Diagnostic value of breast MRI for predicting metastatic axillary lymph nodes in breast cancer patients: diffusion-weighted MRI and conventional MRI

Machine learning approaches in the prediction of positive axillary lymph nodes post neoadjuvant chemotherapy using MRI, CT, or ultrasound: A systematic review

Background and objective

Neoadjuvant chemotherapy is a standard treatment approach for locally advanced breast cancer. Conventional imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound, have been used for axillary lymph node evaluation which is crucial for treatment planning and prognostication. This systematic review aims to comprehensively examine the current research on applying machine learning algorithms for predicting positive axillary lymph nodes following neoadjuvant chemotherapy utilizing imaging modalities, including MRI, CT, and ultrasound.

Methods

A systematic search was conducted across databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published up to December 2023. Articles employing machine learning algorithms to predict positive axillary lymph nodes using MRI, CT, or ultrasound data after neoadjuvant chemotherapy were included. The review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, encompassing data extraction and quality assessment.

Results

Seven studies were included, comprising 1502 patients. Four studies used MRI, two used CT, and one applied ultrasound. Two studies developed deep-learning models, while five used classic machine-learning models mainly based on multiple regression. Across the studies, the models showed high predictive accuracy, with the best-performing models combining radiomics and clinical data.

Conclusion

This systematic review demonstrated the potential of utilizing advanced data analysis techniques, such as deep learning radiomics, in improving the prediction of positive axillary lymph nodes in breast cancer patients following neoadjuvant chemotherapy.

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.

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer

Objective

To investigate the value of predicting axillary lymph node (ALN) metastasis based on intratumoral and peritumoral dynamic contrast-enhanced MRI (DCE-MRI) radiomics and clinico-radiological characteristics in breast cancer.

Methods

A total of 473 breast cancer patients who underwent preoperative DCE-MRI from Jan 2017 to Dec 2020 were enrolled. These patients were randomly divided into training (n=378) and testing sets (n=95) at 8:2 ratio. Intratumoral regions (ITRs) of interest were manually delineated, and peritumoral regions of 3 mm (3 mmPTRs) were automatically obtained by morphologically dilating the ITR. Radiomics features were extracted, and ALN metastasis-related radiomics features were selected by the Mann-Whitney U test, Z score normalization, variance thresholding, K-best algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. Clinico-radiological risk factors were selected by logistic regression and were also used to construct predictive models combined with radiomics features. Then, 5 models were constructed, including ITR, 3 mmPTR, ITR+3 mmPTR, clinico-radiological and combined (ITR+3 mmPTR+ clinico-radiological) models. The performance of models was assessed by sensitivity, specificity, accuracy, F1 score and area under the curve (AUC) of receiver operating characteristic (ROC), calibration curves and decision curve analysis (DCA).

Results

A total of 2264 radiomics features were extracted from each region of interest (ROI), 3 and 10 radiomics features were selected for the ITR and 3 mmPTR, respectively. 5 clinico-radiological risk factors were selected, including lesion size, human epidermal growth factor receptor 2 (HER2) expression, vascular cancer thrombus status, MR-reported ALN status, and time-signal intensity curve (TIC) type. In the testing set, the combined model showed the highest AUC (0.839), specificity (74.2%), accuracy (75.8%) and F1 Score (69.3%) among the 5 models. DCA showed that it had the greatest net clinical benefit compared to the other models.

Conclusion

The intra- and peritumoral radiomics models based on DCE-MRI could be used to predict ALN metastasis in breast cancer, especially for the combined model with clinico-radiological characteristics showing promising clinical application value.

Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography

OBJECTIVES

We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications.

METHODS

We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images. After feature selection, three random forest models were built using the retained features, respectively. Significant clinical factors were integrated with these radiomics models to construct a multi-modality model. The multi-modality model was compared to radiologists' diagnoses on axillary ultrasound and MRI. It was also used to assist radiologists in making a secondary diagnosis on MRI.

RESULTS

The multi-modality model showed superior performance with AUCs of 0.964 in the training cohort, 0.916 in the internal validation cohort, and 0.892 in the external validation cohort. It surpassed single-modality models and radiologists' ALN diagnosis on MRI and axillary ultrasound in all validation cohorts. Additionally, the multi-modality model improved radiologists' MRI-based ALN diagnostic ability, increasing the average accuracy from 70.70 to 78.16% for radiologist A and from 75.42 to 81.38% for radiologist B.

CONCLUSION

The multi-modality model can predict ALN metastasis of breast cancer accurately. Moreover, the artificial intelligence (AI) model also assisted the radiologists to improve their diagnostic ability on MRI.

CLINICAL RELEVANCE STATEMENT

The multi-modality model based on both MRI and mammography images allows preoperative prediction of axillary lymph node metastasis in breast cancer patients. With the assistance of the model, the diagnostic efficacy of radiologists can be further improved.

KEY POINTS

• We developed a novel multi-modality model that combines MRI and mammography radiomics with clinical factors to accurately predict axillary lymph node (ALN) metastasis, which has not been previously reported. • Our multi-modality model outperformed both the radiologists' ALN diagnosis based on MRI and axillary ultrasound, as well as single-modality radiomics models based on MRI or mammography. • The multi-modality model can serve as a potential decision support tool to improve the radiologists' ALN diagnosis on MRI.

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

[Radiologic evaluation of lymph nodes in cancer patients]

BACKGROUND

In solid tumors, the detection of locoregional lymph node metastases is of decisive importance not only for the prognosis but also for selecting the correct treatment. Various noninvasive imaging methods or, classically, lymph node dissection are available for this purpose.

OBJECTIVE

This article presents the general principles of noninvasive lymph node diagnostics and discusses the value of the clinically available imaging modalities, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). In addition, recent new technical developments of each modality are highlighted.

MATERIAL AND METHODS

Literature search and summary of the clinical and scientific experience of the authors.

RESULTS

The available imaging procedures are divided into (1) morphological (US, CT, MRI) and (2) functional modalities (PET, special MRI). The former capture structural lymph node parameters, such as size and shape, while the latter address properties that go beyond morphology (e.g. glucose metabolism). The high diagnostic accuracy required for future treatment algorithms will require a combination of both aspects.

DISCUSSION/CONCLUSION

Currently, none of the available modalities have sufficient accuracy to replace lymph node dissection in all oncological scenarios. One of the major challenges for interdisciplinary oncological research is to define the optimal interaction between imaging and lymph node dissection for different malignancies and tumor stages.

Diagnostic Performance of PET/MRI in Breast Cancer: A Systematic Review and Bayesian Bivariate Meta-analysis

INTRODUCTION

By performing a systematic review and meta-analysis, the diagnostic value of ¹⁸F-FDG PET/MRI in breast lesions, lymph nodes, and distant metastases was assessed, and the merits and demerits of PET/MRI in the application of breast cancer were comprehensively reviewed.

METHODS

Breast cancer-related studies using ¹⁸F-FDG PET/MRI as a diagnostic tool published before September 12, 2022 were included. The pooled sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC) were calculated using Bayesian bivariate meta-analysis in a lesion-based and patient-based manner.

RESULTS

We ultimately included 24 studies (including 1723 patients). Whether on a lesion-based or patient-based analysis, PET/MRI showed superior overall pooled sensitivity (0.95 [95% CI: 0.92-0.98] & 0.93 [95% CI: 0.88-0.98]), specificity (0.94 [95% CI: 0.90-0.97] & 0.94 [95% CI: 0.92-0.97]), LDOR (5.79 [95% CI: 4.95-6.86] & 5.64 [95% CI: 4.58-7.03]) and AUC (0.98 [95% CI: 0.94-0.99] & 0.98[95% CI: 0.92-0.99]) for diagnostic applications in breast cancer. In the specific subgroup analysis, PET/MRI had high pooled sensitivity and specificity for the diagnosis of breast lesions and distant metastatic lesions and was especially excellent for bone lesions. PET/MRI performed poorly for diagnosing axillary lymph nodes but was better than for lymph nodes at other sites (pooled sensitivity, specificity, LDOR, AUC: 0.86 vs. 0.58, 0.90 vs. 0.82, 4.09 vs. 1.98, 0.89 vs. 0.84).

CONCLUSION

¹⁸F-FDG PET/MRI performed excellently in diagnosing breast lesions and distant metastases. It can be applied to the initial diagnosis of suspicious breast lesions, accurate staging of breast cancer patients, and accurate restaging of patients with suspected recurrence.

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.

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.

Multiple-parameter MRI after neoadjuvant systemic therapy combining clinicopathologic features in evaluating axillary pathologic complete response in patients with clinically node-positive breast cancer

OBJECTIVE

This study aimed to evaluate axillary pathologic complete response (pCR) after neoadjuvant systemic therapy (NST) in clinically node-positive breast cancer (BC) patients based on post-NST multiple-parameter MRI and clinicopathological characteristics.

METHODS

In this retrospective study, females with clinically node-positive BC who received NST and followed by surgery between January 2017 and September 2021 were included. All axillary lymph nodes (ALNs) on MRI were matched with pathology by ALN markers or sizes. MRI morphological parameters, signal intensity curve (TIC) patterns and apparent diffusion coefficient (ADC) values of post-NST ALNs were measured. The clinicopathological characteristics was also collected and analyzed. Univariable and multivariable logistic regression analyses were performed to evaluate the independent predictors of axillary pCR.

RESULTS

Pathologically confirmed 137 non-pCR ALNs in 71 patients and 87 pCR ALNs in 87 patients were included in this study. Cortical thickness, fatty hilum, and TIC patterns of ALNs, hormone receptor, and human epidermal growth factor receptor 2 (HER2) status were significantly different between the two groups (all, p < 0.05). There was no significant difference for ADC values (p = 0.875). On multivariable analysis, TIC patterns (odds ratio [OR], 2.67, 95% confidence interval [CI]: 1.33, 5.34, p = 0.006), fatty hilum (OR, 2.88, 95% CI:1.39, 5.98, p = 0.004), hormone receptor (OR, 8.40, 95% CI: 2.48, 28.38, p = 0.001) and HER2 status (OR, 8.57, 95% CI: 3.85, 19.08, p < 0.001) were identified as independent predictors associated with axillary pCR. The area under the curve of the multivariate analysis using these predictors was 0.85 (95% CI: 0.79, 0.91).

CONCLUSION

Combining post-NST multiple-parameter MRI and clinicopathological characteristics allowed more accurate identification of BC patients who had received axillary pCR after NST.

ADVANCES IN KNOWLEDGE

A combined model incorporated multiple-parameter MRI and clinicopathologic features demonstrated good performance in evaluating axillary pCR preoperatively and non-invasively.

Axillary Lymphadenopathy after Pfizer-BioNTech and Moderna COVID-19 Vaccination: MRI Evaluation

Background COVID-19 vaccination-related axillary lymphadenopathy has become an important problem in cancer imaging. Data are needed to update or support imaging guidelines for conducting appropriate follow-up. Purpose To investigate the prevalence, predisposing factors, and MRI characteristics of COVID-19 vaccination-related axillary lymphadenopathy. Materials and Methods Prospectively collected prevaccination and postvaccination chest MRI scans were secondarily analyzed. Participants who underwent two doses of either the Pfizer-BioNTech or Moderna COVID-19 vaccine and chest MRI from June to October 2021 were included. Enlarged axillary lymph nodes were identified on postvaccination MRI scans compared with prevaccination scans. The lymph node diameter, signal intensity with T2-weighted imaging, and apparent diffusion coefficient (ADC) of the largest enlarged lymph nodes were measured. These values were compared between prevaccination and postvaccination MRI by using the Wilcoxon signed-rank test. Results Overall, 433 participants (mean age, 65 years ± 11 [SD]; 300 men and 133 women) were included. The prevalence of axillary lymphadenopathy in participants 1-14 days after vaccination was 65% (30 of 46). Participants with lymphadenopathy were younger than those without lymphadenopathy (P < .001). Female sex and the Moderna vaccine were predisposing factors (P = .005 and P = .003, respectively). Five or more enlarged lymph nodes were noted in 2% (eight of 433) of participants. Enlarged lymph nodes greater than or equal to 10 mm in the short axis were noted in 1% (four of 433) of participants. The median signal intensity relative to the muscle on T2-weighted images was 4.0; enlarged lymph nodes demonstrated a higher signal intensity (P = .002). The median ADC of enlarged lymph nodes after vaccination in 90 participants was 1.1 × 10^-3 mm²/sec (range, 0.6-2.0 × 10^-3 mm²/sec), thus ADC values remained normal. Conclusion Axillary lymphadenopathy after the second dose of the Pfizer-BioNTech or Moderna COVID-19 vaccines was frequent within 2 weeks after vaccination, was typically less than 10 mm in size, and had a normal apparent diffusion coefficient. © RSNA, 2022.

Radiomic Signature Based on Dynamic Contrast-Enhanced MRI for Evaluation of Axillary Lymph Node Metastasis in Breast Cancer

Background. To construct and validate a radiomic-based model for estimating axillary lymph node (ALN) metastasis in patients with breast cancer by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods. In this retrospective study, a radiomic-based model was established in a training cohort of 236 patients with breast cancer. Radiomic features were extracted from breast DCE-MRI scans. A method named the least absolute shrinkage and selection operator (LASSO) was applied to select radiomic features based on highly reproducible features. A radiomic signature was built by a support vector machine (SVM). Multivariate logistic regression analysis was adopted to establish a clinical characteristic-based model. The performance of models was analysed through discrimination ability and clinical benefits. Results. The radiomic signature comprised 6 features related to ALN metastasis and showed significant differences between the patients with ALN metastasis and without ALN metastasis ( $P<0.001$ ). The area under the curve (AUC) of the radiomic model was 0.990 and 0.858, respectively, in the training and validation sets. The clinical feature-based model, including MRI-reported status and palpability, performed slightly worse, with an AUC of 0.784 in the training cohort and 0.789 in the validation cohort. The radiomic signature was confirmed to provide more clinical benefits by decision curve analysis. Conclusions. The radiomic-based model developed in this study can successfully diagnose the status of lymph nodes in patients with breast cancer, which may reduce unnecessary invasive clinical operations.

Multiparametric MRI radiomics nomogram for predicting lymph-vascular space invasion in early-stage cervical cancer

OBJECTIVE

To develop a radiomics nomogram based on multiparametric MRI (mpMRI) to pre-operatively predict lymph-vascular space invasion (LVSI) in patients with early-stage cervical cancer.

METHODS

This retrospective study included 233 consecutive patients with Stage IB-IIB cervical cancer. According to the ratio of 2:1, 154 patients and 79 patients were randomly assigned to the primary and validation cohorts, respectively. Features with intraclass and interclass correlation coefficient (ICCs) greater than 0.75 were selected for radiomics features. The significant features for predicting LVSI were selected using the least absolute shrinkage and selection operator (LASSO) algorithm based on the primary cohort. The rad-score for each patient was constructed via a linear combination of selected features that were weighted by their respective coefficients. The radiomics nomogram was developed using multivariable logistic regression analysis by incorporating the rad-score and clinical risk factors.

RESULTS

A total of 19 radiomics features and 3 clinical risk factors were selected. The rad-score exhibited a good performance in discriminating LVSI with a C-index of 0.76 and 0.81 in the primary and validation cohorts, respectively. The radiomics nomogram also exhibited a good discriminating performance in two cohorts (C-index of 0.78 and 0.82). The calibration curve of the radiomics nomogram demonstrated no significant differences was found between prediction and observation outcomes for the probability of LVSI in two cohorts (p = 0.86 and 0.98, respectively). The decision curve analysis indicated that clinician and patients could benefit from the use of radiomics nomogram and rad-score.

CONCLUSION

The nomogram and rad-score could be used conveniently and individually to predict LVSI in patients with early-stage cervical cancer and facilitate the treatment decision for clinician and patients.

ADVANCES IN KNOWLEDGE

The nomogram could pre-operatively predict LVSI in early-stage cervical cancer.

Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer

Purpose

The goal of this study is to develop and validate a radiomics nomogram integrating the radiomics features from DCE-MRI and clinical factors for the preoperative diagnosis of axillary lymph node (ALN) metastasis in breast cancer patients.

Procedures

A total of 432 patients with breast cancer were enrolled in this retrospective study and divided into a training cohort (n = 296) and a validation cohort (n = 136). Radiomics features were extracted from the second phase of dynamic contrast enhanced (DCE) MRI images. The least absolute shrinkage and selection operator (LASSO) regression method was used to screen optimal features and construct a radiomics signature in the training cohort. Multivariable logistic regression analysis was used to establish a radiomics nomogram model based on the radiomics signature and clinical factors. The predictive performance of the nomogram was quantified with respect to discrimination and calibration, which was further evaluated in the independent validation cohort.

Results

Fourteen ALN metastasis-related features were selected to construct the radiomics signature, with an area under the curve (AUC) of 0.847 and 0.805 in the training and validation cohorts, respectively. The nomogram was established by incorporating the histological grade, multifocality, MRI report lymph node status and radiomics signature and showed good calibration and excellent performance for ALN detection (AUC of 0.907 and 0.874 in the training and validation cohorts, respectively). The decision curve, which demonstrated the radiomics nomogram, displayed promising clinical utility.

Conclusions

The radiomics nomogram can be used as a noninvasive and reliable tool to assist clinicians in accurately predicting ALN metastasis in breast cancer preoperatively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-022-00450-w.

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%.

Multiparametric Magnetic Resonance Imaging in Evaluation of Benign and Malignant Breast Masses with Pathological Correlation

Background

Dynamic contrast-enhanced (DCE) MRI sequences plays a vital role in diagnosing breast masses with high sensitivity and specificity as compared to other diagnostic modalities. The addition of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) values significantly improves diagnostic accuracy. This study aimed to study the breast masses on DCE-MRI, restricted diffusion on DWI, ADC values, and choline peak on spectroscopy in breast cancer diagnosis.

Material and methods

This study was a prospective observational study which involved subjects with breast lumps. Baseline data was collected from the patients along with pertinent clinical history and relevant laboratory investigations. MR mammography (MRM) was performed on a 1.5 Tesla MR Scanner (MAGNETOM® Avanto, Siemens AG, Munich Germany) using a dedicated double breast coil.

Results

Forty-one subjects were included with a total of 54 breast masses in them. The mean age of the study population was 47.1±14.7 years. From the MRI final diagnosis, the majority (53.70%) were diagnosed as malignant lesions and 46.30% as benign. Out of 20 lesions diagnosed as benign on histopathology, only five percent had ADC value <1.3 ×10⁻³mm²/s, and the majority (95%) had ADC value >1.3 ×10⁻³mm²/s. All 20 lesions were circumscribed, ovoid, or round in shape showing no restricted diffusion on DWI, with corresponding ADC value of >1.3×10⁻³mm²/s, homogeneous post-contrast enhancement, or with dark internal septations, type I kinetic enhancement curve, and they showed no choline peak on spectroscopy. Out of 34 malignant lesions diagnosed on histopathology, the majority (85.29%) displayed restricted diffusion on DWI and had an ADC value of <1.3×10⁻³mm²/s, most of them had spiculated margins, type II/ III kinetic curve with choline peak on spectroscopy.

Conclusion

Multiparametric MR mammography, which included DCE-MRM, DWI, ADC values, and spectroscopy, correlated well with the histopathological diagnosis of benign and malignant breast masses.

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.

MRI Findings Suggestive of Metastatic Axillary Lymph Nodes in Patients with Invasive Breast Cancer

Purpose

This study aimed to investigate the diagnostic performance of features suggestive of nodal metastasis on preoperative MRI in patients with invasive breast cancer.

Materials and Methods

We retrospectively reviewed the preoperative breast MRI of 192 consecutive patients with surgically proven invasive breast cancer. We analyzed MRI findings of axillary lymph nodes with regard to the size, long/short ratio, cortical thickness, shape and margin of the cortex, loss of hilum, asymmetry, signal intensity (SI) on T2-weighted images (T2WI), degree of enhancement in the early phase, and enhancement kinetics. Receiver operating characteristic (ROC) analysis, chi-square test, t test, and McNemar’s test were used for statistical analysis.

Results

Increased shorter diameter, uneven cortical shape, increased cortical thickness, loss of hilum, asymmetry, irregular cortical margin, and low SI on T2WI were significantly suggestive of metastasis. ROC analysis revealed the cutoff value for the shorter diameter and cortical thickness as 8.05 mm and 2.75 mm, respectively. Increased cortical thickness (> 2.75 mm) and uneven cortical shape showed significantly higher sensitivity than other findings in McNemar’s test. Irregular cortical margins showed the highest specificity (100%).

Conclusion

Cortical thickness > 2.75 mm and uneven cortical shape are more sensitive parameters than other findings, and an irregular cortical margin is the most specific parameter for predicting axillary metastasis in patients with invasive breast cancer.

Avoiding unnecessary intraoperative sentinel lymph node frozen section biopsy of patients with early breast cancer

Purpose

After the publication of the ACOSOG (American College of Surgeons Oncology Group) Z0011 trial, the rate of axillary lymph node dissection has reduced. Thus, the need for intraoperative frozen section biopsy of sentinel lymph nodes (SLNs) has become controversial. We identified patients for whom intraoperative SLN frozen section biopsy could be omitted and found that frozen section biopsy rate can be reduced.

Methods

We reviewed the records of patients with tumors ≤5 cm in diameter who underwent breast-conserving surgery between January 2013 and December 2019 at Seoul St. Mary’s Hospital. Clinicopathological and imaging characteristics were compared according to number of positive SLNs (0–2 SLNs positive vs. ≥3 SLNs positive).

Results

A total of 1,983 patients were included in this study. Thirty-two patients (1.6%) had at least 3 positive SLNs. Patients with ≥3 positive SLNs had significantly larger tumors and were more frequently high-grade tumors (P < 0.001 and P = 0.002, respectively). Identification of suspicious lymph nodes on imaging studies was also associated with the presence of ≥3 positive SLNs (hazard ratio, 11.54; 95% confidence interval, 4.42–30.10). All patients with none or only 1 suspicious lymph node on any imaging modality (n = 647, 32.6%) had 0–2 positive SLNs. Also, among patients with clinical T1-stage tumors and at least 2 suspicious lymph nodes on only 1 imaging modality (n = 514, 25.9%), only 2 cases had ≥3 positive SLNs.

Conclusion

We found that intraoperative SLN frozen biopsy could be omitted in patients using tumor size and axillary lymph node status on imaging modality.

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.

Extra-axillary nodal metastases in breast cancer: comparison of ultrasound, MRI, PET/CT, and CT

PURPOSE

To evaluate how ultrasound (US), MRI, PET/CT, and CT predict extra-axillary nodal metastases.

SUBJECTS AND METHODS

This IRB approved, retrospective study consisted of 124 suspicious supraclavicular and 88 internal mammary (IM) nodal cases with US and at least one additional cross-sectional examination (MRI, PET/CT or CT) from a total of 1472 invasive cancers with staging nodal US between January 2016-January 2019. Imaging findings were compared with the true node status, determined by fine needle aspirate (FNA) biopsy or evidence of response to chemotherapy on follow up imaging.

RESULTS

In the supraclavicular region, US had accuracy 98.2%, consisting of 97 true positives (TP), 27 false positives (FP), and 1348 true negative (TN). 93.5% of suspicious supraclavicular nodes had FNA for a PPV 78.2%. PET/CT had accuracy 88.6% (26 TP, 5 TN and 4 false negatives (FN)). CT exams had accuracy 61.7% (42 TP, 16 TN, 7 FP, and 29 FN). In the IM region, US had accuracy 93.2% (82 TP, 1 FP, 5 FN, and 1384 TN) but only 43.2% of suspicious IM nodes had FNA for a PPV 98.8%. MRI had accuracy 100.0% (all 47 TP). PET/CT exams had accuracy 96.8% (30 TP and 1FN). CT exams had accuracy 62.7% (36 TP, 1 TN, and 22 FN).

CONCLUSION

US/FNA has accuracy 98.2% and 93.2% in the supraclavicular and IM regions, however only 43.2% of suspicious IM nodes are directly sampled. In these cases, MRI or PET/CT can be used to problem solve and guide treatment decisions.

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

•

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

•

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

•

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.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Diagnostic performance of standard breast MR imaging compared to dedicated axillary MR imaging in the evaluation of axillary lymph node

BACKGROUND

Breast magnetic resonance (MR) imaging does not usually assess axillary lymph nodes -using dedicated axillary sequence. The additional utility of dedicated axillary sequence is poorly understood. We evaluated the diagnostic performance of dedicated axillary imaging sequence for evaluation of axillary lymph node.

METHODS

In this retrospective study from January 2018 to March 2018, 750 consecutive women underwent breast MR imaging. 263 patients were excluded, due to neoadjuvant chemotherapy (n = 235), incomplete histopathological information (n = 14) and follow-up loss (n = 14), 487 women were included. Two radiologists scored lymph node on confidence level scale from 0 (definitely benign) to 4 (definitely malignant), -using standard MR and dedicated axillary imaging sequences. Diagnostic performance parameters were compared and calculated correlation coefficient of quantitative features (largest dimension, cortical thickness, and the ratio of cortical thickness to largest dimension of lymph node).

RESULTS

68 (14.0%) were node-positive and 419 (86.0%) were node-negative. The sensitivity, specificity, positive, negative predictive values and accuracy were respectively, 66.2, 93.3, 61.6, 94.4, and 89.5% for dedicated axillary sequence and 64.7, 94.0, 63.8, 94.3, 89.9% for standard MR sequence The dedicated axillary and standard sequences s did not exhibit significant differences in detection of positive lymph nodes (AUC, 0.794 for standard and 0.798 for dedicated axillary sequence, P = 0.825). The cortical thickness appeared to be the most discriminative quantitative measurement using both axillary (AUC, 0.846) and standard sequences (AUC, 0.823), with high correlation coefficient (0.947).

CONCLUSION

Evaluation of axillary nodal status using standard breast MR imaging is comparable to dedicated axillary MR imaging.

Axillary lymph node dissection is not obligatory in breast cancer patients with biopsy-proven axillary lymph node metastasis

PURPOSE

The ACOSOG Z0011 trial demonstrated that axillary lymph node dissection (ALND) is unnecessary in select patients with cT1-2N0 tumors undergoing breast-conserving therapy with 1-2 positive sentinel lymph nodes (SLNs). However, patients with preoperatively confirmed ALN metastasis were not included and may be subjected to unnecessary ALND. The aim of this study is to identify patients who can be considered for ALND omission when the preoperative ALN biopsy results are positive.

METHODS

Breast cancer patients who underwent preoperative ALN biopsy and primary surgery were retrospectively reviewed. Among patients with positive ALN biopsy results, clinicopathological and imaging characteristics were compared according to LN disease burden (1-2 positive LNs vs. ≥ 3 positive LNs).

RESULTS

A total of 542 patients were included in the analysis. Among them, 225 (41.5%) patients had a preoperative positive ALN biopsy. More than 40% of the patients (n = 99, 44.0%) with a positive biopsy had only 1-2 positive ALNs. The association between nodal burden and imaging factors was strongest when ≥ 2 suspicious LNs were identified on PET/CT images (HR 8.795, 95% CI 4.756 to 13.262). More than one imaging modality showing ≥ 2 suspicious LNs was also strongly correlated with ≥ 3 positive ALNs (HR 5.148, 95% CI 2.881 to 9.200).

CONCLUSIONS

Nearly half of patients with a preoperative biopsy-proven ALN metastasis had only 1-2 positive LNs on ALND. Patients meeting ACOSOG Z0011 criteria with only one suspicious LN on PET/CT or those presenting with few abnormal ALNs on only one imaging modality appear appropriate for SLNB and consideration of ALND omission.

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.

Differentiation of Breast Lesions and Distinguishing Their Histological Subtypes Using Diffusion-Weighted Imaging and ADC Values

Diffusion-weighted imaging (DWI) has not been well explored in differentiation of malignant from benign breast lesions. The aims of this study were to examine the role of apparent diffusion coefficient (ADC) values in differentiation of malignant from benign tumors and distinguishing histological subtypes of malignant lesions, and to determine correlations between ADC values and breast tumors structure. This cohort-study included 174 female patients who underwent contrast-enhanced breast MR examination on a 3T scanner and were divided into two groups: patient group (114 patients with proven tumors) and control group (60 healthy patients). One-hundred-thirty-nine lesions (67 malignant and 72 benign) were detected and pathohistologically analyzed. Differences between variables were tested using chi-square test; correlations were determined using Pearson's correlation test. For determination of cut off values for diagnostic potential, Receiver Operating Characteristic curves were constructed. Statistical significance was set at p < 0.05. Mean ADC values were significantly lower in malignant compared to benign lesions (0.68 × 10^-3mm²/s vs. 1.12 × 10^-3mm²/s, p < 0.001). The cut off value of ADC for benign lesions was 0.792 × 10^-3mm²/s (sensitivity 98.6%, specificity 65.7%), and for malignant 0.993 × 10^-3mm²/s (98.5, 80.6%). There were no significant correlations between malignant lesion subtypes and ADC values. DWI is a clinically useful tool for differentiation of malignant from benign lesions based on mean ADC values. The cut off value for benign lesions was higher than reported recently, due to high amount of fibrosis in included benign lesions. Finally, ADC values might have implications in determination of the biological nature of the malignant lesions.

Optimal radiotherapy for patients with internal mammary lymph node metastasis from breast cancer

Background

This study aimed to determine the optimal radiotherapy (RT) regimen for patients with clinical metastasis to the internal mammary lymph node (cIMN+) from breast cancer.

Methods

We retrospectively reviewed the medical records of 84 patients with cIMN+ breast cancer treated with curative surgery, taxane-based chemotherapy, and postoperative RT between January 2009 and December 2014. Postoperative RT was administered to the whole breast or chest wall using 50 Gy in 2 Gy fractions. Boost RT to the internal mammary lymph node (IMN) was administered at the physician’s discretion. We categorized patients into two groups according to the IMN dose as follows: low-dose IMN RT (50.0–63.5 Gy) and high-dose IMN RT (63.6–70.4 Gy).

Results

After a median follow-up of 58 months (range, 12–111 months), IMN recurrence was observed in 2 patients (2.4%), and all IMN recurrences developed simultaneously with distant metastases. The 5-year locoregional recurrence-free survival, disease-free survival (DFS), and overall survival rates were 89.1, 72.0, and 81.2%, respectively. The triple-negative subtype, IMN size ≥1.0 cm, old age, and low-dose IMN were significantly associated with poor DFS. Among the patients with IMN size ≥1.0 cm, the 5-year DFS was significantly higher in those treated with high-dose IMN RT than in those treated with low-dose IMN RT (69.3% vs. 33.3%, p = 0.019).

Conclusions

IMN RT without IMN dissection resulted in favorable outcomes in cIMN+ breast cancer. For patients with a large IMN, a higher IMN radiation dose might be needed for disease control.

Multiparametric MRI-Based Radiomics Nomogram for Predicting Lymph Node Metastasis in Early-Stage Cervical Cancer

BACKGROUND

Lymph node metastasis (LNM) is a critical risk factor affecting treatment strategy and prognosis in patients with early-stage cervical cancer.

PURPOSE

To establish a multiparametric MRI (mpMRI)-based radiomics nomogram for preoperatively predicting LNM status.

STUDY TYPE

Retrospective.

POPULATION

Among 233 consecutive patients, 155 patients were randomly allocated to the primary cohort and 78 patients to the validation cohort.

FIELD STRENGTH

Radiomic features were extracted from a 1.5T mpMRI scan (T₁ -weighted imaging [T₁ WI], fat-saturated T₂ -weighted imaging [FS-T₂ WI], contrast-enhanced [CE], diffusion-weighted imaging [DWI], and apparent diffusion coefficient [ADC] maps).

ASSESSMENT

The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. The area under the receiver operating characteristics curve (ROC AUC), accuracy, sensitivity, and specificity were also calculated.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) method was used for dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the radiomics nomogram. An independent sample t-test and chi-squared test were used to compare the differences in continuous and categorical variables, respectively.

RESULTS

The radiomic signature allowed a good discrimination between the LNM and non-LNM groups, with a C-index of 0.856 (95% confidence interval [CI], 0.794-0.918) in the primary cohort and 0.883 (95% CI, 0.809-0.957) in the validation cohort. Additionally, the radiomics nomogram also had a good discriminating performance and yielded good calibration both in the primary and validation cohorts (C-index, 0.882 [95% CI, 0.827-0.937], C-index, 0.893 [95% CI, 0.822-0.964], respectively). Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.

DATA CONCLUSION

A radiomics nomogram was developed by incorporating the radiomics signature with the MRI-reported LN status and FIGO stage. This nomogram might be used to facilitate the individualized prediction of LNM in patients with early-stage cervical cancer.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:885-896.

Measurement of apparent diffusion coefficient in discrimination of benign and malignant axillary lymph nodes

Purpose

We aimed to determine the contribution of the apparent diffusion coefficient (ADC) value in the detection of axillary lymph node metastasis.

Material and methods

Breast magnetic resonance of 58 patients, performed in the radiology clinic of our hospital between 2015 and 2017 were examined retrospectively, and 43 lymph nodes in 43 patients were included in the study. They were evaluated morphologically on T1W and T2W sequences, and the lymph nodes showing rounded shape, focal or diffuse cortical thickness of more than 3 mm, and partial or total effacement of fatty hilum were included in the study. Subsequently, their ADC values were measured.

Results

There were 43 lymph nodes, 20 of which were malignant and 23 of which were benign. While the mean ADC value of malignant axillary lymph nodes was 0.749 10^-3 mm²/s (0.48-1.342), it was 0.982 10^-3 mm²/s (0.552-1.986) for benign lymph nodes. When the ADC cut-off value was taken as ≤ 0.753 × 10^-3 mm²/s, its discrimination power between benign and malignant axillary lymph nodes was as follows: sensitivity - 60%; specificity - 91.3%; accuracy - 76.7%; positive predictive value - 85.7%; and negative predictive value - 72.4%.

Conclusions

There was no significant difference between mean ADC value of 12 lymphadenopathies (LAP) associated with inflammatory breast diseases (granulomatous mastitis and acute suppurative mastitis) and mean ADC value of metastatic lymph nodes. However, the ADC value of lymph nodes showing thickened cortex due to systemic inflammatory diseases was over 1, and there was a statistically significant difference when compared with metastatic lymph nodes.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

KEY POINTS

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

Heterogeneous Enhancement Pattern in DCE-MRI Reveals the Morphology of Normal Lymph Nodes: An Experimental Study

Purpose

To investigate the heterogeneous enhancement pattern in normal lymph nodes of healthy mice by different albumin-binding contrast agents.

Methods

The enhancement of normal lymph nodes was assessed in mice by dynamic contrast-enhanced MRI (DCE-MRI) after the administration of two contrast agents characterized by different albumin-binding properties: gadopentetate dimeglumine (Gd-DTPA) and gadobenate dimeglumine (Gd-BOPTA). To take into account potential heterogeneities of the contrast uptake in the lymph nodes, k-means cluster analysis was performed on DCE-MRI data. Cluster spatial distribution was visually assessed. Statistical comparison among clusters and contrast agents was performed on semiquantitative parameters (AUC, wash-in rate, and wash-out rate) and on the relative size of the segmented clusters.

Results

Cluster analysis of DCE-MRI data revealed at least two main clusters, localized in the outer portion and in the inner portion of each lymph node. With both contrast agents, AUC (p < 0.01) and wash-in (p < 0.05) rates were greater in the inner cluster, which also showed a steeper wash-out rate than the outer cluster (Gd-BOPTA, p < 0.01; Gd-DTPA, p=0.056). The size of the outer cluster was greater than that of the inner cluster by Gd-DTPA (p < 0.05) and Gd-BOPTA (p < 0.01). The enhancement pattern of Gd-DTPA was not significantly different from the enhancement pattern of Gd-BOPTA.

Conclusion

DCE-MRI in normal lymph nodes shows a characteristic heterogeneous pattern, discriminating the periphery and the central portion of the lymph nodes. Such a pattern deserves to be investigated as a diagnostic marker for lymph node staging.

Preoperative axillary nodal staging with ultrasound and magnetic resonance imaging: predictive values of quantitative and semantic features

OBJECTIVE:

Although axillary imaging has recently received renewed interest for preoperative staging in tandem with the evolving minimally invasive surgical approaches, axillary imaging is limited by the lack of standardization in the interpretation. We aimed to classify imaging features in ultrasound and MRI into quantitative and semantic features and evaluate predictive value of each feature for predicting nodal metastases.

METHODS:

A total of 316 breast cancers patients who underwent ultrasound and MRI prior to axillary surgery were included. Retrospective reviews of our breastimaging database were done for the quantitative features [cortical thickness (CT) and CT-derived parameters, long diameter (LD), short diameter (SD), and LD/SD ratio] and semantic features (eccentricity, loss of fatty hilum, and irregularity) of the axillary lymph node in images. Odd ratios (ORs) for each imaging feature were calculated with adjustment for clinicopathological characteristics significantly associated with nodal metastases.

RESULTS:

All CT-derived parameters were significantly associated with nodal metastases in both ultrasound and MRI (OR, 3.3-3.5 for ultrasound and 3.3-3.9 for MRI, respectively; Ps < .05). For the ultrasound, LD/SD ratio (OR, 2.1), eccentricity (OR, 2.4), and fatty hilum loss (OR, 27.2) were significantly associated with nodal metastases (Ps < .05). For the MRI, SD (OR, 2.1) and eccentricity (OR, 3.0) were significantly associated with nodal metastases (Ps < .05).

CONCLUSION:

Among the quantitative features, all CT-derived parameters can be used for predicting nodal metastases. Significant predictors of semantic features were heterogeneous between ultrasound and MRI.

ADVANCES IN KNOWLEDGE:

(1) Imaging features of ultrasound and MRI for preoperative axillary nodal staging can be classified into quantitative and semantic features. (2) Predictive values of each imaging features are heterogeneous for predicting nodal metastases.

Axillary lymph node metastasis detection by magnetic resonance imaging in patients with breast cancer: A meta-analysis

BACKGROUND

The study was conducted to evaluate the diagnostic performance of magnetic resonance imaging (MRI) for the detection of axillary lymph node metastasis in patients with breast cancer.

METHODS

PubMed, Medline, Web of Science, Cochrane Embase, Chinese Biomedical Literature, and China National Knowledge Infrastructure databases were searched for open published studies relevant to the use of MRI for the detection of axillary lymph node metastasis in breast cancer patients. The pooled diagnostic sensitivity, specificity, and the symmetric receiver operating characteristic (SROC) curve was calculated by combining the individual data extracted from 26 included studies.

RESULTS

The pooled diagnostic sensitivity and specificity of MRI to detect axillary lymph node metastasis in patients with breast cancer were 0.77 (95% confidence interval [CI] 0.75-0.80) and 0.90 (95% CI 0.89-0.91), respectively. The pooled positive and negative likelihood ratios were 7.67 (95% CI 5.09-11.53) and 0.23 (95% CI 0.17-0.32), respectively, by random effect method. The area under the SROC curve was 0.93 for MRI to detect axillary lymph node metastasis in breast cancer patients.

CONCLUSION

With high sensitivity, specificity, and area under the curve, MRI is an effective method to differentiate metastatic axillary lymph node in breast cancer patients, which can provide useful information for surgical procedure selection.

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.

Developments in Breast Imaging: Update on New and Evolving MR Imaging and Molecular Imaging Techniques

This article reviews new developments in breast imaging. There is growing interest in creating a shorter, less expensive MR protocol with broader applicability. There is an increasing focus on and consideration for the additive impact that functional analysis of breast pathology have on identifying and characterizing lesions. These developments apply to MR imaging and molecular imaging. This article reviews evolving breast imaging techniques with attention to strengths, weaknesses, and applications of these approaches. We aim to give the reader familiarity with the state of current developments in the field and to increase awareness of what to expect in breast imaging.

Magnetic resonance molecular imaging of metastatic breast cancer by targeting extradomain-B fibronectin in the tumor microenvironment

PURPOSE

Non-invasive early accurate detection of malignant breast cancer is paramount to the clinical management of the life-threatening disease. Here, we aim to test a small peptide targeted MRI contrast agent, ZD2-Gd(HP-DO3A), specific to an oncoprotein, extradomain-B fibronectin (EDB-FN), in the tumor microenvironment for MR molecular imaging of breast cancer.

METHOD

EDB-FN expression in 4T1 and MDA-MB-231 cancers was analyzed with quantitative real-time PCR and western blot. Primary and metastatic triple negative breast cancer mouse models were developed using 4T1 and MDA-MB-231 cells. Contrast-enhanced MRI was carried out to evaluate the use of ZD2-Gd(HP-DO3A) in detecting 4T1 and MDA-MB-231 primary and metastatic tumors.

RESULTS

EDB-FN was abundantly expressed in the extracellular matrix (ECM) of both the primary and metastatic TNBC tumors. In T₁ -weighted MRI, ZD2-Gd(HP-DO3A) generated superior contrast enhancement in primary TNBC tumors than a nonspecific clinical agent Gd(HP-DO3A), during 30 min after contrast injection. ZD2-Gd(HP-DO3A) also produced a significant increase in contrast-to-noise ratio (CNR) of TNBC metastases, enabling sensitive localization and delineation of metastases that occulted in non-contrast-enhanced or Gd(HP-DO3A)-enhanced MRI.

CONCLUSIONS

These findings potentiate the use of ZD2-Gd(HP-DO3A) for MR molecular imaging of malignant breast cancers to improve the healthcare of breast cancer patients. Magn Reson Med 79:3135-3143, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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.

Breast Magnetic Resonance Imaging for Assessment of Internal Mammary Lymph Node Status in Breast Cancer

Purpose

The purpose of this study was to assess magnetic resonance imaging (MRI) features of malignant internal mammary lymph nodes (IMLNs) and benign IMLNs in breast cancer patients.

Methods

From 2009 to 2014, the records of 85 patients with IMLNs were archived using MRI report data; 26 patients with small size (long axis diameter <5 mm) nodes were subsequently excluded. The current study evaluated internal mammary lymph nodes in 59 patients who underwent breast MRI for breast cancer staging and for posttherapy follow-up. All MRI findings were retrospectively evaluated. Malignancy was determined based on pathologic examination and positron emission tomography computed tomography findings. Independent t-tests, Mann-Whitney U tests, chi-square tests, and receiver operating characteristics (ROC) curve analysis were used.

Results

Among MRI features, there were statistically significant differences between benign and malignant IMLN groups, in short axis length (3.6±1.3 vs. 8.2±2.9 mm, respectively), long axis length (8.1±2.4 vs. 14.5±4.8 mm, respectively), short/long axis ratio (0.45±0.10 vs. 0.59±0.17, respectively), absent fatty hilum (mean, 0% vs. 95%, respectively), and restricted diffusion (15.8% vs. 85.0%, respectively) (p<0.050). Multiplicity and location of intercostal spaces was not different between the two groups. Short axis length was the most discriminative variable for predicting metastatic nodes (area under the ROC curve, 0.951; threshold, 4 mm; sensitivity, 92.5%; specificity, 84.2%).

Conclusion

Conventional MRI and diffusion-weighted MRI are helpful to detect metastasis of internal mammary lymph nodes in breast cancer.

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.

MRI sequences for the detection of individual lymph nodes in regional breast radiotherapy planning

OBJECTIVE

In regional radiotherapy (RT) for patients with breast cancer, lymph node (LN) targets are delineated on CT, defined by anatomical boundaries. By identifying individual LNs, MRI-based delineations may reduce target volumes and thereby toxicity. We optimized MRI sequences for this purpose. Our aim was to evaluate the techniques for LN delineation in RT planning.

METHODS

Supine MRI was explored at 1.5 T in RT position (arms in abduction). 5 MRI techniques were optimized in 10 and evaluated in 12 healthy female volunteers. The scans included one T1 weighted (T1w), three T2 weighted (T2w) and a diffusion-weighted imaging (DWI) technique. Quantitative evaluation was performed by scoring LN numbers per volunteer and per scan. Qualitatively, scans were assessed on seven aspects, including LN contrast, anatomical information and insensitivity to motion during acquisition.

RESULTS

Two T2w fast spin-echo (FSE) methods showed the highest LN numbers (median 24 axillary), high contrast, excellent fat suppression and relative insensitivity to motion during acquisition. A third T2w sequence and DWI showed significantly fewer LNs (14 and 10) and proved unsuitable due to motion sensitivity and geometrical uncertainties. T1w MRI showed an intermediate number of LNs (17), provided valuable anatomical information, but lacked LN contrast.

CONCLUSION

Explicit LN imaging was achieved, in supine RT position, using MRI. Two T2w FSE techniques had the highest detection rates and were motion insensitive. T1w MRI showed anatomical information. MRI enables direct delineation of individual LNs.

ADVANCES IN KNOWLEDGE

Our optimized MRI scans enable accurate target definition in MRI-guided regional breast RT and development of personalized treatments.

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.