was 0.80 compared to 0.76 for morphological criteria (p =  0.29). Lymph node FF yielded sensitivity 0.91, specificity 0.69, positive predictive value (PPV) 0.83, and negative predictive value (NPV) 0.82, while morphological criteria yielded sensitivity 0.91, specificity 0.62, PPV 0.80, and NPV 0.80 (p =  0.71). Combining lymph node FF and morphological criteria increased diagnostic performance with sensitivity 1.00, specificity 0.67, PPV 0.86, NPV 1.00, and AUC 0.83.

Conclusions

Lymph node FF from high-resolution 3D Dixon images are a promising quantitative indicator of metastases in axillary lymph nodes.

Does axillary lymph node size predict better metastatic involvement than apparent diffusion coefficient (ADC) value in women with newly diagnosed breast cancer?

BACKGROUND

It has been demonstrated that the number of metastatic axillary lymph nodes (mALNs) influence disease-free and overall survival in patients with breast cancer.

PURPOSE

To determine if the ALN size is more accurate than the ALN apparent diffusion coefficient (ADC) value to predict metastatic involvement in newly diagnosed breast cancer.

MATERIAL AND METHODS

A total of 44 patients with breast cancer were included. Magnetic resonance imaging (MRI) examinations were performed on a 1.5-T system with sagittal T1-weighted fast spin-echo non-fat saturated, sagittal T2-weighted fast spin-echo non-fat saturated, axial diffusion-weighted imaging echo-planar (b values of 0 and 700 s/mm²), and non-contrast axial VIBRANT sequences. The size and the ADC value were obtained for ALN ipsilateral and contralateral to breast cancer. The reference standard was the histopathologic lymph node status.

RESULTS

mALN had a greater cortical thickness compared to contralateral non-mALN (10.3 ± 5.32 mm vs. 4 ± 1.17 mm, P ≤ 0.001). The threshold of ≥6.7 mm for predicting axillary metastatic involvement had a sensitivity and a specificity of 80.0% and 97.7%, respectively. The ADC value of mALN was significantly higher than the contralateral non-mALN (0.90 ± 0.12 × 10^-3mm²/s vs. 0.78 ± 0.12 × 10^-3mm²/s; P = 0.001). The threshold of ≥0.86 × 10^-3mm²/s had a sensitivity and a specificity of 66.7% and 76.7%, respectively.

CONCLUSION

Our results indicate that the cortical thickness has a better diagnostic performance in the differentiation of metastatic and non-metastatic lymph nodes than the lymph node ADC.

Reduced and standard field-of-view diffusion weighted imaging in patients with rectal cancer at 3 T-Comparison of image quality and apparent diffusion coefficient measurements

PURPOSE

To compare a zoomed EPI-DWI (z-EPI) with a standard EPI-DWI (s-EPI) in the primary diagnostics of rectal cancer and assess its potential of reduced image artifacts.

METHOD

22 therapy-naïve patients with rectal cancer underwent rectal MRI at a 3 T-system. The protocols consisted of a z-EPI DWI and s-EPI DWI sequence. Images were assessed by two independent and experienced readers regarding overall image quality and artifacts on a 5-point Likert scale, as well as overall sequence preference. In a lesion-based analysis, tumor and lymph node detection were rated on a 4-point Likert scale. Apparent diffusion coefficient (ADC) measurements were performed.

RESULTS

Overall Image quality score for z-EPI and s-EPI showed no statistically significant differences (p = 0.80/0.54, reader 1/2) with a median score of 4 ("good" image quality) for both sequences. The image quality preference rank for z-EPI and s-EPI was given the category 'no preference' in 64 % (reader 1) and 50 % (reader 2). Most artifact-related scores (susceptibility, motion and distortion) did not show reproducible significant differences between z-EPI and s-EPI. The two sequences exhibited comparable, mostly good and excellent quality scores for tumor and lymph node detection (p = 0.19-0.99). ADC values were significantly lower for z-EPI than for s-EPI (p = 0.001/0.002, reader 1/2) with good agreement of ADC measurements between both readers.

CONCLUSION

Our data showed comparable image quality and lesion detection for the z-EPI and the s-EPI sequence in MRI of rectal cancer, whereas the mean ADC of the tumor was significantly lower in z-EPI compared to s-EPI.

Diffusion-Weighted Magnetic Resonance Imaging of the Breast: Standardization of Image Acquisition and Interpretation

Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a rapid, unenhanced imaging technique that measures the motion of water molecules within tissues and provides information regarding the cell density and tissue microstructure. DW MRI has demonstrated the potential to improve the specificity of breast MRI, facilitate the evaluation of tumor response to neoadjuvant chemotherapy and can be employed in unenhanced MRI screening. However, standardization of the acquisition and interpretation of DW MRI is challenging. Recently, the European Society of Breast Radiology issued a consensus statement, which described the acquisition parameters and interpretation of DW MRI. The current article describes the basic principles, standardized acquisition protocols and interpretation guidelines, and the clinical applications of DW MRI in breast imaging.

Best MRI sequences for identifying axillary lymph node markers in patients with metastatic breast cancer: an inter-reader observational study

Background

We assessed confidence in visualization of markers within metastatic axillary lymph nodes (LNs) on magnetic resonance imaging (MRI), which were placed post-ultrasound (US)-guided biopsy.

Methods

A retrospective review was performed on 55 MRI cases between May 2015 and October 2017. Twenty-two MRIs were performed before neoadjuvant therapy, and 33 MRIs were after its initiation. There were 34/55 HydroMARK®, 10/55 Tumark®, and 11/55 other marker types. Time interval between marker placement and MRI examination was 103 ± 81 days (mean ± standard deviation). Three readers with 1–30 years of experience independently assessed four axial sequences: unenhanced fat-suppressed three-dimensional T1-weighted spoiled gradient-recalled (SPGR), first contrast-enhanced fat-suppressed SPGR, T2-weighted water-only fast spin-echo (T2-WO), and T2-weighted fat-only fast-spin-echo (T2-FO).

Results

Markers were 5.2× more likely to be visualized on T2-WO than on unenhanced images (p = < 0.001), and 3.3× more likely to be visualized on contrast-enhanced than on unenhanced sequences (p = 0.009). HydroMARK markers demonstrated a 3× more likelihood of being visualized than Tumark (p = 0.003). Markers were 8.4× more likely to be visualized within morphologically abnormal LNs (p < 0.001). After 250 days post-placement, confidence in marker brightness of HydroMARK markers on T2-WO images was less than 50% (p < 0.001). Inter-rater agreement was excellent for T2-WO and contrast-enhanced SPGR, good for unenhanced SPGR, and poor for T2-FO images.

Conclusion

T2-WO and contrast-enhanced images should be used for marker identification. HydroMARK was the best visualized marker. Markers were easier to identify when placed in abnormal LNs. The visibility of HydroMARK markers was reduced with time.

Axillary Nodal Evaluation in Breast Cancer: State of the Art

Axillary lymph node (LN) metastasis is the most important predictor of overall recurrence and survival in patients with breast cancer, and accurate assessment of axillary LN involvement is an essential component in staging breast cancer. Axillary management in patients with breast cancer has become much less invasive and individualized with the introduction of sentinel LN biopsy (SLNB). Emerging evidence indicates that axillary LN dissection may be avoided in selected patients with node-positive as well as node-negative cancer. Thus, assessment of nodal disease burden to guide multidisciplinary treatment decision making is now considered to be a critical role of axillary imaging and can be achieved with axillary US, MRI, and US-guided biopsy. For the node-positive patients treated with neoadjuvant chemotherapy, restaging of the axilla with US and MRI and targeted axillary dissection in addition to SLNB is highly recommended to minimize the false-negative rate of SLNB. Efforts continue to develop prediction models that incorporate imaging features to predict nodal disease burden and to select proper candidates for SLNB. As methods of axillary nodal evaluation evolve, breast radiologists and surgeons must work closely to maximize the potential role of imaging and to provide the most optimized treatment for patients.

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

Objectives

To investigate whether breast MRI has comparable diagnostic performance as dedicated axillary MRI regarding assessment of node-negative and node-positive breast cancer.

Methods

Forty-seven patients were included. All had undergone both breast MRI and dedicated axillary MRI, followed by surgery. All included breast MRI exams had complete field of view (FOV) of the axillary region. First, unenhanced T2-weighted (T2W) and subsequent diffusion-weighted (DW) images of both MRI exams were independently analyzed by two breast radiologists using a confidence scale and compared to histopathology. ADC values were measured by two researchers independently. Diagnostic performance parameters were calculated on a patient-by-patient basis.

Results

T2W breast MRI had the following diagnostic performance: sensitivity of 50.0% and 62.5%, specificity of 92.3%, PPV of 57.1% and 62.5%, NPV of 90.0% and 92.3%, and AUC of 0.72 for reader 1 and 0.78 for reader 2. T2W dedicated axillary MRI had the following diagnostic performance: sensitivity of 37.5% and 62.5%, specificity of 82.1% and 92.3%, PPV of 44.6% and 50.0%, NPV of 87.8% and 91.4%, and AUC of 0.65 for reader 1 and 0.73 for reader 2. In both evaluations, addition of DW images resulted in comparable diagnostic performance. For both breast MRI and dedicated axillary MRI, there was no significant difference between mean ADC values of benign and malignant lymph nodes.

Conclusions

T2W breast MRI with complete FOV of the axillary region has comparable diagnostic performance as T2W dedicated axillary MRI regarding assessment of node-negative and node-positive breast cancer. Optimization of T2W breast MRI protocol by including a complete FOV of the axillary region can, therefore, be recommended in clinical practice.

Key Points

• Breast MRI with complete field of view of the axillary region has comparable diagnostic performance as dedicated axillary MRI regarding assessment of node-negative and node-positive breast cancer.

• Optimization of breast MRI protocol by including a complete field of view of the axillary region is recommended in clinical practice.

• For both breast MRI and dedicated axillary MRI, DW imaging (including ADC measurements) is of no added value.

Diagnostic performance of axillary ultrasound and standard breast MRI for differentiation between limited and advanced axillary nodal disease in clinically node-positive breast cancer patients

Preoperative differentiation between limited (pN1; 1–3 axillary metastases) and advanced (pN2–3; ≥4 axillary metastases) nodal disease can provide relevant information regarding surgical planning and guiding adjuvant radiation therapy. The aim was to evaluate the diagnostic performance of preoperative axillary ultrasound (US) and breast MRI for differentiation between pN1 and pN2–3 in clinically node-positive breast cancer. A total of 49 patients were included with axillary metastasis confirmed by US-guided tissue sampling. All had undergone breast MRI between 2008–2014 and subsequent axillary lymph node dissection. Unenhanced T2-weighted MRI exams were reviewed by two radiologists independently. Each lymph node on the MRI exams was scored using a confidence scale (0–4) and compared with histopathology. Diagnostic performance parameters were calculated for differentiation between pN1 and pN2–3. Interobserver agreement was determined using Cohen’s kappa coefficient. At final histopathology, 67.3% (33/49) and 32.7% (16/49) of patients were pN1 and pN2–3, respectively. Breast MRI was comparable to US in terms of accuracy (MRI reader 1 vs US, 71.4% vs 69.4%, p = 0.99; MRI reader 2 vs US, 73.5% vs 69.4%, p = 0.77). In the case of 1–3 suspicious lymph nodes, pN2–3 was observed in 30.4% on US (positive predictive value (PPV) 69.6%) and in 22.2–24.3% on MRI (PPV 75.7–77.8%). In the case of ≥4 suspicious lymph nodes, pN1 was observed in 33.3% on US (negative predictive value (NPV) 66.7%) and in 38.5–41.7% on MRI (NPV 58.3–61.5%). Interobserver agreement was considered good (k = 0.73). In clinically node-positive patients, the diagnostic performance of axillary US and breast MRI is comparable and limited for accurate differentiation between pN1 and pN2–3. Therefore, there seems no added clinical value of preoperative breast MRI regarding nodal staging in patients with positive axillary US.

MRI evaluation of axillary and intramammary lymph nodes in the postoperative period

Our study aimed to evaluate if breast‐conserving surgery and adjuvant treatment could affect the morphological features of axillary and intramammary lymph nodes on magnetic resonance imaging (MRI) in patients with invasive breast cancer and clinically negative axilla. In this single‐center study, we retrospectively evaluated 50 patients who had (a) breast‐conserving surgery, (b) clinically negative axilla, (c) preoperative MRI within 3 months before surgery, and (d) postoperative MRI within 12 months after surgery. Axillary and intramammary lymph nodes on postoperative MRI were identified and then compared with preoperative MRI by two breast radiologists with regards to the following: enlargement, cortical thickening, presence of fatty hilum, irregularity, heterogeneity, matting, and axillary lymph node asymmetry. Three hundred and two axillary and eight intramammary lymph nodes were evaluated. Enlargement and cortical thickening were seen in 5/50 (10%) patients in three axillary and two intramammary lymph nodes. None of the lymph nodes on postoperative MRI demonstrated occurrence of lack of fatty hilum, irregularity, heterogeneity, matting or axillary lymph node asymmetry. No evidence of recurrence was observed on 2‐year follow‐up. Lymph node enlargement and cortical thickening may be observed in a few patients in the postoperative period. Nevertheless, in patients with clinically negative axilla, these changes in morphology are often related to treatment rather than malignancy and favor short‐term follow‐up as an alternative to lymph node biopsy.

Predicting lymph node metastasis in pancreatobiliary cancer with magnetic resonance imaging: A prospective analysis

OBJECTIVES

To prospectively investigate the diagnostic potential of lymph node (LN) magnetic resonance (MR) imaging features.

METHODS

A radiologist determined the maximum diameters in the short and long axes, shape, signal intensities on T1- and T2-weighted imaging, pattern of enhancement, and apparent diffusion coefficient (ADC) on diffusion-weighted MR images of LNs and annotated measurable (≥5 mm in short-axis diameter) LNs. Surgically harvested LNs were correlated with the pathologic findings. Univariable and multivariable generalized estimating equation analyses were performed to evaluate predictive power.

RESULTS

Of 80 LNs, 29 (36.3%) were positive and 51 (63.7%) negative for metastasis. The mean short-axis diameter of metastatic LNs (10.59 ± 4.30 mm) was larger than that of benign LNs (7.96 ± 2.10 mm). The ADC was significantly (P < 0.001) lower in metastatic than non-metastatic LNs. The area under the curve (AUC) of a univariable model using only the mean ADC was 0.845 (95% confidence interval [CI], 0.743-0.927), and the mean-ADC cutoff value for predicting LN metastasis was 0.901 × 10^-3 mm²/s. The AUC of a multivariable model including round shape, heterogeneous enhancement, and the mean ADC was 0.917 (95% CI, 0.845-0.972), with a sensitivity, specificity, overall accuracy, and positive and negative predictive values of 89.7%, 82.4%, 85.0%, 74.3%, and 93.3%, respectively.

CONCLUSION

The short-axis diameter and ADC were different between benign and metastatic LNs in pancreatobiliary cancer. However, round shape, heterogeneous enhancement, and a low ADC value (<0.901 × 10^-3 mm²/s) may be the most reliable diagnostic features of multiple metastatic LNs.

Value of Axillary Ultrasound after Negative Axillary MRI for Evaluating Nodal Status in High-Risk Breast Cancer

BACKGROUND

It is assumed that axillary ultrasound (AxUS) is the best method for axillary nodal evaluation in newly diagnosed breast cancer patients. However, few have evaluated the efficacy of preoperative axillary MRI. We compared the statistical accuracy of AxUS and MRI in detecting nodal metastases among breast cancer patients who were selected for neoadjuvant chemotherapy.

STUDY DESIGN

We retrospectively analyzed 219 breast cancer patients undergoing neoadjuvant chemotherapy from 2007 to 2015, all of whom had AxUS and breast MRI before chemotherapy. Two breast radiologists, blinded to clinical, pathologic, and AxUS findings, re-reviewed all breast MRIs, specifically focusing on axillary nodal characteristics. We correlated clinico-pathologic characteristics, AxUS, and MRI findings, and quantified predictive values of both imaging modalities.

RESULTS

Overall, 101 of 219 (47%) patients had T2 tumors. The most common abnormal nodal finding was size >10 mm. Axillary ultrasound and MRI agreed on nodal status in 192 of 219 patients (87.6%). When correlated with pre-chemotherapy needle biopsy in 129 patients, AxUS and axillary MRI performed similarly (sensitivity of 99.1% vs 97.4% and specificity 15.4% vs 15.4%, respectively). Only 4 of 129 (3.1%) patients had a negative MRI and positive AxUS; 3 of 4 of these patients (75%) had a positive biopsy and 2 of 3 had positive lymph nodes on final pathology, therefore suggesting MRI missed clinically significant disease in only 2 of 129 (1.5%) patients.

CONCLUSIONS

In a high-risk patient population, AxUS and MRI have similar statistical profiles in evaluating axillary nodal status. Routine use of AxUS after a normal axillary MRI is not warranted.

Prediction of high nodal burden with ultrasound and magnetic resonance imaging in clinically node-negative breast cancer patients

Background

Although the role of axillary imaging has been redirected for predicting high nodal burden rather than predicting nodal metastases since ACOSOG Z1011 trial, it remains unclear whether and how axillary lymph node (ALN) characteristics predicts high nodal burden. Our study was aimed to evaluate the predictive value of imaging characteristics of ALNs at ultrasound and magnetic resonance imaging (MRI) for prediction of high nodal burden (≥3 metastatic ALNs) in clinically node-negative breast cancer patients.

Methods

Clinicopathological and imaging characteristics were evaluated in patients with ultrasound (n = 312) and MRI (n = 256). Imaging characteristics include number of suspicious ALNs and cortical morphologic changes (grade 1, cortical thickness < 2 mm; grade 2, 2–5 mm; grade 3, ≥5 mm or fatty hilum loss). Odds ratios (ORs) were calculated using multivariate analysis.

Results

For ultrasound, higher (≥2) T stage (OR = 5.65, P = .005), higher number of suspicious ALNs (2 suspicious ALNs, OR = 6.52, P = .019; ≥ 3 suspicious ALNs, OR = 21.08, P = .005), and grade 3 of cortical morphologic changes (OR = 9.85, P = .023) independently associated with high nodal burden. For MRI, higher (≥2) T stage (OR = 5.17, P = .011) and higher number of suspicious ALNs (2 suspicious ALNs, OR = 69.00, P = .001; ≥ 3 suspicious ALNs, OR = 93.55, P < .001) were independently associated with high nodal burden. Among patients with 2 suspicious ALNs, those with grade 3 cortical morphologic change at ultrasound had a higher rate of high nodal burden than those with grade 2 (60.0% [3/5] vs. 25.0% [2/8]).

Conclusions

A higher number of suspicious ALNs is an independent predictor for high nodal burden. Further stratification can be achieved by incorporating assessment of ultrasound-based cortical morphologic changes.

Development and Validation of Nomograms Predictive of Axillary Nodal Status to Guide Surgical Decision-Making in Early-Stage Breast Cancer

Purpose: To develop and validate nomogram models using noninvasive imaging parameters with related clinical variables to predict the extent of axillary nodal involvement and stratify treatment options based on the essential cut-offs for axillary surgery according to the ACOSOG Z0011 criteria.

Materials and Methods: From May 2007 to December 2017, 1799 patients who underwent preoperative breast and axillary magnetic resonance imaging (MRI) were retrospectively studied. Patients with data on axillary ultrasonography (AUS) were enrolled. The MRI images were interpreted according to Breast Imaging Reporting and Data system (BI-RADS). Using logistic regression analyses, nomograms were developed to visualize the associations between the predictors and each lymph node (LN) status endpoint. Predictive performance was assessed based on the area under the receiver operating characteristic curve (AUC). Bootstrap resampling was performed for internal validation. Goodness-of-fit of the models was evaluated using the Hosmer-Lemeshow test.

Results: Of 397 early breast cancer patients, 200 (50.4%) had disease-free axilla, 119 (30.0%) had 1 or 2 positive LNs, and 78 (19.6%) had ≥3 positive LNs. Patient age, MRI features (mass margin, LN margin, presence/absence of LN hilum, and LN symmetry/asymmetry), and AUS descriptors (presence of cortical thickening or hilum) were identified as predictors of nodal disease. Nomograms with these predictors showed good calibration and discrimination; the AUC was 0.809 for negative axillary node (N0) vs. any LN metastasis, 0.749 for 1 or 2 involved nodes vs. N0, and 0.874 for ≥3 nodes vs. ≤2 metastatic nodes. The predictive ability of the 3 nomograms with additional pathological variables was significantly greater.

Conclusion: The nomograms could predict the extent of ALN metastasis and facilitate decision-making preoperatively.

Intravoxel incoherent motion diffusion-weighted magnetic resonance imaging in characterization of axillary lymph nodes: Preliminary animal experience

PURPOSE

To investigate the diagnostic value of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) for discriminating axillary metastatic from non-metastatic lymph nodes (LNs) in rabbit models.

MATERIALS AND METHODS

The institutional animal care and use committee approved this study. Forty New Zealand white rabbits were randomly divided into two groups. The axillary LN models were created by inoculating VX2 cell suspension and complete Freund's adjuvant in the mammary glands of 20 female rabbits of each group, respectively. Conventional MRI and IVIM DWI were performed after animal models successfully established. Images of axillary LNs were analyzed with regard to long-axis diameter (L), short-axis diameter (S), apparent diffusion coefficient (ADC) and IVIM parameters (D, D*, f). Receiver operating characteristic analyses were conducted to determine the diagnostic performance of aforementioned criteria.

RESULTS

A total of 42 metastatic and 30 non-metastatic LNs were successfully isolated. ADC and D of metastatic LNs were significantly lower than those of non-metastatic ones (all P < 0.001), whereas D* was statistically higher (P = 0.033). L, S, and f showed no significant difference between the two groups (P = 0.089, 0.058, 0.054, respectively). Optimal cutoff values, area under the curve, sensitivity, and specificity for differentiation were as follows: ADC = 1.101 × 10^-3 mm²/s, 0.886, 78.6%, 90.0%; D = 0.938 × 10^-3 mm²/s, 0.927, 83.3%, 93.3%; and D* = 12.635 × 10^-3 mm²/s, 0.657, 52.4%, 80.0%.

CONCLUSION

IVIM DWI is useful to distinguish metastatic from non-metastatic LNs in axilla. D was the most discriminative variable for predicting metastatic LNs.

Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI

OBJECTIVES

To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T₂-weighted fat suppression (T₂-FS) and diffusion-weighted MRI (DWI).

METHODS

We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T₂-FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1-10) based on different combination of image features using stepwise forward method.

RESULTS

For T₂-FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T₂-FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set.

CONCLUSIONS

Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice.

KEY POINTS

• SLN biopsy to access breast cancer metastasis has multiple complications. • Radiomics uses features extracted from medical images to characterise intratumour heterogeneity. • We combined T ₂ -FS and DWI textural features to predict SLN metastasis non-invasively.

Use of Breast Magnetic Resonance Imaging and Ultrasonography for Identifying Nonpalpable Axillae Metastases in Newly Diagnosed Breast Cancer Patients

BACKGROUND

The metastasis of axillary lymph node (ALNs) is a critical step in the initial cancer staging of newly diagnosed breast cancer (BC) patients. Various imaging modalities can enhance the sensitivity of clinical examination in assessing the ALN status.

PATIENTS AND METHODS

We enrolled 135 patients with BC, confirmed via histopathology, including 4 bilateral BC cases. A total of 139 ipsilateral ALNs adjacent to the breast lesion were examined via physical examination, ultrasonography (US), and magnetic resonance imaging (MRI); of these, 100 were nonpalpable ALNs, as determined by experienced breast surgeons and physicians. The relative size parameters on MRI and US images were recorded. Receiver operating characteristic (ROC) analyses were conducted, and the area under the ROC curve (AUC) was compared.

RESULTS

Of 139 ALNs, 67 (48%) were malignant and 72 (52%) were benign on pathological examination. In all of the ALNs, the US short diameter appeared to be the most discriminative quantitative measurement for detecting positive findings (AUC, 0.854). In nonpalpable ALNs as well, the US short diameter exhibited the greatest discriminability (AUC, 0.746). However, the 2-dimensional and 3-dimensional parameters on MRI did not exhibit any significant differences between the enrolled and nonpalpable ALNs (P > .05).

CONCLUSION

The shortest diameter on US exhibited better discriminative ability than MRI for predicting positive ALNs in nonpalpable axillae. Moreover, the 2-dimensional and 3-dimensional parameters on MRI did not differ in terms of discriminability.

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.

The Diagnosis of Metastatic Axillary Lymph Nodes of Breast Cancer By Diffusion Weighted Imaging: a meta-analysis and systematic review

Background

The purpose of this meta-analysis was to evaluate the clinical significance of diffusion-weighted imaging in assessing the status of axillary lymph nodes in patients with breast cancer.

Methods

We searched the PubMed, Cochrane, and EMBASE databases, selected studies by inclusion and exclusion criteria, and assessed the quality of selected studies. We explored the source of heterogeneity; calculated sensitivity, specificity, positive and negative likelihood ratios, and pretest probability. A summary receiver operating characteristic curve was performed. Student’s t test was used to compare the different mean apparent diffusion coefficient values of different status lymph nodes.

Results

In selected 10 studies, a total of 801 patients and 2305 lymph nodes were included following inclusion criteria. All scores of the quality assessment of the included studies were greater than or equal to 10 points. The sensitivity was 0.89 (95 % CI 0.79–0.95), the specificity was 0.83 (95 % CI 0.71–0.91), the positive and negative likelihood ratios were 3.86 (95 % CI 2.75–5.41) and 0.17 (95 % CI 0.09–0.32), the pretest probabilities were 53 and 54 %, the area under the curve were 0.93 (95 % CI 0.90–0.95), respectively. The mean apparent diffusion coefficient value of metastatic lymph nodes was significantly lower than that of nonmetastatic axillary lymph nodes.

Conclusions

Diffusion-weighted imaging is a promising tool to discriminate between metastatic and nonmetastatic axillary lymph nodes. Combined with the mean apparent diffusion coefficient value, it can quantitatively diagnose lymph node metastases. Conducting large-scale, high-quality researches can improve the clinical significance of diffusion-weighted imaging to distinguish metastatic and nonmetastatic axillary lymph nodes in patients with breast cancer and provide the evidence to assess the status of axillary lymph nodes.

Meta analysis of lymph node metastasis of breast cancer patients: Clinical value of DWI and ADC value

OBJECTIVES

To evaluate the diagnostic utility of DWI in the assessment of node metastases and investigate whether the ADC value could be used to discriminate between metastatic and non-metastatic lymph nodes in breast cancer patients.

MATERIALS AND METHODS

13 studies with a total of 676 metastatic and 811 non-metastatic lymph nodes were included.

RESULTS

(1) The pooled sensitivity, specificity, PPV and NPV of DWI were 0.83, 0.82, 0.83 and 0.85, respectively. The PLR and NLR were 4.95 and 0.23, respectively. The AUC and Q* index were 0.91 and 0.85, respectively. (2) The ADC value of metastatic lymph nodes was lower than non-metastatic lymph nodes (WMD=-0.213, 95% CI -0.349 to -0.076, Z=3.05, P<0.05). (3) Subgroup meta-analysis of the group of b(0800): The pooled sensitivity, specificity, PPV and NPV of DWI were 0.86, 0.86, 0.82 and 0.90, respectively. The PLR and NLR were 6.76 and 0.18, respectively. The AUC and Q* index were 0.93 and 0.87. The ADC value of metastatic lymph nodes was lower than non-metastatic lymph nodes(WMD=-0.267, 95% CI -0.348 to -0.185, Z=6.40, P<0.05).

CONCLUSIONS

DWI and ADC value appear to be a reliable method to differentiate metastatic and non-metastatic lymph nodes. The combination of b=0 and 800s/mm(2) resulted in higher diagnostic accuracy and more pronounced ADC value difference. If only a couple of b values are used, those of b=0 and 800s/mm(2) are recommended.