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Sun C, Gong X, Hou L, Yang D, Li Q, Li L, Wang Y. A nomogram based on conventional and contrast-enhanced ultrasound radiomics for the noninvasively prediction of axillary lymph node metastasis in breast cancer patients. Front Oncol 2024; 14:1400872. [PMID: 38800371 PMCID: PMC11116775 DOI: 10.3389/fonc.2024.1400872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 04/25/2024] [Indexed: 05/29/2024] Open
Abstract
Background This study aimed to investigate whether quantitative radiomics features extracted from conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) of primary breast lesions can help noninvasively predict axillary lymph nodes metastasis (ALNM) in breast cancer patients. Method A total of 111 breast cancer patients with 111 breast lesions were prospectively enrolled. All the included patients received presurgical CUS screening and CEUS examination and were randomly assigned to the training and validation sets at a ratio of 7:3 (n = 78 versus 33). Radiomics features were respectively extracted based on CUS and CEUS using the PyRadiomics package. The max-relevance and min-redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) analyses were used for feature selection and radiomics score calculation in the training set. The variance inflation factor (VIF) was performed to check the multicollinearity among selected predictors. The best performing model was selected to develop a nomogram using binary logistic regression analysis. The calibration and clinical utility of the nomogram were assessed. Results The model combining CUS reported ALN status, CUS radiomics score (CUS-radscore) and CEUS radiomics score (CEUS-radscore) exhibited the best performance. The areas under the curves (AUC) of our proposed nomogram in the training and external validation sets were 0.845 [95% confidence interval (CI), 0.739-0.950] and 0.901 (95% CI, 0.758-1). The calibration curves and decision curve analysis (DCA) demonstrated the nomogram's robust consistency and clinical utility. Conclusions The established nomogram is a promising prediction tool for noninvasive prediction of ALN status. The radiomics features based on CUS and CEUS can help improve the predictive performance.
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Affiliation(s)
- Chao Sun
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xuantong Gong
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lu Hou
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Di Yang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qian Li
- Department of Ultrasound, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yong Wang
- Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Sivakanthan T, Tanner J, Mahata B, Agrawal A. Investigating the role of tumour-to-skin proximity in predicting nodal metastasis in breast cancer. Breast Cancer Res Treat 2024; 205:109-116. [PMID: 38308767 PMCID: PMC11063104 DOI: 10.1007/s10549-023-07230-5] [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: 08/30/2023] [Accepted: 12/11/2023] [Indexed: 02/05/2024]
Abstract
BACKGROUND Understanding the factors influencing nodal status in breast cancer is vital for axillary staging, therapy, and patient survival. The nodal stage remains a crucial factor in prognostication indices. This study investigates the relationship between tumour-to-skin distance (in T1-T3 tumours where the skin is not clinically involved) and the risk of nodal metastasis. METHODS We retrospectively reviewed data from 100 patients who underwent neoadjuvant chemotherapy (NACT). Besides patient demographics and tumour variables, a radiologist retrospectively reviewed pre-operative MRI to measure tumour-to-skin distance. R core packages were used for univariate (χ2 and T-Wilcoxon tests) and bivariate logistic regression statistical analysis. RESULTS Of 95 analysable datasets, patients' median age was 51 years (IQR: 42-61), 97% were symptomatic (rest screen detected), and the median tumour size was 43 mm (IQR, 26-52). On multivariate analysis, increasing invasive tumour size (p = 0.02), ER positivity (p = 0.007) and shorter tumour-to-skin distance (p = 0.05) correlated with nodal metastasis. HER2 was not included in multivariate analysis as there was no association with nodal status on univariate analysis. In node-positive tumours, as tumour size increased, the tumour-to-skin distance decreased (r = - 0.34, p = 0.026). In node-negative tumours, there was no correlation (r = + 0.18, p = 0.23). CONCLUSION This study shows that non-locally advanced cancers closer to the skin (and consequent proximity to subdermal lymphatics) are associated with a greater risk of nodal metastasis. Pre-operative identification of those more likely to be node positive may suggest the need for a second-look USS since a higher nodal stage may lead to a change in therapeutic strategies, such as upfront systemic therapy, node marking, and axillary clearance without the need to return to theatre following sentinel node biopsy.
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Affiliation(s)
| | - J Tanner
- Cambridge University Hospitals, Cambridge, CB2 0QQ, UK
| | - B Mahata
- University of Cambridge, Cambridge, UK
| | - A Agrawal
- Cambridge University Hospitals, Cambridge, CB2 0QQ, UK.
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Gallagher J, Elleson KM, Englander K, Chintapally N, Sun W, Whiting J, Laronga C, Lee MC. Factors Associated With Node-Positive Disease in Estrogen Receptor-Positive Breast Cancer Patients. J Surg Res 2024; 295:327-331. [PMID: 38061237 DOI: 10.1016/j.jss.2023.11.030] [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: 03/01/2023] [Revised: 10/18/2023] [Accepted: 11/12/2023] [Indexed: 02/25/2024]
Abstract
INTRODUCTION Larger tumor size and shorter tumor-to-nipple distance at diagnosis are associated with greater risk of lymph node involvement in breast cancer. However, the relationship between receptor subtype status and lymph node metastasis remains unclear. Our objective was to examine the association between primary tumor size, location, and nodal metastasis across estrogen receptor (ER)+/ progesterone receptor (PR)+/ human epidermal growth factor receptor 2 (HER2)-, ER+/PR-/HER2-, ER+/PR+/HER2+, and ER+/PR-/HER2+ tumors. METHODS A single-institution retrospective chart review was conducted of breast cancer patients diagnosed between 1998 and 2019 who underwent nodal evaluation during primary surgery. Neoadjuvant chemotherapy, pure ductal carcinoma in situ, inflammatory, recurrent, metastatic, bilateral, multicentric, and multifocal disease were excluded. Descriptive statistics (proportions and frequencies for categorical variables and medians [Q1-Q3] for continuous variables) were used to summarize patient characteristics. Kruskal-Wallis test was applied to test the association of outcome variables and continuous variables. Chi-square test or Fisher exact test was applied to test the association of outcome variables and categorical variables. RESULTS Six hundred eighteen ER + patients had a median tumor size of 1.7 cm (1.1-2.5 cm). Two hundred ninety six out of 618 (47.9%) were node-positive and 188/618 (30.4%) had axillary dissection. Eighty four point three percent of patients were ER+/PR+/HER2-, 6.31% were ER+/PR-/HER2-, 6.96% were ER+/PR+/HER2+, and 1.13% were ER+/PR-/HER2+. Median tumor size was significantly larger in node-positive cases compared to node-negative cases in ER+/PR+/HER2-, ER+/PR+/HER2+, and ER+/PR-/HER2- subgroups. In ER+/PR+/HER2-patients, median tumor-nipple distance was significantly shorter in node-positive patients compared to node-negative patients. Upper outer quadrant location was significantly associated with nodal positivity in ER+/PR-/HER2- patients. CONCLUSIONS Across ER + patients, the significance between tumor size, location, and lymph node positivity varied significantly when differentiating by PR and HER2 status.
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Affiliation(s)
- Julia Gallagher
- University of South Florida Morsani College of Medicine, Tampa, Florida
| | - Kelly M Elleson
- Regional Breast Care, Fort Myers, Florida; Genesis Care, Fort Myers, Florida
| | | | - Neha Chintapally
- University of South Florida Morsani College of Medicine, Tampa, Florida
| | - Weihong Sun
- Department of Breast Oncology, Moffitt Cancer Center, Tampa, Florida
| | - Junmin Whiting
- Moffitt Cancer Center and Research Institute, Department of Biostatistics and Bioinformatics, Tampa, Florida
| | - Christine Laronga
- Department of Breast Oncology, Moffitt Cancer Center, Tampa, Florida
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Yao J, Zhou W, Zhu Y, Zhou J, Chen X, Zhan W. Predictive nomogram using multimodal ultrasonographic features for axillary lymph node metastasis in early‑stage invasive breast cancer. Oncol Lett 2024; 27:95. [PMID: 38288042 PMCID: PMC10823315 DOI: 10.3892/ol.2024.14228] [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: 08/10/2023] [Accepted: 12/19/2023] [Indexed: 01/31/2024] Open
Abstract
Axillary lymph node (ALN) status is a key prognostic factor in patients with early-stage invasive breast cancer (IBC). The present study aimed to develop and validate a nomogram based on multimodal ultrasonographic (MMUS) features for early prediction of axillary lymph node metastasis (ALNM). A total of 342 patients with early-stage IBC (240 in the training cohort and 102 in the validation cohort) who underwent preoperative conventional ultrasound (US), strain elastography, shear wave elastography and contrast-enhanced US examination were included between August 2021 and March 2022. Pathological ALN status was used as the reference standard. The clinicopathological factors and MMUS features were analyzed with uni- and multivariate logistic regression to construct a clinicopathological and conventional US model and a MMUS-based nomogram. The MMUS nomogram was validated with respect to discrimination, calibration, reclassification and clinical usefulness. US features of tumor size, echogenicity, stiff rim sign, perfusion defect, radial vessel and US Breast Imaging Reporting and Data System category 5 were independent risk predictors for ALNM. MMUS nomogram based on these factors demonstrated an improved calibration and favorable performance [area under the receiver operator characteristic curve (AUC), 0.927 and 0.922 in the training and validation cohorts, respectively] compared with the clinicopathological model (AUC, 0.681 and 0.670, respectively), US-depicted ALN status (AUC, 0.710 and 0.716, respectively) and the conventional US model (AUC, 0.867 and 0.894, respectively). MMUS nomogram improved the reclassification ability of the conventional US model for ALNM prediction (net reclassification improvement, 0.296 and 0.288 in the training and validation cohorts, respectively; both P<0.001). Taken together, the findings of the present study suggested that the MMUS nomogram may be a promising, non-invasive and reliable approach for predicting ALNM.
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Affiliation(s)
- Jiejie Yao
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Wei Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Ying Zhu
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Jianqiao Zhou
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Xiaosong Chen
- Comprehensive Breast Health Center, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, P.R. China
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Pang W, Wang Y, Zhu Y, Jia Y, Nie F. Predictive value for axillary lymph node metastases in early breast cancer: Based on contrast-enhanced ultrasound characteristics of the primary lesion and sentinel lymph node. Clin Hemorheol Microcirc 2024; 86:357-367. [PMID: 37955082 DOI: 10.3233/ch-231973] [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] [Indexed: 11/14/2023]
Abstract
OBJECTIVE To evaluate the value of contrast-enhanced ultrasound (CEUS) characteristics based on primary lesion combined with lymphatic contrast-enhanced ultrasound (LCEUS) patterns of SLN in predicting axillary lymph node metastasis (ALNM) with T1-2N0 breast cancer. METHODS A retrospective study was conducted in 118 patients with clinically confirmed T1-2N0 breast cancer. Conventional ultrasound (CUS) and CEUS characteristics of the primary lesion and enhancement patterns of SLN were recorded. The risk factors associated with ALNM were selected by univariate and binary logistic regression analysis, and the receiver operating characteristic (ROC) curve was drawn for the evaluation of predictive ALNM metastasis performance. RESULTS Univariate analysis showed that age, HER-2 status, tumor size, nutrient vessels, extended range of enhancement lesion, and the enhancement patterns of SLN were significant predictive features of ALNM. Further binary logistic regression analysis indicated that the extended range of enhancement lesion (p < 0.001) and the enhancement patterns of SLN (p < 0.001) were independent risk factors for ALNM. ROC analysis showed that the AUC of the combination of these two indicators for predicting ALNM was 0.931 (95% CI: 0.887-0.976, sensitivity: 75.0%, specificity: 99.8%). CONCLUSION The CEUS characteristics of primary lesion combined with enhancement patterns of SLN are highly valuable in predicting ALNM and can guide clinical axillary surgery decision-making in early breast cancer.
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Affiliation(s)
- Wenjing Pang
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
| | - Yao Wang
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
| | - Yangyang Zhu
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
| | - Yingying Jia
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
| | - Fang Nie
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
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Vongsaisuwon M, Vacharathit V, Lerttiendamrong B, Manasnayakorn S, Tantiphlachiva K, Vongwattanakit P, Treeratanapun N. Reconsidering the Role of Frozen Section in Sentinel Lymph Node Biopsy for Mastectomy Patients. J Surg Res 2024; 293:64-70. [PMID: 37716102 DOI: 10.1016/j.jss.2023.08.013] [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: 06/23/2023] [Revised: 08/02/2023] [Accepted: 08/21/2023] [Indexed: 09/18/2023]
Abstract
INTRODUCTION Axillary lymph node dissection was recommended for mastectomy patients with more than two nodal metastases from sentinel lymph node biopsy. Conventionally, intraoperative frozen section was sent routinely to reduce the need for second-stage axillary lymph node dissection; however, recent global trend has seen decreasing usage of the intraoperative analyses. This pilot study conducted in Thailand aimed to evaluate the role of intraoperative frozen section of sentinel lymph node biopsy in early-stage breast cancer patients who underwent mastectomy. METHODS A 5-y retrospective study of 1773 patients was conducted in Thailand. The inclusion criteria were early-stage breast cancer patients with either radiologically negative nodes, or radiographically borderline nodes found to be negative on fine needle aspiration who underwent mastectomy and sentinel lymph node biopsy. Reoperations were indicated when three or more nodal metastases were detected on the pathological analysis. The reoperation rate prevented by frozen section and the reoperation rate needed for those with permanent section alone were reported. RESULTS Among 265 patients, 202 patients underwent concomitant intraoperative frozen section while the remaining 63 patients underwent permanent section alone. Six patients (3.0%) from the frozen section group and one patient (1.6%) from the permanent section group were found with more than two nodal metastases. Despite using intraoperative frozen sections, only one patient from each group required reoperation. There was no significant difference in the number of patients requiring reoperation between the frozen section group and the permanent section group. CONCLUSIONS Our study provides strong evidence to all surgeons that in early breast cancer patients undergoing mastectomy, sentinel lymph node biopsy with permanent section analysis alone may not lower the standard of care compared to using additional intraoperative frozen section analysis. Adopting this practice may lead to decreased operation costs, operative time, and anesthetic side effects.
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Affiliation(s)
- Mawin Vongsaisuwon
- Department of Surgery, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | | | | | - Sopark Manasnayakorn
- Department of Surgery, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Kasaya Tantiphlachiva
- Department of Surgery, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | | | - Nattanan Treeratanapun
- Department of Surgery, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
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Zhao M, Zheng Y, Chu J, Liu Z, Dong F. Ultrasound-based radiomics combined with immune status to predict sentinel lymph node metastasis in primary breast cancer. Sci Rep 2023; 13:16918. [PMID: 37805562 PMCID: PMC10560203 DOI: 10.1038/s41598-023-44156-w] [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: 07/18/2023] [Accepted: 10/04/2023] [Indexed: 10/09/2023] Open
Abstract
In the past few years, the axillary lymph node dissection technique has been steadily replaced by sentinel lymph node biopsy for treating and diagnosing breast cancer, thereby minimizing the complications and sequelae of the patients. Nevertheless, sentinel lymph node biopsy still presents limitations, such as high operation requirements, prolonged surgical duration, and adverse reactions to tracer agents. This study developed a novel non-invasive method to predict sentinel lymph node metastasis in breast cancer by analyzing the ultrasound imaging characteristics of the primary tumor, combined with the analysis of peripheral blood T-cell subsets that reflect the immune status of the body. The radiomic features analyzed in this study were extracted from preoperative ultrasound images of 199 solitary breast cancer patients, who were undergoing surgery and were pathologically diagnosed at the Yancheng First People's Hospital. All cases were randomly categorized in a 4:1 ratio to the training (n = 159) and validation (n = 40) cohorts. The extracted radiomics features were subjected to dimensional reduction with the help of the least absolute shrinkage and selection operator technique, resulting in the inclusion of 19 radiomics features. Four classifiers, including naïve Bayesian, logistic regression, classification decision tree, and support vector machine, were utilized to model the radiomics features, conventional ultrasound features, and peripheral blood T cell subsets in the training dataset, and validated using the validation dataset. The best-performing model was chosen for constructing the combined model. The radiomics model constructed using the logistic regression showed the best performance, with the training and validation cohorts showing areas under the curve (AUCs) of 0.77 and 0.68, respectively. The conventional ultrasound and peripheral blood T cell models constructed using the classification decision tree showed the best performance, wherein the training cohort presented AUCs of 0.71 and 0.81, respectively, while the validation cohort presented AUCs of 0.68 and 0.69, respectively. The combined model constructed by logistic regression showed AUCs of 0.91 and 0.79 in the training and validation datasets, respectively. The resulting combined model can be considered a simple, non-invasive method with strong reproducibility and clinical significance. Thus, it can be utilized to predict sentinel lymph node metastasis in breast cancer. Furthermore, the combined model can be effectively used to guide clinical decisions related to the selection of surgical procedures in breast surgery.
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Affiliation(s)
- Miaomiao Zhao
- Department of Ultrasound, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, 66 Renmin Road, Yancheng, 224005, China
| | - Yan Zheng
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, 215000, China
| | - Jian Chu
- Department of General Surgery, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, 66 Renmin Road, Yancheng, 224005, China
| | - Zhenhua Liu
- Department of Radiotherapy, The Yancheng Clinical College of Xuzhou Medical University, The First People's Hospital of Yancheng, 66 Renmin Road, Yancheng, 224005, China.
| | - Fenglin Dong
- Department of Ultrasound, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, 215000, China.
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Li WB, Du ZC, Liu YJ, Gao JX, Wang JG, Dai Q, Huang WH. Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning. Front Oncol 2023; 13:1219838. [PMID: 37719009 PMCID: PMC10503049 DOI: 10.3389/fonc.2023.1219838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Objective To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients. Methods A total of 271 US videos from 271 early breast cancer patients collected from Xiang'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models. Results Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers' review. Conclusion A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.
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Affiliation(s)
- Wei-Bin Li
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Department of Ultrasonic Medicine Affiliated Hospital of Xizang Minzu University, Xianyang, China
| | - Zhi-Cheng Du
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yue-Jie Liu
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Department of Ultrasonic Medicine, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jun-Xue Gao
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Department of Ultrasonic Medicine, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
| | - Jia-Gang Wang
- Department of Ultrasonic Medicine of Shantou Central Hospital, Shantou, China
| | - Qian Dai
- School of Informatics, Xiamen University, Xiamen, China
| | - Wen-He Huang
- Cancer Center and Department of Breast and Thyroid Surgery, Xiang’an Hospital, School of Medicine, Xiamen University, Xiamen, China
- Fujian Key Laboratory of Precision Diagnosis and Treatment in Breast Cancer, Xiamen, China
- Xiamen Key Laboratory of Endocrine-Related Cancer Precision Medicine, Xiamen, China
- Xiamen Research Center of Clinical Medicine in Breast and Thyroid Cancers, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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Song SE, Cho KR, Cho Y, Jung SP, Park KH, Woo OH, Seo BK. Value of Breast MRI and Nomogram After Negative Axillary Ultrasound for Predicting Axillary Lymph Node Metastasis in Patients With Clinically T1-2 N0 Breast Cancer. J Korean Med Sci 2023; 38:e251. [PMID: 37644678 PMCID: PMC10462481 DOI: 10.3346/jkms.2023.38.e251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 04/21/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND There are increasing concerns about that sentinel lymph node biopsy (SLNB) could be omitted in patients with clinically T1-2 N0 breast cancers who has negative axillary ultrasound (AUS). This study aims to assess the false negative result (FNR) of AUS, the rate of high nodal burden (HNB) in clinically T1-2 N0 breast cancer patients, and the diagnostic performance of breast magnetic resonance imaging (MRI) and nomogram. METHODS We identified 948 consecutive patients with clinically T1-2 N0 cancers who had negative AUS, subsequent MRI, and breast conserving therapy between 2013 and 2020 from two tertiary medical centers. Patients from two centers were assigned to development and validation sets, respectively. Among 948 patients, 402 (mean age ± standard deviation, 57.61 ± 11.58) were within development cohort and 546 (54.43 ± 10.02) within validation cohort. Using logistic regression analyses, clinical-imaging factors associated with lymph node (LN) metastasis were analyzed in the development set from which nomogram was created. The performance of MRI and nomogram was assessed. HNB was defined as ≥ 3 positive LNs. RESULTS The FNR of AUS was 20.1% (81 of 402) and 19.2% (105 of 546) and the rates of HNB were 1.2% (5/402) and 2.2% (12/546), respectively. Clinical and imaging features associated with LN metastasis were progesterone receptor positivity, outer tumor location on mammography, breast imaging reporting and data system category 5 assessment of cancer on ultrasound, and positive axilla on MRI. In validation cohorts, the positive predictive value (PPV) and negative predictive value (NPV) of MRI and clinical-imaging nomogram was 58.5% and 86.5%, and 56.0% and 82.0%, respectively. CONCLUSION The FNR of AUS was approximately 20% but the rate of HNB was low. The diagnostic performance of MRI was not satisfactory with low PPV but MRI had merit in reaffirming negative AUS with high NPV. Patients who had low probability scores from our clinical-imaging nomogram might be possible candidates for the omission of SLNB.
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Affiliation(s)
- Sung Eun Song
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Kyu Ran Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea.
| | - Yongwon Cho
- Department of Radiology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Seung Pil Jung
- Department of Surgery, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Kyong-Hwa Park
- Department of Oncology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
| | - Ok Hee Woo
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Korea
| | - Bo Kyoung Seo
- Department of Radiology, Korea University Ansan Hospital, Korea University College of Medicine, Ansan, Korea
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Yur M, Aygen E, İlhan YS, Lale A, Ebiloğlu MF. The effect of the tumor-to-skin distance on axillary lymph node metastasis in breast cancer. REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2023; 69:e20221277. [PMID: 37098931 PMCID: PMC10176633 DOI: 10.1590/1806-9282.20221277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 01/20/2023] [Indexed: 04/27/2023]
Abstract
OBJECTIVE Tumor-to-skin distance is known to have an effect on axillary lymph node metastasis but has no clinical use with nomograms. This study aimed to investigate the effect of tumor-to-skin distance on axillary lymph node metastasis alone and in combination with nomogram for clinical use. METHODS A total of 145 patients who underwent surgery for breast cancer (T1-T2 stage) and whose axillary lymph nodes were evaluated (axillary dissection or sentinel lymph node biopsy) between January 2010 and December 2020 were included in the study. Tumor-to-skin distance and other pathological data of the patients were evaluated. RESULTS Of the 145 patients, 83 (57.2%) had metastatic lymph nodes in the axilla. Tumor-to-skin distance was different in terms of lymph node metastasis (p=0.045). In the receiver operating characteristic curve for tumor-to-skin distance, area under curve was 0.597 (95%CI 0.513-0.678, p=0.046), area under curve of the nomogram was 0.740 (95%CI 0.660-0.809), p<0.001) and nomogram+tumor-to-skin distance was 0.753 (95%CI 0.674-0.820), p<0.001). No statistical difference was found for axillary lymph node metastasis between the nomogram+tumor-to-skin distance and the nomogram alone (p=0.433). CONCLUSION Although tumor-to-skin distance demonstrated a significant difference in axillary lymph node metastasis, it had a poor association with an area under curve value of 0.597 and did not produce a significant improvement in predicting lymph node metastasis when combined with the nomogram. The tumor-to-skin distance may be unlikely to enter clinical practice.
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Affiliation(s)
- Mesut Yur
- Firat Üniversitesi, Department of Surgical Oncology - Elâzığ, Turkey
| | - Erhan Aygen
- Firat Üniversitesi, Department of Surgical Oncology - Elâzığ, Turkey
| | - Yavuz Selim İlhan
- Firat Üniversitesi, Department of Surgical Oncology - Elâzığ, Turkey
| | - Azmi Lale
- Fethi Sekin State Hospital, Department of Surgical Oncology - Elâzığ, Turkey
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Xiong J, Zuo W, Wu Y, Wang X, Li W, Wang Q, Zhou H, Xie M, Qin X. Ultrasonography and clinicopathological features of breast cancer in predicting axillary lymph node metastases. BMC Cancer 2022; 22:1155. [PMID: 36352378 PMCID: PMC9647900 DOI: 10.1186/s12885-022-10240-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 10/27/2022] [Indexed: 11/11/2022] Open
Abstract
Background Early identification of axillary lymph node metastasis (ALNM) in breast cancer (BC) is still a clinical difficulty. There is still no good method to replace sentinel lymph node biopsy (SLNB). The purpose of our study was to develop and validate a nomogram to predict the probability of ALNM preoperatively based on ultrasonography (US) and clinicopathological features of primary tumors. Methods From September 2019 to April 2022, the preoperative US) and clinicopathological data of 1076 T1-T2 BC patients underwent surgical treatment were collected. Patients were divided into a training set (875 patients from September 2019 to October 2021) and a validation set (201 patients from November 2021 to April 2022). Patients were divided into positive and negative axillary lymph node (ALN) group according pathology of axillary surgery. Compared the US and clinicopathological features between the two groups. The risk factors for ALNM were determined using multivariate logistic regression analysis, and a nomogram was constructed. AUC and calibration were used to assess its performance. Results By univariate and multivariate logistic regression analysis, age (p = 0.009), histologic grades (p = 0.000), molecular subtypes (p = 0.000), tumor location (p = 0.000), maximum diameter (p = 0.000), spiculated margin (p = 0.000) and distance from the skin (p = 0.000) were independent risk factors of ALNM. Then a nomogram was developed. The model was good discriminating with an AUC of 0.705 and 0.745 for the training and validation set, respectively. And the calibration curves demonstrated high agreement. However, in further predicting a heavy nodal disease burden (> 2 nodes), none of the variables were significant. Conclusion This nomogram based on the US and clinicopathological data can predict the presence of ALNM good in T1-T2 BC patients. But it cannot effectively predict a heavy nodal disease burden (> 2 nodes).
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Zhou T, Yang M, Wang M, Han L, Chen H, Wu N, Wang S, Wang X, Zhang Y, Cui D, Jin F, Qin P, Wang J. Prediction of axillary lymph node pathological complete response to neoadjuvant therapy using nomogram and machine learning methods. Front Oncol 2022; 12:1046039. [PMID: 36353547 PMCID: PMC9637839 DOI: 10.3389/fonc.2022.1046039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 10/10/2022] [Indexed: 11/28/2022] Open
Abstract
Purpose To determine the feasibility of predicting the rate of an axillary lymph node pathological complete response (apCR) using nomogram and machine learning methods. Methods A total of 247 patients with early breast cancer (eBC), who underwent neoadjuvant therapy (NAT) were included retrospectively. We compared pre- and post-NAT ultrasound information and calculated the maximum diameter change of the primary lesion (MDCPL): [(pre-NAT maximum diameter of primary lesion – post-NAT maximum diameter of preoperative primary lesion)/pre-NAT maximum diameter of primary lesion] and described the lymph node score (LNS) (1): unclear border (2), irregular morphology (3), absence of hilum (4), visible vascularity (5), cortical thickness, and (6) aspect ratio <2. Each description counted as 1 point. Logistic regression analyses were used to assess apCR independent predictors to create nomogram. The area under the curve (AUC) of the receiver operating characteristic curve as well as calibration curves were employed to assess the nomogram’s performance. In machine learning, data were trained and validated by random forest (RF) following Pycharm software and five-fold cross-validation analysis. Results The mean age of enrolled patients was 50.4 ± 10.2 years. MDCPL (odds ratio [OR], 1.013; 95% confidence interval [CI], 1.002–1.024; p=0.018), LNS changes (pre-NAT LNS – post-NAT LNS; OR, 2.790; 95% CI, 1.190–6.544; p=0.018), N stage (OR, 0.496; 95% CI, 0.269–0.915; p=0.025), and HER2 status (OR, 2.244; 95% CI, 1.147–4.392; p=0.018) were independent predictors of apCR. The AUCs of the nomogram were 0.74 (95% CI, 0.68–0.81) and 0.76 (95% CI, 0.63–0.90) for training and validation sets, respectively. In RF model, the maximum diameter of the primary lesion, axillary lymph node, and LNS in each cycle, estrogen receptor status, progesterone receptor status, HER2, Ki67, and T and N stages were included in the training set. The final validation set had an AUC value of 0.85 (95% CI, 0.74–0.87). Conclusion Both nomogram and machine learning methods can predict apCR well. Nomogram is simple and practical, and shows high operability. Machine learning makes better use of a patient’s clinicopathological information. These prediction models can assist surgeons in deciding on a reasonable strategy for axillary surgery.
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Affiliation(s)
- Tianyang Zhou
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Mengting Yang
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Mijia Wang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Linlin Han
- Health Management Center, The Second Hospital of Dalian Medical University, Dalian, China
| | - Hong Chen
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Nan Wu
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Shan Wang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Xinyi Wang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Yuting Zhang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
| | - Di Cui
- Information Center, The Second Hospital of Dalian Medical University, Dalian, China
| | - Feng Jin
- Department of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Pan Qin
- Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Jia Wang
- Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Jia Wang,
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Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis. Breast Cancer Res Treat 2022; 195:161-169. [PMID: 35864309 PMCID: PMC9374610 DOI: 10.1007/s10549-022-06672-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/02/2022] [Indexed: 11/13/2022]
Abstract
Purpose Axillary staging is an important prognostic factor in breast cancer. Sentinel lymph node biopsy (SNB) is currently used to stage patients who are clinically and radiologically node-negative. Since the establishment that axillary node clearance (ANC) does not improve overall survival in breast-conserving surgery for patients with low-risk biological cancers, axillary management has become increasingly conservative. This study aims to identify and assess the clinical predictive value of variables that could play a role in the quantification of axillary burden, including the accuracy of quantifying abnormal axillary nodes on ultrasound. Methods A retrospective analysis was conducted of hospital data for female breast cancer patients receiving an ANC at our centre between January 2018 and January 2020. The reference standard for axillary burden was surgical histology following SNB and ANC, allowing categorisation of the patients under ‘low axillary burden’ (2 or fewer pathological macrometastases) or ‘high axillary burden’ (> 2). After exploratory univariate analysis, multivariate logistic regression was conducted to determine relationships between the outcome category and candidate predictor variables: patient age at diagnosis, tumour focality, tumour size on ultrasound and number of abnormal lymph nodes on axillary ultrasound. Results One hundred and thirty-five patients were included in the analysis. Logistic regression showed that the number of abnormal lymph nodes on axillary ultrasound was the strongest predictor of axillary burden and statistically significant (P = 0.044), with a sensitivity of 66.7% and specificity of 86.8% (P = 0.011). Conclusion Identifying the number of abnormal lymph nodes on preoperative ultrasound can help to quantify axillary nodal burden and identify patients with high axillary burden, and should be documented as standard in axillary ultrasound reports of patients with breast cancer.
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Treeratanapun N, Lerttiendamrong B, Vacharathit V, Tantiphlachiva K, Vongwattanakit P, Manasnayakorn S, Vongsaisuwon M. Is sentinel lymph node biopsy without frozen section in early stage breast cancer sufficient in accordance with ACOSOG-Z0011? A retrospective review from King Chulalongkorn Memorial Hospital. BMC Surg 2022; 22:261. [PMID: 35794594 PMCID: PMC9260991 DOI: 10.1186/s12893-022-01709-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/27/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In 2021, there is an increased global trend for sending sentinel lymph node biopsy (SLNB) specimens for permanent section (PS) without intraoperative frozen sections (FS). This pilot study conducted in Thailand determines the re-operation rate for SLNB without FS. METHOD We retrospectively reviewed 239 SLNB cases without FS at King Chulalongkorn Memorial Hospital from April 2016 to April 2021. The patients were diagnosed with primary invasive breast cancer with clinically negative nodes. The clinical nodal status was assessed from physical examination. The re-operation rate was determined by the number of positive SLNs; where 3 more nodal metastases were subjected to a second surgical procedure. RESULT Between April 2016 and April 2021, 239 patients who had undergone SLNB in accordance with ACOSOG Z0011 criteria with PS alone was enrolled. A total of 975 SLNs were removed from these 239 patients, with an average of 4.15 nodes per patient. Out of 239 patients, 21 (8.8%) and 6 (2.5%) had metastatic disease in 1 and 2 nodes, respectively. The remaining 212 (88.7%) patients had no nodal metastasis. None of the patients were subjected to a second surgical procedure. CONCLUSION We conclude that the implementation of SLNB with PS analysis alone in patients who satisfy the ACOSOG Z0011 criteria, with a re-operation rate of 0%, does not have outcomes that would be altered by the standard of care additional FS analysis. With ommision of FS analysis, operation cost, operative time and anesthetic side effects are projected to decrease.
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Affiliation(s)
- Nattanan Treeratanapun
- Department of Surgery, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Sirindhorn Building 1873, Rama 4 Rd., Lumphini, Bangkok, 10330, Thailand
| | - Bhoowit Lerttiendamrong
- Department of Surgery, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Sirindhorn Building 1873, Rama 4 Rd., Lumphini, Bangkok, 10330, Thailand
| | - Voranaddha Vacharathit
- Department of Surgery, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Sirindhorn Building 1873, Rama 4 Rd., Lumphini, Bangkok, 10330, Thailand
| | - Kasaya Tantiphlachiva
- Department of Surgery, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Sirindhorn Building 1873, Rama 4 Rd., Lumphini, Bangkok, 10330, Thailand
| | - Phuphat Vongwattanakit
- Department of Surgery, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Sirindhorn Building 1873, Rama 4 Rd., Lumphini, Bangkok, 10330, Thailand
| | - Sopark Manasnayakorn
- Department of Surgery, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Sirindhorn Building 1873, Rama 4 Rd., Lumphini, Bangkok, 10330, Thailand
| | - Mawin Vongsaisuwon
- Department of Surgery, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Chulalongkorn University, Sirindhorn Building 1873, Rama 4 Rd., Lumphini, Bangkok, 10330, Thailand.
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Li C, Guo Y, Jia L, Yao M, Shao S, Chen J, Xu Y, Wu R. A Convolutional Neural Network Based on Ultrasound Images of Primary Breast Masses: Prediction of Lymph-Node Metastasis in Collaboration With Classification of Benign and Malignant Tumors. Front Physiol 2022; 13:882648. [PMID: 35721528 PMCID: PMC9205241 DOI: 10.3389/fphys.2022.882648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/10/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: A convolutional neural network (CNN) can perform well in either of two independent tasks [classification and axillary lymph-node metastasis (ALNM) prediction] based on breast ultrasound (US) images. This study is aimed to investigate the feasibility of performing the two tasks simultaneously. Methods: We developed a multi-task CNN model based on a self-built dataset containing 5911 breast US images from 2131 patients. A hierarchical loss (HL) function was designed to relate the two tasks. Sensitivity, specificity, accuracy, precision, F1-score, and analyses of receiver operating characteristic (ROC) curves and heatmaps were calculated. A radiomics model was built by the PyRadiomics package. Results: The sensitivity, specificity and area under the ROC curve (AUC) of our CNN model for classification and ALNM tasks were 83.5%, 71.6%, 0.878 and 76.9%, 78.3%, 0.836, respectively. The inconsistency error of ALNM prediction corrected by HL function decreased from 7.5% to 4.2%. Predictive ability of the CNN model for ALNM burden (≥3 or ≥4) was 77.3%, 62.7%, and 0.752, and 66.6%, 76.8%, and 0.768, respectively, for sensitivity, specificity and AUC. Conclusion: The proposed multi-task CNN model highlights its novelty in simultaneously distinguishing breast lesions and indicating nodal burden through US, which is valuable for “personalized” treatment.
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Affiliation(s)
- Chunxiao Li
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuanfan Guo
- Shanghai Key Lab of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
| | - Liqiong Jia
- Department of Ultrasound, Zhongshan Hospital Wusong Branch, Fudan University, Shanghai, China
| | - Minghua Yao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sihui Shao
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jing Chen
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yi Xu
- Shanghai Key Lab of Digital Media Processing and Transmission, Shanghai Jiao Tong University, Shanghai, China
- *Correspondence: Rong Wu, ; Yi Xu,
| | - Rong Wu
- Department of Ultrasound, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Rong Wu, ; Yi Xu,
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16
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Radosa JC, Solomayer EF, Deeken M, Minko P, Zimmermann JSM, Kaya AC, Radosa MP, Stotz L, Huwer S, Müller C, Karsten MM, Wagenpfeil G, Radosa CG. Preoperative Sonographic Prediction of Limited Axillary Disease in Patients with Primary Breast Cancer Meeting the Z0011 Criteria: an Alternative to Sentinel Node Biopsy? Ann Surg Oncol 2022; 29:4764-4772. [PMID: 35486266 PMCID: PMC9246792 DOI: 10.1245/s10434-022-11829-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/13/2022] [Indexed: 12/19/2022]
Abstract
PURPOSE To assess the accuracy of preoperative sonographic staging for prediction of limited axillary disease (LAD, one or two metastatic lymph nodes) and to identify factors associated with high prediction-pathology concordance in patients with early-stage breast cancer meeting the Z0011 criteria. MATERIALS AND METHODS Patients treated between January 2015 and January 2020 were included in this retrospective, multicentric analysis of prospectively acquired service databases. The accuracy of LAD prediction was assessed separately for patients with one and two suspicious lymph nodes on preoperative sonography. Test validity outcomes for LAD prediction were calculated for both groups, and a multivariate model was used to identify factors associated with high accuracy of LAD prediction. RESULTS Of 2059 enrolled patients, 1513 underwent sentinel node biopsy, 436 primary and 110 secondary axillary dissection. For LAD prediction in patients with one suspicious lymph node on preoperative ultrasound, sensitivity was 92% (95% CI 87-95%), negative predictive value (NPV) was 92% (95% CI 87-95%), and the false-negative rate (FNR) was 8% (95% CI 5-13%). For patients with two preoperatively suspicious nodes, the sensitivity, NPV, and FNR were 89% (95% CI 84-93%), 73% (62-83%), and 11% (95% CI 7-16%), respectively. On multivariate analysis, the number of suspicious lymph nodes was associated inversely with correct LAD prediction ([OR 0.01 (95% CI 0.01-0.93), p ≤ 0.01]. CONCLUSIONS Sonographic axillary staging in patients with one metastatic lymph node predicted by preoperative ultrasound showed high accuracy and a false-negative rate comparable to sentinel node biopsy for prediction of limited axillary disease.
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Affiliation(s)
- Julia Caroline Radosa
- Department of Gynaecology and Obstetrics, Saarland University Hospital, Homburg, Saar, Germany.
| | - Erich-Franz Solomayer
- Department of Gynaecology and Obstetrics, Saarland University Hospital, Homburg, Saar, Germany
| | - Martin Deeken
- Department of Gynaecology and Obstetrics, Knappschaftsklinikum Puettlingen, Puettlingen, Germany
| | - Peter Minko
- Department for Diagnostic and Interventionel Radiology, Duesseldorf University Hospital, Duesseldorf, Germany
| | | | - Askin Canguel Kaya
- Department of Gynaecology and Obstetrics, Saarland University Hospital, Homburg, Saar, Germany
| | - Marc Philipp Radosa
- Department of Gynaecology & Obstetrics, Klinikum Bremen-Nord, Bremen, Germany
| | - Lisa Stotz
- Department of Gynaecology and Obstetrics, Saarland University Hospital, Homburg, Saar, Germany
| | - Sarah Huwer
- Department of Gynaecology and Obstetrics, Saarland University Hospital, Homburg, Saar, Germany
| | - Carolin Müller
- Department of Gynaecology and Obstetrics, Saarland University Hospital, Homburg, Saar, Germany
| | - Maria Margarete Karsten
- Charité - University Medicine Berlin, Corporate Member of Freie University Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,Department of Gynecology with Breast Center, Berlin Institute of Health, Berlin, Germany
| | - Gudrun Wagenpfeil
- Institute of Medical Biometry, Epidemiology and Medical Informatics, Saarland University Hospital, Homburg, Saar, Germany
| | - Christoph Georg Radosa
- Department of Gynaecology and Obstetrics, Saarland University Hospital, Homburg, Saar, Germany.,Department of Diagnostic and Interventional Radiology, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
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Sun S, Mutasa S, Liu MZ, Nemer J, Sun M, Siddique M, Desperito E, Jambawalikar S, Ha RS. Deep learning prediction of axillary lymph node status using ultrasound images. Comput Biol Med 2022; 143:105250. [PMID: 35114444 DOI: 10.1016/j.compbiomed.2022.105250] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 01/18/2022] [Accepted: 01/19/2022] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To investigate the ability of our convolutional neural network (CNN) to predict axillary lymph node metastasis using primary breast cancer ultrasound (US) images. METHODS In this IRB-approved study, 338 US images (two orthogonal images) from 169 patients from 1/2014-12/2016 were used. Suspicious lymph nodes were seen on US and patients subsequently underwent core-biopsy. 64 patients had metastatic lymph nodes. A custom CNN was utilized on 248 US images from 124 patients in the training dataset and tested on 90 US images from 45 patients. The CNN was implemented entirely of 3 × 3 convolutional kernels and linear layers. The 9 convolutional kernels consisted of 6 residual layers, totaling 12 convolutional layers. Feature maps were down-sampled using strided convolutions. Dropout with a 0.5 keep probability and L2 normalization was utilized. Training was implemented by using the Adam optimizer and a final SoftMax score threshold of 0.5 from the average of raw logits from each pixel was used for two class classification (metastasis or not). RESULTS Our CNN achieved an AUC of 0.72 (SD ± 0.08) in predicting axillary lymph node metastasis from US images in the testing dataset. The model had an accuracy of 72.6% (SD ± 8.4) with a sensitivity and specificity of 65.5% (SD ± 28.6) and 78.9% (SD ± 15.1) respectively. Our algorithm is available to be shared for research use. (https://github.com/stmutasa/MetUS). CONCLUSION It's feasible to predict axillary lymph node metastasis from US images using a deep learning technique. This can potentially aid nodal staging in patients with breast cancer.
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Affiliation(s)
- Shawn Sun
- Department of Radiology, Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Simukayi Mutasa
- Department of Radiology, Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Michael Z Liu
- Department of Radiology, Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | | | - Mary Sun
- Department of Radiology, Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Maham Siddique
- Department of Radiology, Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Elise Desperito
- Department of Radiology, Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA
| | - Richard S Ha
- Breast Imaging Section Columbia University Medical Center, 622 West 168th Street, PB-1-301, New York, NY, 10032, USA.
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Zhang H, Dong Y, Jia X, Zhang J, Li Z, Chuan Z, Xu Y, Hu B, Huang Y, Chang C, Xu J, Dong F, Xia X, Wu C, Hu W, Wu G, Li Q, Chen Q, Deng W, Jiang Q, Mou Y, Yan H, Xu X, Yan H, Zhou P, Shao Y, Cui L, He P, Qian L, Liu J, Shi L, Zhao Y, Xu Y, Song Y, Zhan W, Zhou J. Comprehensive Risk System Based on Shear Wave Elastography and BI-RADS Categories in Assessing Axillary Lymph Node Metastasis of Invasive Breast Cancer—A Multicenter Study. Front Oncol 2022; 12:830910. [PMID: 35359391 PMCID: PMC8960926 DOI: 10.3389/fonc.2022.830910] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/14/2022] [Indexed: 12/07/2022] Open
Abstract
Purpose To develop a risk stratification system that can predict axillary lymph node (LN) metastasis in invasive breast cancer based on the combination of shear wave elastography (SWE) and conventional ultrasound. Materials and Methods A total of 619 participants pathologically diagnosed with invasive breast cancer underwent breast ultrasound examinations were recruited from a multicenter of 17 hospitals in China from August 2016 to August 2017. Conventional ultrasound and SWE features were compared between positive and negative LN metastasis groups. The regression equation, the weighting, and the counting methods were used to predict axillary LN metastasis. The sensitivity, specificity, and the areas under the receiver operating characteristic curve (AUC) were calculated. Results A significant difference was found in the Breast Imaging Reporting and Data System (BI-RADS) category, the “stiff rim” sign, minimum elastic modulus of the internal tumor and peritumor region of 3 mm between positive and negative LN groups (p < 0.05 for all). There was no significant difference in the diagnostic performance of the regression equation, the weighting, and the counting methods (p > 0.05 for all). Using the counting method, a 0–4 grade risk stratification system based on the four characteristics was established, which yielded an AUC of 0.656 (95% CI, 0.617–0.693, p < 0.001), a sensitivity of 54.60% (95% CI, 46.9%–62.1%), and a specificity of 68.99% (95% CI, 64.5%–73.3%) in predicting axillary LN metastasis. Conclusion A 0–4 grade risk stratification system was developed based on SWE characteristics and BI-RADS categories, and this system has the potential to predict axillary LN metastases in invasive breast cancer.
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Affiliation(s)
- Huiting Zhang
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Yijie Dong
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Xiaohong Jia
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jingwen Zhang
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Zhiyao Li
- Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhirui Chuan
- Department of Medical Ultrasound, Yunnan Cancer Hospital & The Third Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yanjun Xu
- Department of Ultrasound in Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Bin Hu
- Department of Ultrasound, Minhang Hospital, Fudan University, Shanghai, China
| | - Yunxia Huang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cai Chang
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jinfeng Xu
- Department of Ultrasound, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, and The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Fajin Dong
- Department of Ultrasound, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, and The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China
| | - Xiaona Xia
- Department of Ultrasound Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Chengrong Wu
- Department of Ultrasound Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Wenjia Hu
- Department of Ultrasound, People’s Hospital of Henan Province, Zhengzhou, China
| | - Gang Wu
- Department of Ultrasound, People’s Hospital of Henan Province, Zhengzhou, China
| | - Qiaoying Li
- Department of Ultrasound Diseases, Tangdu Hospital, Four Military Medical University, Xi’an, China
| | - Qin Chen
- Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Wanyue Deng
- Department of Ultrasound, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Qiongchao Jiang
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yonglin Mou
- Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, China
| | - Huannan Yan
- Department of Ultrasound, General Hospital of Northern Theater Command, Shenyang, China
| | - Xiaojing Xu
- Department of Ultrasound, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Hongju Yan
- Department of Ultrasound, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ping Zhou
- Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Yang Shao
- Department of Ultrasound, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Ligang Cui
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Ping He
- Department of Ultrasound, Peking University Third Hospital, Beijing, China
| | - Linxue Qian
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Jinping Liu
- Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Liying Shi
- Department of Ultrasound, Affiliated Hospital of Guizhou Medical University, Guizhou, China
| | - Yanan Zhao
- Department of Ultrasound, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Yongyuan Xu
- Department of Ultrasound, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Yanyan Song
- Department of Biostatistics, Institute of Medical Sciences, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Jianqiao Zhou, ; Yanyan Song, ; Weiwei Zhan,
| | - Weiwei Zhan
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Jianqiao Zhou, ; Yanyan Song, ; Weiwei Zhan,
| | - Jianqiao Zhou
- Department of Ultrasound, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- *Correspondence: Jianqiao Zhou, ; Yanyan Song, ; Weiwei Zhan,
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Prediction of axillary nodal burden in patients with invasive lobular carcinoma using MRI. Breast Cancer Res Treat 2021; 186:463-473. [PMID: 33389406 DOI: 10.1007/s10549-020-06056-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 12/09/2020] [Indexed: 01/07/2023]
Abstract
PURPOSE To investigate clinical and imaging features associated with a high nodal burden (≥ 3 metastatic lymph nodes [LNs]) and compare diagnostic performance of US and MRI in patients with invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC). METHODS Retrospective search revealed 239 patients with ILC and 999 with IDC who underwent preoperative US and MRI between January 2016 and June 2019. Patients with ILC were propensity-score-matched with patients with IDC. Univariate and multivariate logistic regression analyses were performed to determine factors associated with ≥ 3 metastatic LNs. RESULTS 412 patients (206 ILC and 206 IDC) were evaluated. Of all patients with ILC, 27.2% (56/206) were node-positive and 7.8% (16/206) showed a high nodal burden. In multivariate analysis, the clinical N stage was the only independent factor associated with a high nodal burden in patients with IDC (odds ratio [OR] 6.24; 95% confidence interval [CI] 1.57-24.73; P = 0.009), but not in patients with ILC. Increased cortical thickness with loss of fatty hilum on US was associated with a high nodal burden in patients with ILC (OR 58.40; 95% CI 5.09-669.71; P = 0.001) and IDC (OR 24.14; 95% CI 3.52-165.37; P = 0.001), while suspicious LN findings at MRI were independently associated with a high nodal burden in ILC only (OR 13.94; 95% CI 2.61-74.39; P = 0.002). CONCLUSION In patients with ILC, MRI findings of suspicious LNs were helpful to predict a high nodal disease burden.
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20
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Chang JM, Leung JWT, Moy L, Ha SM, Moon WK. Axillary Nodal Evaluation in Breast Cancer: State of the Art. Radiology 2020; 295:500-515. [PMID: 32315268 DOI: 10.1148/radiol.2020192534] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Axillary lymph node (LN) metastasis is the most important predictor of overall recurrence and survival in patients with breast cancer, and accurate assessment of axillary LN involvement is an essential component in staging breast cancer. Axillary management in patients with breast cancer has become much less invasive and individualized with the introduction of sentinel LN biopsy (SLNB). Emerging evidence indicates that axillary LN dissection may be avoided in selected patients with node-positive as well as node-negative cancer. Thus, assessment of nodal disease burden to guide multidisciplinary treatment decision making is now considered to be a critical role of axillary imaging and can be achieved with axillary US, MRI, and US-guided biopsy. For the node-positive patients treated with neoadjuvant chemotherapy, restaging of the axilla with US and MRI and targeted axillary dissection in addition to SLNB is highly recommended to minimize the false-negative rate of SLNB. Efforts continue to develop prediction models that incorporate imaging features to predict nodal disease burden and to select proper candidates for SLNB. As methods of axillary nodal evaluation evolve, breast radiologists and surgeons must work closely to maximize the potential role of imaging and to provide the most optimized treatment for patients.
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Affiliation(s)
- Jung Min Chang
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Jessica W T Leung
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Linda Moy
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Su Min Ha
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
| | - Woo Kyung Moon
- From the Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea (J.M.C., S.M.H., W.K.M.); Department of Breast Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex (J.W.T.L.); Department of Radiology, New York University Langone Medical Center, New York, NY (L.M.); NYU Center for Advanced Imaging Innovation and Research, New York, NY (L.M.)
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21
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Lee SE, Sim Y, Kim S, Kim EK. Predictive performance of ultrasonography-based radiomics for axillary lymph node metastasis in the preoperative evaluation of breast cancer. Ultrasonography 2020; 40:93-102. [PMID: 32623841 PMCID: PMC7758097 DOI: 10.14366/usg.20026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 04/01/2020] [Indexed: 12/29/2022] Open
Abstract
PURPOSE The purpose of this study was to evaluate the predictive performance of ultrasonography (US)-based radiomics for axillary lymph node metastasis and to compare it with that of a clinicopathologic model. METHODS A total of 496 patients (mean age, 52.5±10.9 years) who underwent breast cancer surgery between January 2014 and December 2014 were included in this study. Among them, 306 patients who underwent surgery between January 2014 and August 2014 were enrolled as a training cohort, and 190 patients who underwent surgery between September 2014 and December 2014 were enrolled as a validation cohort. To predict axillary lymph node metastasis in breast cancer, we developed a preoperative clinicopathologic model using multivariable logistic regression and constructed a radiomics model using 23 radiomic features selected via least absolute shrinkage and selection operator regression. RESULTS In the training cohort, the areas under the curve (AUC) were 0.760, 0.812, and 0.858 for the clinicopathologic, radiomics, and combined models, respectively. In the validation cohort, the AUCs were 0.708, 0.831, and 0.810, respectively. The combined model showed significantly better diagnostic performance than the clinicopathologic model. CONCLUSION A radiomics model based on the US features of primary breast cancers showed additional value when combined with a clinicopathologic model to predict axillary lymph node metastasis.
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Affiliation(s)
- Si Eun Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Yongsik Sim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Sungwon Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Eun-Kyung Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science and Center for Clinical Image Data Science, Yonsei University College of Medicine, Seoul, Korea
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22
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Choong WL, Evans A, Purdie CA, Wang H, Donnan PT, Lawson B, Macaskill EJ. Mode of presentation and skin thickening on ultrasound may predict nodal burden in breast cancer patients with a positive axillary core biopsy. Br J Radiol 2020; 93:20190711. [PMID: 31971817 DOI: 10.1259/bjr.20190711] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE A number of pre-operative factors predicting nodal burden in females with breast cancer have recently been identified. The aim of this study is to assess if these factors independently influence nodal burden in females with a positive axillary core biopsy. METHODS All node positive patients detected on axillary core biopsy were identified in our cancer audit database. Mode of presentation, age, core tumour grade, core tumour type, ER and HER2 status were evaluated. Tumours were assessed for ultrasound size, distance of tumour-to-skin, presence of invasion of skin and diffuse skin thickening. Axillary lymph nodes were assessed for cortical thickness and presence of ultrasound replaced nodes. Statistical significance was ascertained using univariate logistic regression. A predictive model was produced following a multiple logistic regression model incorporating cross-validation and assessed using receiving operating characteristic curve. RESULTS 115 patients' data were analysed. Patients referred because of symptoms (70% vs 38%, p = 0.005), and those with ultrasound skin thickening (87% vs 59%, p = 0.055) have higher nodal burden than those referred from screening or without skin thickening. These factors were significant after multivariate analysis. The final predictive model included mode of presentation, ultrasound tumour size, cortical thickness and presence of ultrasound skin thickening. The area under curve is 0.77. CONCLUSION We have shown that mode of presentation and ultrasound skin thickening are independent predictors of high nodal burden at surgery. A model has been developed to predict nodal burden pre-operatively, which may lead to avoidance of axillary node clearance in patients with lower nodal burden. ADVANCES IN KNOWLEDGE Method of presentation and skin involvement/proximity to skin by the primary tumour are known to influence outcome and nodal involvement respectively but have not been studied with regard to nodal burden. We have shown that mode of presentation and skin thickening at ultrasound are independent predictors of high nodal burden at surgery.
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Affiliation(s)
- Wen Ling Choong
- Department of Breast Surgery, Ninewells Hospital and Medical School, Level 6, Dundee, UK
| | - Andrew Evans
- Department of Radiology, Ninewells Hospital and Medical School, Level 6, Dundee, UK
| | - Colin A Purdie
- Department of Pathology, Ninewells Hospital and Medical School, Dundee, UK
| | - Huan Wang
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Peter T Donnan
- Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Brooke Lawson
- Department of Radiology, Ninewells Hospital and Medical School, Level 6, Dundee, UK
| | - E Jane Macaskill
- Department of Breast Surgery, Ninewells Hospital and Medical School, Level 6, Dundee, UK
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23
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Bae MS. Using Deep Learning to Predict Axillary Lymph Node Metastasis from US Images of Breast Cancer. Radiology 2020; 294:29-30. [DOI: 10.1148/radiol.2019192339] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Min Sun Bae
- From the Department of Radiology, Inha University Hospital, 27 Inhang-ro, Jung-gu, Incheon 22332, Korea
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Zhou LQ, Wu XL, Huang SY, Wu GG, Ye HR, Wei Q, Bao LY, Deng YB, Li XR, Cui XW, Dietrich CF. Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning. Radiology 2020; 294:19-28. [PMID: 31746687 DOI: 10.1148/radiol.2019190372] [Citation(s) in RCA: 155] [Impact Index Per Article: 38.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models' performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.
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Affiliation(s)
- Li-Qiang Zhou
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - Xing-Long Wu
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - Shu-Yan Huang
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - Ge-Ge Wu
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - Hua-Rong Ye
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - Qi Wei
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - Ling-Yun Bao
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - You-Bin Deng
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - Xing-Rui Li
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - Xin-Wu Cui
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
| | - Christoph F Dietrich
- From the Sino-German Tongji-Caritas Research Center of Ultrasound in Medicine, Department of Medical Ultrasound, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China (L.Q.Z., G.G.W., Q.W., Y.B.D., X.W.C., C.F.D.); School of Mathematics and Computer Science, Wuhan Textile University, Wuhan, Hubei Province, China (X.L.W.); Department of Ultrasound, The First People's Hospital of Huaihua, University of South China, Huaihua, China (S.Y.H.); Department of Ultrasound, China Resources & Wisco General Hospital, Wuhan, Hubei Province, China (H.R.Y.); Department of Ultrasound, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China (L.Y.B.); Department of Thyroid and Breast Surgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China (X.R.L.); and Medical Clinic 2, Caritas-Krankenhaus Bad Mergentheim, Academic Teaching Hospital of the University of Wuerzburg, Bad Mergentheim, Germany (C.F.D.)
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Zhang Y, Li J, Fan Y, Li X, Qiu J, Zhu M, Li H. Risk factors for axillary lymph node metastases in clinical stage T1-2N0M0 breast cancer patients. Medicine (Baltimore) 2019; 98:e17481. [PMID: 31577783 PMCID: PMC6783158 DOI: 10.1097/md.0000000000017481] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 07/16/2019] [Accepted: 09/12/2019] [Indexed: 02/05/2023] Open
Abstract
Axillary lymph node metastasis (ALNM) is commonly the earliest detectable clinical manifestation of breast cancer when distant metastasis emerges. This study aimed to explore the influencing factors of ALNM and develop models that can predict its occurrence preoperatively.Cases of sonographically visible clinical stage T1-2N0M0 breast cancers treated with breast and axillary surgery at West China Hospital were retrospectively reviewed. Univariate and multivariate logistic regression analyses were performed to evaluate associations between ALNM and variables. Decision tree analyses were performed to construct predictive models using the C5.0 packages.Of the 1671 tumors, 541 (32.9%) showed axillary lymph node positivity on final surgical histopathologic analysis. In multivariate logistic regression analysis, tumor size (P < .001), infiltration of subcutaneous adipose tissue (P < .001), infiltration of the interstitial adipose tissue (P = .031), and tumor quadrant locations (P < .001) were significantly correlated with ALNM. Furthermore, the accuracy in the decision tree model was 69.52%, and the false-negative rate (FNR) was 74.18%. By using the error-cost matrix algorithm, the FNR significantly decreased to 14.75%, particularly for nodes 5, 8, and 13 (FNR: 11.4%, 9.09%, and 14.29% in the training set and 18.1%,14.71%, and 20% in the test set, respectively).In summary, our study demonstrated that tumor lesion boundary, tumor size, and tumor quadrant locations were the most important factors affecting ALNM in cT1-2N0M0 stage breast cancer. The decision tree built using these variables reached a slightly higher FNR than sentinel lymph node dissection in predicting ALNM in some selected patients.
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Affiliation(s)
| | - Ji Li
- Department of Breast Surgery
- Anesthesia surgery center
| | | | | | | | - Mou Zhu
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China
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Zhang H, Sui X, Zhou S, Hu L, Huang X. Correlation of Conventional Ultrasound Characteristics of Breast Tumors With Axillary Lymph Node Metastasis and Ki-67 Expression in Patients With Breast Cancer. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2019; 38:1833-1840. [PMID: 30480840 DOI: 10.1002/jum.14879] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 10/26/2018] [Indexed: 06/09/2023]
Abstract
OBJECTIVES To explore the association between the ultrasound (US) characteristics of breast tumors with axillary lymph node metastasis (ALNM) and Ki-67 expression in patients with breast cancer. METHODS In total, 527 consecutive patients with breast cancer who had undergone US examinations and curative surgery with axillary lymph node evaluations were included. The size, shape, aspect ratio, margin, internal echo, internal calcification, posterior echo attenuation, lymphatic hilar structure, cortical thickness, and blood flow of the axillary lymph nodes or primary breast lesions were observed with conventional US. Pathologic prognostic factors, including the histologic type of the tumor, histologic grade, estrogen and progesterone receptor status, lymph node status, and Ki-67 expression were determined. A logistic regression model was used to evaluate whether the US characteristics of primary breast lesions were associated with ALNM and Ki-67 expression. RESULTS The maximum tumor diameter (odds ratio [OR], 1.54; 95% confidence interval [CI], 1.05-2.27; P = .028), tumor margin (OR, 2.89; 95% CI, 1.69-4.94; P < .001), internal echo (OR, 2.17; 95% CI, 1.47-3.20; P < .001), and Ki-67 status (OR, 3.57; 95% CI, 2.29-5.58; P < .001) had significant value as independent predictors of ALNM. Only the internal echo (OR, 1.95; 95% CI, 1.28-2.95; P = .002) of breast cancer was an independent predictor of the Ki-67 status. The heterogeneity in the internal echo indicated faster cancer cell proliferation and was associated with a worse prognosis in patients with breast carcinoma. CONCLUSIONS Certain conventional US characteristics may be useful predictors of ALNM and the Ki-67 status in patients with breast cancer.
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Affiliation(s)
- Hang Zhang
- the Department of Ultrasound, Affiliated Provincial Hospital of Anhui Medical University, First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Hospital, Hefei, China
| | - Xiufang Sui
- the Department of Ultrasound, Affiliated Provincial Hospital of Anhui Medical University, First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Hospital, Hefei, China
| | - Suzhi Zhou
- Department of Ultrasound, Children's Hospital of Anhui Province, Hefei, China
| | - Lei Hu
- the Department of Ultrasound, Affiliated Provincial Hospital of Anhui Medical University, First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Hospital, Hefei, China
| | - Xian Huang
- the Department of Ultrasound, Affiliated Provincial Hospital of Anhui Medical University, First Affiliated Hospital of the University of Science and Technology of China, Anhui Provincial Hospital, Hefei, China
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Li JW, Tong YY, Jiang YZ, Shui XJ, Shi ZT, Chang C. Clinicopathologic and Ultrasound Variables Associated With a Heavy Axillary Nodal Tumor Burden in Invasive Breast Carcinoma. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2019; 38:1747-1755. [PMID: 30480341 DOI: 10.1002/jum.14863] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 10/12/2018] [Indexed: 06/09/2023]
Abstract
OBJECTIVES To identify clinicopathologic and ultrasound (US) variables that were associated with a heavy nodal tumor burden, which was defined as 3 or more lymph nodes involved with metastasis to the axilla after invasive breast carcinoma. METHODS With ethical approval, 621 patients with a pathologic diagnosis of invasive breast carcinoma were retrospectively analyzed for clinical, pathologic, and US data. Pathologic findings were ascertained by the final paraffin pathologic analysis. Ultrasound characteristics were evaluated on the basis of the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS). Univariate and multivariate logistic regression analyses were used to assess the clinicopathologic and US variables that were associated with a heavy nodal tumor burden at the axilla. RESULTS There were 107 cases (17.2%) of invasive breast carcinoma with a heavy tumor burden at the axilla. The independent clinicopathologic variables for a heavy tumor burden at the axilla included a tumor size of 2 to 5 cm (odds ratio [OR], 1.86; P = .036), the presence of lymphovascular invasion (OR, 23.52; P < .001), the presence of papillary invasion (OR, 2.93; P = .043), and a non-triple-negative subtype (OR, 2.34; P = .04). The independent US features of breast tumors that were associated with a heavy tumor burden at the axilla included BI-RADS category 5 (OR, 5.50; P = .024) and a posterior acoustic shadow (OR, 1.94; P = .024). CONCLUSIONS A large tumor size, lymphovascular invasion, papillary invasion, and a non-triple-negative subtype on the pathologic analysis as well as BI-RADS category 5 and a posterior acoustic shadow on a US assessment were associated with a heavy nodal tumor burden at the axilla. These US characteristics of the primary breast carcinoma might provide additional information to axillary US for the prediction of axillary nodal tumor loads.
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Affiliation(s)
- Jia-Wei Li
- Departments of Medical Ultrasound, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yang Tong
- Departments of Medical Ultrasound, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Zhou Jiang
- Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xu-Juan Shui
- Department of Medical Ultrasound, Wenzhou People's Hospital, Third Clinical Institute, affiliated with Wenzhou Medical University, Wenzhou, China
| | - Zhao-Ting Shi
- Departments of Medical Ultrasound, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Cai Chang
- Departments of Medical Ultrasound, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
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