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Wang S, Wang D, Wen X, Xu X, Liu D, Tian J. Construction and validation of a nomogram prediction model for axillary lymph node metastasis of cT1 invasive breast cancer. Eur J Cancer Prev 2024; 33:309-320. [PMID: 37997911 DOI: 10.1097/cej.0000000000000860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
OBJECTIVE Based on the ultrasonic characteristics of the breast mass and axillary lymph nodes as well as the clinicopathological information, a model was developed for predicting axillary lymph node metastasis in cT1 breast cancer, and relevant features associated with axillary lymph node metastasis were identified. METHODS Our retrospective study included 808 patients with cT1 invasive breast cancer treated at the Second Affiliated Hospital and the Cancer Hospital Affiliated with Harbin Medical University from February 2012 to August 2021 (250 cases in the positive axillary lymph node group and 558 cases in the negative axillary lymph node group). We allocated 564 cases to the training set and 244 cases to the verification set. R software was used to compare clinicopathological data and ultrasonic features between the two groups. Based on the results of multivariate logistic regression analysis, a nomogram prediction model was developed and verified for axillary lymph node metastasis of cT1 breast cancer. RESULTS Univariate and multivariate logistic regression analysis indicated that palpable lymph nodes ( P = 0.003), tumor location ( P = 0.010), marginal contour ( P < 0.001), microcalcification ( P = 0.010), surrounding tissue invasion ( P = 0.046), ultrasonic detection of lymph nodes ( P = 0.001), cortical thickness ( P < 0.001) and E-cadherin ( P < 0.001) are independently associated with axillary lymph node metastasis. Using these features, a nomogram was developed for axillary lymph node metastasis. The training set had an area under the curve of 0.869, while the validation set had an area under the curve of 0.820. Based on the calibration curve, the model predicted axillary lymph node metastases were in good agreement with reality ( P > 0.05). Nomogram's net benefit was good based on decision curve analysis. CONCLUSION The nomogram developed in this study has a high negative predictive value for axillary lymph node metastasis in invasive cT1 breast c ancer. Patients with no axillary lymph node metastases can be accurately screened using this nomogram, potentially allowing this group of patients to avoid invasive surgery.
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Affiliation(s)
- Shuqi Wang
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang
| | - Dongmo Wang
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang
| | - Xin Wen
- The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai
| | - Xiangli Xu
- The second hospital of Harbin, Harbin, Heilongjiang, China
| | - Dongmei Liu
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang
| | - Jiawei Tian
- Department of Ultrasound, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang
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Zeng F, Cai W, Lin L, Chen C, Tang X, Yang Z, Chen Y, Chen L, Chen L, Li J, Chen S, Wang C, Xue Y. Development of a Preoperative Prediction Model Based on Spectral CT to Evaluate Axillary Lymph Node With Macrometastases in Clinical T1/2N0 Invasive Breast Cancer. Clin Breast Cancer 2024:S1526-8209(24)00174-5. [PMID: 39030158 DOI: 10.1016/j.clbc.2024.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 06/07/2024] [Accepted: 06/13/2024] [Indexed: 07/21/2024]
Abstract
OBJECTIVES To develop a prediction model based on spectral computed tomography (CT) to evaluate axillary lymph node (ALN) with macrometastases in clinical T1/2N0 invasive breast cancer. METHODS A total of 217 clinical T1/2N0 invasive breast cancer patients who underwent spectral CT scans were retrospectively enrolled and categorized into a training cohort (n = 151) and validation cohort (n = 66). These patients were classified into ALN nonmacrometastases (stage pN0 or pN0 [i+] or pN1mi) and ALN macrometastases (stage pN1-3) subgroups. The morphologic criteria and quantitative spectral CT parameters of the most suspicious ALN were measured and compared. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the models. RESULTS The combined arterial-venous phase spectral CT model yielded the best diagnostic performance in discrimination of ALN nonmacrometastases and ALN macrometastases with the highest AUC (0.963 in the training cohort and 0.945 in validation cohorts). Among single phase spectral CT models, the venous phase spectral CT model showed the best performance (AUC = 0.960 in the training cohort and 0.940 in validation cohorts). There was no significant difference in AUCs among the 3 models (DeLong test, P > .05 for each comparison). CONCLUSION A Lasso-logistic model that combined morphologic features and quantitative spectral CT parameters based on contrast-enhanced spectral imaging potentially be used as a noninvasive tool for individual preoperative prediction of ALN status in clinical T1/2N0 invasive breast cancers.
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Affiliation(s)
- Fang Zeng
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Weifeng Cai
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Lin Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Cong Chen
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Xiaoxue Tang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Zheting Yang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Yilin Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Lihong Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Lili Chen
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Jing Li
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China
| | - Suping Chen
- GE Healthcare, Changsha, Hunan Province, China
| | - Chuang Wang
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Department of General Surgery, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Breast Cancer Institute, Fujian Medical University, Fuzhou, Fujian Province, China.
| | - Yunjing Xue
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China; Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors, Fujian Medical University, Fuzhou, Fujian Province, China.
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Li Z, Ma Q, Gao Y, Qu M, Li J, Lei J. Diagnostic performance of MRI for assessing axillary lymph node status after neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis. Eur Radiol 2024; 34:930-942. [PMID: 37615764 DOI: 10.1007/s00330-023-10155-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 06/09/2023] [Accepted: 07/08/2023] [Indexed: 08/25/2023]
Abstract
OBJECTIVE This systematic review examined the diagnostic performance of magnetic resonance imaging (MRI) for assessing axillary lymph node status (ALNS) after neoadjuvant chemotherapy (NAC) in breast cancer patients. METHODS We searched PubMed, Embase, Cochrane Library, and Web of Science to identify relevant studies and used the QUADAS-2 tool to assess methodological quality of eligible studies. We used STATA version 12.0 to perform data pooling, heterogeneity testing, subgroup analysis, and sensitivity analysis. RESULTS For the 21 enrolled studies, including 2875 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were respectively 0.63 (95% CI: 0.53-0.72), 0.75 (95% CI: 0.68-0.81), 2.52 (95% CI: 1.98-3.19), 0.50 (95% CI: 0.39-0.63), and 5.08 (95% CI: 3.38-7.63). The AUC was 0.76 (95% CI: 0.72-0.79). I2 values of sensitivity (I2 = 94.41%) and specificity (I2 = 88.97%) were both > 50%. For the initial positive ALN patients, the pooled sensitivity and specificity were 0.64 (95% CI: 0.53-0.75) and 0.74 (95% CI: 0.64-0.82), respectively. Sensitivity analyses by focusing on studies with MRI performed post-NAC, studies using DCE-MRI, or studies with low risk of bias showed similar results to the primary analyses. CONCLUSION MRI may have suboptimal diagnostic value in assessing ALNS after NAC for breast cancer patients. Due to the inconsistency of NAC regimens, the variability of axillary surgery, and the lack of time interval between MRI and surgery, further studies are needed to confirm our findings. CLINICAL RELEVANCE STATEMENT Our study provided the diagnostic value of MRI in assessing axillary lymph node status after neoadjuvant chemotherapy for breast cancer patients. KEY POINTS • MRI may have suboptimal diagnostic value in assessing axillary lymph node status after NAC for general breast cancer patients. • The initial axillary lymph node status has little impact on the diagnostic efficacy of MRI. • The substantial heterogeneity among studies highlights the need for further studies to provide more high-quality evidence in this field.
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Affiliation(s)
- Zhifan Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Qinqin Ma
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - Mengmeng Qu
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Jinkui Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
- Department of Radiology, the First Hospital of Lanzhou University, Chengguan District, No. 1 Donggang West Road, Lanzhou, 730000, Gansu Province, China
| | - Junqiang Lei
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China.
- Department of Radiology, the First Hospital of Lanzhou University, Chengguan District, No. 1 Donggang West Road, Lanzhou, 730000, Gansu Province, China.
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Chen ST, Lai HW, Chang JHM, Liao CY, Wen TC, Wu WP, Wu HK, Lin YJ, Chang YJ, Chen ST, Chen DR, Huang HI, Hung CL. Diagnostic accuracy of pre-operative breast magnetic resonance imaging (MRI) in predicting axillary lymph node metastasis: variations in intrinsic subtypes, and strategy to improve negative predictive value-an analysis of 2473 invasive breast cancer patients. Breast Cancer 2023; 30:976-985. [PMID: 37500823 PMCID: PMC10587219 DOI: 10.1007/s12282-023-01488-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 07/18/2023] [Indexed: 07/29/2023]
Abstract
BACKGROUND The value and utility of axillary lymph node (ALN) evaluation with MRI in breast cancer were not clear for various intrinsic subtypes. The aim of the current study is to test the potential of combining breast MRI and clinicopathologic factors to identify low-risk groups of ALN metastasis and improve diagnostic performance. MATERIAL AND METHODS Patients with primary operable invasive breast cancer with pre-operative breast MRI and post-operative pathologic reports were retrospectively collected from January 2009 to December 2021 in a single institute. The concordance of MRI and pathology of ALN status were determined, and also analyzed in different intrinsic subtypes. A stepwise strategy was designed to improve MRI-negative predictive value (NPV) on ALN metastasis. RESULTS 2473 patients were enrolled. The diagnostic performance of MRI in detecting metastatic ALN was significantly different between intrinsic subtypes (p = 0.007). Multivariate analysis identified tumor size and histologic type as independent predictive factors of ALN metastases. Patients with HER-2 (MRI tumor size ≤ 2 cm), or TNBC (MRI tumor size ≤ 2 cm) were found to have MRI-ALN-NPV higher than 90%, and these false cases were limited to low axillary tumor burden. CONCLUSION The diagnostic performance of MRI to predict ALN metastasis varied according to the intrinsic subtype. Combined pre-operative clinicopathologic factors and intrinsic subtypes may increase ALN MRI NPV, and further identify some groups of patients with low risks of ALN metastasis, high NPV, and low burdens of axillary disease even in false-negative cases.
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Affiliation(s)
- Shu-Tian Chen
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital - Chiayi Branch, Chiayi, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, No.155, Sec. 2, Linong St., Beitou Dist., Taipei, 11221, Taiwan
| | - Hung-Wen Lai
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Endoscopy and Oncoplastic Breast Surgery Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan.
- Division of General Surgery, Changhua Christian Hospital, Changhua, Taiwan.
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua, Taiwan.
- Tumor Center, Changhua Christian Hospital, Changhua, Taiwan.
- Kaohsiung Medical University, Kaohsiung, Taiwan.
- Division of Breast Surgery, Yuanlin Christian Hospital, Yuanlin, Taiwan.
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan.
| | | | - Chiung-Ying Liao
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan
| | - Tzu-Cheng Wen
- Endoscopy and Oncoplastic Breast Surgery Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan
| | - Wen-Pei Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan
- Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Hwa-Koon Wu
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan
| | - Ying-Jen Lin
- Tumor Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Yu-Jun Chang
- Big Data Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Shou-Tung Chen
- Division of General Surgery, Changhua Christian Hospital, Changhua, Taiwan
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Dar-Ren Chen
- Division of General Surgery, Changhua Christian Hospital, Changhua, Taiwan
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Hsin-I Huang
- Department of Information Management, National Sun Yat-Sen University, Kaohsiung, Taiwan
- We-Sing Breast Hospital, Kaohsiung, Taiwan
| | - Che-Lun Hung
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, No.155, Sec. 2, Linong St., Beitou Dist., Taipei, 11221, Taiwan.
- Department of Computer Science and Communication Engineering, Providence University, Taichung, Taiwan.
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Adam R, Dell'Aquila K, Hodges L, Maldjian T, Duong TQ. Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review. Breast Cancer Res 2023; 25:87. [PMID: 37488621 PMCID: PMC10367400 DOI: 10.1186/s13058-023-01687-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 07/11/2023] [Indexed: 07/26/2023] Open
Abstract
Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.
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Affiliation(s)
- Richard Adam
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Kevin Dell'Aquila
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Laura Hodges
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Takouhie Maldjian
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA
| | - Tim Q Duong
- Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
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Zhang X, Liu M, Ren W, Sun J, Wang K, Xi X, Zhang G. Predicting of axillary lymph node metastasis in invasive breast cancer using multiparametric MRI dataset based on CNN model. Front Oncol 2022; 12:1069733. [PMID: 36561533 PMCID: PMC9763602 DOI: 10.3389/fonc.2022.1069733] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/15/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose To develop a multiparametric MRI model for predicting axillary lymph node metastasis in invasive breast cancer. Methods Clinical data and T2WI, DWI, and DCE-MRI images of 252 patients with invasive breast cancer were retrospectively analyzed and divided into the axillary lymph node metastasis (ALNM) group and non-ALNM group using biopsy results as a reference standard. The regions of interest (ROI) in T2WI, DWI, and DCE-MRI images were segmented using MATLAB software, and the ROI was unified into 224 × 224 sizes, followed by image normalization as input to T2WI, DWI, and DCE-MRI models, all of which were based on ResNet 50 networks. The idea of a weighted voting method in ensemble learning was employed, and then T2WI, DWI, and DCE-MRI models were used as the base models to construct a multiparametric MRI model. The entire dataset was randomly divided into training sets and testing sets (the training set 202 cases, including 78 ALNM, 124 non-ALNM; the testing set 50 cases, including 20 ALNM, 30 non-ALNM). Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of models were calculated. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to evaluate the diagnostic performance of each model for axillary lymph node metastasis, and the DeLong test was performed, P< 0.05 statistically significant. Results For the assessment of axillary lymph node status in invasive breast cancer on the test set, multiparametric MRI models yielded an AUC of 0.913 (95% CI, 0.799-0.974); T2WI-based model yielded an AUC of 0.908 (95% CI, 0.792-0.971); DWI-based model achieved an AUC of 0.702 (95% CI, 0.556-0.823); and the AUC of the DCE-MRI-based model was 0.572 (95% CI, 0.424-0.711). The improvement in the diagnostic performance of the multiparametric MRI model compared with the DWI and DCE-MRI-based models were significant (P< 0.01 for both). However, the increase was not meaningful compared with the T2WI-based model (P = 0.917). Conclusion Multiparametric MRI image analysis based on an ensemble CNN model with deep learning is of practical application and extension for preoperative prediction of axillary lymph node metastasis in invasive breast cancer.
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Affiliation(s)
- Xiaodong Zhang
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Menghan Liu
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Wanqing Ren
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Jingxiang Sun
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,Postgraduate Department, Shandong First Medical University (Shandong Academy of Medical Sciences), Jinan, China
| | - Kesong Wang
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Xiaoming Xi
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, China
| | - Guang Zhang
- Department of Health Management, The First Affiliated Hospital of Shandong First Medical University, Jinan, China,*Correspondence: Guang Zhang,
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Zahran AMH, Maarouf RA, Hussein A, Sheha AS. The role of diffusion-weighted MR imaging in discrimination between benign and malignant axillary lymph nodes in breast cancer patients. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2022. [DOI: 10.1186/s43055-022-00801-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Noninvasive preoperative evaluation of axillary lymph nodes proved to have a significant role not only on the protocol of treatment of breast cancer but also impact the whole life of the patient. Complications of lymph node biopsy or axillary clearance increase the need for noninvasive reliable diagnostic tool. We aimed in the current study to evaluate the role of diffusion-weighted magnetic resonance imaging (DW-MRI) and apparent diffusion coefficient (ADC) in discrimination between benign and malignant axillary lymph nodes. We included 44 suspicious lymph nodes from 29 patients. Qualitative DW-MRI was analyzed into restricted or not; ADC maps and cut-off value were calculated, and they were correlated with histopathological results, which were the gold standard tool of the current study.
Results
The cut-off value of ADC-differentiated between malignant and benign lymph nodes was 0.89 × 10−3 mm2/s. The statistical indices including the sensitivity, specificity, PPV, NPV and accuracy were 89.66%, 86.67%, 93.9, 81.2% and 87.8%, respectively, with P value < 0.001, while DW-MRI results were classified into restricted or not restricted with qualitative statistical indices of 96.6%, 80%, 90.3%, 92.3% and 90.9% for sensitivity, specificity, PPV, NPV and accuracy, respectively, with P value < 0.001.
Conclusion
DW-MRI and ADC both have significant role in discrimination between benign and malignant axillary lymph nodes increasing the accuracy of MRI examination in breast cancer patients.
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Kawaguchi S, Tamura N, Tanaka K, Kobayashi Y, Sato J, Kinowaki K, Shiiba M, Ishihara M, Kawabata H. Clinical prediction model based on 18F-FDG PET/CT plus contrast-enhanced MRI for axillary lymph node macrometastasis. Front Oncol 2022; 12:989650. [PMID: 36176414 PMCID: PMC9513385 DOI: 10.3389/fonc.2022.989650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 08/23/2022] [Indexed: 11/15/2022] Open
Abstract
Purpose Positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) are useful for detecting axillary lymph node (ALN) metastasis in invasive ductal breast cancer (IDC); however, there is limited clinical evidence to demonstrate the effectiveness of the combination of PET/CT plus MRI. Further axillary surgery is not recommended against ALN micrometastasis (lesion ≤2 mm) seen in sentinel lymph nodes, especially for patients who received proper adjuvant therapy. We aimed to evaluate the efficacy of a prediction model based on PET/CT plus MRI for ALN macrometastasis (lesion >2 mm) and explore the possibility of risk stratification of patients using the preoperative PET/CT plus MRI and biopsy findings. Materials and methods We retrospectively investigated 361 female patients (370 axillae; mean age, 56 years ± 12 [standard deviation]) who underwent surgery for primary IDC at a single center between April 2017 and March 2020. We constructed a prediction model with logistic regression. Patients were divided into low-risk and high-risk groups using a simple integer risk score, and the false negative rate for ALN macrometastasis was calculated to assess the validity. Internal validation was also achieved using a 5-fold cross-validation. Results The PET/CT plus MRI model included five predictor variables: maximum standardized uptake value of primary tumor and ALN, primary tumor size, ALN cortical thickness, and histological grade. In the derivation (296 axillae) and validation (74 axillae) cohorts, 54% and 61% of patients, respectively, were classified as low-risk, with a false-negative rate of 11%. Five-fold cross-validation yielded an accuracy of 0.875. Conclusions Our findings demonstrate the validity of the PET/CT plus MRI prediction model for ALN macrometastases. This model may aid the preoperative identification of low-risk patients for ALN macrometastasis and provide helpful information for PET/MRI interpretation.
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Affiliation(s)
- Shun Kawaguchi
- Breast and Endocrine Surgery, Toranomon Hospital, Tokyo, Japan
| | - Nobuko Tamura
- Breast and Endocrine Surgery, Toranomon Hospital, Tokyo, Japan
| | - Kiyo Tanaka
- Breast and Endocrine Surgery, Toranomon Hospital, Tokyo, Japan
| | - Yoko Kobayashi
- Breast and Endocrine Surgery, Toranomon Hospital, Tokyo, Japan
| | | | | | - Masato Shiiba
- Diagnostic Imaging Center, Toranomon Hospital, Tokyo, Japan
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Li Z, Gao Y, Gong H, Feng W, Ma Q, Li J, Lu X, Wang X, Lei J. Different Imaging Modalities for the Diagnosis of Axillary Lymph Node Metastases in Breast Cancer: A Systematic Review and Network Meta-Analysis of Diagnostic Test Accuracy. J Magn Reson Imaging 2022; 57:1392-1403. [PMID: 36054564 DOI: 10.1002/jmri.28399] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Accurate diagnosis of axillary lymph node metastasis (ALNM) of breast cancer patients is important to guide local and systemic treatment. PURPOSE To evaluate the diagnostic performance of different imaging modalities for ALNM in patients with breast cancer. STUDY TYPE Systematic review and network meta-analysis (NMA). SUBJECTS Sixty-one original articles with 8011 participants. FIELD STRENGTH 1.5 T and 3.0 T. ASSESSMENT We used the QUADAS-2 and QUADAS-C tools to assess the risk of bias in eligible studies. The identified articles assessed ultrasonography (US), MRI, mammography, ultrasound elastography (UE), PET, CT, PET/CT, scintimammography, and PET/MRI. STATISTICAL ANALYSIS We used random-effects conventional meta-analyses and Bayesian network meta-analyses for data analyses. We used sensitivity and specificity, relative sensitivity and specificity, superiority index, and summary receiver operating characteristic curve (SROC) analysis to compare the diagnostic value of different imaging modalities. RESULTS Sixty-one studies evaluated nine imaging modalities. At patient level, sensitivities of the nine imaging modalities ranged from 0.27 to 0.84 and specificities ranged from 0.84 to 0.95. Patient-based NMA showed that UE had the highest superiority index (5.95) with the highest relative sensitivity of 1.13 (95% confidence interval [CI]: 0.93-1.29) among all imaging methods when compared to US. At lymph node level, MRI had the highest superiority index (6.91) with highest relative sensitivity of 1.13 (95% CI: 1.01-1.23) and highest relative specificity of 1.11 (95% CI: 0.95-1.23) among all imaging methods when compared to US. SROCs also showed that UE and MRI had the largest area under the curve (AUC) at patient level and lymph node level of 0.92 and 0.94, respectively. DATA CONCLUSION UE and MRI may be superior to other imaging modalities in the diagnosis of ALNM in breast cancer patients at the patient level and the lymph node level, respectively. Further studies are needed to provide high-quality evidence to validate our findings. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Zhifan Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Ya Gao
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.,Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Hengxin Gong
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Wen Feng
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Qinqin Ma
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Jinkui Li
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Xingru Lu
- The First Clinical Medical College of Lanzhou University, Lanzhou, China.,Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Xiaohui Wang
- Department of Obstetrics and Gynecology, the First Hospital of Lanzhou University, Lanzhou, China
| | - Junqiang Lei
- Department of Radiology, the First Hospital of Lanzhou University, Lanzhou, China
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Evaluation of different imaging modalities for axillary lymph node staging in breast cancer patients to provide a personalized and optimized therapy algorithm. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04221-9. [PMID: 35948829 DOI: 10.1007/s00432-022-04221-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 07/18/2022] [Indexed: 10/15/2022]
Abstract
PURPOSE The reliable detection of tumor-infiltrated axillary lymph nodes for breast cancer [BC] patients plays a decisive role in further therapy. We aimed to find out whether cross-sectional imaging techniques could improve sensitivity for pretherapeutic axillary staging in nodal-positive BC patients compared to conventional imaging such as mammography and sonography. METHODS Data for breast cancer patients with tumor-infiltrated axillary lymph nodes having received surgery between 2014 and 2020 were included in this study. All examinations (sonography, mammography, computed tomography [CT] and magnetic resonance imaging [MRI]) were interpreted by board-certified specialists in radiology. The sensitivity of different imaging modalities was calculated, and binary logistic regression analyses were performed to detect variables influencing the detection of positive lymph nodes. RESULTS All included 382 breast cancer patients had received conventional imaging, while 52.61% of the patients had received cross-sectional imaging. The sensitivity of the combination of all imaging modalities was 68.89%. The combination of MRI and CT showed 63.83% and the combination of sonography and mammography showed 36.11% sensitivity. CONCLUSION We could demonstrate that cross-sectional imaging can improve the sensitivity of the detection of tumor-infiltrated axillary lymph nodes in breast cancer patients. Only the safe detection of these lymph nodes at the time of diagnosis enables the evaluation of the response to neoadjuvant therapy, thereby allowing access to prognosis and improving new post-neoadjuvant therapies.
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11
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Zheng M, Huang Y, Peng J, Xia Y, Cui Y, Han X, Wang S, Xie H. Optimal Selection of Imaging Examination for Lymph Node Detection of Breast Cancer With Different Molecular Subtypes. Front Oncol 2022; 12:762906. [PMID: 35912264 PMCID: PMC9326026 DOI: 10.3389/fonc.2022.762906] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 05/30/2022] [Indexed: 11/30/2022] Open
Abstract
Objective Axillary lymph node management is an important part of breast cancer surgery and the accuracy of preoperative imaging evaluation can provide adequate information to guide operation. Different molecular subtypes of breast cancer have distinct imaging characteristics. This article was aimed to evaluate the predictive ability of imaging methods in accessing the status of axillary lymph node in different molecular subtypes. Methods A total of 2,340 patients diagnosed with primary invasive breast cancer after breast surgery from 2013 to 2018 in Jiangsu Breast Disease Center, the First Affiliated Hospital with Nanjing Medical University were included in the study. We collected lymph node assessment results from mammography, ultrasounds, and MRIs, performed receiver operating characteristic (ROC) analysis, and calculated the sensitivity and specificity of each test. The C-statistic among different imaging models were compared in different molecular subtypes to access the predictive abilities of these imaging models in evaluating the lymph node metastasis. Results In Her-2 + patients, the C-statistic of ultrasound was better than that of MRI (0.6883 vs. 0.5935, p=0.0003). The combination of ultrasound and MRI did not raise the predictability compared to ultrasound alone (p=0.492). In ER/PR+HER2- patients, the C-statistic of ultrasound was similar with that of MRI (0.7489 vs. 0.7650, p=0.5619). Ultrasound+MRI raised the prediction accuracy compared to ultrasound alone (p=0.0001). In ER/PR-HER2- patients, the C-statistics of ultrasound was similar with MRI (0.7432 vs. 0.7194, p=0.5579). Combining ultrasound and MRI showed no improvement in the prediction accuracy compared to ultrasound alone (p=0.0532). Conclusion From a clinical perspective, for Her-2+ patients, ultrasound was the most recommended examination to assess the status of axillary lymph node metastasis. For ER/PR+HER2- patients, we suggested that the lymph node should be evaluated by ultrasound plus MRI. For ER/PR-Her2- patients, ultrasound or MRI were both optional examinations in lymph node assessment. Furthermore, more new technologies should be explored, especially for Her2+ patients, to further raise the prediction accuracy of lymph node assessment.
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Affiliation(s)
| | | | | | | | | | | | - Shui Wang
- *Correspondence: Shui Wang, ; Hui Xie,
| | - Hui Xie
- *Correspondence: Shui Wang, ; Hui Xie,
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12
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Le-Petross HT, Slanetz PJ, Lewin AA, Bao J, Dibble EH, Golshan M, Hayward JH, Kubicky CD, Leitch AM, Newell MS, Prifti C, Sanford MF, Scheel JR, Sharpe RE, Weinstein SP, Moy L. ACR Appropriateness Criteria® Imaging of the Axilla. J Am Coll Radiol 2022; 19:S87-S113. [PMID: 35550807 DOI: 10.1016/j.jacr.2022.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 02/19/2022] [Indexed: 11/26/2022]
Abstract
This publication reviews the current evidence supporting the imaging approach of the axilla in various scenarios with broad differential diagnosis ranging from inflammatory to malignant etiologies. Controversies on the management of axillary adenopathy results in disagreement on the appropriate axillary imaging tests. Ultrasound is often the appropriate initial imaging test in several clinical scenarios. Clinical information (such as age, physical examinations, risk factors) and concurrent complete breast evaluation with mammogram, tomosynthesis, or MRI impact the type of initial imaging test for the axilla. Several impactful clinical trials demonstrated that selected patient's population can received sentinel lymph node biopsy instead of axillary lymph node dissection with similar overall survival, and axillary lymph node dissection is a safe alternative as the nodal staging procedure for clinically node negative patients or even for some node positive patients with limited nodal tumor burden. This approach is not universally accepted, which adversely affect the type of imaging tests considered appropriate for axilla. This document is focused on the initial imaging of the axilla in various scenarios, with the understanding that concurrent or subsequent additional tests may also be performed for the breast. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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Affiliation(s)
| | - Huong T Le-Petross
- The University of Texas MD Anderson Cancer Center, Houston, Texas; Director of Breast MRI.
| | - Priscilla J Slanetz
- Panel Chair, Boston University School of Medicine, Boston, Massachusetts; Vice Chair of Academic Affairs, Department of Radiology, Boston Medical Center; Associate Program Director, Diagnostic Radiology Residency, Boston Medical Center; Program Director, Early Career Faculty Development Program, Boston University Medical Campus; Co-Director, Academic Writing Program, Boston University Medical Group; President, Massachusetts Radiological Society; Vice President, Association of University Radiologists
| | - Alana A Lewin
- Panel Vice-Chair, New York University School of Medicine, New York, New York; Associate Program Director, Breast Imaging Fellowship, NYU Langone Medical Center
| | - Jean Bao
- Stanford University Medical Center, Stanford, California; Society of Surgical Oncology
| | | | - Mehra Golshan
- Smilow Cancer Hospital, Yale Cancer Center, New Haven, Connecticut; American College of Surgeons; Deputy CMO for Surgical Services and Breast Program Director, Smilow Cancer Hospital at Yale; Executive Vice Chair for Surgery, Yale School of Medicine
| | - Jessica H Hayward
- University of California San Francisco, San Francisco, California; Co-Fellowship Direction, Breast Imaging Fellowship
| | | | - A Marilyn Leitch
- UT Southwestern Medical Center, Dallas, Texas; American Society of Clinical Oncology
| | - Mary S Newell
- Emory University Hospital, Atlanta, Georgia; Interim Director, Division of Breast Imaging at Emory; ACR: Chair of BI-RADS; Chair of PP/TS
| | - Christine Prifti
- Boston Medical Center, Boston, Massachusetts, Primary care physician
| | | | | | | | - Susan P Weinstein
- Perelman School of Medicine of the University of Pennsylvania, Philadelphia, Pennsylvania; Associate Chief of Radiology, San Francisco VA Health Systems
| | - Linda Moy
- Specialty Chair, NYU Clinical Cancer Center, New York, New York; Chair of ACR Practice Parameter for Breast Imaging, Chair ACR NMD
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Chen K, Yin G, Xu W. Predictive Value of 18F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer. Diagnostics (Basel) 2022; 12:diagnostics12040997. [PMID: 35454045 PMCID: PMC9030613 DOI: 10.3390/diagnostics12040997] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/08/2022] [Accepted: 04/13/2022] [Indexed: 01/26/2023] Open
Abstract
Background: To develop and validate a radiomics model based on 18F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31–82 years) with pathologically proven IDC and a preoperative 18F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661–0.929 (mean AUC, 0.817), and the accuracy range was 65.3–93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative 18F-FDG PET/CT radiomic features.
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Affiliation(s)
- Kun Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, China; (K.C.); (G.Y.)
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin 300060, China
| | - Guotao Yin
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, China; (K.C.); (G.Y.)
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin 300060, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, China; (K.C.); (G.Y.)
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin 300060, China
- Correspondence: or
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Nanocarbon Tracer and Areola Injection Site Are Superior in the Sentinel Lymph Node Biopsy Procedure for Breast Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4066179. [PMID: 35321201 PMCID: PMC8938060 DOI: 10.1155/2022/4066179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/12/2022] [Accepted: 01/18/2022] [Indexed: 11/18/2022]
Abstract
Background. Axillary lymph node (ALN) staging is the most effective method to evaluate the condition of patients with breast cancer, their choice of treatment options, and prognosis. The sentinel lymph node (SLN) status assessment is the key to sentinel lymph node biopsy (SLNB) in patients with breast cancer. The choice of tracer and tracer injection sites affects SLNB. Objective. This study mainly analyzes the best tracer for SLNB and the best choice of tracer injection site. Methods. A total of 165 breast cancer patients who underwent SLNB were selected and injected with methylene blue or 99mTc-labeled sodium phytate or nanocarbon 20 min before biopsy. The number of SLNs detected by different tracers in different injection sites such as peritumoral tissue (PT) and subareolar area (SA) was counted, and the sensitivity, specificity, and positive/negative prediction rates were recorded and compared. Results. The detection success rate, average detection number of SLNs, and detection accuracy of the nanocarbon tracer were higher than the other two. The detection sensitivity, specificity, and positive and negative prediction rates of nanocarbon for SLNB were also higher than those of the other two tracers. When comparing the performance of tracers in different injection sites, it was found that the detection of three tracers injected in the SA was better than the injection in the PT. Conclusion. For women with early-stage breast cancer, nanocarbon can be used as the preferred tracer for SLNB to determine the status of the patient’s ALNs, and the areola area can be used as the best injection site.
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15
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18F-Alfatide II for the evaluation of axillary lymph nodes in breast cancer patients: comparison with 18F-FDG. Eur J Nucl Med Mol Imaging 2022; 49:2869-2876. [PMID: 35138445 DOI: 10.1007/s00259-021-05333-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 03/23/2021] [Indexed: 11/04/2022]
Abstract
PURPOSE 18F-Alfatide II has been translated into clinical use and been proven to have good performance in identifying breast cancer. In this study, we investigated 18F-Alfatide II for evaluation of axillary lymph nodes (ALN) in breast cancer patients and compared the performance with 18F-FDG. METHODS A total of 44 female patients with clinically suspected breast cancer were enrolled and underwent 18F-Alfatide II and 18F-FDG PET/CT within a week. Tracer uptakes in ALN were evaluated by visual analysis, semi-quantitative analysis with maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), and SUVmax ratio of target/non-target (T/NT). RESULTS Among 44 patients, 37 patients were pathologically diagnosed with breast cancer with metastatic (17 cases) or non-metastatic (20 cases) ALN. The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of visual analysis were 70.6%, 90%, 81.1%, 85.7%, and 78.3% for 18F-Alfatide II, 64.7%, 90%, 78.4%, 84.6%, and 75% for 18F-FDG, respectively. By combining 18F-Alfatide II and 18F-FDG, the sensitivity significantly increased to 82.4%, the specificity was 85%, the accuracy increased to 83.8%, the PPV was 82.4%, and the NPV significantly increased to 85.0%. Three cases of luminal B subtype were false negative for both 18F-Alfatide II and 18F-FDG. The other 2 false negative cases of 18F-Alfatide II were triple-negative subtype and 3 false negative cases of 18F-FDG were luminal B subtype too. The AUCs of three semi-quantitative parameters (SUVmax, SUVmean, T/NT) for 18F-Alfatide II were between 0.8 and 0.9, whereas those for 18F-FDG were more than 0.9. 18F-Alfatide II T/NT had the highest Youden index (76.5%), specificity (100%), accuracy (89.2%), and PPV (100%) among these semi-quantitative parameters. 18F-Alfatide II uptake as well as 18F-FDG uptake in metastatic axillary lymph nodes (MALN) was significantly higher than that in benign axillary lymph nodes (BALN). Both 18F-Alfatide II and 18F-FDG did not show difference in primary tumor uptake irrespective of ALN status. CONCLUSION 18F-Alfatide II can be used in breast cancer patients to detect metastatic ALN, however, like 18F-FDG, with high specificity but relatively low sensitivity. The combination of 18F-Alfatide II and 18F-FDG can significantly improve sensitivity and NPV. 18F-Alfatide II T/NT may serve as the most important semi-quantitative parameter to evaluate ALN.
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Rasool R, Ullah I, Mubeen B, Alshehri S, Imam SS, Ghoneim MM, Alzarea SI, Al-Abbasi FA, Murtaza BN, Kazmi I, Nadeem MS. Theranostic Interpolation of Genomic Instability in Breast Cancer. Int J Mol Sci 2022; 23:ijms23031861. [PMID: 35163783 PMCID: PMC8836911 DOI: 10.3390/ijms23031861] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Revised: 01/26/2022] [Accepted: 01/27/2022] [Indexed: 12/14/2022] Open
Abstract
Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.
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Affiliation(s)
- Rabia Rasool
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore 54000, Pakistan; (R.R.); (I.U.); (B.M.)
| | - Inam Ullah
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore 54000, Pakistan; (R.R.); (I.U.); (B.M.)
| | - Bismillah Mubeen
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore 54000, Pakistan; (R.R.); (I.U.); (B.M.)
| | - Sultan Alshehri
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (S.A.); (S.S.I.)
| | - Syed Sarim Imam
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (S.A.); (S.S.I.)
| | - Mohammed M. Ghoneim
- Department of Pharmacy Practice, College of Pharmacy, AlMaarefa University, Ad Diriyah 13713, Saudi Arabia;
| | - Sami I. Alzarea
- Department of Pharmacology, College of Pharmacy, Jouf University, Sakaka 72341, Saudi Arabia;
| | - Fahad A. Al-Abbasi
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Bibi Nazia Murtaza
- Department of Zoology, Abbottabad University of Science and Technology (AUST), Abbottabad 22310, Pakistan;
| | - Imran Kazmi
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Correspondence: (I.K.); (M.S.N.)
| | - Muhammad Shahid Nadeem
- Department of Biochemistry, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Correspondence: (I.K.); (M.S.N.)
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Ou X, Zhu J, Qu Y, Wang C, Wang B, Xu X, Wang Y, Wen H, Ma A, Liu X, Zou X, Wen Z. Imaging features of sentinel lymph node mapped by multidetector-row computed tomography lymphography in predicting axillary lymph node metastasis. BMC Med Imaging 2021; 21:193. [PMID: 34911489 PMCID: PMC8675471 DOI: 10.1186/s12880-021-00722-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 11/26/2021] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Accurately assessing axillary lymph node (ALN) status in breast cancer is vital for clinical decision making and prognosis. The purpose of this study was to evaluate the predictive value of sentinel lymph node (SLN) mapped by multidetector-row computed tomography lymphography (MDCT-LG) for ALN metastasis in breast cancer patients. METHODS 112 patients with breast cancer who underwent preoperative MDCT-LG examination were included in the study. Long-axis diameter, short-axis diameter, ratio of long-/short-axis and cortical thickness were measured. Logistic regression analysis was performed to evaluate independent predictors associated with ALN metastasis. The prediction of ALN metastasis was determined with related variables of SLN using receiver operating characteristic (ROC) curve analysis. RESULTS Among the 112 cases, 35 (30.8%) cases had ALN metastasis. The cortical thickness in metastatic ALN group was significantly thicker than that in non-metastatic ALN group (4.0 ± 1.2 mm vs. 2.4 ± 0.7 mm, P < 0.001). Multi-logistic regression analysis indicated that cortical thickness of > 3.3 mm (OR 24.53, 95% CI 6.58-91.48, P < 0.001) had higher risk for ALN metastasis. The best sensitivity, specificity, negative predictive value(NPV) and AUC of MDCT-LG for ALN metastasis prediction based on the single variable of cortical thickness were 76.2%, 88.5%, 90.2% and 0.872 (95% CI 0.773-0.939, P < 0.001), respectively. CONCLUSION ALN status can be predicted using the imaging features of SLN which was mapped on MDCT-LG in breast cancer patients. Besides, it may be helpful to select true negative lymph nodes in patients with early breast cancer, and SLN biopsy can be avoided in clinically and radiographically negative axilla.
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Affiliation(s)
- Xiaochan Ou
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Jianbin Zhu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Yaoming Qu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Chengmei Wang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Baiye Wang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Xirui Xu
- Department of Breast Surgery, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510828, Guangdong, China
| | - Yanyu Wang
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Haitao Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Andong Ma
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Xinzi Liu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Xia Zou
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China
| | - Zhibo Wen
- Department of Radiology, Zhujiang Hospital, Southern Medical University, 253 Gongye Middle Avenue, Haizhu District, Guangzhou, 510282, Guangdong, China.
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Mberu V, McFarlane J, Macaskill EJ, Evans A. A retrospective review of MRI features associated with metastasis-free survival in women with breast cancer: focusing on skin thickening and skin enhancement. Br J Radiol 2021; 94:20210472. [PMID: 34591686 DOI: 10.1259/bjr.20210472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES To identify associations between MRI-detected skin thickening and enhancement and metastasis-free survival (MFS) given recent reports of skin thickening on ultrasound being a poorer prognostic indicator. METHODS Interrogation of a prospectively collected database of ultrasound-visible breast lesions showed 214 lesions with pre-treatment MRIs available for analysis in a single centre. Data on MFS was prospectively collected. Retrospective MRI review was performed blinded to outcome. Imaging factors recorded were presence of skin thickening and enhancement, non-mass-enhancement (NME) and abnormal nodes, mass characteristics, perilesional oedema and background parenchymal enhancement. Statistical analysis used chi-squared test, Kaplan-Meier survival curves, the Log-rank test and receiver-operator characteristic (ROC) curves. RESULTS During a median follow-up period of 5.6 years, 21 (10%) of 212 patients developed distant metastases. Skin thickening [24 of 30 (80%) vs 169 of 184 (92%), p = 0.043] and skin enhancement [15 of 20 (75%) vs 178 of 194 (92%), p = 0.016] were associated with poorer MFS. Large index lesion size [p < 0.001, AUC 0.823], large sum of masses [p < 0.001, AUC 0.813], increasing total lesion extent including NME [p < 0.001, AUC 0.749] and abnormal axillary nodes [55 of 66 (83%) vs 138 of 148 (93%), p = 0.024] were also associated with poorer MFS. CONCLUSIONS Skin thickening and enhancement on breast MRI are associated with poorer MFS. These findings should be taken into account when managing patients with invasive breast cancer. ADVANCES IN KNOWLEDGE Skin enhancement on breast MRI at diagnosis is associated with metastases development. Skin thickening on breast MRI is associated with future metastatic disease.
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Affiliation(s)
- Valentine Mberu
- University of Dundee, School of Medicine, Ninewells Hospital, Dundee, UK
| | | | | | - Andrew Evans
- University of Dundee, School of Medicine, Ninewells Hospital, Dundee, UK
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Ren T, Lin S, Huang P, Duong TQ. Convolutional Neural Network of Multiparametric MRI Accurately Detects Axillary Lymph Node Metastasis in Breast Cancer Patients With Pre Neoadjuvant Chemotherapy. Clin Breast Cancer 2021; 22:170-177. [PMID: 34384696 DOI: 10.1016/j.clbc.2021.07.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/29/2021] [Accepted: 07/06/2021] [Indexed: 12/21/2022]
Abstract
BACKGROUND Accurate assessment of the axillary lymph nodes (aLNs) in breast cancer patients is essential for prognosis and treatment planning. Current radiological staging of nodal metastasis has poor accuracy. This study aimed to investigate the machine learning convolutional neural networks (CNNs) on multiparametric MRI to detect nodal metastasis with 18FDG-PET as ground truths. MATERIALS AND METHODS Data were obtained via a retrospective search. Inclusion criteria were patients with bilateral breast MRI and 18FDG-PETand/or CT scans obtained before neoadjuvant chemotherapy. In total, 238 aLNs were obtained from 56 breast cancer patients with 18FDG-PET and/or CT and breast MRI data. Radiologists scored each node based on all MRI as diseased and non-diseased nodes. Five models were built using T1-W MRI, T2-W MRI, DCE MRI, T1-W + T2-W MRI, and DCE + T2-W MRI model. Performance was evaluated using receiver operating curve (ROC) analysis, including area under the curve (AUC). RESULTS All CNN models yielded similar performance with an accuracy ranging from 86.08% to 88.50% and AUC ranging from 0.804 to 0.882. The CNN model using T1-W MRI performed better than that using T2-W MRI in detecting nodal metastasis. CNN model using combined T1- and T2-W MRI performed the best compared to all other models (accuracy = 88.50%, AUC = 0.882), but similar in AUC to the DCE + T2-W MRI model (accuracy = 88.02%, AUC = 0.880). All CNN models performed better than radiologists in detecting nodal metastasis (accuracy = 65.8%). CONCLUSION xxxxxx.
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Affiliation(s)
- Thomas Ren
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, New York, NY
| | - Stephanie Lin
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, New York, NY
| | - Pauline Huang
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, New York, NY
| | - Tim Q Duong
- Department of Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, New York, NY.
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20
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Li L, Yu T, Sun J, Jiang S, Liu D, Wang X, Zhang J. Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI. Acta Radiol 2021; 63:1014-1022. [PMID: 34162234 DOI: 10.1177/02841851211025857] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
BACKGROUND The number of metastatic axillary lymph nodes (ALNs) play a crucial role in the staging, prognosis and therapy of patients with breast cancer. PURPOSE To predict the number of metastatic ALNs in breast cancer via radiomics. MATERIAL AND METHODS We enrolled 197 patients with breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A total of 3386 radiomic features were extracted from the early- and delayed-phase subtraction images. To classify the number of metastatic ALNs, logistic regression was used to develop a radiomic signature and nomogram. RESULTS The radiomic signature were constructed to distinguish the N0 group from the N+ (metastatic ALNs ≥ 1) group, which yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and test group, respectively. Based on the radiomic signature and BI-RADS category, a nomogram was further developed and showed excellent predictive performance with AUC values of 0.85 and 0.89 in the training and test groups, respectively. Another radiomic signature was constructed to distinguish the N1 (1-3 ALNs) group from the N2-3 (≥4 metastatic ALNs) group and showed encouraging performance with AUC values of 0.94 and 0.84 in training and test group, respectively. CONCLUSIONS We developed a nomogram and a radiomic signature that can be used to predict ALN metastasis and distinguish the N1 from the N2-3 group. Both nomogram and radiomic signature may be potential tools to assist clinicians in assessing ALN metastasis in patients with breast cancer.
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Affiliation(s)
- Lan Li
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, PR China
| | - Tao Yu
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, PR China
| | - Jianqing Sun
- Clinical Science, Philips Healthcare, Shanghai, PR China
| | - Shixi Jiang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, PR China
| | - Daihong Liu
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, PR China
| | - Xiaoxia Wang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, PR China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital & Chongqing Cancer Institute & Chongqing Cancer Hospital, Chongqing, PR China
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21
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Sun X, Zhang Q, Niu L, Huang T, Wang Y, Zhang S. Establishing a prediction model of axillary nodal burden based on the combination of CT and ultrasound findings and the clinicopathological features in patients with early-stage breast cancer. Gland Surg 2021; 10:751-760. [PMID: 33708557 DOI: 10.21037/gs-20-899] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background Axillary lymph node (ALN) management in early-stage breast cancer (ESBC) patients has become less invasive during the past decades. Here, we tried to explore whether high nodal burden (HNB) in ESBC patients could be predicted preoperatively, so as to avoid unnecessary sentinel lymph node biopsy (SLNB). Methods The clinicopathological and imaging data of patients with early invasive breast cancer (cT1-2N0M0) were analyzed retrospectively. Univariate and multivariate analyses were performed for the risk factors of axillary HNB in ESBC patients, and a risk prediction model of HNB was established. Results HNB was identified in 105 (8.0%) of 1,300 ESBC patients. Multivariate analysis showed that estrogen receptors (ER) status, human epidermal growth factor receptor 2 (HER2) status, number of abnormal lymph nodes (LNs) on computed tomography (CT), and axillary score on ultrasound (US) were the risk factors of HNB (all P<0.05). The area under the receiver operating characteristic (ROC) curve in the prediction model was 0.914, with the sensitivity being 85.7% and the specificity being 82.4%. The calibration curve showed that the prediction model had good performance. Conclusions As a valuable tool for predicting HNB in ESBC patients, this newly established model helps clinicians to make reasonable axillary surgery decisions and thus avoid unnecessary SLNB.
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Affiliation(s)
- Xianfu Sun
- Department of Breast Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Qiang Zhang
- Department of Breast Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Lianjie Niu
- Department of Breast Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Tao Huang
- Department of Breast Surgery, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Yingjie Wang
- Department of Oncology, Affiliated Zhengzhou Cancer Hospital of Henan University, Zhengzhou Cancer Hospital, Zhengzhou, China
| | - Shengze Zhang
- Department of Thyroid and Breast III, Cangzhou Central Hospital, Cangzhou, China
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22
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Atallah D, Moubarak M, Arab W, El Kassis N, Chahine G, Salem C. MRI‐based predictive factors of axillary lymph node status in breast cancer. Breast J 2020; 26:2177-2182. [DOI: 10.1111/tbj.14089] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 09/26/2020] [Accepted: 09/29/2020] [Indexed: 12/18/2022]
Affiliation(s)
- David Atallah
- Faculty of Medicine Saint Joseph University Achrafieh Lebanon
- Department of Gynecology and Obstetrics Hôtel‐Dieu de France University Hospital Achrafieh Lebanon
| | - Malak Moubarak
- Faculty of Medicine Saint Joseph University Achrafieh Lebanon
- Department of Gynecology and Obstetrics Hôtel‐Dieu de France University Hospital Achrafieh Lebanon
| | - Wissam Arab
- Faculty of Medicine Saint Joseph University Achrafieh Lebanon
- Department of Gynecology and Obstetrics Hôtel‐Dieu de France University Hospital Achrafieh Lebanon
| | - Nadine El Kassis
- Faculty of Medicine Saint Joseph University Achrafieh Lebanon
- Department of Gynecology and Obstetrics Hôtel‐Dieu de France University Hospital Achrafieh Lebanon
| | - Georges Chahine
- Faculty of Medicine Saint Joseph University Achrafieh Lebanon
- Department of Oncology Hôtel‐Dieu de France University Hospital Achrafieh Lebanon
| | - Christine Salem
- Faculty of Medicine Saint Joseph University Achrafieh Lebanon
- Department of Radiology Hôtel‐Dieu de France University Hospital Achrafieh Lebanon
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23
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Wang RY, Zhang YW, Gao ZM, Wang XM. Role of sonoelastography in assessment of axillary lymph nodes in breast cancer: a systematic review and meta-analysis. Clin Radiol 2019; 75:320.e1-320.e7. [PMID: 31892406 DOI: 10.1016/j.crad.2019.11.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 11/29/2019] [Indexed: 12/24/2022]
Abstract
AIM To evaluate the effectiveness of shear-wave elastography (SWE) and strain elastography (SE) for axillary lymph nodes (ALNs). MATERIALS AND METHODS PubMed, Embase, and Cochrane Library databases were searched until September 2018. Weighted mean difference was calculated for continuous variables. The accuracy of sonoelastography was assessed by calculating pooled sensitivity, specificity, area under the curve (AUC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). All data were analysed using Stata 12.0. RESULTS Ten studies with 1,038 ALNs were included in the meta-analysis. Five studies evaluated the use of SE, and the other five evaluated the SWE. The SWE stiffness values of malignant ALNs were significantly higher than those of benign nodes. Both SE and SWE have relatively high specificity and sensitivity. The max stiffness in SWE showed the highest specificity (0.94; 95% confidence interval [CI], 0.81-0.98), PLR (12.1; 95% CI, 4-36.5), NLR (0.29; 95% CI, 0.12-0.69), AUC (0.94; 95% CI, 0.91-0.96), and DOR (42; 95% CI, 12-154); in contrast, the mean stiffness showed the highest sensitivity (0.80; 95% CI, 0.61-0.91). CONCLUSION Sonoelastography demonstrated high sensitivity and specificity for differentiating between malignant and benign ALNs. The max and mean stiffness on SWE appeared to exhibit the highest accuracy. Thus, SWE is an effective accompaniment to sentinel node biopsy, and is appropriate for preoperative assessment of ALNs in the post-Z0011 era.
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Affiliation(s)
- R Y Wang
- Department of Ultrasound, The First Affiliated Hospital of China Medical University, Heping District, Shenyang City, 110001, China
| | - Y W Zhang
- Department of Second Clinical College, China Medical University, Heping District, Shenyang City, 110001, China
| | - Z M Gao
- Department of Surgical Oncology and General Surgery, The First Affiliated Hospital of China Medical University, Heping District, Shenyang City, 110001, China
| | - X M Wang
- Department of Ultrasound, The First Affiliated Hospital of China Medical University, Heping District, Shenyang City, 110001, China.
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24
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Ren T, Cattell R, Duanmu H, Huang P, Li H, Vanguri R, Liu MZ, Jambawalikar S, Ha R, Wang F, Cohen J, Bernstein C, Bangiyev L, Duong TQ. Convolutional Neural Network Detection of Axillary Lymph Node Metastasis Using Standard Clinical Breast MRI. Clin Breast Cancer 2019; 20:e301-e308. [PMID: 32139272 DOI: 10.1016/j.clbc.2019.11.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 11/18/2019] [Accepted: 11/30/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Axillary lymph node status is important for breast cancer staging and treatment planning as the majority of breast cancer metastasis spreads through the axillary lymph nodes. There is currently no reliable noninvasive imaging method to detect nodal metastasis associated with breast cancer. MATERIALS AND METHODS Magnetic resonance imaging (MRI) data were those from the peak contrast dynamic image from 1.5 Tesla MRI scanners at the pre-neoadjuvant chemotherapy stage. Data consisted of 66 abnormal nodes from 38 patients and 193 normal nodes from 61 patients. Abnormal nodes were those determined by expert radiologist based on 18Fluorodeoxyglucose positron emission tomography images. Normal nodes were those with negative diagnosis of breast cancer. The convolutional neural network consisted of 5 convolutional layers with filters from 16 to 128. Receiver operating characteristic analysis was performed to evaluate prediction performance. For comparison, an expert radiologist also scored the same nodes as normal or abnormal. RESULTS The convolutional neural network model yielded a specificity of 79.3% ± 5.1%, sensitivity of 92.1% ± 2.9%, positive predictive value of 76.9% ± 4.0%, negative predictive value of 93.3% ± 1.9%, accuracy of 84.8% ± 2.4%, and receiver operating characteristic area under the curve of 0.91 ± 0.02 for the validation data set. These results compared favorably with scoring by radiologists (accuracy of 78%). CONCLUSION The results are encouraging and suggest that this approach may prove useful for classifying lymph node status on MRI in clinical settings in patients with breast cancer, although additional studies are needed before routine clinical use can be realized. This approach has the potential to ultimately be a noninvasive alternative to lymph node biopsy.
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Affiliation(s)
- Thomas Ren
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY
| | - Renee Cattell
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY; Department of Biomedical Engineering
| | - Hongyi Duanmu
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY; Department of Computer Science, Stony Brook University, Stony Brook, NY
| | - Pauline Huang
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY
| | - Haifang Li
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY
| | - Rami Vanguri
- Department of Radiology, Columbia University Medical Center, New York, NY; Data Science Institute, Columbia University, New York, NY
| | - Michael Z Liu
- Department of Radiology, Columbia University Medical Center, New York, NY
| | | | - Richard Ha
- Department of Radiology, Columbia University Medical Center, New York, NY
| | - Fusheng Wang
- Department of Computer Science, Stony Brook University, Stony Brook, NY; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY
| | - Jules Cohen
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY
| | - Clifford Bernstein
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY
| | - Lev Bangiyev
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY
| | - Timothy Q Duong
- Department of Radiology, Stony Brook School of Medicine, Stony Brook, NY.
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25
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Chen CF, Zhang YL, Cai ZL, Sun SM, Lu XF, Lin HY, Liang WQ, Yuan MH, Zeng D. Predictive Value of Preoperative Multidetector-Row Computed Tomography for Axillary Lymph Nodes Metastasis in Patients With Breast Cancer. Front Oncol 2019; 8:666. [PMID: 30671386 PMCID: PMC6331431 DOI: 10.3389/fonc.2018.00666] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Accepted: 12/17/2018] [Indexed: 02/05/2023] Open
Abstract
Introduction: Axillary lymph nodes (ALN) status is an essential component in tumor staging and treatment planning for patients with breast cancer. The aim of present study was to evaluate the predictive value of preoperative multidetector-row computed tomography (MDCT) for ALN metastasis in breast cancer patients. Methods: A total of 148 cases underwent preoperative MDCT examination and ALN surgery were eligible for the study. Logistic regression analysis of MDCT variates was used to estimate independent predictive factors for ALN metastasis. The prediction of ALN metastasis was determined with MDCT variates through receiver operating characteristic (ROC) analysis. Results: Among the 148 cases, 61 (41.2%) cases had ALN metastasis. The cortical thickness in metastatic ALN was significantly thicker than that in non-metastatic ALN (7.5 ± 5.0 mm vs. 2.6 ± 2.8 mm, P < 0.001). Multi-logistic regression analysis indicated that cortical thickness of >3 mm (OR: 12.32, 95% CI: 4.50–33.75, P < 0.001) and non-fatty hilum (OR: 5.38, 95% CI: 1.51–19.19, P = 0.009) were independent predictors for ALN metastasis. The sensitivity, specificity and AUC of MDCT for ALN metastasis prediction based on combined-variated analysis were 85.3%, 87.4%, and 0.893 (95% CI: 0.832–0.938, P < 0.001), respectively. Conclusions: Cortical thickness (>3 mm) and non-fatty hilum of MDCT were independent predictors for ALN metastasis. MDCT is a potent imaging tool for predicting ALN metastasis in breast cancer. Future prospective study on the value of contrast enhanced MDCT in preoperative ALN evaluation is warranted.
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Affiliation(s)
- Chun-Fa Chen
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Yu-Ling Zhang
- Department of Information, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Ze-Long Cai
- Department of Medical Imaging, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Shu-Ming Sun
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Xiao-Feng Lu
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Hao-Yu Lin
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Wei-Quan Liang
- Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Shantou University Medical College, Shantou, China
| | - Ming-Heng Yuan
- Cancer Research Center, Shantou University Medical College, Shantou, China
| | - De Zeng
- Department of Medical Oncology, Cancer Hospital of Shantou University Medical College, Shantou, China
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