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Liu Y, Li X, Zhu L, Zhao Z, Wang T, Zhang X, Cai B, Li L, Ma M, Ma X, Ming J. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE-MRI Radiomics Nomogram. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:6729473. [PMID: 36051932 PMCID: PMC9410821 DOI: 10.1155/2022/6729473] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/10/2022] [Accepted: 07/13/2022] [Indexed: 11/22/2022]
Abstract
Objective To investigate the value of preoperative prediction of breast cancer axillary lymph node metastasis based on intratumoral and peritumoral dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) radiomics nomogram. Material and Methods. In this study, a radiomics model was developed based on a training cohort involving 250 patients with breast cancer (BC) who had undergone axillary lymph node (ALN) dissection between June 2019 and January 2021. The intratumoral and peritumoral radiomics features were extracted from the second postcontrast images of DCE-MRI. Based on filtered radiomics features, the radiomics signature was built by using the least absolute shrinkage and selection operator method. The Support Vector Machines (SVM) learning algorithm was used to construct intratumoral, periatumoral, and intratumoral combined periatumoral models for predicting axillary lymph node metastasis (ALNM) in BC. Nomogram performance was determined by its discrimination, calibration, and clinical value. Multivariable logistic regression was adopted to establish a radiomics nomogram. Results The intratumoral combined peritumoral radiomics signature, which was composed of fifteen ALN status-related features, showed the best predictive performance and was associated with ALNM in both the training and validation cohorts (P < 0.001). The prediction efficiency of the intratumoral combined peritumoral radiomics model was higher than that of the intratumoral radiomics model and the peritumoral radiomics model. The AUCs of the training and verification cohorts were 0.867 and 0.785, respectively. The radiomics nomogram, which incorporated the radiomics signature, MR-reported ALN status, and MR-reported maximum diameter of the lesion, showed good calibration and discrimination in the training (AUC = 0.872) and validation cohorts (AUC = 0.863). Conclusion The intratumoral combined peritumoral radiomics model derived from DCE-MRI showed great predictive value for ALNM and may help to improve clinical decision-making for BC.
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Affiliation(s)
- Ying Liu
- Special Needs Comprehensive Department, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Xing Li
- Medical Imaging Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Lina Zhu
- Medical Imaging Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Zhiwei Zhao
- Medical Imaging Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Tuan Wang
- Medical Imaging Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Xi Zhang
- Medical Imaging Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Bing Cai
- Medical Imaging Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Li Li
- Medical Imaging Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Mingrui Ma
- Information Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Xiaojian Ma
- Information Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
| | - Jie Ming
- Medical Imaging Center, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi 830011, Xinjiang, China
- Medical Imaging Center, Bachu County People's Hospital, Bachu 843800, Xinjiang, China
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He L, Liang P, Zeng H, Huang G, Wu J, Zhang Y, Cui Y, Huang W. A Predictive Model for Nonsentinel Node Status after Sentinel Lymph Node Biopsy in Sentinel Lymph Node-Positive Chinese Women with Early Breast Cancer. JOURNAL OF ONCOLOGY 2022; 2022:7704686. [PMID: 35251176 PMCID: PMC8894031 DOI: 10.1155/2022/7704686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 01/11/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Axial lymph node dissection (ALND) is needed in patients with positive sentinel lymph node (SLN). ALND is easy to cause upper limb edema. Therefore, accurate prediction of nonsentinel lymph nodes (non-SLN) which may not need ALND can avoid excessive dissection and reduce complications. We constructed a new prognostic model to predict the non-SLN metastasis of Chinese breast cancer patients. METHODS We enrolled 736 patients who underwent sentinel lymph node biopsy (SLNB); 228 (30.98%) were diagnosed with SLNB metastasis which was determined by intraoperative pathological detection and further accepted ALND. We constructed a prediction model by univariate analysis, multivariate analysis, "R" language, and binary logistic regression in the abovementioned 228 patients and verified this prediction model in 60 patients. RESULTS Based on univariate analysis using α = 0.05 as the significance level for type I error, we found that age (P=0.045), tumor size (P=0.006), multifocality (P=0.011), lymphovascular invasion (P=0.003), positive SLN number (P=0.009), and negative SLN number (P=0.034) were statistically significant. Age was excluded in multivariate analysis, and we constructed a predictive equation to assess the risk of non-SLN metastasis: Logit(P)=Ln(P/1 - P)=0.267∗a+1.443∗b+1.078∗c+0.471∗d - 0.618∗e - 2.541 (where "a" represents tumor size, "b" represents multifocality, "c" represents lymphovascular invasion, "d" represents the number of metastasis of SLN, and "e" represents the number of SLNs without metastasis). AUCs for the training group and validation group were 0.715 and 0.744, respectively. When setting the risk value below 22.3%, as per the prediction equation's low-risk interval, our model predicted that about 4% of patients could avoid ALND. CONCLUSIONS This study established a model which demonstrated good prognostic performance in assessing the risk of non-SLN metastasis in Chinese patients with positive SLNs.
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Affiliation(s)
- Lifang He
- Breast Center, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
| | - Peide Liang
- Breast Center, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
- Department of Thyroid and Breast Surgery, Dongguan Houjie Hospital, Dongguan 523000, Guangdong Province, China
| | - Huancheng Zeng
- Breast Center, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
| | - Guangsheng Huang
- Breast Center, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
| | - Jundong Wu
- Breast Center, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
| | - Yiwen Zhang
- Breast Center, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
| | - Yukun Cui
- Guangdong Provincial Key Laboratory for Breast Cancer Diagnosis and Treatment, Cancer Hospital of Shantou University Medical College, Shantou 515000, Guangdong Province, China
| | - Wenhe Huang
- Department of Breast and Thyroid Surgery, Xiang'an Hospital of Xiamen University, No. 2000, Xiang'an East Road, Xiamen 361101, Fujian Province, China
- Key Laboratory for Endocrine-Related Cancer Precision Medicine of Xiamen, Xiamen 361101, Fujian Province, China
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Peyroteo M, Canotilho R, Margarida Correia A, Baía C, Ribeiro C, Reis P, de Sousa A. Predictive factors of non-sentinel lymph node disease in breast cancer patients with positive sentinel lymph node. Cir Esp 2022; 100:81-87. [PMID: 35123939 DOI: 10.1016/j.cireng.2022.01.003] [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: 10/17/2020] [Accepted: 11/16/2020] [Indexed: 06/14/2023]
Abstract
INTRODUCTION Management of positive sentinel lymph node biopsy (SLNB) in breast cancer remains a matter of debate. Our aim was to evaluate the incidence and identify predictive factors of non-sentinel lymph node metastases. METHODS Retrospective review of all cN0 breast cancer patients treated between January 2013 and December 2017, with positive SLNB that were submitted to ALND. RESULTS Of the 328 patients included, the majority of tumors were cT1 or cT2, with lymphovascular invasion in 58.4% of cases. The mean isolated nodes in SLNB was 2.7, with a mean of 1.6 positive nodes, 60.7% with extracapsular extension. Regarding ALND, a mean of 13.9 nodes were isolated, with a mean of 2.1 positive nodes. There was no residual disease in the ALND in 50.9% of patients, with 18.9% having ≥4 positive nodes. In the multivariate analysis, lymphovascular invasion, extracapsular extension in SLN, largest SLN metastases size (>10 mm) and ratio of positive SNL (>50%) were independent predictors of non-sentinel lymph node metastases. These four factors were used to build a non-pondered score to predict the probability of a positive ALND after a positive SLNB. The AUC of the model was 0.69 and 81% of patients with score = 0 and 65.6% with score = 1 had no additional disease in ALND. CONCLUSION The absence of non-sentinel lymph node metastases in the majority of patients with 1-2 positive SLN with low risk score questions the need of ALND in this population. The identified predictive factors may help select patients in which ALND can be omitted.
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Affiliation(s)
- Mariana Peyroteo
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal.
| | - Rita Canotilho
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Ana Margarida Correia
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Catarina Baía
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Cátia Ribeiro
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Paulo Reis
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Abreu de Sousa
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
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Peyroteo M, Canotilho R, Correia AM, Baía C, Ribeiro C, Reis P, de Sousa A. Predictive factors of non-sentinel lymph node disease in breast cancer patients with positive sentinel lymph node. Cir Esp 2020; 100:S0009-739X(20)30386-9. [PMID: 33358014 DOI: 10.1016/j.ciresp.2020.11.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 11/15/2020] [Accepted: 11/16/2020] [Indexed: 11/20/2022]
Abstract
INTRODUCTION Management of positive sentinel lymph node biopsy (SLNB) in breast cancer remains a matter of debate. Our aim was to evaluate the incidence and identify predictive factors of non-sentinel lymph node metastases. METHODS Retrospective review of all cN0 breast cancer patients treated between January 2013 and December 2017, with positive SLNB that were submitted to ALND. RESULTS Of the 328 patients included, the majority of tumors were cT1 or cT2, with lymphovascular invasion in 58.4% of cases. The mean isolated nodes in SLNB was 2.7, with a mean of 1.6 positive nodes, 60.7% with extracapsular extension. Regarding ALND, a mean of 13.9 nodes were isolated, with a mean of 2.1 positive nodes. There was no residual disease in the ALND in 50.9% of patients, with 18.9% having ≥ four positive nodes. In the multivariate analysis, lymphovascular invasion, extracapsular extension in SLN, largest SLN metastases size (>10 mm) and ratio of positive SNL (> 50%) were independent predictors of non-sentinel lymph node metastases. These four factors were used to build a non-pondered score to predict the probability of a positive ALND after a positive SLNB. The AUC of the model was 0.69 and 81% of patients with score = 0 and 65.6% with score = 1 had no additional disease in ALND. CONCLUSION The absence of non-sentinel lymph node metastases in the majority of patients with 1-2 positive SLN with low risk score questions the need of ALND in this population. The identified predictive factors may help select patients in which ALND can be omitted.
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Affiliation(s)
- Mariana Peyroteo
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal.
| | - Rita Canotilho
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Ana Margarida Correia
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Catarina Baía
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Cátia Ribeiro
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Paulo Reis
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
| | - Abreu de Sousa
- Surgical Oncology Department, Instituto Português de Oncologia do Porto, Porto, Portugal
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Guo CG, Zhao DB, Liu Q, Zhou ZX, Zhao P, Wang GQ, Cai JQ. A nomogram to predict lymph node metastasis in patients with early gastric cancer. Oncotarget 2017; 8:12203-12210. [PMID: 28099943 PMCID: PMC5355337 DOI: 10.18632/oncotarget.14660] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2016] [Accepted: 12/25/2016] [Indexed: 02/07/2023] Open
Abstract
Background Lymph node status is crucial to determining treatment for early gastric cancer (EGC). We aim to establish a nomogram to predict the possibility of lymph node metastasis (LNM) in EGC patients. Methods Medical records of 952 EGC patients with curative resection, from 2002 to 2014, were retrospectively retrieved. Univariate and multivariate analysis were performed to examine risk factors associated with LNM. A nomogram for predicting LNM was established and internally validated. Results Five variables significantly associated with LNM were included in our model, these are sex (Odd ratio [OR] = 1.961, 95% confidence index [CI], 1.334 to 2.883; P = 0.001), depth of tumor (OR = 2.875, 95% CI, 1.872 to 4.414; P = 0.000), tumor size (OR = 1.986, 95% CI, 1.265 to 3.118; P = 0.003), histology type (OR = 2.926, 95% CI, 1.854 to 4.617; P = 0.000) and lymphovascular invasion (OR = 4.967, 95% CI, 2.996 to 8.235; P = 0.000). The discrimination of the prediction model was 0.786. Conclusions A nomogram for predicting lymph node metastasis in patients with early gastric cancer was successfully established, which was superior to the absolute endoscopic submucosal dissection (ESD) indication in terms of the clinical performance.
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Affiliation(s)
- Chun Guang Guo
- Department of Abdominal Surgical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dong Bing Zhao
- Department of Abdominal Surgical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qian Liu
- Department of Abdominal Surgical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhi Xiang Zhou
- Department of Abdominal Surgical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ping Zhao
- Department of Abdominal Surgical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Gui Qi Wang
- Department of Endoscopy, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jian Qiang Cai
- Department of Abdominal Surgical Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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MRI and FDG-PET/CT based assessment of axillary lymph node metastasis in early breast cancer: a meta-analysis. Clin Radiol 2017; 72:295-301. [DOI: 10.1016/j.crad.2016.12.001] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Revised: 09/13/2016] [Accepted: 12/05/2016] [Indexed: 11/18/2022]
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