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Xie S, Tang W, Zhang C, Wang J, Wang M, Zhou Y. Classification of breast edema on T2-weighted imaging for predicting sentinel lymph node metastasis and biological behavior in breast cancer. Clin Radiol 2024; 79:e1003-e1009. [PMID: 38763808 DOI: 10.1016/j.crad.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/16/2024] [Accepted: 04/24/2024] [Indexed: 05/21/2024]
Abstract
OBJECTIVE To determine whether preoperative classification of breast edema on T2-weighted imaging (T2WI) is useful for predicting sentinel lymph node (SLN) metastasis and biological behavior in patients with early-stage breast cancer. METHODS This retrospective study involved 341 women with breast cancer who underwent breast MRI from January 2019 to March 2022. Breast edema was scored on a scale of 1-4 on T2WI (1, no edema; 2, peritumoral edema; 3, prepectoral edema; and 4, subcutaneous edema). A logistic regression model was employed for univariate and multivariate analyses. A clinicopathological model was established using independent influencing factors identified in the multivariate analyses, excluding breast edema score (BES). Subsequently, BES was incorporated into this model to establish a combined BES model. The AUC and Delong test were used to examine the additional predictive value of the BES. RESULTS Logistic regression analysis showed that breast edema was an independent risk factor for SLN metastasis. The combined BES model significantly improved the predictive performance of SLN metastasis compared with the clinicopathological model alone (AUC, 0.77 vs. 0.71; p=0.005). In addition, the BES was significantly positively correlated with the tumor diameter (p<0.001), histologic grade (p=0.001), Ki-67 index (p<0.001), and non-luminal subtypes (p<0.001). CONCLUSION The BES on T2WI is useful for predicting SLN metastasis. A higher grade of breast edema is associated with breast cancer aggressiveness and increases the probability of SLN metastasis.
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Affiliation(s)
- S Xie
- Departments of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241001, Anhui, China; Departments of Radiology, Fuyang Hospital of Anhui Medical University, Fuyang 236000, Anhui, China
| | - W Tang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - C Zhang
- Departments of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241001, Anhui, China
| | - J Wang
- Departments of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241001, Anhui, China
| | - M Wang
- Departments of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241001, Anhui, China
| | - Y Zhou
- Departments of Radiology, Yijishan Hospital of Wannan Medical College, Wuhu 241001, Anhui, China.
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Wang X, Nie L, Zhu Q, Zuo Z, Liu G, Sun Q, Zhai J, Li J. Artificial intelligence assisted ultrasound for the non-invasive prediction of axillary lymph node metastasis in breast cancer. BMC Cancer 2024; 24:910. [PMID: 39075447 PMCID: PMC11285453 DOI: 10.1186/s12885-024-12619-6] [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: 04/19/2023] [Accepted: 07/09/2024] [Indexed: 07/31/2024] Open
Abstract
PURPOSE A practical noninvasive method is needed to identify lymph node (LN) status in breast cancer patients diagnosed with a suspicious axillary lymph node (ALN) at ultrasound but a negative clinical physical examination. To predict ALN metastasis effectively and noninvasively, we developed an artificial intelligence-assisted ultrasound system and validated it in a retrospective study. METHODS A total of 266 patients treated with sentinel LN biopsy and ALN dissection at Peking Union Medical College & Hospital(PUMCH) between the year 2017 and 2019 were assigned to training, validation and test sets (8:1:1). A deep learning model architecture named DeepLabV3 + was used together with ResNet-101 as the backbone network to create an ultrasound image segmentation diagnosis model. Subsequently, the segmented images are classified by a Convolutional Neural Network to predict ALN metastasis. RESULTS The area under the receiver operating characteristic curve of the model for identifying metastasis was 0.799 (95% CI: 0.514-1.000), with good end-to-end classification accuracy of 0.889 (95% CI: 0.741-1.000). Moreover, the specificity and positive predictive value of this model was 100%, providing high accuracy for clinical diagnosis. CONCLUSION This model can be a direct and reliable tool for the evaluation of individual LN status. Our study focuses on predicting ALN metastasis by radiomic analysis, which can be used to guide further treatment planning in breast cancer.
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Affiliation(s)
- Xuefei Wang
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China
| | - Lunyiu Nie
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Qingli Zhu
- Ultrasonography Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China
| | - Zhichao Zuo
- Radiology Department, Xiangtan Central Hospital, Hunan, China
| | - Guanmo Liu
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China
| | - Qiang Sun
- Breast Surgery Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China.
| | - Jidong Zhai
- Department of Computer Science and Technology, Tsinghua University, Beijing, China.
| | - Jianchu Li
- Ultrasonography Department, Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical College and Hospital, No. 3 Dongdan, Dongcheng District, Beijing, China.
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Shahriarirad R, Meshkati Yazd SM, Fathian R, Fallahi M, Ghadiani Z, Nafissi N. Prediction of sentinel lymph node metastasis in breast cancer patients based on preoperative features: a deep machine learning approach. Sci Rep 2024; 14:1351. [PMID: 38228684 PMCID: PMC10791698 DOI: 10.1038/s41598-024-51244-y] [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: 09/15/2023] [Accepted: 01/02/2024] [Indexed: 01/18/2024] Open
Abstract
Sentinel lymph node (SLN) biopsy is the standard surgical approach to detect lymph node metastasis in breast cancer. Machine learning is a novel tool that provides better accuracy for predicting positive SLN involvement in breast cancer patients. This study obtained data from 2890 surgical cases of breast cancer patients from two referral hospitals in Iran from 2000 to 2021. Patients whose SLN involvement status was identified were included in our study. The dataset consisted of preoperative features, including patient features, gestational factors, laboratory data, and tumoral features. In this study, TabNet, an end-to-end deep learning model, was proposed to predict SLN involvement in breast cancer patients. We compared the accuracy of our model with results from logistic regression analysis. A total of 1832 patients with an average age of 51 ± 12 years were included in our study, of which 697 (25.5%) had SLN involvement. On average, the TabNet model achieved an accuracy of 75%, precision of 81%, specificity of 70%, sensitivity of 87%, and AUC of 0.74, while the logistic model demonstrated an accuracy of 70%, precision of 73%, specificity of 65%, sensitivity of 79%, F1 score of 73%, and AUC of 0.70 in predicting the SLN involvement in patients. Vascular invasion, tumor size, core needle biopsy pathology, age, and FH had the most contributions to the TabNet model. The TabNet model outperformed the logistic regression model in all metrics, indicating that it is more effective in predicting SLN involvement in breast cancer patients based on preoperative data.
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Affiliation(s)
- Reza Shahriarirad
- Thoracic and Vascular Surgery Research Center, Shiraz University of Medical Science, Shiraz, Iran
| | | | - Ramin Fathian
- Faculty of Engineering, University of Alberta, Edmonton, AB, Canada
| | | | - Zahra Ghadiani
- Department of Breast, Rasoul Akram Hospital Clinical Research Development Center (RCRDC), Iran University of Medical Sciences, Tehran, Iran
| | - Nahid Nafissi
- Department of Breast, Rasoul Akram Hospital Clinical Research Development Center (RCRDC), Iran University of Medical Sciences, Tehran, Iran.
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Wang Z, Zhang H, Lin F, Zhang R, Ma H, Shi Y, Yang P, Zhang K, Zhao F, Mao N, Xie H. Intra- and Peritumoral Radiomics of Contrast-Enhanced Mammography Predicts Axillary Lymph Node Metastasis in Patients With Breast Cancer: A Multicenter Study. Acad Radiol 2023; 30 Suppl 2:S133-S142. [PMID: 37088646 DOI: 10.1016/j.acra.2023.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 02/03/2023] [Accepted: 02/03/2023] [Indexed: 04/25/2023]
Abstract
RATIONALE AND OBJECTIVES This multicenter study aimed to explore the feasibility of radiomics based on intra- and peritumoral regions on preoperative breast cancer contrast-enhanced mammography (CEM) to predict axillary lymph node (ALN) metastasis. MATERIALS AND METHODS A total of 809 patients with preoperative breast cancer CEM images from two centers were retrospectively recruited. Least absolute shrinkage and selection operator (LASSO) regression was used to select radiomics features extracted from CEM images in regions of the tumor and peritumoral area of five and ten mm as well as construct radiomics signature. A nomogram, including the optimal radiomics signature and clinicopathological factors, was then constructed. Nomogram performance was evaluated using AUC and compared with breast radiologists directly. RESULTS In the internal testing set, AUCs of peritumoral signatures decreased when the peritumoral area increased and signaturetumor + 10mm demonstrated the best performance with an AUC of 0.712. The nomogram incorporating signaturetumor + 10mm, tumor diameter, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and CEM-reported lymph node status yielded maximum AUCs of 0.753 and 0.732 in internal and external testing sets, respectively. Moreover, the nomogram outperformed radiologists and improved diagnostic performance of radiologists. CONCLUSION The nomogram based on CEM intra- and peritumoral regions may provide a noninvasive auxiliary tool to guide treatment strategy of ALN metastasis in breast cancer.
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Affiliation(s)
- Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000; Institute of medical imaging, Binzhou Medical University, Yantai, Shandong, P. R. China, 264000
| | - Ran Zhang
- Artificial Intelligence and Clinical Innovation Institute, Huiying Medical Technology Co., Ltd, P. R. China, 100192
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Ping Yang
- Department of Pathology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Kun Zhang
- Department of Breast Surgery, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, P. R. China, 264000
| | - Feng Zhao
- School of Compute Science and Technology, Shandong Technology and Business University, Yantai, Shandong, People's Republic of China, 264000
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, No. 20 Yuhuangding east road, Yantai, Shandong, P. R. China, 264000.
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Takada K, Kashiwagi S, Iimori N, Kouhashi R, Yabumoto A, Goto W, Asano Y, Tauchi Y, Morisaki T, Ogisawa K, Shibutani M, Tanaka H, Maeda K. Impact of oral statin therapy on clinical outcomes in patients with cT1 breast cancer. BMC Cancer 2023; 23:224. [PMID: 36894884 PMCID: PMC9999569 DOI: 10.1186/s12885-023-10631-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 02/09/2023] [Indexed: 03/11/2023] Open
Abstract
PURPOSE A previous meta-analysis examining the relationship between statin use and breast cancer reported that the inhibitory effect of statins on breast cancer may be more pronounced in early-stage cases. In this study, we aimed to investigate the effects of hyperlipidemia treatment at the time of breast cancer diagnosis and to examine its correlation with metastasis to axillary lymph nodes among patients with so-called cT1 breast cancer whose primary lesion was 2 cm or less and was pathologically evaluated by sentinel lymph node biopsy or axillary lymph node dissection. We also investigated the effects of hyperlipidemic drugs on the prognosis of patients with early-stage breast cancer. METHODS After excluding cases that did not meet the criteria, we analyzed data from 719 patients who were diagnosed with breast cancer, with a primary lesion of 2 cm or less identified by preoperative imaging, and who underwent surgery without preoperative chemotherapy. RESULTS Regarding hyperlipidemia drugs, no correlation was found between statin use and lymph node metastasis (p = 0.226), although a correlation was found between lipophilic statin use and lymph node metastasis (p = 0.042). Also, the disease-free survival periods were prolonged following treatment of hyperlipidemia (p = 0.047, hazard ratio: 0.399) and statin administration (p = 0.028, hazard ratio: 0.328). CONCLUSION In cT1 breast cancer, the results suggest that oral statin therapy may contribute to favorable outcomes.
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Affiliation(s)
- Koji Takada
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Shinichiro Kashiwagi
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Nozomi Iimori
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Rika Kouhashi
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Akimichi Yabumoto
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Wataru Goto
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yuka Asano
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Yukie Tauchi
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Tamami Morisaki
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Kana Ogisawa
- Department of Breast Surgical Oncology, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Masatsune Shibutani
- Department of Gastroenterological Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Hiroaki Tanaka
- Department of Gastroenterological Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Kiyoshi Maeda
- Department of Gastroenterological Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
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Cheng Y, Xu S, Wang H, Wang X, Niu S, Luo Y, Zhao N. Intra- and peri-tumoral radiomics for predicting the sentinel lymph node metastasis in breast cancer based on preoperative mammography and MRI. Front Oncol 2022; 12:1047572. [PMID: 36578933 PMCID: PMC9792138 DOI: 10.3389/fonc.2022.1047572] [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: 09/18/2022] [Accepted: 11/21/2022] [Indexed: 12/14/2022] Open
Abstract
Purpose This study aims to investigate values of intra- and peri-tumoral regions in the mammography and magnetic resonance imaging (MRI) image for prediction of sentinel lymph node metastasis (SLNM) in invasive breast cancer (BC). Methods This study included 208 patients with invasive BC between Spe. 2017 and Apr. 2021. All patients underwent preoperative digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI) scans. Radiomics features were extracted from manually outlined intratumoral regions, and automatically dilated peritumoral tumor regions in each modality. The least absolute shrinkage and selection operator (LASSO) regression was used to select key features from each region to develop radiomics signatures (RSs). Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity and negative predictive value (NPV) were calculated to evaluate performance of the RSs. Results Intra- and peri-tumoral regions of BC can provide complementary information on the SLN status. In each modality, the Com-RSs derived from combined intra- and peri-tumoral regions always yielded higher AUCs than the Intra-RSs or Peri-RSs. A total of 10 and 11 features were identified as the most important predictors from mammography (DM plus DBT) and MRI (DCE-MRI plus DWI), respectively. The DCE-MRI plus DWI generated higher AUCs compared with DM plus DBT in the training (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.897 vs. 0.846) and validation (AUCs, DCE-MRI plus DWI vs. DM plus DBT, 0.826 vs. 0.786) cohort. Conclusions Radiomics features from intra- and peri-tumoral regions can provide complementary information to identify the SLNM in both mammography and MRI. The DCE-MRI plus DWI generated lower specificity, but higher AUC, accuracy, sensitivity and negative predictive value compared with DM plus DBT.
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Affiliation(s)
- Yuan Cheng
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Shu Xu
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Haotian Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Xiaoyu Wang
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Shuxian Niu
- Department of Biomedical Engineering, School of Intelligent Medicine, China Medical University, Shenyang, China
| | - Yahong Luo
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Nannan Zhao
- Department of Radiology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China,*Correspondence: Nannan Zhao,
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Ceylan C, Pehlevan Ozel H, Agackiran I, Altun Ozdemir B, Atas H, Menekse E. Preoperative predictive factors affecting sentinel lymph node positivity in breast cancer and comparison of their effectiveness with existing nomograms. Medicine (Baltimore) 2022; 101:e32170. [PMID: 36482614 PMCID: PMC9726412 DOI: 10.1097/md.0000000000032170] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
This study aimed to establish a strong regression model by revealing the preoperative predictive factors for sentinel lymph node (SLN) positivity in patients with early stage breast cancer (ESBC). In total, 445 patients who underwent SLN dissection for ESBC were included. All data that may be potential predictors of SLN positivity were retrospectively analyzed. Tumor size >2 cm, human epidermal growth factor receptor 2 (HER2) + status, lymphovascular invasion (LVI), palpable tumor, microcalcifications, multifocality or multicentricity, and axillary ultrasonographic findings were defined as independent predictors of SLN involvement. The area under the receiver operating characteristic (ROC) curve (AUC) values were 0.797, 0.808, and 0.870 for the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram, MD Anderson Cancer Center (MDACC) nomogram, and our regression model, respectively (P < .001). The recent model for predicting SLN status in ESBC was found to be stronger than existing nomograms. Parameters not included in current nomograms, such as palpable tumors, microcalcifications, and axillary ultrasonographic findings, are likely to make this model more meaningful.
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Affiliation(s)
- Cengiz Ceylan
- Department of Surgery, Inönü University, Malatya, Turkey
- * Correspondence: Cengiz Ceylan, Department of Surgery, Inönü University, Malatya, Yeşilyurt 44915, Turkey (e-mail: )
| | | | - Ibrahim Agackiran
- Department of Surgery, Elaziğ Fethi Sekin City Hospital, Elazıital, Elaziğ, Turkey
| | | | - Hakan Atas
- Department of Surgery, Ankara City Hospital, Ankara, Turkey
| | - Ebru Menekse
- Department of Surgery, Ankara City Hospital, Ankara, Turkey
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Orozco JIJ, Le J, Ensenyat-Mendez M, Baker JL, Weidhaas J, Klomhaus A, Marzese DM, DiNome ML. Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer. Ann Surg Oncol 2022; 29:6407-6414. [PMID: 35842534 PMCID: PMC10413094 DOI: 10.1245/s10434-022-12143-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/24/2022] [Indexed: 12/16/2022]
Abstract
BACKGROUND In the era of molecular stratification and effective multimodality therapies, surgical staging of the axilla is becoming less relevant for patients with estrogen receptor (ER)-positive early-stage breast cancer (EBC). Therefore, a nonsurgical method for accurately predicting lymph node disease is the next step in the de-escalation of axillary surgery. This study sought to identify epigenetic signatures in the primary tumor that accurately predict lymph node status. PATIENTS AND METHODS We selected a cohort of patients in The Cancer Genome Atlas (TCGA) with ER-positive, HER2-negative invasive ductal carcinomas, and clinically-negative axillae (n = 127). Clinicopathological nomograms from the Memorial Sloan Kettering Cancer Center (MSKCC) and the MD Anderson Cancer Center (MDACC) were calculated. DNA methylation (DNAm) patterns from primary tumor specimens were compared between patients with pN0 and those with > pN0. The cohort was divided into training (n = 85) and validation (n = 42) sets. Random forest was employed to obtain the combinations of DNAm features with the highest accuracy for stratifying patients with > pN0. The most efficient combinations were selected according to the area under the curve (AUC). RESULTS Clinicopathological models displayed a modest predictive potential for identifying > pN0 disease (MSKCC AUC 0.76, MDACC AUC 0.69, p = 0.15). Differentially methylated sites (DMS) between patients with pN0 and those with > pN0 were identified (n = 1656). DMS showed a similar performance to the MSKCC model (AUC = 0.76, p = 0.83). Machine learning approaches generated five epigenetic classifiers, which showed higher discriminative potential than the clinicopathological variables tested (AUC > 0.88, p < 0.05). CONCLUSIONS Epigenetic classifiers based on primary tumor characteristics can efficiently stratify patients with no lymph node involvement from those with axillary lymph node disease, thereby providing an accurate method of staging the axilla.
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Affiliation(s)
- Javier I J Orozco
- Saint John's Cancer Institute, Providence Saint John's Health Center, Santa Monica, CA, USA
| | - Julie Le
- Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Miquel Ensenyat-Mendez
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Carretera de Valldemosa 79, -1F, Palma, Spain
| | - Jennifer L Baker
- Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Joanne Weidhaas
- Department of Radiation Oncology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Alexandra Klomhaus
- Department of Medicine Statistics Core, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Diego M Marzese
- Cancer Epigenetics Laboratory at the Cancer Cell Biology Group, Institut d'Investigació Sanitària Illes Balears (IdISBa), Carretera de Valldemosa 79, -1F, Palma, Spain.
| | - Maggie L DiNome
- Department of Surgery, Duke University School of Medicine, Durham, NC, USA.
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Fong W, Tan L, Tan C, Wang H, Liu F, Tian H, Shen S, Gu R, Hu Y, Jiang X, Mei J, Liang J, Hu T, Chen K, Yu F. Predicting the risk of axillary lymph node metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features and the use of nomograms: a prospective single-center observational study. Eur Radiol 2022; 32:8200-8212. [PMID: 36169686 DOI: 10.1007/s00330-022-08855-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 04/24/2022] [Accepted: 05/01/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES The purpose of this study was to establish two preoperative nomograms to evaluate the risk for axillary lymph node (ALN) metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features. METHODS We prospectively evaluated 593 consecutive female participants who were diagnosed with cT1-3N0-1M0 breast cancer between March 2018 and May 2019 at Sun Yat-Sen Memorial Hospital. The participants were randomly classified into training and validation sets in a 4:1 ratio for the development and validation of the nomograms, respectively. Multivariate logistic regression analysis was performed to identify independent predictors of ALN status. We developed Nomogram A and Nomogram B to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2), respectively. RESULTS A total of 528 participants were evaluated in the final analyses. Multivariable analysis revealed that the number of suspicious lymph nodes, long axis, short-to-long axis ratio, cortical thickness, tumor location, and histological grade were independent predictors of ALN status. The AUCs of nomogram A in the training and validation groups were 0.83 and 0.78, respectively. The AUCs of nomogram B in the training and validation groups were 0.87 and 0.87, respectively. Both nomograms were well-calibrated. CONCLUSION We developed two preoperative nomograms that can be used to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2) in early breast cancer patients. Both nomograms are useful tools that will help clinicians predict the risk of ALN metastasis and facilitate therapy decision-making about axillary surgery. KEY POINTS • We developed two preoperative nomograms to predict axillary lymph node status based on ultrasonographic-clinicopathologic features. • Nomogram A was used to predict axillary lymph node metastasis (presence vs. absence). The AUCs in the training and validation groups were 0.83 and 0.78, respectively. Nomogram B was used to estimate the number of metastatic lymph nodes ( ≤ 2 vs. > 2). The AUCs in the training and validation group were 0.87 and 0.87, respectively. • Our nomograms may help clinicians weigh the risks and benefits of axillary surgery more appropriately.
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Affiliation(s)
- Wengcheng Fong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China
| | - Luyuan Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China
| | - Cui Tan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Pathology, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Hongli Wang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Fengtao Liu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Huan Tian
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China
| | - Shiyu Shen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ran Gu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yue Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Xiaofang Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jingsi Mei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jing Liang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Tingting Hu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China
| | - Kai Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China. .,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China. .,Artificial Intelligence Laboratory, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Fengyan Yu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China. .,Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, Guangzhou, 510120, China.
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10
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Wang C, Chen X, Luo H, Liu Y, Meng R, Wang M, Liu S, Xu G, Ren J, Zhou P. Development and Internal Validation of a Preoperative Prediction Model for Sentinel Lymph Node Status in Breast Cancer: Combining Radiomics Signature and Clinical Factors. Front Oncol 2021; 11:754843. [PMID: 34820327 PMCID: PMC8606782 DOI: 10.3389/fonc.2021.754843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose To develop and internally validate a nomogram combining radiomics signature of primary tumor and fibroglandular tissue (FGT) based on pharmacokinetic dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical factors for preoperative prediction of sentinel lymph node (SLN) status in breast cancer patients. Methods This study retrospectively enrolled 186 breast cancer patients who underwent pretreatment pharmacokinetic DCE-MRI with positive (n = 93) and negative (n = 93) SLN. Logistic regression models and radiomics signatures of tumor and FGT were constructed after feature extraction and selection. The radiomics signatures were further combined with independent predictors of clinical factors for constructing a combined model. Prediction performance was assessed by receiver operating characteristic (ROC), calibration, and decision curve analysis. The areas under the ROC curve (AUCs) of models were corrected by 1,000-times bootstrapping method and compared by Delong's test. The added value of each independent model or their combinations was also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices. This report referred to the "Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis" (TRIPOD) statement. Results The AUCs of the tumor radiomic model (eight features) and the FGT radiomic model (three features) were 0.783 (95% confidence interval [CI], 0.717-0.849) and 0.680 (95% CI, 0.604-0.757), respectively. A higher AUC of 0.799 (95% CI, 0.737-0.862) was obtained by combining tumor and FGT radiomics signatures. By further combining tumor and FGT radiomics signatures with progesterone receptor (PR) status, a nomogram was developed and showed better discriminative ability for SLN status [AUC 0.839 (95% CI, 0.783-0.895)]. The IDI and NRI indices also showed significant improvement when combining tumor, FGT, and PR compared with each independent model or a combination of any two of them (all p < 0.05). Conclusion FGT and clinical factors improved the prediction performance of SLN status in breast cancer. A nomogram integrating the DCE-MRI radiomics signature of tumor and FGT and PR expression achieved good performance for the prediction of SLN status, which provides a potential biomarker for clinical treatment decision-making.
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Affiliation(s)
- Chunhua Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiaoyu Chen
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongbing Luo
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuanyuan Liu
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Ruirui Meng
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Wang
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Siyun Liu
- Pharmaceutical Diagnostics, General Electric (GE) Company (Healthcare), Beijing, China
| | - Guohui Xu
- Department of Interventional Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
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11
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Guan X, Dong Y, Fan Z, Zhan Y, Xie X, Xu G, Zhang Y, Guo G, Shi A. Aldehyde dehydrogenase 1 (ALDH1) immunostaining in axillary lymph node metastases is an independent prognostic factor in ALDH1-positive breast cancer. J Int Med Res 2021; 49:3000605211047279. [PMID: 34644211 PMCID: PMC8642120 DOI: 10.1177/03000605211047279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Objective To determine whether aldehyde dehydrogenase 1 (ALDH1) immunostaining in axillary lymph node metastases in patients with breast cancer is associated with poor clinical prognosis. Methods This retrospective study reviewed data from the medical records of patients with immunohistochemistry-confirmed invasive ductal carcinoma (IDC) and 1–3 metastatic lymph nodes in the ipsilateral axilla between December 2012 and July 2015. The association between ALDH1 immunostaining in axillary lymph node metastases and clinical parameters and prognosis was analysed using χ2-test, Kaplan–Meier survival analysis, univariate and multivariate Cox regression analyses. Results A total of 229 patients with IDC were enrolled in the study. The median follow-up was 61 months (range, 20–89 months). Patients with ALDH1-positive axillary lymph node metastases had significantly shorter relapse-free survival and overall survival compared with those with ALDH1-negative axillary lymph node metastases. ALDH1 immunostaining in axillary lymph node metastases was a significant predictor of poor prognosis in univariate and multivariate analyses. Conclusion This large study with long-term follow-up suggests that ALDH1 immunostaining in axillary lymph node metastases is an independent predictor of poor prognosis in patients with breast cancer. The clinical relevance of this finding should be confirmed in further well-designed prospective studies.
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Affiliation(s)
- Xin Guan
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Yi Dong
- The Second Breast Surgery Department, 377382Jilin Cancer Hospital, Jilin Cancer Hospital, Changchun, Jilin Province, China
| | - Zhimin Fan
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Yue Zhan
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Xinpeng Xie
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Gege Xu
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Yu Zhang
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Guoqiang Guo
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, China
| | - Aiping Shi
- Department of Breast Surgery, The First Hospital of Jilin University, Changchun, Jilin Province, China
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12
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Yang C, Dong J, Liu Z, Guo Q, Nie Y, Huang D, Qin N, Shu J. Prediction of Metastasis in the Axillary Lymph Nodes of Patients With Breast Cancer: A Radiomics Method Based on Contrast-Enhanced Computed Tomography. Front Oncol 2021; 11:726240. [PMID: 34616678 PMCID: PMC8488257 DOI: 10.3389/fonc.2021.726240] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/27/2021] [Indexed: 12/29/2022] Open
Abstract
Background The use of traditional techniques to evaluate breast cancer is restricted by the subjective nature of assessment, variation across radiologists, and limited data. Radiomics may predict axillary lymph node metastasis (ALNM) of breast cancer more accurately. Purpose The aim was to evaluate the diagnostic performance of a radiomics model based on ALNs themselves that used contrast-enhanced computed tomography (CECT) to detect ALNM of breast cancer. Methods We retrospectively enrolled 402 patients with breast cancer confirmed by pathology from January 2016 to October 2019. Three hundred and ninety-six features were extracted for all patients from axial CECT images of 825 ALNs using Artificial Intelligent Kit software (GE Medical Systems, Version V3.1.0.R). Next, the radiomics model was trained, validated, and tested for predicting ALNM in breast cancer by using a support vector machine algorithm. Finally, the performance of the radiomics model was evaluated in terms of its classification accuracy and the value of the area under the curve (AUC). Results The radiomics model yielded the best classification accuracy of 89.1% and the highest AUC of 0.92 (95% CI: 0.91-0.93, p=0.002) for discriminating ALNM in breast cancer in the validation cohorts. In the testing cohorts, the model also demonstrated better performance, with an accuracy of 88.5% and an AUC of 0.94 (95% CI: 0.93-0.95, p=0.005) for predicting ALNM in breast cancer. Conclusion The radiomics model based on CECT images can be used to predict ALNM in breast cancer and has significant potential in clinical noninvasive diagnosis and in the prediction of breast cancer metastasis.
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Affiliation(s)
- Chunmei Yang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Jing Dong
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ziyi Liu
- The Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu, China
| | - Qingxi Guo
- Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yue Nie
- Department of Radiology, Luzhou People's Hospital, Luzhou, China
| | - Deqing Huang
- The Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu, China
| | - Na Qin
- The Institute of Systems Science and Technology, Southwest Jiaotong University, Chengdu, China
| | - Jian Shu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.,Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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13
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3T MRI-Radiomic Approach to Predict for Lymph Node Status in Breast Cancer Patients. Cancers (Basel) 2021; 13:cancers13092228. [PMID: 34066451 PMCID: PMC8124168 DOI: 10.3390/cancers13092228] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/11/2021] [Accepted: 05/04/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary Breast cancer is the most common cancer in women worldwide. The axillary lymph node status is one of the main prognostic factors. Currently, the methods to define the lymph node status are invasive and not without sequelae (from biopsy to lymphadenectomy). Radiomics is a new tool, and highly varied, but with high potential that has already shown excellent results in numerous fields of application. In our study, we have developed a classifier validated on a relatively large number of patients, which is able to predict lymph node status using a combination of patients clinical features, primary breast cancer histological features and radiomics features based on 3 Tesla post contrast—MR images. This approach can accurately select breast cancer patients who may avoid unnecessary biopsy and lymphadenectomy in a non-invasive way. Abstract Background: axillary lymph node (LN) status is one of the main breast cancer prognostic factors and it is currently defined by invasive procedures. The aim of this study is to predict LN metastasis combining MRI radiomics features with primary breast tumor histological features and patients’ clinical data. Methods: 99 lesions on pre-treatment contrasted 3T-MRI (DCE). All patients had a histologically proven invasive breast cancer and defined LN status. Patients’ clinical data and tumor histological analysis were previously collected. For each tumor lesion, a semi-automatic segmentation was performed, using the second phase of DCE-MRI. Each segmentation was optimized using a convex-hull algorithm. In addition to the 14 semantics features and a feature ROI volume/convex-hull volume, 242 other quantitative features were extracted. A wrapper selection method selected the 15 most prognostic features (14 quantitative, 1 semantic), used to train the final learning model. The classifier used was the Random Forest. Results: the AUC-classifier was 0.856 (label = positive or negative). The contribution of each feature group was lower performance than the full signature. Conclusions: the combination of patient clinical, histological and radiomics features of primary breast cancer can accurately predict LN status in a non-invasive way.
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14
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Li H, Tang L, Chen Y, Mao L, Xie H, Wang S, Guan X. Development and validation of a nomogram for prediction of lymph node metastasis in early-stage breast cancer. Gland Surg 2021; 10:901-913. [PMID: 33842235 DOI: 10.21037/gs-20-782] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Background Lymph node status is an important factor in determining the prognosis of early-stage breast cancer. We endeavored to build and validate a simple nomogram to predict lymph node metastasis (LNM) in patients with early-stage breast cancer. Methods Patients with T1-2 and non-metastasis (M0) breast cancer registered in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled. All patients were divided into primary cohort and validation cohort in a 2:1 ratio. In order to assess risk factors for LNM, we performed univariate and multivariate binary logistic regression, and based on results of multivariable analysis, we built the predictive nomogram model. The C-index, receiver operating characteristic (ROC) and calibration plots were applied to assess LNM model performance. Moreover, the nomogram efficiency was further validated through the validation cohort, part of which was from the First Affiliated Hospital of Nanjing Medical University database. Results Totally, 184,531 female breast cancer with T1-2 tumor size from SEER database and 1,222 patients from the Chinese institutional data were included. There were 123,019 patients in the primary cohort and 62,734 patients in validation cohort. The LNM nomogram was composed of seven features including age at diagnosis, race, primary site, histologic type, grade, tumor size and subtype. The model showed good discrimination, with a C-index of 0.720 [95% confidence interval (CI): 0.717-0.723] and good calibration. Similar C-index was 0.718 (95% CI: 0.713-0.723) in validation cohort. Consistently, ROC curves presented good discrimination in the primary cohort [area under the curve (AUC) =0.720] and the validation set (AUC =0.718) for the LNM nomogram. Calibration curve of the nomogram demonstrated good agreement. Conclusions With the prediction of novel validated nomogram for women with early-stage breast cancer, doctors may distinguish patients with high possibility of LNM and devise individualize treatments.
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Affiliation(s)
- Huan Li
- Department of Respiratory Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Lin Tang
- Department of Medical Oncology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Yajuan Chen
- Department of Respiratory Medicine, Jinling Hospital, Medical School of Nanjing University, Nanjing, China
| | - Ling Mao
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hui Xie
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shui Wang
- Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoxiang Guan
- Department of Medical Oncology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.,Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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15
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Deep Learning Signature Based on Staging CT for Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer. Acad Radiol 2020; 27:1226-1233. [PMID: 31818648 DOI: 10.1016/j.acra.2019.11.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 11/10/2019] [Accepted: 11/13/2019] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES To evaluate the noninvasive predictive performance of deep learning features based on staging CT for sentinel lymph node (SLN) metastasis of breast cancer. MATERIALS AND METHODS A total of 348 breast cancer patients were enrolled in this study, with their SLN metastases pathologically confirmed. All patients received contrast-enhanced CT preoperative examinations and CT images were segmented and analyzed to extract deep features. After the feature selection, deep learning signature was built with the selected key features. The performance of the deep learning signatures was assessed with respect to discrimination, calibration, and clinical usefulness in the primary cohort (184 patients from January 2016 to March 2017) and then validated in the independent validation cohort (164 patients from April 2017 to December 2018). RESULTS Ten deep learning features were automatically selected in the primary cohort to establish the deep learning signature of SLN metastasis. The deep learning signature shows favorable discriminative ability with an area under curve of 0.801 (95% confidence interval: 0.736-0.867) in primary cohort and 0.817 (95% confidence interval: 0.751-0.884) in validation cohort. To further distinguish the number of metastatic SLNs (1-2 or more than two metastatic SLN), another deep learning signature was constructed and also showed moderate performance (area under curve 0.770). CONCLUSION We developed the deep learning signatures for preoperative prediction of SLN metastasis status and numbers (1-2 or more than two metastatic SLN) in patients with breast cancer. The deep learning signature may potentially provide a noninvasive approach to assist clinicians in predicting SLN metastasis in patients with breast cancer.
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16
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Takada K, Kashiwagi S, Asano Y, Goto W, Kouhashi R, Yabumoto A, Morisaki T, Shibutani M, Takashima T, Fujita H, Hirakawa K, Ohira M. Prediction of lymph node metastasis by tumor-infiltrating lymphocytes in T1 breast cancer. BMC Cancer 2020; 20:598. [PMID: 32590956 PMCID: PMC7318528 DOI: 10.1186/s12885-020-07101-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 06/22/2020] [Indexed: 11/22/2022] Open
Abstract
Background Lymph node metastasis is more likely in early-stage breast cancer with lower tumor-infiltrating lymphocyte (TIL) density. Therefore, we investigated the correlation between TILs and lymph node metastasis in cT1 breast cancer patients undergoing surgery and the usefulness of TILs in predicting sentinel lymph node metastasis (SLNM) in cT1N0M0 breast cancer. Methods We investigated 332 breast cancer patients who underwent surgery as the first-line treatment after preoperative diagnosis of cT1. A positive diagnosis of SLNM as an indication for axillary clearance was defined as macrometastasis in the sentinel lymph node (SLN) (macrometastasis: tumor diameter > 2 mm). Semi-quantitative evaluation of lymphocytes infiltrating the peritumoral stroma as TILs in primary tumor biopsy specimens prior to treatment was conducted. Results For SLN biopsy (SLNB), a median of 2 (range, 1–8) SLNs were pathologically evaluated. Sixty cases (19.4%) of SLNM (macrometastasis: 46, micrometastasis: 16) were observed. Metastasis was significantly greater in breast cancers with tumor diameter > 10 mm than in those with diameter ≤ 10 mm (p = 0.016). Metastasis was significantly associated with lymphatic invasion (p < 0.001). These two clinicopathological factors correlated with SLNM even in patients diagnosed with cN0 (tumor size; p = 0.017, lymphatic invasion; p = 0.002). Multivariate analysis for SLNM predictors revealed lymphatic invasion (p = 0.008, odds ratio [OR] = 2.522) and TILs (p < 0.001, OR = 0.137) as independent factors. Conclusions Our results suggest a correlation between lymph node metastasis and tumor immune-microenvironment in cT1 breast cancer. TIL density may be a predictor of SLNM in breast cancer without lymph node metastasis on preoperative imaging.
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Affiliation(s)
- Koji Takada
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Shinichiro Kashiwagi
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.
| | - Yuka Asano
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Wataru Goto
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Rika Kouhashi
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Akimichi Yabumoto
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Tamami Morisaki
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Masatsune Shibutani
- Department of Gastrointestinal Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Tsutomu Takashima
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Hisakazu Fujita
- Department of Scientific and Linguistic Fundamentals of Nursing, Osaka City University Graduate School of Nursing, 1-5-17 Asahi-machi, Abeno-ku, Osaka, 545-0051, Japan
| | - Kosei Hirakawa
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.,Department of Gastrointestinal Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Masaichi Ohira
- Department of Breast and Endocrine Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan.,Department of Gastrointestinal Surgery, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
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17
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Cai YL, Lin YX, Jiang LS, Ye H, Li FY, Cheng NS. A Novel Nomogram Predicting Distant Metastasis in T1 and T2 Gallbladder Cancer: A SEER-based Study. Int J Med Sci 2020; 17:1704-1712. [PMID: 32714073 PMCID: PMC7378661 DOI: 10.7150/ijms.47073] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 06/19/2020] [Indexed: 02/05/2023] Open
Abstract
Background: Gallbladder cancer (GBC) is the most common malignancy of the biliary system. Early T stage GBC patients with distant metastasis are proven to have a worse prognosis. In this study, our aim was to construct and validate a novel nomogram for predicting distant metastasis in T1 and T2 GBC. Methods: Between 2004 and 2014, patients with T1 and T2 GBC were identified in the Surveillance, Epidemiology, and End Results (SEER) database. All of the eligible patients were randomly divided into training and validation cohorts. Univariate and multivariate analyses were used to assess significant predictive factors associated with distant metastasis. A nomogram was developed and validated by a calibration curve and receptor operating characteristic curve (ROC) analysis. Results: According to the inclusion and exclusion criteria, 3013 patients with historically confirmed AJCC stage T1 and T2 GBC were enrolled. Younger age, high pathological grade, nonadenocarcinoma, T1, N1 and larger tumor size correlated positively with the risk of distant metastasis. A novel nomogram was established to predict distant metastasis in early T stage GBC patients. Internal validation with a calibration plot in the training cohort showed that this nomogram was well calibrated. Through ROC curve analysis, the areas under the ROC curves in the training and validation cohorts were 0.723 and 0.679, respectively. Conclusions: Although some limitations exist in this predictive model, the nomogram revealed the relationship between the clinicopathological characteristics of T1 and T2 GBC patients and the risk of distant metastasis. The novel nomogram will assist in patient counseling and guide treatment decision making for T1 and T2 GBC patients.
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Affiliation(s)
- Yu-Long Cai
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yi-Xin Lin
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Li-Sheng Jiang
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Hui Ye
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Fu-Yu Li
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Nan-Sheng Cheng
- Department of Biliary Surgery, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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A N0 Predicting Model for Sentinel Lymph Node Biopsy Omission in Early Breast Cancer Upstaged From Ductal Carcinoma in Situ. Clin Breast Cancer 2019; 20:e281-e289. [PMID: 32147404 DOI: 10.1016/j.clbc.2019.11.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/16/2019] [Accepted: 11/30/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND A prediction model with high sensitivity for the detection of negative axillary involvement can reduce additional axillary surgery in patients with ductal carcinoma in situ (DCIS) upstaged to invasive cancer while saving patients with pure DCIS from unnecessary axillary surgeries. Using a nationwide database, we developed and validated a scoring system for guidance in selective sentinel lymph node biopsy omission. PATIENTS AND METHODS A total of 41,895 patients with clinically node-negative breast cancer from the Korean Breast Cancer Registry were included. The study cohort was randomly divided for the development and validation of the prediction model. Missing data were filled in using multiple imputation. Factors that were significantly associated with axillary lymph node (ALN) metastasis in > 50% of datasets were included in the final prediction model. RESULTS The frequency of ALN metastasis in the total cohort was 24.5%. After multivariable logistic regression analysis, variables that were associated with ALN metastasis were palpability, multifocality, location, size, histologic type, grade, lymphovascular invasion, hormone receptor expression, and Ki-67 level. A scoring system was developed using these factors. The areas under the receiver operating characteristic curve for the scoring system was 0.750 in both training and validating sets. The cutoff value for performing sentinel lymph node biopsy was determined as a score of 4 to obtain prediction sensitivity higher than 95%. CONCLUSIONS A scoring system to predict the probability of ALN metastasis was developed and validated. The application of this system in the clinic may reduce unnecessary axillary surgeries in patients with DCIS and minimize additional axillary surgery for upstaged patients with invasive cancer.
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19
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Liu J, Sun D, Chen L, Fang Z, Song W, Guo D, Ni T, Liu C, Feng L, Xia Y, Zhang X, Li C. Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer. Front Oncol 2019; 9:980. [PMID: 31632912 PMCID: PMC6778833 DOI: 10.3389/fonc.2019.00980] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Accepted: 09/16/2019] [Indexed: 12/13/2022] Open
Abstract
Purpose: To investigate whether a combination of radiomics and automatic machine learning applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of primary breast cancer can non-invasively predict axillary sentinel lymph node (SLN) metastasis. Methods: 62 patients who received a DCE-MRI breast scan were enrolled. Tumor resection and sentinel lymph node (SLN) biopsy were performed within 1 week after the DCE-MRI examination. According to the time signal intensity curve, the volumes of interest (VOIs) were delineated on the whole tumor in the images with the strongest enhanced phase. Datasets were randomly divided into two sets including a training set (~80%) and a validation set (~20%). A total of 1,409 quantitative imaging features were extracted from each VOI. The select K best and least absolute shrinkage and selection operator (Lasso) were used to obtain the optimal features. Three classification models based on the logistic regression (LR), XGboost, and support vector machine (SVM) classifiers were constructed. Receiver Operating Curve (ROC) analysis was used to analyze the prediction performance of the models. Both feature selection and models construction were firstly performed in the training set, then were further tested in the validation set by the same thresholds. Results: There is no significant difference between all clinical and pathological variables in breast cancer patients with and without SLN metastasis (P > 0.05), except histological grade (P = 0.03). Six features were obtained as optimal features for models construction. In the validation set, with respect to the accuracy and MSE, the SVM demonstrated the highest performance, with an accuracy, AUC, sensitivity (for positive SLN), specificity (for positive SLN) and Mean Squared Error (MSE) of 0.85, 0.83, 0.71, 1, 0.26, respectively. Conclusions: We demonstrated the feasibility of combining artificial intelligence and radiomics from DCE-MRI of primary tumors to predict axillary SLN metastasis in breast cancer. This non-invasive approach could be very promising in application.
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Affiliation(s)
- Jia Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dong Sun
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Linli Chen
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng Fang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weixiang Song
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dajing Guo
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tiangen Ni
- Department of Breast and Thyroid Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chuan Liu
- Department of Radiology, Affiliated Hospital of Chuanbei Medical College, Nanchong, China
| | - Lin Feng
- Department of Radiology, Affiliated Hospital of Chuanbei Medical College, Nanchong, China
| | - Yuwei Xia
- Huiying Medical Technology, Beijing, China
| | | | - Chuanming Li
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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20
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Zhao YX, Liu YR, Xie S, Jiang YZ, Shao ZM. A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program. J Cancer 2019; 10:2443-2449. [PMID: 31258749 PMCID: PMC6584352 DOI: 10.7150/jca.30386] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 04/22/2019] [Indexed: 01/21/2023] Open
Abstract
Background: Patients with early stage breast cancer with lymph nodes metastasis were proven to have more aggressive biologically phenotypes. This study aimed to build a nomogram to predict lymph node metastasis in patients with T1 breast cancer. Methods: We identified female patients with T1 breast cancer diagnosed between 2010 and 2014 in the Surveillance, Epidemiology and End Results database. The patients were randomized into training and validation sets. Univariate and multivariate logistic regressions were carried out to assess the relationships between lymph node metastasis and clinicopathological characteristics. A nomogram was developed and validated by a calibration curve and receptor operating characteristic curve analysis. Result: Age, race, tumour size, tumour primary site, pathological grade, oestrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER2) status were independent predictive factors of positive lymph node metastasis in T1 breast cancer. Increasing age, tumour size and pathological grade were positively correlated with the risk of lymph node metastasis. We developed a nomogram to predict lymph node metastasis and further validated it in a validation set, with areas under the receiver operating characteristic curves of 0.733 and 0.741 in the training and validation sets, respectively. Conclusions: A better understanding of the clinicopathological characteristics of T1 breast cancer patients might important for assessing their lymph node status. The nomogram developed here, if further validated in other large cohorts, might provide additional information regarding lymph node metastasis. Together with sentinel lymph node biopsy, this nomogram can help comprehensively predict lymph node metastasis.
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Affiliation(s)
- Ya-Xin Zhao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Cancer Institute, Fudan University Shanghai Cancer Center, 270 Dong-An Road, Shanghai 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, P. R. China
| | - Yi-Rong Liu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Cancer Institute, Fudan University Shanghai Cancer Center, 270 Dong-An Road, Shanghai 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, P. R. China
| | - Shao Xie
- Department of Oncology, Shanghai Medical College, Fudan University, P. R. China
| | - Yi-Zhou Jiang
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Cancer Institute, Fudan University Shanghai Cancer Center, 270 Dong-An Road, Shanghai 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, P. R. China
| | - Zhi-Ming Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center; Cancer Institute, Fudan University Shanghai Cancer Center, 270 Dong-An Road, Shanghai 200032, People's Republic of China.,Department of Oncology, Shanghai Medical College, Fudan University, P. R. China.,Institutes of Biomedical Sciences, Fudan University, Shanghai, P. R. China
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Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI. Sci Rep 2019; 9:2240. [PMID: 30783148 PMCID: PMC6381163 DOI: 10.1038/s41598-019-38502-0] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Accepted: 12/31/2018] [Indexed: 02/06/2023] Open
Abstract
The accurate and noninvasive preoperative prediction of the state of the axillary lymph nodes is significant for breast cancer staging, therapy and the prognosis of patients. In this study, we analyzed the possibility of axillary lymph node metastasis directly based on Magnetic Resonance Imaging (MRI) of the breast in cancer patients. After mass segmentation and feature analysis, the SVM, KNN, and LDA three classifiers were used to distinguish the axillary lymph node state in 5-fold cross-validation. The results showed that the effect of the SVM classifier in predicting breast axillary lymph node metastasis was significantly higher than that of the KNN classifier and LDA classifier. The SVM classifier performed best, with the highest accuracy of 89.54%, and obtained an AUC of 0.8615 for identifying the lymph node status. Each feature was analyzed separately and the results showed that the effect of feature combination was obviously better than that of any individual feature on its own.
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22
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Luo J, Ning Z, Zhang S, Feng Q, Zhang Y. Bag of deep features for preoperative prediction of sentinel lymph node metastasis in breast cancer. ACTA ACUST UNITED AC 2018; 63:245014. [DOI: 10.1088/1361-6560/aaf241] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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23
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Wu P, Zhao K, Liang Y, Ye W, Liu Z, Liang C. Validation of Breast Cancer Models for Predicting the Nonsentinel Lymph Node Metastasis After a Positive Sentinel Lymph Node Biopsy in a Chinese Population. Technol Cancer Res Treat 2018; 17:1533033818785032. [PMID: 30033828 PMCID: PMC6055247 DOI: 10.1177/1533033818785032] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Objectives: Over the years, completion axillary lymph node dissection is recommended for the patients with breast cancer if sentinel lymph node metastasis is found. However, not all of these patients had nonsentinel lymph node metastasis on final histology. Some predicting models have been developed for calculating the risk of nonsentinel lymph node metastasis. The aim of our study was to validate some of the predicting models in a Chinese population. Method: Two hundred thirty-six patients with positive sentinel lymph node and complete axillary lymph node dissection were included. Patients were applied to 6 models for evaluation of the risk of nonsentinel lymph node involvement. The receiver–operating characteristic curves were shown in our study. The calculation of area under the curves and false negative rate was done for each model to assess the discriminative power of the models. Results: There are 105 (44.5%) patients who had metastatic nonsentinel lymph node(s) in our population. Primary tumor size, the number of metastatic sentinel lymph node, and the proportion of metastatic sentinel lymph nodes/total sentinel lymph nodes were identified as the independent predictors of nonsentinel lymph node metastasis. The Seoul National University Hospital and Louisville scoring system outperformed the others, with area under the curves of 0.706 and 0.702, respectively. The area under the curve values were 0.677, 0.673, 0.432, and 0.674 for the Memorial Sloan-Kettering Cancer Center, Tenon, Stanford, and Shanghai Cancer Hospital models, respectively. With adjusted cutoff points, the Louisville scoring system outperformed the others by classifying 26.51% of patients with breast cancer to the low-risk group. Conclusion: The Louisville and Seoul National University Hospital scoring system were found to be more predictive among the 6 models when applied to the Chinese patients with breast cancer in our database. Models developed at other institutions should be used cautiously for decision-making regarding complete axillary lymph node dissection after a positive biopsy in sentinel lymph node.
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Affiliation(s)
- Peiqi Wu
- 1 Department of Radiology, Guangdong General Hospital (Guangdong Academy of Medical Sciences), Guangzhou, China.,2 The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.,3 Department of Radiology, Shenzhen Yantian District Peoples's Hospital, Shenzhen City, China
| | - Ke Zhao
- 1 Department of Radiology, Guangdong General Hospital (Guangdong Academy of Medical Sciences), Guangzhou, China
| | - Yanli Liang
- 1 Department of Radiology, Guangdong General Hospital (Guangdong Academy of Medical Sciences), Guangzhou, China.,2 The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Weitao Ye
- 1 Department of Radiology, Guangdong General Hospital (Guangdong Academy of Medical Sciences), Guangzhou, China
| | - Zaiyi Liu
- 1 Department of Radiology, Guangdong General Hospital (Guangdong Academy of Medical Sciences), Guangzhou, China
| | - Changhong Liang
- 1 Department of Radiology, Guangdong General Hospital (Guangdong Academy of Medical Sciences), Guangzhou, China.,2 The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China
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Elmadahm A, Lord SJ, Hudson HM, Lee CK, Buizen L, Farshid G, Gebski VJ, Gill PG. Performance of four published risk models to predict sentinel lymph-node involvement in Australian women with early breast cancer. Breast 2018; 41:82-88. [DOI: 10.1016/j.breast.2018.05.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 05/24/2018] [Accepted: 05/27/2018] [Indexed: 01/12/2023] Open
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25
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Liu C, Ding J, Spuhler K, Gao Y, Serrano Sosa M, Moriarty M, Hussain S, He X, Liang C, Huang C. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J Magn Reson Imaging 2018; 49:131-140. [PMID: 30171822 DOI: 10.1002/jmri.26224] [Citation(s) in RCA: 125] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 05/29/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Sentinel lymph node (SLN) status is an important prognostic factor for patients with breast cancer, which is currently determined in clinical practice by invasive SLN biopsy. PURPOSE To noninvasively predict SLN metastasis in breast cancer using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) intra- and peritumoral radiomics features combined with or without clinicopathologic characteristics of the primary tumor. STUDY TYPE Retrospective. POPULATION A total of 163 breast cancer patients (55 positive SLN and 108 negative SLN). FIELD STRENGTH/SEQUENCE 1.5T, T1 -weighted DCE-MRI. ASSESSMENT A total of 590 radiomic features were extracted for each patient from both intratumoral and peritumoral regions of interest. To avoid overfitting, the dataset was randomly separated into a training set (∼67%) and a validation set (∼33%). The prediction models were built with the training set using logistic regression on the most significant radiomic features in the training set combined with or without clinicopathologic characteristics. The prediction performance was further evaluated in the independent validation set. STATISTICAL TESTS Mann-Whitney U-test, Spearman correlation, least absolute shrinkage selection operator (LASSO) regression, logistic regression, and receiver operating characteristic (ROC) analysis were performed. RESULTS Combining radiomic features with clinicopathologic characteristics, six features were automatically selected in the training set to establish the prediction model of SLN metastasis. In the independent validation set, the area under ROC curve (AUC) was 0.869 (NPV = 0.886). Using radiomic features alone in the same procedure, 4 features were selected and the validation set AUC was 0.806 (NPV = 0.824). DATA CONCLUSION This is the first attempt to demonstrate the feasibility of using DCE-MRI radiomics to predict SLN metastasis in breast cancer. Clinicopathologic characteristics improved the prediction performance. This study provides noninvasive methods to evaluate SLN status for guiding further treatment of breast cancer patients, and can potentially benefit those with negative SLN, by eliminating unnecessary invasive lymph node removal and the associated complications, which is a step further towards precision medicine. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:131-140.
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Affiliation(s)
- Chunling Liu
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, China
- Department of Radiology, Stony Brook Medicine, Stony Brook, New York, USA
| | - Jie Ding
- Department of Radiology, Stony Brook Medicine, Stony Brook, New York, USA
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA
| | - Karl Spuhler
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA
| | - Yi Gao
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Mario Serrano Sosa
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA
| | - Meghan Moriarty
- Department of Radiology, Stony Brook Medicine, John T Mather Memorial Hospital, Port Jefferson, New York, USA
| | - Shahid Hussain
- Department of Radiology, Stony Brook Medicine, Stony Brook, New York, USA
| | - Xiang He
- Department of Radiology, Stony Brook Medicine, Stony Brook, New York, USA
| | - Changhong Liang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chuan Huang
- Department of Radiology, Stony Brook Medicine, Stony Brook, New York, USA
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York, USA
- Department of Psychiatry, Stony Brook Medicine, Stony Brook, New York, USA
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
- Stony Brook University Cancer Center, Stony Brook, New York, USA
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26
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Dong Y, Feng Q, Yang W, Lu Z, Deng C, Zhang L, Lian Z, Liu J, Luo X, Pei S, Mo X, Huang W, Liang C, Zhang B, Zhang S. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol 2018; 28:582-591. [PMID: 28828635 DOI: 10.1007/s00330-017-5005-7] [Citation(s) in RCA: 168] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 07/12/2017] [Accepted: 07/24/2017] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To predict sentinel lymph node (SLN) metastasis in breast cancer patients using radiomics based on T2-weighted fat suppression (T2-FS) and diffusion-weighted MRI (DWI). METHODS We enrolled 146 patients with histologically proven breast cancer. All underwent pretreatment T2-FS and DWI MRI scan. In all, 10,962 texture and four non-texture features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (AUC) were used to select the features. We constructed ten logistic regression models (orders of 1-10) based on different combination of image features using stepwise forward method. RESULTS For T2-FS, model 10 with ten features yielded the highest AUC of 0.847 in the training set and 0.770 in the validation set. For DWI, model 8 with eight features reached the highest AUC of 0.847 in the training set and 0.787 in the validation set. For joint T2-FS and DWI, model 10 with ten features yielded an AUC of 0.863 in the training set and 0.805 in the validation set. CONCLUSIONS Full utilisation of breast cancer-specific textural features extracted from anatomical and functional MRI images improves the performance of radiomics in predicting SLN metastasis, providing a non-invasive approach in clinical practice. KEY POINTS • SLN biopsy to access breast cancer metastasis has multiple complications. • Radiomics uses features extracted from medical images to characterise intratumour heterogeneity. • We combined T 2 -FS and DWI textural features to predict SLN metastasis non-invasively.
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Affiliation(s)
- Yuhao Dong
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
- Graduate College, Shantou University Medical College, Shantou, Guangdong, People's Republic of China
| | - Qianjin Feng
- The Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Wei Yang
- The Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Zixiao Lu
- The Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Chunyan Deng
- The Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, People's Republic of China
| | - Lu Zhang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
| | - Zhouyang Lian
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
| | - Jing Liu
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
| | - Xiaoning Luo
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
| | - Shufang Pei
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
| | - Xiaokai Mo
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
- Graduate College, Shantou University Medical College, Shantou, Guangdong, People's Republic of China
| | - Wenhui Huang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
| | - Changhong Liang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
| | - Bin Zhang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China
| | - Shuixing Zhang
- Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, No. 106 Zhongshan Er Road, 510080, Guangzhou, Guangdong Province, People's Republic of China.
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Liu H, Xu G, Yao MH, Pu H, Fang Y, Xiang LH, Wu R. Association of conventional ultrasound, elastography and clinicopathological factors with axillary lymph node status in invasive ductal breast carcinoma with sizes > 10 mm. Oncotarget 2018; 9:2819-2828. [PMID: 29416814 PMCID: PMC5788682 DOI: 10.18632/oncotarget.18969] [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: 11/22/2016] [Accepted: 06/18/2017] [Indexed: 11/25/2022] Open
Abstract
Background To evaluate whether conventional ultrasound, elastography [conventional strain elastography of elasticity imaging, acoustic radiation force impulse induced strain elastography of virtual touch tissue imaging, and a novel two-dimensional shear wave elastography of virtual touch tissue imaging quantification] and clinicopathological factors are associated with axillary lymph node metastasis in invasive ductal breast carcinoma with sizes > 10 mm. Materials and Methods We evaluated 150 breast lesions from 148 patients using the above methods and the clinicopathological factors. Univariate and multivariate logistic regression analysis were performed to determine the axillary lymph node metastasis risk factors. Diagnostic performance was evaluated using receiver operating characteristic curve analysis. Results Sixty-three tumors (42%) were node-positive, 87 (58%) were node-negative. Aspect ratio, virtual touch tissue imaging grade, shear wave velocity, pathological invasive tumor size, and histological grade maintained independent significance in predicting nodal involvement. The mean tumor shear wave velocitys (4.60, 6.49, 7.16) increased in proportion to metastatic node number (0, 1-3, ≥ 4, respectively; P < 0.001). For all tumors in this study, the cut-off shear wave velocity was 6.16 m/s and was associated with 64.1% sensitivity, 78.0% specificity and an area under the ROC curve of 0.799 (95% confidence interval, 0.731-0.868). Conclusions Aspect ratio, virtual touch tissue imaging grade, shear wave velocity, pathological invasive tumor size and histological grade are independently associated with axillary lymph node metastasis in invasive ductal breast carcinoma with sizes > 10 mm.
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Affiliation(s)
- Hui Liu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai 200072, China
| | - Guang Xu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai 200072, China
| | - Ming-Hua Yao
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai 200072, China
| | - Huan Pu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai 200072, China
| | - Yan Fang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai 200072, China
| | - Li-Hua Xiang
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai 200072, China
| | - Rong Wu
- Department of Medical Ultrasound, Shanghai Tenth People's Hospital, Ultrasound Research and Education Institute, Tongji University School of Medicine, Shanghai 200072, China
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Kondov B, Isijanovska R, Milenkovikj Z, Petrusevska G, Jovanovski-Srceva M, Bogdanovska-Todorovska M, Kondov G. Impact of Size of the Tumour, Persistence of Estrogen Receptors, Progesterone Receptors, HER2Neu Receptors and Ki67 Values on Positivity of Axillary Lymph Nodes in Patients with Early Breast Cancer with Clinically Negative Axillary Examination. Open Access Maced J Med Sci 2017; 5:825-830. [PMID: 29362604 PMCID: PMC5771280 DOI: 10.3889/oamjms.2017.213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Revised: 10/20/2017] [Accepted: 10/24/2017] [Indexed: 11/22/2022] Open
Abstract
AIM: The study aimed to identify factors that influence the positivity of axillary lymph nodes in patients with early breast cancer and clinically negative axillary lymph nodes, who were subjected for modified radical mastectomy and axillary lymphadenectomy. MATERIAL AND METHODS: This study included 81 surgically treated, early breast cancer patients during the period from 08-2015 to 05-2017. All the cases have been analysed by standard histological analysis including macroscopic and microscopic examination by routine H&E staining. For determination of molecular receptors, immunostaining by PT LINK immunoperoxidase has been done for HER2neu, ER, PR, p53 and Ki67. RESULTS: Patients age ranged between 31-73 years, an average of 56.86 years. The mean size of a primary tumour in the surgically treated patient was 20.33 ± 6.0 mm. Axillary dissection revealed from 5 to 32 lymph nodes, with an average of 14. Metastases have been found in 1 to 7 lymph nodes, with an average 0.7. Only 26 (32.1%) of the patients showed metastases in the axillary lymph nodes. The univariant regression analysis showed that the size of a tumour and presence of HER2neu receptors on cancer cells influence the positivity of the axillary lymph nodes. The presence of the estrogen receptors, progesterone receptors have no influence on the positivity for metastatic deposits of lymph nodes. Multivariant model and logistic regression analysis as significant independent factors or predictors of positivity of the axillary lymph nodes are influenced by the tumour size only. CONCLUSION: Our study showed that the metastatic involvement of the axillary lymph nodes is mainly influenced by the size of a tumour and presence of HER2neu receptors in the univariant analysis. This point to the important influence of positivity of the axillary lymph nodes but, in multi-variant regressive analysis the lymph node status correlates with the tumour size only.
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Affiliation(s)
- Borislav Kondov
- University Clinic for Thoracic and Vascular Surgery, Clinical Centre "Mother Theresa", Faculty of Medicine, Ss Cyril and Methodius University of Skopje, Skopje, Republic of Macedonia
| | - Rosalinda Isijanovska
- Institute for Epidemiology, Faculty of Medicine, Ss Cyril and Methodius University of Skopje, Skopje, Republic of Macedonia
| | - Zvonko Milenkovikj
- University Clinic for Infective Diseases and Febrile Conditions, Clinical Centre "Mother Theresa", Faculty of Medicine, Ss Cyril and Methodius University of Skopje, Skopje, Republic of Macedonia
| | - Gordana Petrusevska
- Institute for Pathology, Faculty of Medicine, Ss Cyril and Methodius University of Skopje, Skopje, Republic of Macedonia
| | - Marija Jovanovski-Srceva
- University Clinic for Anesthesia and Reanimation, Clinical Centre "Mother Theresa", Faculty of Medicine, Ss Cyril and Methodius University of Skopje, Skopje, Republic of Macedonia
| | | | - Goran Kondov
- University Clinic for Thoracic and Vascular Surgery, Clinical Centre "Mother Theresa", Faculty of Medicine, Ss Cyril and Methodius University of Skopje, Skopje, Republic of Macedonia
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Sun L, Chen G, Zhou Y, Zhang L, Jin Z, Liu W, Wu G, Jin F, Li K, Chen B. Clinical significance of MSKCC nomogram on guiding the application of touch imprint cytology and frozen section in intraoperative assessment of breast sentinel lymph nodes. Oncotarget 2017; 8:78105-78112. [PMID: 29100452 PMCID: PMC5652841 DOI: 10.18632/oncotarget.17490] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Accepted: 04/07/2017] [Indexed: 11/25/2022] Open
Abstract
The widely practiced intra-operative methods for rapid evaluation and detection of sentinel lymph node (SLN) status include frozen section (FS) and touch imprint cytology (TIC). This study optimized the use of TIC and FS in the intra-operative detection of breast SLNs based on the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram. Three hundred forty-two SLNs were removed from 79 patients. SLN metastatic probability was assessed by the MSKCC nomogram. The SLNs underwent intra-operative TIC and FS, as well as routine post-operative paraffin sections (RPSs). The relationships between TIC, FS, and SLN metastatic probability were analyzed. Overall, TIC was more sensitive than FS (92.31% vs. 76.92%), while TIC specificity was inferior to FS specificity (84.85% vs. 100%). In addition, the best cut-off value for TIC based on the MSKCC nomogram was inferior to the best FS cut-off value (22.5% vs. 34.5%). All patients with a MSKCC value <22.5% in the present study were negative based on FS and RPS, while the true-negative and false-positive rates for TIC were 92.5% and 7.5%, respectively. Thus, early breast cancer patients, based on a MSKCC value <22.5%, can safely avoid FS, but should have TIC performed intra-operatively. Patients with a MSKCC value >22.5% should have TIC and FS to determine the size of metastases, whether or not to proceed with axillary lymph node dissection, and to avoid easily missed metastases.
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Affiliation(s)
- Lisha Sun
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China.,Department of Surgical Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Guanglei Chen
- Department of Breast Disease and Reconstruction Center, Breast Cancer Key Laboratory of Dalian, The Second Hospital of Dalian Medical University, Dalian, China
| | - Yizhen Zhou
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Lei Zhang
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Zining Jin
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Weiguang Liu
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Guangping Wu
- Department of Pathology, The First Hospital of China Medical University, Shenyang, China
| | - Feng Jin
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China
| | - Kai Li
- Department of Surgical Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Bo Chen
- Department of Breast Surgery, The First Hospital of China Medical University, Shenyang, China
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30
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Kondov B, Kondov G, Spirovski Z, Milenkovikj Z, Colanceski R, Petrusevska G, Pesevska M. Prognostic Factors on the Positivity for Metastases of the Axillary Lymph Nodes from Primary Breast Cancer. ACTA ACUST UNITED AC 2017; 38:81-90. [PMID: 28593885 DOI: 10.1515/prilozi-2017-0011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
AIM The aim of the study was to identify the impact of T stage, the presence of estrogen, progesterone, HER2neu receptors and the values of the Ki67 on the positivity for metastases of the axillary lymph nodes, from primary breast cancer. MATERIAL AND METHODS 290 surgically treated patients for breast cancer were included in the study. All cases have been analyzed by standard histological analysis including microscopic analysis on standard H&E staining. For determining the molecular receptors - HER2neu, ER, PR, p53 and Ki67, immunostaining by PT LINK immunoperoxidase has been done. RESULTS Patients age was ranged between 18-90 years, average of 57.6+11.9. The mean size of the primary tumor in the surgically treated patient was 30.27 + 18.3 mm. On dissection from the axillary pits 8 to 39 lymph nodes were taken out, an average of 13.81+5.56. Metastases have been found in 1 to 23 lymph nodes, an average 3.14+4.71. In 59% of the patients there have been found metastases in the axillary lymph nodes. The univariate regression analysis showed that the location, size of tumor, differentiation of the tumor, stage, the value of the Ki67 and presence of lymphovascular invasion influence on the positivity of the axillary lymph nodes. The presence of the estrogen receptors, progesterone receptors and HER2neu receptors showed that they do not have influence on the positivity for metastatic deposits in axillary lymph nodes. The multivariate model and the logistic regression analysis as independent significant factors or predictors of positivity of the axillary lymph nodes are influenced by the tumor size and the positive lymphovascular invasion. CONCLUSION Our study showed that the involving of the axillary lymph nodes is mainly influenced by the size of the tumor and the presence of lymphovascular invasion in the tumor. Ki67 determined proliferative index in the univariate analysis points the important influence of positivity in the axillary lymph nodes, but not in the multivariate regressive analysis.
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Affiliation(s)
- Borislav Kondov
- University Clinic for Thoracic and Vascular Surgery, Skopje, Majka Tereza 17, 1000 Skopje
| | - Goran Kondov
- University Clinic for Thoracic and Vascular Surgery - Medical Faculty Skopje
| | - Zoran Spirovski
- University Clinic for Thoracic and Vascular Surgery - Medical Faculty Skopje
| | - Zvonko Milenkovikj
- University Clinic for Infective Disease and Febrile Conditions - Medical Faculty Skopje
| | - Risto Colanceski
- University Clinic for Thoracic and Vascular Surgery - Medical Faculty Skopje
| | | | - Meri Pesevska
- University Clinic for Oncology and Radiotherapy- Medical Faculty Skopje
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POU5F1/Oct-4 expression in breast cancer tissue is significantly associated with non-sentinel lymph node metastasis. BMC Cancer 2016; 16:175. [PMID: 26931354 PMCID: PMC4774000 DOI: 10.1186/s12885-015-1966-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2015] [Accepted: 11/30/2015] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND At present, few studies have explored the significance of POU5F1 (also known as octamer-bingding factor, Oct-4 or Oct-3) expression in breast cancer tissues. METHODS A total of 121 patients were retrospectively selected between May 2010 and March 2013 to investigate the relationship between POU5F1/Oct-4 expression in breast cancer tissues and non-sentinel lymph node (non-SLN) metastases and to validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram. All patients had early-stage breast cancer, which was histologically confirmed by the Department of Surgical Oncology, The First Affiliated Hospital of China Medical University. Histological type and grade of tumors were determined from tissue samples by hematoxylin and eosin staining, while the presence of POU5F1/Oct-4 protein was determined by immunohistochemistry. POU5F1/Oct-4 expression levels in tissues obtained from patients with sentinel lymph node (SLN) and non-SLN metastasis and in tissues obtained from patients without lymph node metastases were compared. RESULTS POU5F1/Oct-4 expression levels in breast cancer tissues were significantly higher in both the SLN metastasis and non-SLN metastasis groups (P = 0.003 and P = 0.030, respectively). Furthermore, POU5F1/Oct-4 expression was found to be associated to both histological (P = 0.01) and molecular type (P = 0.03). Thus, our data once again confirms the validity of the MSKCC nomogram. The area under curve (AUC) was 0.919 (95 % CI: 0.869-0.969, P < 0.001). The probability of non-SLN metastasis generated from the MSKCC nomgram was significantly higher in the POU5F1/Oct-4 positive group than in the POU5F1/Oct-4 negative group. Both univariate and multivariate analysis revealed that Oct-4 expression levels were significantly associated with non-SLN metastases (P = 0.030 and P = 0.034, respectively). CONCLUSIONS POU5F1/Oct-4 expression levels are significantly associated with non-SLN metastases. Patients with higher probabilities of metastasis generated from the MSKCC nomogram may also have higher POU5F1/Oct-4 expression levels.
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Qiu SQ, Zeng HC, Zhang F, Chen C, Huang WH, Pleijhuis RG, Wu JD, van Dam GM, Zhang GJ. A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound. Sci Rep 2016; 6:21196. [PMID: 26875677 PMCID: PMC4753408 DOI: 10.1038/srep21196] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 01/19/2016] [Indexed: 02/05/2023] Open
Abstract
Among patients with a preoperative positive axillary ultrasound, around 40% of them are pathologically proved to be free from axillary lymph node (ALN) metastasis. We aimed to develop and validate a model to predict the probability of ALN metastasis as a preoperative tool to support clinical decision-making. Clinicopathological features of 322 early breast cancer patients with positive axillary ultrasound findings were analyzed. Multivariate logistic regression analysis was performed to identify independent predictors of ALN metastasis. A model was created from the logistic regression analysis, comprising lymph node transverse diameter, cortex thickness, hilum status, clinical tumour size, histological grade and estrogen receptor, and it was subsequently validated in another 234 patients. Coefficient of determination (R(2)) and the area under the ROC curve (AUC) were calculated to be 0.9375 and 0.864, showing good calibration and discrimination of the model, respectively. The false-negative rates of the model were 0% and 5.3% for the predicted probability cut-off points of 7.1% and 13.8%, respectively. This means that omission of axillary surgery may be safe for patients with a predictive probability of less than 13.8%. After further validation in clinical practice, this model may support increasingly limited surgical approaches to the axilla in breast cancer.
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Affiliation(s)
- Si-Qi Qiu
- The Breast Center, Cancer Hospital of Shantou University Medical College, Guangdong, China
- Department of Medical Oncology, University of Groningen, University Medical Center Groningen, the Netherlands
| | - Huan-Cheng Zeng
- The Breast Center, Cancer Hospital of Shantou University Medical College, Guangdong, China
| | - Fan Zhang
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Guangdong, China
| | - Cong Chen
- Department of Ultrasound Diagnosis, Cancer Hospital of Shantou University Medical College, Guangdong, China
| | - Wen-He Huang
- The Breast Center, Cancer Hospital of Shantou University Medical College, Guangdong, China
| | - Rick G. Pleijhuis
- Department of Internal Medicine, Medical Spectrum Twente, Enschede, The Netherlands
| | - Jun-Dong Wu
- The Breast Center, Cancer Hospital of Shantou University Medical College, Guangdong, China
| | - Gooitzen M. van Dam
- Department of Surgery, Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, the Netherlands
| | - Guo-Jun Zhang
- The Breast Center, Cancer Hospital of Shantou University Medical College, Guangdong, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Guangdong, China
- Cancer Research Center, Shantou University Medical College, Guangdong, China
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Evans A, Rauchhaus P, Whelehan P, Thomson K, Purdie CA, Jordan LB, Michie CO, Thompson A, Vinnicombe S. Does shear wave ultrasound independently predict axillary lymph node metastasis in women with invasive breast cancer? Breast Cancer Res Treat 2013; 143:153-7. [PMID: 24305976 PMCID: PMC4363519 DOI: 10.1007/s10549-013-2747-z] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Accepted: 10/21/2013] [Indexed: 11/25/2022]
Abstract
Shear wave elastography (SWE) shows promise as an adjunct to greyscale ultrasound examination in assessing breast masses. In breast cancer, higher lesion stiffness on SWE has been shown to be associated with features of poor prognosis. The purpose of this study was to assess whether lesion stiffness at SWE is an independent predictor of lymph node involvement. Patients with invasive breast cancer treated by primary surgery, who had undergone SWE examination were eligible. Data were retrospectively analysed from 396 consecutive patients. The mean stiffness values were obtained using the Aixplorer® ultrasound machine from SuperSonic Imagine Ltd. Measurements were taken from a region of interest positioned over the stiffest part of the abnormality. The average of the mean stiffness value obtained from each of two orthogonal image planes was used for analysis. Associations between lymph node involvement and mean lesion stiffness, invasive cancer size, histologic grade, tumour type, ER expression, HER-2 status and vascular invasion were assessed using univariate and multivariate logistic regression. At univariate analysis, invasive size, histologic grade, HER-2 status, vascular invasion, tumour type and mean stiffness were significantly associated with nodal involvement. Nodal involvement rates ranged from 7 % for tumours with mean stiffness <50 kPa to 41 % for tumours with a mean stiffness of >150 kPa. At multivariate analysis, invasive size, tumour type, vascular invasion, and mean stiffness maintained independent significance. Mean stiffness at SWE is an independent predictor of lymph node metastasis and thus can confer prognostic information additional to that provided by conventional preoperative tumour assessment and staging.
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Affiliation(s)
- Andrew Evans
- Dundee Cancer Centre, Ninewells Hospital & Medical School, Mailbox 4, Dundee, DD1 9SY, UK,
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