Sensitive and Specific Detection of Breast Cancer Lymph Node Metastasis Through Dual-Modality Magnetic Particle Imaging and Fluorescence Molecular Imaging: a Preclinical Evaluation

2021 ◽  
Author(s):  
Guorong Wang ◽  
Guangyuan Shi ◽  
Yu Tian ◽  
Lingyan Kong ◽  
Ning Ding ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Magnetic Particle ◽  
Cancer Targeting ◽  
Node Metastasis ◽  
Magnetic Particle Imaging ◽  
Particle Imaging ◽  
Fluorescence Molecular Imaging ◽  
Biosafety Evaluation

Abstract Purpose: A sensitive and specific imaging method to detect metastatic cancer cells in lymph nodes (LNs) to detect the early-stage breast cancer is urgently needed. The purpose of this study was to investigate a novel breast cancer-targeting and tumour microenvironment ATP-responsive superparamagnetic iron oxide (SPIOs) imaging probe that was developed to detect lymph node metastasis (LNMs) through fluorescence molecular imaging (FMI) and magnetic particle imaging (MPI). The imaging nanoprobe comprised of SPIOs conjugated with breast cancer-targeting peptides (CREKA) and an ATP-responsive DNA aptamer (dsDNA-Cy5.5), abbreviated as SPIOs@A-T. Methods: SPIOs@A-T was synthesised and characterized for its imaging properties, targeting ability and toxicity in vitro. Mice with metastatic lymph node (MLN) of breast cancer were established to evaluate the FMI and MPI imaging strategy in vivo. Healthy mice with normal lymph node (NLN) were used as control group. Histological examination and biosafety evaluation were performed for further assessment. Results: After injection with SPIO@A-T, the obvious high fluorescent intensity and MPI signal were observed in MLN group than those in NLN group. MPI could also complement the limitation of imaging depth from FMI, thus could detect MLN more sensitively. The combination of the imaging strengths of FMI and MPI ensured the detection of breast cancer metastases with high sensitivity and specificity, thereby facilitating the precision differentiation of malignant from benign LNs. Besides, the biosafety evaluation results showed SPIO@A-T had good biocompatibility. Conclusion: Due to the superior properties of tumour-targeting, detection specificity, and biosafety, the SPIOs@A-T imaging probe in combination with FMI and MPI can provide a promising novel method for the early and precise detection of LNMs in clinical practice.

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

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 757
Author(s):  
Sanaz Samiei ◽  
Renée W. Y. Granzier ◽  
Abdalla Ibrahim ◽  
Sergey Primakov ◽  
Marc B. I. Lobbes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lymph Nodes ◽  
Axillary Lymph Node ◽  
Axillary Lymph Node Metastasis ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Mr Images ◽  
Node Metastasis

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

Axillary schwannoma mimicking a breast cancer lymph node metastasis

2021 ◽  
Author(s):  
Hanae Ramdani ◽  
Siham El Haddad ◽  
Latifa Chat ◽  
Abdelilah Souadka ◽  
Nazik Allali
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Node Metastasis
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