Biopsy or Follow-up: AI Improves the Clinical Strategy of US BI-RADS 4A Breast Nodules Using a Convolutional Neural Network

OBJECTIVES

To develop predictive nomograms based on clinical and ultrasound features and to improve the clinical strategy for US BI-RADS 4A lesions.

METHODS

Patients with US BI-RADS 4A lesions from 3 hospitals between January 2016 and June 2020 were retrospectively included. Clinical and ultrasound features were extracted to establish nomograms CE (based on clinical experience) and DL (based on deep-learning algorithm). The performances of nomograms were evaluated by receiver operator characteristic curves, calibration curves and decision curves. Diagnostic performances with DL of radiologists were analyzed.

RESULTS

1616 patients from 2 hospitals were randomly divided into training and internal validation cohorts at a ratio of 7:3. Hundred patients from another hospital made up external validation cohort. DL achieved more optimized AUCs than CE (internal validation: 0.916 vs. 0.863, P < .01; external validation: 0.884 vs. 0.776, P = .05). The sensitivities of DL were higher than CE (internal validation: 81.03% vs. 72.41%, P = .044; external validation: 93.75% vs. 81.25%, P = .4795) without losing specificity (internal validation: 84.91% vs. 86.47%, P = .353; external validation: 69.14% vs. 71.60%, P = .789). Decision curves indicated DL adds more clinical net benefit. With DL's assistance, both radiologists achieved higher AUCs (0.712 vs. 0.801; 0.547 vs. 0.800), improved specificities (70.93% vs. 74.42%, P < .001; 59.3% vs. 81.4%, P = .004), and decreased unnecessary biopsy rates by 6.7% and 24%.

CONCLUSION

DL was developed to discriminate US BI-RADS 4A lesions with a higher diagnostic power and more clinical net benefit than CE. Using DL may guide clinicians to make precise clinical decisions and avoid overtreatment of benign lesions.

The Role of MRI in Breast Cancer and Breast Conservation Therapy

Contrast-enhanced breast MRI has an established role in aiding in the detection, evaluation, and management of breast cancer. This article discusses MRI sequences, the clinical utility of MRI, and how MRI has been evaluated for use in breast radiotherapy treatment planning. We highlight the contribution of MRI in the decision-making regarding selecting appropriate candidates for breast conservation therapy and review the emerging role of MRI-guided breast radiotherapy.

Preoperative Role of Superb Microvascular Imaging and Shear-Wave Elastography for Prediction of Axillary Lymph Node Metastasis in Patients With Breast Cancer

ABSTRACT

This study aims to evaluate the role of shearwave elastography (SWE) and superb microvascular imaging (SMI) for preoperative prediction of axillary lymph node metastasis (ALNM) in patients with breast cancer. In a cohort of 214 women with breast cancer, B-Mode ultrasonography (US), SMIvascular-index (SMIvi), and SWE (E-mean, E-ratio) values were recorded before tru-cut biopsy. Axillary fine-needle aspiration biopsy (FNAB) and sentinel lymph node sampling results were collected. Imaging findings and histopathological data were statistically compared. Receiver operating characteristic curve analysis was used to evaluate diagnostic performance. Reverse stepwise logistical regression analysis was conducted. Although ALNM was negative in 111 cases, it was positive in 103 patients. Axillary lymph node metastasis (+) group had larger size ( P < 0.001), higher vascularization (SMIvi: 8.0 ± 6.0 versus 5.0 ± 4.3, P < 0.001), and higher elasticity value (E-mean: 129 ± 31 kPa versus 117.3 ± 40 kPa, P = 0.014). Axillary lymph node metastasis was observed statistically more frequently in Her-2 positive cases ( P = 0.005). There was no significant difference between other B-mode US findings ( P > 0.05), SMI Adler ( P = 0.878), and E-ratio ( P = 0.212). The most appropriate cutoff value for the prediction of ALNM was 23.5 mm for size, 3.8 for SMIvi, and 138.5 kPa for E-mean. The most sensitive (77%) method was the SMIvi measurement, while the most specific (86%) finding was Her-2 positivity. The combined model (being Her-2 positive, >23.5 cm, and >3.8 SMIvi) increased the specificity (78%), PPV (71%), and accuracy (68%). Although the increased size is a previously studied parameter in predicting the risk of ALNM, Her-2 and data obtained by SWE, and SMI can be used to assist conventional US.

ACR Appropriateness Criteria® Imaging of Invasive Breast Cancer

As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. 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 process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

A Radiation Therapy Contouring Atlas for Delineation of the Level I and II Axillae in the Prone Position: A Single-Institution Experience

PURPOSE

With transition from supine to prone position, tenting of the pectoralis major occurs, displacing the muscle from the chest wall and shifting the level I and II axillary spaces. For patients for whom we aim to treat the level I and II axillae using the prone technique, accurate delineation of these nodal regions is necessary. Although different consensus guidelines exist for delineation of nodal anatomy in supine position, to our knowledge, there are no contouring guidelines in the prone position that account for this change in nodal anatomy.

METHODS AND MATERIALS

The level I and II nodal contours from the Radiation Therapy Oncology Group (RTOG) breast cancer supine atlas were adapted for prone position by 2 radiation oncologists and a breast radiologist based on anatomic changes observed from supine to prone positioning on preoperative diagnostic imaging. Forty-three patients from a single institution treated with prone high tangents from 2012 to 2018 were identified as representative cases to delineate the revised level I and II axillae on noncontrast computed tomography (CT) scans obtained during radiation simulation. The revised nodal contours were reviewed by an expanded expert multidisciplinary panel including breast radiologists, radiation oncologists, and surgical oncologists for consistency and reproducibility.

RESULTS

Consensus was achieved among the panel in order to create modifications from the RTOG breast atlas for CT-based contouring of the level I and II axillae in prone position using bone, muscle, and skin as landmarks. This atlas provides representative examples and accompanying descriptions for the changes described to the caudal and anterior borders of level II and the anterior, posterior, medial, and lateral borders of level I. A step-by-step guide is provided for properly identifying the revised anterior border of the level I axilla.

CONCLUSIONS

The adaptations to the RTOG breast cancer atlas for prone positioning will enable radiation oncologists to more accurately target the level I and II axillae when the axillae are targets in addition to the breast.

Development and Validation of MRI Radiomics Models to Differentiate HER2-Zero, -Low, and -Positive Breast Cancer

BACKGROUND. Breast cancer HER2 expression has been redefined using a three-tiered system, with HER2-zero cancers considered ineligible for HER2-targeted therapy, HER2-low cancers considered candidates for novel HER2-targeted drugs, and HER2-positive cancers treated with traditional HER2-targeted medications. OBJECTIVE. The purpose of this study was to assess MRI radiomics models for a three-tiered classification of HER2 expression of breast cancer. METHODS. This retrospective study included 592 patients with pathologically confirmed breast cancer (mean age, 47.0 ± 18.0 [SD] years) who underwent breast MRI at either of a health system's two hospitals from April 2016 through June 2022. Three-tiered HER2 status was pathologically determined. Radiologists assessed the conventional MRI features of tumors and manually segmented the tumors on multiparametric sequences (T2-weighted images, DWI, ADC maps, and T1-weighted delayed contrast-enhanced images) to extract radiomics features. Least absolute shrinkage and selection operator analysis was used to develop two radiomics signatures, to differentiate HER2-zero cancers from HER2-low or HER2-positive cancers (task 1) as well as to differentiate HER2-low cancers from HER2-positive cancers (task 2). Patients from hospital 1 were randomly assigned to a discovery set (task 1: n = 376; task 2: n = 335) or an internal validation set (task 1: n = 161; task 2: n = 143); patients from hospital 2 formed an external validation set (task 1: n = 55; task 2: n = 50). Multivariable logistic regression analysis was used to create nomograms combining radiomics signatures with clinicopathologic and conventional MRI features. RESULTS. AUC, sensitivity, and specificity in the discovery, internal validation, and external validation sets were as follows: for task 1, 0.89, 99.4%, and 69.0%; 0.86, 98.6%, and 76.5%; and 0.78, 100.0%, and 0.0%, respectively; for task 2, 0.77, 93.8%, and 32.3%; 0.75, 92.9%, and 6.8%; and 0.77, 97.0%, and 29.4%, respectively. For task 1, no nomogram was created because no clinicopathologic or conventional MRI feature was associated with HER2 status independent of the MRI radiomics signature. For task 2, a nomogram including an MRI radiomics signature and three pathologic features (histologic grade of III, high Ki-67 index, and positive progesterone receptor status) that were independently associated with HER2-low expression had an AUC of 0.87, 0.83, and 0.80 in the three sets. CONCLUSION. MRI radiomics features were used to differentiate HER2-zero from HER2-low cancers or HER2-positives cancers as well as to differentiate HER2-low cancers from HER2-positive cancers. CLINICAL IMPACT. MRI radiomics may help select patients for novel or traditional HER2-targeted therapies, particularly those patients with ambiguous results of immunohistochemical staining results or limited access to fluorescence in situ hybridization.

Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer

This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) for the preoperative evaluation of axillary lymph node (ALN) metastasis status in patients with a newly diagnosed unifocal breast cancer. A total of 883 eligible patients with breast cancer who underwent preoperative breast and axillary ultrasound were retrospectively enrolled between April 1, 2016, and June 30, 2022. The training cohort comprised 621 patients from Hospital I; the external validation cohorts comprised 112, 87, and 63 patients from Hospitals II, III, and IV, respectively. A DLR signature was created based on the deep learning and handcrafted features, and the DLRN was then developed based on the signature and four independent clinical parameters. The DLRN exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) of 0.914, 0.929, and 0.952 in the three external validation cohorts, respectively. Decision curve and calibration curve analyses demonstrated the favorable clinical value and calibration of the nomogram. In addition, the DLRN outperformed five experienced radiologists in all cohorts. This has the potential to guide appropriate management of the axilla in patients with breast cancer, including avoiding overtreatment.

A machine-learning approach based on multiparametric MRI to identify the risk of non-sentinel lymph node metastasis in patients with early-stage breast cancer

BACKGROUND

It has been reported that patients with early breast cancer with 1-2 positive sentinel lymph nodes have a lower risk of non-sentinel lymph node (NSLN) metastasis and cannot benefit from axillary lymph node dissection.

PURPOSE

To develop the potential of machine learning based on multiparametric magnetic resonance imaging (MRI) and clinical factors for predicting the risk of NSLN metastasis in breast cancer.

MATERIAL AND METHODS

This retrospective study included 144 patients with 1-2 positive sentinel lymph node breast cancer. Multiparametric MRI morphologic findings and the detailed demographical characteristics of the primary tumor and axillary lymph node were extracted. The logistic regression, support vector classification, extreme gradient boosting, and random forest algorithm models were established to predict the risk of NSLN metastasis. The prediction efficiency of a machine-learning-based model was evaluated. Finally, the relative importance of each input variable was analyzed for the best model.

RESULTS

Of the 144 patients, 80 (55.6%) developed NSLN metastasis. A total of 24 imaging features and 14 clinicopathological features were analyzed. The extreme gradient boosting algorithm had the strongest prediction efficiency with an area under curve of 0.881 and 0.781 in the training set and test set, respectively. Five main factors for the metastasis of NSLN were found, including histological grade, cortical thickness, fatty hilum, short axis of lymph node, and age.

CONCLUSION

The machine-learning model incorporating multiparametric MRI features and clinical factors can predict NSLN metastasis with high accuracy for breast cancer and provide predictive information for clinical protocol.

Diagnostic value of applying preoperative breast ultrasound and clinicopathologic features to predict axillary lymph node burden in early invasive breast cancer: a study of 1247 patients

BACKGROUND

Since the Z0011 trial, the assessment of axillary lymph node status has been redirected from the previous assessment of the occurrence of lymph node metastasis alone to the assessment of the degree of lymph node loading. Our aim was to apply preoperative breast ultrasound and clinicopathological features to predict the diagnostic value of axillary lymph node load in early invasive breast cancer.

METHODS

The 1247 lesions were divided into a high lymph node burden group and a limited lymph node burden group according to axillary lymph node status. Univariate and multifactorial analyses were used to predict the differences in clinicopathological characteristics and breast ultrasound characteristics between the two groups with high and limited lymph node burden. Pathological findings were used as the gold standard.

RESULTS

Univariate analysis showed significant differences in ki-67, maximum diameter (MD), lesion distance from the nipple, lesion distance from the skin, MS, and some characteristic ultrasound features (P < 0.05). In multifactorial analysis, the ultrasound features of breast tumors that were associated with a high lymph node burden at the axilla included MD (odds ratio [OR], 1.043; P < 0.001), shape (OR, 2.422; P = 0.0018), hyperechoic halo (OR, 2.546; P < 0.001), shadowing in posterior features (OR, 2.155; P = 0.007), and suspicious lymph nodes on axillary ultrasound (OR, 1.418; P = 0.031). The five risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.702.

CONCLUSION

Breast ultrasound features and clinicopathological features are better predictors of high lymph node burden in early invasive breast cancer, and this prediction helps to develop more effective treatment plans.

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.

Analysis of clinical factors and ultrasound features associated with COVID-19 vaccine-related axillary lymphadenopathy: A large group study

PURPOSE

To investigate factors that distinguish COVID-19 vaccine-related axillary lymphadenopathy from malignancy or other etiologies.

METHODS

From June 2021 to April 2022, 3859 consecutive female patients had breast and axillary ultrasound (US) at our institution. After exclusions, 592 patients were included in the study. We retrospectively reviewed clinical history and US features of enlarged axillary lymph nodes. Assessed clinical factors included age, vaccination type, dose and vaccination date, and ultrasound features included cortical thickness, shape, marginal irregularity, focal cortical thickening, fatty hilum, and number and anatomic location of enlarged lymph nodes. The seven US features were used to score the severity of lymphadenopathy. Binary logistic models and independent two-sample t-tests were used for statistical analysis.

RESULTS

Among 592 patients (mean age 49.3 ± 10.3 years), 406(68.6%), 90(15.2%), 42(7.1), 4(0.7%) and 50(8.4%) patients received Pfizer, AstraZeneca, Moderna, Janssen and cross inoculation of more than one type, respectively. 185(31.3%), 376(63.5%) and 31(5.2%) patients received a first, second and third dose, respectively. The interval between vaccination and US was 30.9 ± 21.5 days. US showed axillary lymphadenopathy (LAP) in 113 patients (19.1%). Clinical factors associated with LAP were age younger than 50 years, mRNA vaccine, first dose and shorter interval(P < 0.05). US features associated with LAP were mean cortical thickness of 4.6 ± 1.63 mm, oval shape (70.8%), smooth margin (53.1%), focal cortical thickening (62.8%) and preserved fatty hilum (84.1%). Using our scoring method, the mean overall score for vaccine-related LAP was 2.45 ± 1.51 points.

CONCLUSION

Awareness of influencing factors and sonographic features can help differentiate COVID-19 vaccine-related adenopathy from other etiologies.

A multivariable model of ultrasound and clinicopathological features for predicting axillary nodal burden of breast cancer: potential to prevent unnecessary axillary lymph node dissection

BACKGROUND

To develop a clinical model for predicting high axillary nodal burden in patients with early breast cancer by integrating ultrasound (US) and clinicopathological features.

METHODS AND MATERIALS

Patients with breast cancer who underwent preoperative US examination and breast surgery at the Affiliated Hospital of Nantong University (centre 1, n = 250) and at the Affiliated Hospital of Jiangsu University (centre 2, n = 97) between January 2012 and December 2016 and between January 2020 and March 2022, respectively, were deemed eligible for this study (n = 347). According to the number of lymph node (LN) metastasis based on pathology, patients were divided into two groups: limited nodal burden (0-2 metastatic LNs) and heavy nodal burden (≥ 3 metastatic LNs). In addition, US features combined with clinicopathological variables were compared between these two groups. Univariate and multivariate logistic regression analysis were conducted to identify the most valuable variables for predicting ≥ 3 LNs in breast cancer. A nomogram was then developed based on these independent factors.

RESULTS

Univariate logistic regression analysis revealed that the cortical thickness (p < 0.001), longitudinal to transverse ratio (p = 0.001), absence of hilum (p < 0.001), T stage (p = 0.002) and Ki-67 (p = 0.039) were significantly associated with heavy nodal burden. In the multivariate logistic regression analysis, cortical thickness (p = 0.001), absence of hilum (p = 0.042) and T stage (p = 0.012) were considered independent predictors of high-burden node. The area under curve (AUC) of the nomogram was 0.749.

CONCLUSION

Our model based on US variables and clinicopathological characteristics demonstrates that can help select patients with ≥ 3 LNs, which can in turn be helpful to predict high axillary nodal burden in early breast cancer patients and prevent unnecessary axillary lymph node dissection.

Lymph node metastases in breast cancer: Mechanisms and molecular imaging

Breast cancer is the most common malignant disease of women in the world. Breast cancer often metastasizes to axillary lymph nodes. Accurate assessment of the status of axillary lymph nodes is crucial to the staging and treatment of breast cancer. None of the methods used clinically for preoperative noninvasive examination of axillary lymph nodes can accurately identify cancer cells from a molecular level. In recent years, with the in-depth study of lymph node metastases, the mechanisms and molecular imaging of lymph node metastases in breast cancer have been reported. In this review, we highlight the new progress in the study of the main mechanisms of lymph node metastases in breast cancer. In addition, we analyze the advantages and disadvantages of traditional preoperative axillary lymph node imaging methods for breast cancer, and list molecular imaging methods that can accurately identify breast cancer cells in lymph nodes.

Correlation of Perfusion Metrics with Ki-67 Proliferation Index and Axillary Involvement as a Prognostic Marker in Breast Carcinoma Cases: A Dynamic Contrast-Enhanced Perfusion MRI Study

Our study aims to reveal clinically helpful prognostic markers using quantitative radiologic data from perfusion magnetic resonance imaging for patients with locally advanced carcinoma, using the Ki-67 index as a surrogate. Patients who received a breast cancer diagnosis and had undergone dynamic contrast-enhanced magnetic resonance imaging of the breast for pretreatment evaluation and follow-up were searched retrospectively. We evaluated the MRI studies for perfusion parameters and various categories and compared them to the Ki-67 index. Axillary involvement was categorized as low (N0-N1) or high (N2-N3) according to clinical stage. A total sum of 60 patients' data was included in this study. Perfusion parameters and Ki-67 showed a significant correlation with the transfer constant (Ktrans) (ρ = 0.554 p = 0.00), reverse transfer constant (Kep) (ρ = 0.454 p = 0.00), and initial area under the gadolinium curve (IAUGC) (ρ = 0.619 p = 0.00). The IAUGC was also significantly different between axillary stage groups (Z = 2.478 p = 0.013). Outside of our primary hypothesis, associations between axillary stage and contrast enhancement (x2 = 8.023 p = 0.046) and filling patterns (x2 = 8.751 p = 0.013) were detected. In conclusion, these parameters are potential prognostic markers in patients with moderate Ki-67 indices, such as those in our study group. The relationship between axillary status and perfusion parameters also has the potential to determine patients who would benefit from limited axillary dissection.

Value of Breast MRI and Nomogram After Negative Axillary Ultrasound for Predicting Axillary Lymph Node Metastasis in Patients With Clinically T1-2 N0 Breast Cancer

BACKGROUND

There are increasing concerns about that sentinel lymph node biopsy (SLNB) could be omitted in patients with clinically T1-2 N0 breast cancers who has negative axillary ultrasound (AUS). This study aims to assess the false negative result (FNR) of AUS, the rate of high nodal burden (HNB) in clinically T1-2 N0 breast cancer patients, and the diagnostic performance of breast magnetic resonance imaging (MRI) and nomogram.

METHODS

We identified 948 consecutive patients with clinically T1-2 N0 cancers who had negative AUS, subsequent MRI, and breast conserving therapy between 2013 and 2020 from two tertiary medical centers. Patients from two centers were assigned to development and validation sets, respectively. Among 948 patients, 402 (mean age ± standard deviation, 57.61 ± 11.58) were within development cohort and 546 (54.43 ± 10.02) within validation cohort. Using logistic regression analyses, clinical-imaging factors associated with lymph node (LN) metastasis were analyzed in the development set from which nomogram was created. The performance of MRI and nomogram was assessed. HNB was defined as ≥ 3 positive LNs.

RESULTS

The FNR of AUS was 20.1% (81 of 402) and 19.2% (105 of 546) and the rates of HNB were 1.2% (5/402) and 2.2% (12/546), respectively. Clinical and imaging features associated with LN metastasis were progesterone receptor positivity, outer tumor location on mammography, breast imaging reporting and data system category 5 assessment of cancer on ultrasound, and positive axilla on MRI. In validation cohorts, the positive predictive value (PPV) and negative predictive value (NPV) of MRI and clinical-imaging nomogram was 58.5% and 86.5%, and 56.0% and 82.0%, respectively.

CONCLUSION

The FNR of AUS was approximately 20% but the rate of HNB was low. The diagnostic performance of MRI was not satisfactory with low PPV but MRI had merit in reaffirming negative AUS with high NPV. Patients who had low probability scores from our clinical-imaging nomogram might be possible candidates for the omission of SLNB.

Diagnostic Performance of Superb Microvascular Imaging in Differentiating Benign and Malignant Axillary Lymph Nodes

ABSTRACT

The aim was to evaluate the effectiveness of superb microvascular imaging (SMI) in axillary lymph nodes (LNs).Benign and malignant LNs diagnosed via histopathological examination constituted the study subgroups. In addition to grayscale findings for morphological evaluation, vascular patterns and appearance of internal vessels were analyzed by both power Doppler ultrasound (PDUS) and SMI. The number of vascular branches was counted, and a vascularity index (VI) was calculated by SMI.Fifty-two LNs with suspicious findings in terms of metastasis (33 malignant and 19 benign) were evaluated. Diagnostic accuracy according to vascular patterns was 82% for PDUS and 92% for SMI. In the presence of asymmetric cortical thickening, there was a significant difference between benign and malignant LNs in the number of vascular branches of both thin and thick cortical sides ( P < 0.01). Mean VI was significantly higher in the malignant group ( P < 0.05). In differentiating malignancy, when a cutoff VI value was set to 9%, sensitivity was 69.7%, and specificity was 63.2%.Evaluating the vascularity of axillary LNs by SMI is a useful tool in determining the potential of axillary metastasis, especially in the absence of typical sonographic findings. Superb microvascular imaging can beneficially be used to select the most suspicious LN and suspicious area of the LN to sample.

Prediction of Residual Axillary Nodal Metastasis Following Neoadjuvant Chemotherapy for Breast Cancer: Radiomics Analysis Based on Chest Computed Tomography

OBJECTIVE

To evaluate the diagnostic performance of chest computed tomography (CT)-based qualitative and radiomics models for predicting residual axillary nodal metastasis after neoadjuvant chemotherapy (NAC) for patients with clinically node-positive breast cancer.

MATERIALS AND METHODS

This retrospective study included 226 women (mean age, 51.4 years) with clinically node-positive breast cancer treated with NAC followed by surgery between January 2015 and July 2021. Patients were randomly divided into the training and test sets (4:1 ratio). The following predictive models were built: a qualitative CT feature model using logistic regression based on qualitative imaging features of axillary nodes from the pooled data obtained using the visual interpretations of three radiologists; three radiomics models using radiomics features from three (intranodal, perinodal, and combined) different regions of interest (ROIs) delineated on pre-NAC CT and post-NAC CT using a gradient-boosting classifier; and fusion models integrating clinicopathologic factors with the qualitative CT feature model (referred to as clinical-qualitative CT feature models) or with the combined ROI radiomics model (referred to as clinical-radiomics models). The area under the curve (AUC) was used to assess and compare the model performance.

RESULTS

Clinical N stage, biological subtype, and primary tumor response indicated by imaging were associated with residual nodal metastasis during the multivariable analysis (all P < 0.05). The AUCs of the qualitative CT feature model and radiomics models (intranodal, perinodal, and combined ROI models) according to post-NAC CT were 0.642, 0.812, 0.762, and 0.832, respectively. The AUCs of the clinical-qualitative CT feature model and clinical-radiomics model according to post-NAC CT were 0.740 and 0.866, respectively.

CONCLUSION

CT-based predictive models showed good diagnostic performance for predicting residual nodal metastasis after NAC. Quantitative radiomics analysis may provide a higher level of performance than qualitative CT features models. Larger multicenter studies should be conducted to confirm their performance.

Review of the Sonographic Features of Interpectoral (Rotter) Lymph Nodes in Breast Cancer Staging

ABSTRACT

This article reviews the ultrasound evaluation and staging of breast cancer with respect to the involvement of interpectoral (Rotter) lymph nodes. The primary objective is to demonstrate and assess the characteristic sonographic findings of interpectoral (Rotter) lymph nodes to help provide accurate nodal staging information. We aim to provide a comprehensive review and serve as an imaging guide for the identification and evaluation of Rotter lymph nodes. The detection of abnormalities and pathologic features of metastatic axillary nodal disease in the interpectoral region is reviewed, and the impact on clinical management and treatment is discussed. In the radiology literature, there is no comprehensive review of the sonographic appearance and evaluation of Rotter lymph nodes.

Prediction of prognostic factors in breast cancer: A noninvasive method utilizing histogram parameters derived from Adc maps

Objective

The aim of this study is to investigate the relationship between histogram parameters and prognostic factors of breast cancer and to reveal the diagnostic performance of histogram parameters in predicting prognostic factors status.

Materials and methods

Ninety-two patients with a confirmed histopathological diagnosis of breast cancer were included in the study. Magnetic resonance imaging (MRI) was performed using a 1.5T scanner and two different b values were used for diffusion-weighted imaging (DWI) (b values: 0 s/mm², b: 800 s/mm²). For 3D histogram analysis, regions of interest (ROI) were drawn each slice of the lesion on apparent diffusion coefficient (ADC) maps. The following data were derived from the histogram analysis data: percentiles, skewness, kurtosis, and entropy. The relationship between prognostic factors and histogram analysis data was investigated using the Kolmogorov-Smirnov test, Shapiro-Wilk test, skewness-kurtosis test, independent t-test, Mann-Whitney U test, and Kruskal-Wallis test. Receiver operator characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the histogram parameters.

Results

ADC_max, kurtosis, and entropy parameters were statistically significantly correlated with tumor diameter (p = 0.002, p = 0.008, and p = 0.001, respectively). There was a significant difference in ADC_90% and ADC_max values, depending on estrogen receptor (ER) and progesterone receptor (PR) status. These values were lower in ER- and PR-positive than ER- and PR-negative patients (p = 0.02 and p = 0.001 vs. p = 0.018, p = 0.008). All ADC percentage values were lower in patients with a positive Ki-67 proliferation index as compared with those with a negative Ki-67 proliferation index (all p = 0.001). The entropy value was high in high-grade lesions and lesions with axillary involvement (p = 0.039 and p = 0.048, respectively). The highest area under the curve (AUC) for ER and PR status was calculated for the ADC_90% value with ROC curve analysis. The highest AUC for Ki-67 proliferation index was found for the ADC_50%.

Conclusion

Histogram analysis parameters derived from of ADC maps of whole lesions can reflect histopathological features of the tumors. Based on our study, it was concluded that histogram analysis parameters were related to the prognostic factors of the tumor.

Diagnostic Usefulness of Diffusion-Weighted MRI for Axillary Lymph Node Evaluation in Patients with Breast Cancer

This study aimed to determine whether apparent diffusion coefficient (ADC) and morphological features on diffusion-weighted MRI (DW-MRI) can discriminate metastatic axillary lymph nodes (ALNs) from benign in patients with breast cancer. Two radiologists measured ADC, long and short diameters, long-to-short diameter ratio, and cortical thickness and assessed eccentric cortical thickening, loss of fatty hilum, irregular margin, asymmetry in shape or number, and rim sign of ALNs on DW-MRI and categorized them into benign or suspicious ALNs. Pathologic reports were used as a reference standard. Statistical analysis was performed using the Mann-Whitney U test and chi-square test. Overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of DW-MRI were calculated. The ADC of metastatic ALNs was 0.905 × 10^-3 mm²/s, and that of benign ALNs was 0.991 × 10^-3 mm²/s (p = 0.243). All morphologic features showed significant difference between the two groups. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the final categorization on DW-MRI were 77.1%, 93.3%, 79.4%, 92.5%, and 86.2%, respectively. Our results suggest that morphologic evaluation of ALNs on DWI can discriminate metastatic ALNs from benign. The ADC value of metastatic ALNs was lower than that of benign nodes, but the difference was not statistically significant.

The location of unilateral axillary lymphadenopathy after COVID-19 vaccination compared with that of metastasis from breast cancer without vaccination

PURPOSE

Unilateral axillary lymphadenopathy is known to occur after coronavirus disease (COVID-19) vaccination. Post-vaccination lymphadenopathy may mimic the metastatic lymph nodes in breast cancer, and it is challenging to distinguish between them. This study investigated whether the localization of axillary lymphadenopathy on magnetic resonance imaging (MRI) could be used to distinguish reactive lymphadenopathy after COVID-19 vaccines from metastatic nodes.

MATERIALS AND METHODS

We retrospectively examined preoperative MRI images of 684 axillae in 342 patients who underwent breast cancer surgery from June to October 2021. Lymphadenopathy was defined as cortical thickening or short axis ≥ 5 mm. The axilla was divided into ventral and dorsal parts on the axial plane using a perpendicular line extending from the most anterior margin of the muscle group, including the deltoid, latissimus dorsi, or teres major muscles, relative to a line along the lateral chest wall. We recorded the presence or absence of axillary lymphadenopathy in each area and the number of visible lymph nodes.

RESULTS

Of 80 axillae, 41 and 39 were included in the vaccine and metastasis groups, respectively. The median time from the last vaccination to MRI was 19 days in the vaccine group. The number of visible axillary lymph nodes was significantly higher in the vaccine group (median, 15 nodes) than in the metastasis group (7 nodes) (P < 0.001). Dorsal lymphadenopathy was observed in 16 (39.0%) and two (5.1%) axillae in the vaccine and metastasis groups, respectively (P < 0.001). If the presence of both ventral and dorsal lymphadenopathy is considered indicative of vaccine-induced reaction, this finding has a sensitivity of 34.1%, specificity of 97.4%, and positive and negative predictive values of 93.3％ and 58.5%, respectively.

CONCLUSION

The presence of deep axillary lymphadenopathy may be an important factor for distinguishing post-vaccination lymphadenopathy from metastasis. The number of axillary lymph nodes may also help.

Predicting axillary nodal metastasis based on the side of asymmetrical cortical thickening in breast cancer: Evaluation with grayscale and microvascular imaging findings

PURPOSE

To investigate the relationship between sonographic findings and the axillary status, especially the side of thickening in the presence of cortical asymmetry.

METHODS

Patients with biopsy-proven axillary lymph node (ALN) metastasis were included in this study. The lymph nodes were divided into three groups depending on the type of cortical thickening as diffuse, closer (eccentric cortical thickening on the side near the tumor and/or breast) and distant (thickening on the further side) asymmetry. Longitudinal to transverse axis (L/T) ratio, the largest cortical thickness, cortex to hilum ratio (C/H), hilar status (normal/displaced/absent), orientation (parallel/vertical), capsular integrity (sharp/indistinct), vascularisation pattern (hilar/peripheral/penetrant/anarchic/avascular) on superb microvascular imaging (SMI) and presence of conglomeration were recorded for each lymph node. Axillary nodal status on ¹⁸F-FDG PET-CT/MRI scans was recorded, if available. Features of the breast lesions like size, laterality, nuclear grade, hormone receptor status and the level of Ki-67 expression have been added.

RESULTS

A total of 219 metastatic ALNs [diffuse (n = 122), closer asymmetry (n = 71), distant asymmetry (n = 26)] were evaluated. By the univariate analysis, ALN metastasis was significantly associated with the presence of closer asymmetrical cortical thickening (p < 0,0001), C/H ratio (p = 0.001), cortical thickness (p = 0.001), hilar status (p < 0.005) and vascular pattern (p < 0.005). L/T ratio was only a statistically significant parameter for lymph nodes with diffuse cortical enlargement in predicting metastasis, and conglomeration was also observed only in this group (p < 0.05). By multivariate analysis, nodal metastasis was significantly associated with asymmetrical cortical thickening (p = 0.001), C/H ratio (p = 0.005) and vascular pattern (p < 0.0001).

CONCLUSION

Asymmetrical cortical enlargement on the side closer to the breast, C/H ratio and abnormal microvascular pattern are the independent predictors of axillary nodal involvement. Closer asymmetry is an eligible, easy-to-detect grayscale US finding to decide sampling that highly predicts ALN metastasis.

The potential role of nanomedicine in the treatment of breast cancer to overcome the obstacles of current therapies

Breast cancer (BC) is the most frequently diagnosed malignant tumor among women in the world. BC is the heterogeneous tumor with different subtypes including luminal A-like, luminal B-like (HER2-/HER2+), HER2 enriched, and triple-negative BC. The therapeutic strategies including surgery, chemotherapy, radiotherapy, targeted therapy, and endocrine therapy are well developed and commonly used in the treatment of BC. However, some adverse effects of these conventional treatments limited their wide application in clinical. Therefore, it is necessary to develop more safe and more efficient individualized treatment strategies of the BC. Nanomedicine, as the most promising strategy for controlled and targeted drug delivery, is widely used in multiple aspects of cancer therapy. Importantly, accumulative evidences show that nanomedicine has achieved good outcomes in the treatment of BC and a huge amount of BC patients benefited from the nanomedicine related treatments. In this review, we summarized and discussed the major problems occurred during the administration of conventional treatment strategies for BC and the potential roles of nanomedicine in promoting the treatment efficacy of BC by overcoming obstacles of current treatment of BC.

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.

Magnetic resonance imaging evaluation of single axillary lymph node metastasis in breast cancer: Emphasis on the location of lymph nodes

To evaluate the frequency and location of abnormal lymph nodes (LNs) in breast cancer patients with a single axillary lymph node (ALN) metastasis on breast magnetic resonance imaging (MRI). We retrospectively reviewed the MRI findings of 219 consecutive patients with breast cancer with single ALN metastasis who were surgically confirmed at our institution between January 2018 and December 2018. The morphological features and locations of the abnormal LN on MRI were analyzed. Pathology reports were reviewed to evaluate the size of the metastases and whether they were sentinel LNs (SLNs). Of the 219 patients with a single ALN metastasis, 56 (25.6%) showed abnormal MRI findings. Of these, 54 (96.4%) had either the lowest or second-lowest LN in the level I axilla. In 184 (91.5%) of 201 patients who underwent SLN biopsy, the metastatic LN were SLN. Macrometastases were found more frequently in cases with abnormal LNs than in those with normal-looking LNs (P = .004). The most frequent morphological feature of metastatic ALNs was a diffuse cortical thickening of 3 to 5 mm (37.5%). Although MRI findings of single ALN metastasis in breast cancer patients are none or minimal, abnormalities are observed in the lowest or second-lowest LN in the lower axilla when present, suggesting the location of the SLNs.

Iodine map histogram metrics in early-stage breast cancer: prediction of axillary lymph node metastasis status

BACKGROUND

Variations in axillary lymph node (ALN) metastatic potential between different breast cancers lead to microscopical alterations in tumor perfusion heterogeneity. This study investigated the usefulness of histogram metrics from iodine maps in the preoperative diagnosis of metastatic ALNs in patients with early-stage breast cancer.

METHODS

Between October 2020 and November 2021 enhanced spectral computed tomography (CT) was performed in female patients with breast cancer. Quantitative spectral CT parameters and histogram parameters (mean, median, maximum, minimum, 10th percentiles, 90th percentiles, kurtosis, skewness, energy, range, and variance) from iodine maps were compared between patients with metastatic and nonmetastatic ALNs. Continuous variables were compared using Student's t-test or Mann-Whitney U test. Categorical variables were compared using Pearson's chi-square tests or Fisher's exact tests. Associations between ALN status and imaging features were evaluated using Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis.

RESULTS

This study included 113 female patients (62 and 51 in the ALN-negative and ALN-positive groups, respectively). Tumor size, molecular subtypes, and location differed significantly between the ALN-negative and ALN-positive groups (P<0.05). None of the quantitative spectral CT parameters of mass between metastatic and nonmetastatic ALN groups were significantly different (P>0.05). Histogram parameters of iodine maps for breast cancers, including maximum, 10th percentile, range, and energy, were significantly higher in the metastatic ALNs group compared with the nonmetastatic ALNs group (P<0.05). Multivariable logistic regression analyses showed that tumor location and energy were independent predictors of metastatic ALNs in breast cancers. The combination of independent predictors yielded an area under the curve (AUC) of 0.824 (sensitivity 72.5%; specificity 74.2%).

CONCLUSIONS

Whole-lesion histogram parameters derived from spectral CT iodine maps may be used as a complementary noninvasive means for the preoperative identification of ALN metastases in patients with early-stage breast cancer.

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.

SARS-CoV-2 mRNA Vaccination Causes Prolonged Increased Cortical Thickening and Vascularity in Ipsilateral Axillary Lymph Nodes

OBJECTIVES

To describe the serial grey-scale and color Doppler appearance of ipsilateral axillary lymphadenopathy in response to the Pfizer-BioNTech Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) messenger RNA (mRNA) vaccine over 24 to 28 weeks.

METHODS

The data for this study were collected during an observational study to determine whether mRNA vaccination induced a germinal center B cell reaction in blood and draining axillary lymph nodes. The current study evaluated the serial color Doppler and grey-scale sonographic appearance of these lymph nodes. Ten participants who each underwent 6 sonograms and FNAs over 24 to 28 weeks were included in the study. A total of 11 lateral lymph nodes were identified. Cortical thickness was measured and absence or presence of color Doppler flow in the hilum and lymph node cortex was graded (scale: 0-2).

RESULTS

Eleven lateral axillary lymph nodes were biopsied over 24 to 28 weeks. Mean thickness varied through time (P < .001) and was greater weeks 2 to 7 compared to weeks 24 to 28 (mean differences of 2.6 to 1.3; P < .006), but weeks 14 to 17 mean thickness was not different from weeks 24 to 28 (0.57; P = .15). Cortical vascularity was increased in all 11 lymph nodes by week 5. Mean vascularity varied through time (P < .001) and was greater weeks 2 to 14 compared to weeks 24 to 28; mean differences ranged from 1.7 to 0.83 (P < .001).

CONCLUSIONS

Serial grey-scale and color Doppler appearance of ipsilateral axillary lymph nodes after mRNA vaccination manifest as increased and prolonged cortical thickening and vascularity that diminishes and approaches normal by 24 to 28 weeks.

The Value of Fine Needle Aspiration Biopsy in the Pre-Operative Assessment of the Axilla in Breast Cancer Patients

This paper reviews the role of fine needle aspiration biopsy (FNAB) in assessing the axilla prior to definitive surgery or neoadjuvant therapy in breast cancer patients. The radiological criteria for biopsy are discussed and pathological techniques and pitfalls illustrated. The sensitivity and specificity of the technique and the clinical utility are addressed, with particular reference to the current controversies in the management of the axilla in the light of the American College of Surgeons Oncology Group Z0011 trial results. The low morbidity procedure of FNAB is recommended when the radiological and clinical features suggest a high yield from the abnormal axillary nodes, with consideration of core biopsy if an expected positive result is not obtained or the circumstances require tissue for ancillary studies. In conclusion, FNAB of the axilla is a highly sensitive procedure which can offer further valuable information to assist in clinical decision making. The technique is of particular value in the setting of a large primary tumour size and multiple enlarged nodes. A summary flow chart is provided to facilitate pre-operative management of the axilla and to encourage a universal approach.

Predicting the risk of axillary lymph node metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features and the use of nomograms: a prospective single-center observational study

OBJECTIVES

The purpose of this study was to establish two preoperative nomograms to evaluate the risk for axillary lymph node (ALN) metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features.

METHODS

We prospectively evaluated 593 consecutive female participants who were diagnosed with cT_1-3N_0-1M₀ breast cancer between March 2018 and May 2019 at Sun Yat-Sen Memorial Hospital. The participants were randomly classified into training and validation sets in a 4:1 ratio for the development and validation of the nomograms, respectively. Multivariate logistic regression analysis was performed to identify independent predictors of ALN status. We developed Nomogram A and Nomogram B to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2), respectively.

RESULTS

A total of 528 participants were evaluated in the final analyses. Multivariable analysis revealed that the number of suspicious lymph nodes, long axis, short-to-long axis ratio, cortical thickness, tumor location, and histological grade were independent predictors of ALN status. The AUCs of nomogram A in the training and validation groups were 0.83 and 0.78, respectively. The AUCs of nomogram B in the training and validation groups were 0.87 and 0.87, respectively. Both nomograms were well-calibrated.

CONCLUSION

We developed two preoperative nomograms that can be used to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2) in early breast cancer patients. Both nomograms are useful tools that will help clinicians predict the risk of ALN metastasis and facilitate therapy decision-making about axillary surgery.

KEY POINTS

• We developed two preoperative nomograms to predict axillary lymph node status based on ultrasonographic-clinicopathologic features. • Nomogram A was used to predict axillary lymph node metastasis (presence vs. absence). The AUCs in the training and validation groups were 0.83 and 0.78, respectively. Nomogram B was used to estimate the number of metastatic lymph nodes ( ≤ 2 vs. > 2). The AUCs in the training and validation group were 0.87 and 0.87, respectively. • Our nomograms may help clinicians weigh the risks and benefits of axillary surgery more appropriately.

Shear wave elastography as a supplemental tool in the assessment of unsuspicious axillary lymph nodes in patients undergoing breast ultrasound examination

Objectives:

To define reference values for shear wave elastography (SWE) in unsuspicious axillary lymph nodes in patients undergoing breast ultrasound examination.

Methods:

In total, 177 clinically and sonographically unsuspicious axillary lymph nodes were prospectively evaluated with SWE using Virtual Touch Tissue Imaging Quantification (VTIQ) in 175 women. Mean values of tissue stiffness for axillary fatty tissue, lymph node cortex, and lymph node hilus were measured. Additionally, test-retest reliability of SWE in the assessment of axillary lymph node stiffness was evaluated by repeating each measurement three times.

Results:

In 177 axillary lymph nodes, the mean stiffness of lymph node cortex, hilus, and surrounding fatty tissue as quantified by SWE was 1.90 m/s (SD: 0.34 m/s), 2.02 m/s (SD: 0.37 m/s), and 1.75 m/s (SD: 0.38 m/s), respectively. The mean stiffness of cortex and hilus was significantly higher compared to fatty tissue (p < 0.0001). SWE demonstrated good test–retest reliability in the assessment of stiffness of the lymph node hilus, cortex, and the surrounding fatty tissue with an intraclass correlation of 0.79 (95% CI: 0.75; 0.83), 0.75 (95% CI: 0.70; 0.79), and 0.78 (95% CI: 0.74; 0.82), respectively, (p < 0.0001).

Conclusions:

Reference values for SWE in unsuspicious axillary lymph nodes are determined. These results may help to better identify axillary lymph node metastasis for breast cancer patients when combined with other lymph node features. SWE is a reliable method for the objective quantification of tissue stiffness of axillary lymph nodes.

Advances in knowledge:

This study presents physiological reference values for tissue stiffness by examining the axillary lymph nodes with SWE in 175 women with sonomorphologically unsuspicious lymph nodes.

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 Lymphadenopathy in the COVID-19 Era: What the Radiologist Needs to Know

Axillary lymphadenopathy caused by the high immunogenicity of messenger RNA (mRNA) COVID-19 vaccines presents radiologists with new diagnostic dilemmas in differentiating vaccine-related benign reactive lymphadenopathy from that due to malignant causes. Understanding axillary anatomy and lymphatic drainage is key to radiologic evaluation of the axilla. US plays a critical role in evaluation and classification of axillary lymph nodes on the basis of their cortical and hilar morphology, which allows prediction of metastatic disease. Guidelines for evaluation and management of axillary lymphadenopathy continue to evolve as radiologists gain more experience with axillary lymphadenopathy related to COVID-19 vaccines. General guidelines recommend documenting vaccination dates and laterality and administering all vaccine doses contralateral to the site of primary malignancy whenever applicable. Guidelines also recommend against postponing imaging for urgent clinical indications or for treatment planning in patients with newly diagnosed breast cancer. Although conservative management approaches to axillary lymphadenopathy initially recommended universal short-interval imaging follow-up, updates to those approaches as well as risk-stratified approaches recommend interpreting lymphadenopathy in the context of both vaccination timing and the patient’s overall risk of metastatic disease. Patients with active breast cancer in the pretreatment or peritreatment phase should be evaluated with standard imaging protocols regardless of vaccination status. Tissue sampling and multidisciplinary discussion remain useful in management of complex cases, including increasing lymphadenopathy at follow-up imaging, MRI evaluation of extent of disease, response to neoadjuvant treatment, and potentially confounding cases.

An invited commentary by Weinstein is available online.

^©RSNA, 2022

Shedding Light on T2 Bright Masses on Breast MRI: Benign and Malignant Causes

While T2 hyperintense masses on breast MRI are often benign, there are several malignant etiologies that can also be T2 hyperintense. Delineation between benign and malignant entities is important for the accurate interpretation of breast MRI. Common benign T2 hyperintense masses include cysts, fibroadenomas, and lymph nodes. Malignant processes that are T2 hyperintense include metastatic lymph nodes, mucinous breast carcinomas, papillary breast carcinomas, and breast cancers with central necrosis. Evaluation of the morphology and enhancement pattern of a T2 hyperintense mass can help to differentiate a benign process from a malignant one. This educational review will present both benign and malignant causes of T2 hyperintense masses on breast MRI and review common imaging findings and pertinent imaging characteristics that can be used to help accurately identify benign entities while also recognizing suspicious lesions that require additional evaluation.

Predicting axillary lymph node metastasis in breast cancer using the similarity of quantitative dual-energy CT parameters between the primary lesion and axillary lymph node

PURPOSE

To evaluate the similarity of quantitative dual-energy computed tomography (DECT) parameters between the primary breast cancer lesion and axillary lymph node (LN) for predicting LN metastasis.

MATERIALS AND METHODS

This retrospective study included patients with breast cancer who underwent contrast-enhanced DECT between July 2019 and April 2021. Relationships between LN metastasis and simple DECT parameters, similarity of DECT parameters, and pathological and morphological features were analyzed. ROC curve analysis was used to evaluate diagnostic ability.

RESULTS

Overall, 137 LNs (39 metastases and 98 non-metastases) were evaluated. Significant differences were observed in some pathological (nuclear grade, estrogen receptor status, and Ki67 index) and morphological characteristics (shortest and longest diameters of the LN, longest-to-shortest diameter ratio, and hilum), most simple DECT parameters, and all DECT similarity parameters between the LN metastasis and non-metastasis groups (all, P < 0.001-0.004). The shortest diameter of the LN (odds ratio 2.22; 95% confidence interval 1.47, 3.35; P < 0.001) and the similarity parameter of 40-keV attenuation (odds ratio, 2.00; 95% confidence interval 1.13, 3.53; P = 0.017) were independently associated with LN metastasis compared to simple DECT parameters of 40-keV attenuation (odds ratio 1.01; 95% confidence interval 0.99, 1.03; P =0.35). The AUC value of the similarity parameters for predicting metastatic LN was 0.78-0.81, even in cohorts with small LNs (shortest diameter < 5 mm) (AUC value 0.73-0.78).

CONCLUSION

The similarity of the delayed-phase DECT parameters could be a more useful tool for predicting LN metastasis than simple DECT parameters in breast cancer, regardless of LN size.

Axillary lymph node characteristics in breast cancer patients versus post-COVID-19 vaccination: Overview of current evidence per imaging modality

BACKGROUND

Axillary lymph node characteristics on axillary ultrasound (US), breast MRI and 18F-FDG PET/CT are relevant at breast cancer diagnosis. Axillary lymphadenopathy after COVID-19 vaccination has been frequently reported. This may cause a diagnostic dilemma, particularly in the ipsilateral axilla in women who have a either a recent diagnosis of breast cancer or a history of breast cancer. This review provides an overview of the current evidence regarding axillary lymph node characteristics at breast cancer diagnosis versus "post-COVID-19 vaccination".

METHODS

A non-systematic narrative review was performed. Studies describing axillary lymph node characteristics per imaging modality (axillary US, breast MRI and 18F-FDG PET/CT) in breast cancer patients versus post-COVID-19 vaccination were selected and used for the current study.

RESULTS

The morphologic characteristics and distribution of abnormal nodes on US may differ from the appearance of metastatic adenopathy since diffuse cortical thickening of the lymph nodes is the most observed characteristic after vaccination, whereas metastases show as most suspicious characteristics focal cortical thickening and effacement of the fatty hilum. Current evidence on MRI and 18F-FDG on morphologic characteristics of axillary lymphadenopathy is missing, although it was suggested that vaccine related lymphadenopathy is more likely to be present in level 2 and 3 nodes than metastatic nodes. Reported frequencies of lymphadenopathy post-COVID-19 vaccination range from 49% to 85% (US), 29% (breast MRI) and 14.5% to 53.9% (18F-FDG PET/CT). Several factors may impact the presence or extent of lymphadenopathy post-COVID-19 vaccination: injection site, type of vaccine (i.e., mRNA versus vector), time interval (days) between vaccination and imaging, previous history of COVID-19 pneumonia, and first versus second vaccine dose.

CONCLUSION

Although lymph node characteristics differ at breast cancer diagnosis versus post-COVID-19 vaccination, clinical information regarding injection site, vaccine type and vaccination date needs to be documented to improve the interpretation and guide treatment towards the next steps of action.

Axillary Anatomy and Pathology: Pearls and "Pitfalls" for Thoracic Imagers

The axilla contains several important structures which exist in a relatively confined anatomic space between the neck, chest wall, and upper extremity. While neoplastic lymphadenopathy may be among the most common axillary conditions, many other processes may be encountered. For example, expanded use of axillary vessels for access routes for endovascular procedures will increase the need for radiologists to access vessel anatomy, patency, and complications that may arise. Knowledge of axillary anatomy and pathology will allow the imager to systematically evaluate the axillae using various imaging modalities.

Preoperative Sonographic Prediction of Limited Axillary Disease in Patients with Primary Breast Cancer Meeting the Z0011 Criteria: an Alternative to Sentinel Node Biopsy?

PURPOSE

To assess the accuracy of preoperative sonographic staging for prediction of limited axillary disease (LAD, one or two metastatic lymph nodes) and to identify factors associated with high prediction-pathology concordance in patients with early-stage breast cancer meeting the Z0011 criteria.

MATERIALS AND METHODS

Patients treated between January 2015 and January 2020 were included in this retrospective, multicentric analysis of prospectively acquired service databases. The accuracy of LAD prediction was assessed separately for patients with one and two suspicious lymph nodes on preoperative sonography. Test validity outcomes for LAD prediction were calculated for both groups, and a multivariate model was used to identify factors associated with high accuracy of LAD prediction.

RESULTS

Of 2059 enrolled patients, 1513 underwent sentinel node biopsy, 436 primary and 110 secondary axillary dissection. For LAD prediction in patients with one suspicious lymph node on preoperative ultrasound, sensitivity was 92% (95% CI 87-95%), negative predictive value (NPV) was 92% (95% CI 87-95%), and the false-negative rate (FNR) was 8% (95% CI 5-13%). For patients with two preoperatively suspicious nodes, the sensitivity, NPV, and FNR were 89% (95% CI 84-93%), 73% (62-83%), and 11% (95% CI 7-16%), respectively. On multivariate analysis, the number of suspicious lymph nodes was associated inversely with correct LAD prediction ([OR 0.01 (95% CI 0.01-0.93), p ≤ 0.01].

CONCLUSIONS

Sonographic axillary staging in patients with one metastatic lymph node predicted by preoperative ultrasound showed high accuracy and a false-negative rate comparable to sentinel node biopsy for prediction of limited axillary disease.

Dual-Energy Computed Tomography for Evaluation of Breast Cancer Follow-Ups: Comparison of Virtual Monoenergetic Images and Iodine-Map

Differentiating tumor tissue from dense breast tissue can be difficult. Dual-energy CT (DECT) could be suitable for making diagnoses at breast cancer follow-ups. This study investigated the contrast in DECT images and iodine maps for patients with breast cancer being followed-up. Chest CT images captured in 2019 were collected. Five cases of metastatic breast cancer in the lungs were analyzed; the contrast-to-noise ratio (for breast tissue and muscle: CNRb and CNRm, respectively), tumor-to-breast mammary gland ratio (T/B), and tumor-to-muscle ratio (T/M) were calculated. For 84 cases of no metastasis, monochromatic spectral and iodine maps were obtained to compare differences under various breast densities using the K-means algorithm. The optimal T/B, T/M, and CNRb (related to mammary glands) were achieved for the 40-keV image. Conversely, CNRm (related to lungs) was better for higher energy. The optimal balance was achieved at 80 keV. T/B, T/M, and CNR were excellent for iodine maps, particularly for density > 25%. In conclusion, energy of 80 keV is the parameter most suitable for observing the breast and lungs simultaneously by using monochromatic spectral images. Adding iodine mapping can be appropriate when a patient’s breast density is greater than 25%.

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 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.

Stratification of axillary lymph node metastasis risk with breast magnetic resonance imaging in breast cancer

Aims: To develop a model based on breast MRI to stratify axillary lymph node metastasis (ALNM) in breast cancer. Patients & methods: A total of 134 eligible patients were used to build a predicting model, which was validated with an independent group of 57 patients and evaluated for accuracy and sensitivity. Results: A model based on breast MRI was developed and yielded total accuracy of 82.5% and sensitivities of 94.3, 64.3 and 62.5% to predict patients with no, low and heavy ALNM burden, respectively, in the validation group. Conclusion: A noninvasive model based on breast MRI was developed to preoperatively stratify ALNM in breast cancer; its performance needs to be validated and improved in future research.

Preoperative staging of ovarian cancer: comparison between ultrasound, CT and whole-body diffusion-weighted MRI (ISAAC study)

OBJECTIVES

To compare the performance of transvaginal and transabdominal ultrasound with that of the first-line staging method (contrast-enhanced computed tomography (CT)) and a novel technique, whole-body magnetic resonance imaging with diffusion-weighted sequence (WB-DWI/MRI), in the assessment of peritoneal involvement (carcinomatosis), lymph-node staging and prediction of non-resectability in patients with suspected ovarian cancer.

METHODS

Between March 2016 and October 2017, all consecutive patients with suspicion of ovarian cancer and surgery planned at a gynecological oncology center underwent preoperative staging and prediction of non-resectability with ultrasound, CT and WB-DWI/MRI. The evaluation followed a single, predefined protocol, assessing peritoneal spread at 19 sites and lymph-node metastasis at eight sites. The prediction of non-resectability was based on abdominal markers. Findings were compared to the reference standard (surgical findings and outcome and histopathological evaluation).

RESULTS

Sixty-seven patients with confirmed ovarian cancer were analyzed. Among them, 51 (76%) had advanced-stage and 16 (24%) had early-stage ovarian cancer. Diagnostic laparoscopy only was performed in 16% (11/67) of the cases and laparotomy in 84% (56/67), with no residual disease at the end of surgery in 68% (38/56), residual disease ≤ 1 cm in 16% (9/56) and residual disease > 1 cm in 16% (9/56). Ultrasound and WB-DWI/MRI performed better than did CT in the assessment of overall peritoneal carcinomatosis (area under the receiver-operating-characteristics curve (AUC), 0.87, 0.86 and 0.77, respectively). Ultrasound was not inferior to CT (P = 0.002). For assessment of retroperitoneal lymph-node staging (AUC, 0.72-0.76) and prediction of non-resectability in the abdomen (AUC, 0.74-0.80), all three methods performed similarly. In general, ultrasound had higher or identical specificity to WB-DWI/MRI and CT at each of the 19 peritoneal sites evaluated, but lower or equal sensitivity in the abdomen. Compared with WB-DWI/MRI and CT, transvaginal ultrasound had higher accuracy (94% vs 91% and 85%, respectively) and sensitivity (94% vs 91% and 89%, respectively) in the detection of carcinomatosis in the pelvis. Better accuracy and sensitivity of ultrasound (93% and 100%) than WB-DWI/MRI (83% and 75%) and CT (84% and 88%) in the evaluation of deep rectosigmoid wall infiltration, in particular, supports the potential role of ultrasound in planning rectosigmoid resection. In contrast, for the bowel serosal and mesenterial assessment, abdominal ultrasound had the lowest accuracy (70%, 78% and 79%, respectively) and sensitivity (42%, 65% and 65%, respectively).

CONCLUSIONS

This is the first prospective study to document that, in experienced hands, ultrasound may be an alternative to WB-DWI/MRI and CT in ovarian cancer staging, including peritoneal and lymph-node evaluation and prediction of non-resectability based on abdominal markers of non-resectability. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Diagnostic Accuracy of Shear Wave Elastography as an Adjunct Tool in Detecting Axillary Lymph Nodes Metastasis

OBJECTIVES

This study evaluates the diagnostic performance of shear wave elastography (SWE) in differentiating between benign and axillary lymph node (ALN) metastasis in breast carcinoma.

MATERIALS AND METHODS

Breast lesions and axillae of 107 patients were assessed using B-mode ultrasound and SWE. Histopathology was the diagnostic gold standard.

RESULTS

In metastatic axillary lymph nodes, qualitative SWE using color patterns had the highest area under curve (AUC) value, followed by B-mode Ultrasound (cortical thickening >3 mm) and quantitative SWE using Emax of 15.2 kPa (AUC of 81.3%, 70.1%, and 61.2%, respectively). Qualitative SWE exhibited better diagnostic performance than the other two parameters, with sensitivity of 96.0% and specificity of 56.1%. Combination of B-mode Ultrasound (using cortical thickness of >3 mm as cut-off point) and qualitative SWE (Color patterns of 2 to 4) showed sensitivity of 71.6%, specificity of 95%, PPV of 96%, NPV of 66.7%, and accuracy of 80.4%.

CONCLUSION

Qualitative SWE assessment exhibited higher accuracy compared to quantitative values. Qualitative SWE as an adjunct to B-mode ultrasound can further improve the diagnostic accuracy of metastatic ALN in breast cancer.

Predictive value of chest computed tomography for axillary lymph node metastasis in patients with breast cancer: A retrospective cohort study

Purpose: This study aimed to evaluate the predictive value of preoperative chest computed tomography (CT) for axillary lymph node (ALN) metastasis in patients with breast cancer.Methods: CT features of ALNs were retrospectively reviewed in 212 patients with breast cancer who underwent preoperative chest CT examination and ALN dissection. Primary tumor size and CT characteristics of ALNs (cortical thickness, cortical shape, the presence or absence of contrast enhancement of ALNs, and the presence or absence of perinodal infiltration) were recorded and analyzed. A nomogram was developed to visualize the associations between the predictors and each ALN status endpoint.Results: Of 212 patients, 95 (44.8%) had ALN metastasis. Primary tumor size and CT characteristics of ALNs were identified as predictors of ALN metastasis. The nomogram comprising primary tumor size and cortical shape was a good validation model for predicting ALN metastasis. The sensitivity, specificity, and accuracy of the nomogram for predicting ALN metastasis were 88.4%, 79.5%, and 83.5%, respectively.Conclusion: Using preoperative chest CT scans, a nomogram combining the cortical shape of ALNs with the primary tumor size showed good performance in predicting ALN metastasis.

Imaging Protocol and Criteria for Evaluation of Axillary Lymph Nodes in the NAUTILUS Trial

Axillary ultrasonography (US) is the most commonly used imaging modality for nodal evaluation in patients with breast cancer. No Axillary Surgical Treatment in Clinically Lymph Node-Negative Patients after Ultrasonography (NAUTILUS) is a prospective, multicenter, randomized controlled trial investigating whether sentinel lymph node biopsy (SLNB) can be safely omitted in patients with clinically and sonographically node-negative T1–2 breast cancer treated with breast-conserving therapy. In this trial, a standardized imaging protocol and criteria were established for the evaluation of axillary lymph nodes. Women lacking palpable lymph nodes underwent axillary US to dismiss suspicious nodal involvement. Patients with a round hypoechoic node with effaced hilum or indistinct margins were excluded. Patients with T1 tumors and a single node with a cortical thickness ≥ 3 mm underwent US-guided biopsy. Finally, patients with negative axillary US findings were included. The NAUTILUS axillary US nodal assessment criteria facilitate the proper selection of candidates who can omit SLNB.

Characterizing perfusion defects in metastatic lymph nodes at an early stage using high-frequency ultrasound and micro-CT imaging

A perfusion defect in a metastatic lymph node (LN) can be visualized as a localized area of low contrast on contrast-enhanced CT, MRI or ultrasound images. Hypotheses for perfusion defects include abnormal hemodynamics in neovascular vessels or a decrease in blood flow in pre-existing blood vessels in the parenchyma due to compression by LN tumor growth. However, the mechanisms underlying perfusion defects in LNs during the early stage of LN metastasis have not been investigated. We show that tumor mass formation with very few microvessels was associated with a perfusion defect in a non-enlarged LN at the early stage of LN metastasis in a LN adenopathy mouse (LN size circa 10 mm). We found in a mouse model of LN metastasis, induced using non-keratinizing tumor cells, that during the formation of the perfusion defect in a non-enlarged LN, the number of blood vessels ≤ 50 μm in diameter decreased, while those of > 50 μm in diameter increased. The methods used were contrast-enhanced high-frequency ultrasound and contrast-enhanced micro-CT imaging systems, with a maximum spatial resolution of > 30 μm. Furthermore, we found no tumor angiogenesis or oxygen partial pressure (pO₂) changes in the metastatic LN. Our results demonstrate that the perfusion defect appears to be a specific form of tumorigenesis in the LN, which is a vascular-rich organ. We anticipate that a perfusion defect on ultrasound, CT or MRI images will be used as an indicator of a non-enlarged metastatic LN at an early stage.