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Skarping I, Bendahl PO, Szulkin R, Alkner S, Andersson Y, Bergkvist L, Christiansen P, Filtenborg Tvedskov T, Frisell J, Gentilini OD, Kontos M, Kühn T, Lundstedt D, Vrou Offersen B, Olofsson Bagge R, Reimer T, Sund M, Rydén L, de Boniface J. Prediction of High Nodal Burden in Patients With Sentinel Node-Positive Luminal ERBB2-Negative Breast Cancer. JAMA Surg 2024:2824187. [PMID: 39320882 PMCID: PMC11425194 DOI: 10.1001/jamasurg.2024.3944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2024]
Abstract
Importance In patients with clinically node-negative (cN0) breast cancer and 1 or 2 sentinel lymph node (SLN) macrometastases, omitting completion axillary lymph node dissection (CALND) is standard. High nodal burden (≥4 axillary nodal metastases) is an indication for intensified treatment in luminal breast cancer; hence, abstaining from CALND may result in undertreatment. Objective To develop a prediction model for high nodal burden in luminal ERBB2-negative breast cancer (all histologic types and lobular breast cancer separately) without CALND. Design, Setting, and Participants The prospective Sentinel Node Biopsy in Breast Cancer: Omission of Axillary Clearance After Macrometastases (SENOMAC) trial randomized patients 1:1 to CALND or its omission from January 2015 to December 2021 among adult patients with cN0 T1-T3 breast cancer and 1 or 2 SLN macrometastases across 5 European countries. The cohort was randomly split into training (80%) and test (20%) sets, with equal proportions of high nodal burden. Prediction models were developed by multivariable logistic regression in the complete luminal ERBB2-negative cohort and a lobular breast cancer subgroup. Nomograms were constructed. The present diagnostic/prognostic study presents the results of a prespecified secondary analysis of the SENOMAC trial. Herein, only patients with luminal ERBB2-negative tumors assigned to CALND were selected. Data analysis for this article took place from June 2023 to April 2024. Exposure Predictors of high nodal burden. Main Outcomes and Measures High nodal burden was defined as ≥4 axillary nodal metastases. The luminal prediction model was evaluated regarding discrimination and calibration. Results Of 1010 patients (median [range] age, 61 [34-90] years; 1006 [99.6%] female and 4 [0.4%] male), 138 (13.7%) had a high nodal burden and 212 (21.0%) had lobular breast cancer. The model in the training set (n = 804) included number of SLN macrometastases, presence of SLN micrometastases, SLN ratio, presence of SLN extracapsular extension, and tumor size (not included in lobular subgroup). Upon validation in the test set (n = 201), the area under the receiver operating characteristic curve (AUC) was 0.74 (95% CI, 0.62-0.85) and the calibration was satisfactory. At a sensitivity threshold of ≥80%, all but 5 low-risk patients were correctly classified corresponding to a negative predictive value of 94%. The prediction model for the lobular subgroup reached an AUC of 0.74 (95% CI, 0.66-0.83). Conclusions and Relevance The predictive models and nomograms may facilitate systemic treatment decisions without exposing patients to the risk of arm morbidity due to CALND. External validation is needed. Trial Registration ClinicalTrials.gov Identifier: NCT02240472.
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Affiliation(s)
- Ida Skarping
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Clinical Physiology and Nuclear Medicine, Skane University Hospital, Lund, Sweden
| | - Pär-Ola Bendahl
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Robert Szulkin
- Cytel Inc, Sweden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden
| | - Sara Alkner
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Hematology, Oncology and Radiation Physics, Skane University Hospital, Lund, Sweden
| | - Yvette Andersson
- Department of Surgery, Västmanland Hospital, Västerås, Sweden
- Centre for Clinical Research Uppsala University, Västmanland Hospital Västerås, Sweden
| | - Leif Bergkvist
- Centre for Clinical Research Uppsala University, Västmanland Hospital Västerås, Sweden
| | - Peer Christiansen
- Department of Plastic and Breast Surgery, Aarhus University Hosoital, Denmark
| | - Tove Filtenborg Tvedskov
- Department of Breast Surgery, Gentofte Hospital, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jan Frisell
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Breast Center Karolinska, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden
| | - Oreste D Gentilini
- Università Vita-Salute San Raffaele, Milano, Italy
- IRCCS Ospedale San Raffaele, Milano, Italy
| | - Michalis Kontos
- 1st Department of Surgery, National and Kapodistrian University of Athens, Laiko Hospital, Athens, Greece
| | - Thorsten Kühn
- Interdisciplinary Breast Center, University of Ulm, Ulm, Germany
- Breast Center Die Filderklinik, Filderstadt, Germany
| | - Dan Lundstedt
- Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Oncology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Birgitte Vrou Offersen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
- Aarhus University, Faculty of Health, Aarhus, Denmark
| | - Roger Olofsson Bagge
- Sahlgrenska Center for Cancer Research, Department of Surgery, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Surgery, Sahlgrenska University Hospital, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Toralf Reimer
- Department of Obstetrics and Gynecology, University of Rostock, Rostock, Germany
| | - Malin Sund
- Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Diagnostics and Intervention/Surgery, Umeå University, Umeå, Sweden
| | - Lisa Rydén
- Division of Oncology, Department of Clinical Sciences, Lund University, Lund, Sweden
- Department of Surgery and Gastroenterology, Skane University Hospital, Lund, Sweden
| | - Jana de Boniface
- Department of Surgery, Capio St Göran's Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
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Hu B, Xu Y, Gong H, Tang L, Wang L, Li H. Nomogram Utilizing ABVS Radiomics and Clinical Factors for Predicting ≤ 3 Positive Axillary Lymph Nodes in HR+ /HER2- Breast Cancer with 1-2 Positive Sentinel Nodes. Acad Radiol 2024; 31:2684-2694. [PMID: 38383259 DOI: 10.1016/j.acra.2024.01.026] [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: 11/09/2023] [Revised: 01/15/2024] [Accepted: 01/22/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND In HR+ /HER2- breast cancer patients with ≤ 3 positive axillary lymph nodes (ALNs), genomic tests can streamline chemotherapy decisions. Current studies, centered on tumor metrics, miss broader patient insights. Automated Breast Volume Scanning (ABVS) provides advanced 3D imaging, and its potential synergy with radiomics for ALN evaluation is untapped. OBJECTIVE This study sought to combine ABVS radiomics and clinical characteristics in a nomogram to predict ≤ 3 positive ALNs in HR+ /HER2- breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs), guiding clinicians in genetic test candidate selection. METHODS We enrolled 511 early-stage breast cancer patients: 362 from A Hospital for training and 149 from B Hospital for validation. Using LASSO logistic regression, primary features were identified. A clinical-radiomics nomogram was developed to predict the likelihood of ≤ 3 positive ALNs in HR+ /HER2- patients with 1-2 positive SLNs. We assessed the discriminative capability of the nomogram using the ROC curve. The model's calibration was confirmed through a calibration curve, while its fit was evaluated using the Hosmer-Lemeshow (HL) test. To determine the clinical net benefits, we employed the Decision Curve Analysis (DCA). RESULTS In the training group, 81.2% patients had ≤ 3 metastatic ALNs, and 83.2% in the validation group. We developed a clinical-radiomics nomogram by analyzing clinical characteristics and rad-scores. Factors like positive SLNs (OR=0.077), absence of negative SLNs (OR=11.138), lymphovascular invasion (OR=0.248), and rad-score (OR=0.003) significantly correlated with ≤ 3 positive ALNs. The clinical-radiomics nomogram, with an AUC of 0.910 in training and 0.882 in validation, outperformed the rad-score-free clinical nomogram (AUCs of 0.796 and 0.782). Calibration curves and the HL test (P values 0.688 and 0.691) confirmed its robustness. DCA showed the clinical-radiomics nomogram provided superior net benefits in predicting ALN burden across specific threshold probabilities. CONCLUSION We developed a clinical-radiomics nomogram that integrated radiomics from ABVS images and clinical data to predict the presence of ≤ 3 positive ALNs in HR+ /HER2- patients with 1-2 positive SLNs, aiding oncologists in identifying candidates for genomic tests, bypassing ALND. In the era of precision medicine, combining genomic tests with SLN biopsy refines both surgical and systemic patient treatments.
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Affiliation(s)
- Bin Hu
- Department of Ultrasound, Minhang Hospital, Fudan University, 170 Xinsong Rd, Shanghai 201199, China.
| | - Yanjun Xu
- Department of Ultrasonography, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Huiling Gong
- Department of Ultrasound, Minhang Hospital, Fudan University, 170 Xinsong Rd, Shanghai 201199, China
| | - Lang Tang
- Department of Ultrasound, Minhang Hospital, Fudan University, 170 Xinsong Rd, Shanghai 201199, China
| | - Lihong Wang
- Department of Ultrasound, Minhang Hospital, Fudan University, 170 Xinsong Rd, Shanghai 201199, China
| | - Hongchang Li
- Department of General Surgery, Institute of Fudan-Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai, China
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Wang XE, Bi Z, Zhang J, Wang YS. Nomograms for metastasis of non-sentinel lymph nodes or more than three lymph nodes in patients with one or two positive sentinel lymph nodes. Front Oncol 2024; 14:1413936. [PMID: 38835388 PMCID: PMC11148251 DOI: 10.3389/fonc.2024.1413936] [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: 04/08/2024] [Accepted: 04/30/2024] [Indexed: 06/06/2024] Open
Abstract
Purpose The purpose of this study was to provide advice for the indication of regional nodal irradiation (RNI) in patients with one to two positive sentinel lymph nodes (SLNs) without axillary lymph node dissection (ALND). Methods We conducted a retrospective study in Shandong Cancer Hospital, Fudan University Shanghai Cancer Center, and West China Hospital. Logistic analysis was performed in order to explore the influencing factors of positive non-SLNs (NSLNs) and >3 positive nodes among patients with one to two SLNs+. Then, nomograms were constructed. Results Between May 2010 and 2020, among the 2,845 patients with one to two SLNs+ undergoing ALND (1,992 patients in the training set and 853 patients in the validation set), there were 34.3% harbored NSLNs+ and 15.6% harbored >3 positive nodes. Multivariate analysis showed that cN stage, the number of positive/negative SLN, pathological tumor stage, lympho-vascular invasion (LVI), multicenter, and molecular subtypes were significantly associated with NSLN metastasis. Similarly, multivariate analysis also showed that cN stage, the number of positive/negative SLNs, pathological tumor stage, and LVI could be independent predictors of >3 positive nodes. Then, nomograms for NSLN metastasis and >3 positive nodes were constructed using these parameters, respectively. Conclusions The nomograms will be useful in estimating positive NSLNs and >3 positive nodes, and they might provide advice for the optimization of RNI.
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Affiliation(s)
- Xue-Er Wang
- The Third Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Zhao Bi
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jin Zhang
- The Third Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yong-Sheng Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Bi Z, Wang Y. Advances in regional nodal management of early-stage breast cancer. Chin J Cancer Res 2024; 36:215-225. [PMID: 38751438 PMCID: PMC11090791 DOI: 10.21147/j.issn.1000-9604.2024.02.08] [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: 12/18/2023] [Accepted: 03/18/2024] [Indexed: 05/18/2024] Open
Abstract
With the continuous improvement of systemic treatment, reasonable local regional control of early-stage breast cancer can be translated into survival benefits. The optimization of regional nodal management in patients with limited sentinel lymph node (SLN) metastasis needs to be weighed by surgical complications, regional recurrence risk, and lymph node status, as well as other escalating treatment (systemic/radiotherapy) that may result from de-escalating surgery. With the effective support and supplementation of systemic therapy and radiotherapy, the management of axillary surgery is developing in a de-escalating trend. The widespread application of neoadjuvant therapy has contributed to optimizing the management of patients with clinically node-negative/imaging node-positive disease. In clinical practice, it is necessary to consider the residual tumor burden of regional lymph nodes when formulating the optimal irradiation fields in patients with limited positive SLN without axillary lymph node dissection. The combined application of genomic tests and American College of Surgeons Oncology Group Z0011/AMAROS criteria could provide patients with a better strategy of dual de-escalation treatment, which includes the de-escalation of both axillary surgery and systemic treatment. In the era of sentinel lymph node biopsy (SLNB), the regional nodal management of breast cancer should adhere to the concept of "updating ideas, making bold assumptions, and carefully seeking proof", make full use of the benefits of systemic therapy and radiotherapy to reduce the scope of surgery and complications, and expand the "net benefit" of efficacy and quality of life. This review discusses the optimization of regional nodal management in the era of SLNB, in order to provide reference information for clinicians.
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Affiliation(s)
- Zhao Bi
- Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250017, China
| | - Yongsheng Wang
- Breast Cancer Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250017, 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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Murata T, Watase C, Shiino S, Kurita A, Ogawa A, Jimbo K, Iwamoto E, Yoshida M, Takayama S, Suto A. Development and validation of a pre- and intra-operative scoring system that distinguishes between non-advanced and advanced axillary lymph node metastasis in breast cancer with positive sentinel lymph nodes: a retrospective study. World J Surg Oncol 2022; 20:314. [PMID: 36171615 PMCID: PMC9516796 DOI: 10.1186/s12957-022-02779-9] [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: 04/11/2022] [Accepted: 09/16/2022] [Indexed: 11/10/2022] Open
Abstract
Background There are currently no scoring-type predictive models using only easily available pre- and intraoperative data developed for assessment of the risk of advanced axillary lymph node metastasis (ALNM) in patients with breast cancer with metastatic sentinel lymph nodes (SLNs). We aimed to develop and validate a scoring system using only pre- and intraoperative data to distinguish between non-advanced (≤ 3 lymph nodes) and advanced (> 3 lymph nodes) ALNM in patients with breast cancer with metastatic SLNs. Methods We retrospectively identified 804 patients with breast cancer (cT1-3cN0) who had metastatic SLNs and had undergone axillary lymph node dissection (ALND). We evaluated the risk factors for advanced ALNM using logistic regression analysis and developed and validated a scoring system for the prediction of ALNM using training (n = 501) and validation (n = 303) cohorts, respectively. The predictive performance was assessed using the receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration plots. Results Ultrasound findings of multiple suspicious lymph nodes, SLN macrometastasis, the ratio of metastatic SLNs to the total number of SLNs removed, and the number of metastatic SLNs were significant risk factors for advanced ALNM. Clinical tumor size and invasive lobular carcinoma were of borderline significance. The scoring system based on these six variables yielded high AUCs (0.90 [training] and 0.89 [validation]). The calibration plots of frequency compared to the predicted probability showed slopes of 1.00 (training) and 0.85 (validation), with goodness-of-fit for the model. When the cutoff score was set at 4, the negative predictive values (NPVs) of excluding patients with advanced ALNM were 96.8% (training) and 96.9% (validation). The AUC for predicting advanced ALNM using our scoring system was significantly higher than that predicted by a single independent predictor, such as the number of positive SLNs or the proportion of positive SLNs. Similarly, our scoring system also showed good discrimination and calibration ability when the analysis was restricted to patients with one or two SLN metastases. Conclusion Our easy-to-use scoring system can exclude advanced ALNM with high NPVs. It may contribute to reducing the risk of undertreatment with adjuvant therapies in patients with metastatic SLNs, even if ALND is omitted. Supplementary Information The online version contains supplementary material available at 10.1186/s12957-022-02779-9.
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Affiliation(s)
- Takeshi Murata
- Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Chikashi Watase
- Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Sho Shiino
- Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Arisa Kurita
- Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ayumi Ogawa
- Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Kenjiro Jimbo
- Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Eriko Iwamoto
- Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Masayuki Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Shin Takayama
- Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Akihiko Suto
- Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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Bi Z, Qiu PF, Zhang Y, Song XG, Chen P, Xie L, Wang YS, Song XR. A Three lncRNA Set: AC009975.1, POTEH-AS1 and AL390243.1 as Nodal Efficacy Biomarker of Neoadjuvant Therapy for HER-2 Positive Breast Cancer. Front Oncol 2021; 11:779140. [PMID: 34938660 PMCID: PMC8685269 DOI: 10.3389/fonc.2021.779140] [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: 09/18/2021] [Accepted: 11/11/2021] [Indexed: 12/04/2022] Open
Abstract
Purpose The study aimed to explore whether the expression of lncRNAs in primary tumors could predict nodal efficacy after neoadjuvant therapy (NAT) for HER2+ breast cancer. Methods Total RNA was extracted from HER2+ breast cancer tissues before NAT (n=103) and from 48 pairs of cancers and para-cancers tissues that did not receive NAT. Different lncRNAs were selected by microarray, validated by qPCR, and analyzed to illuminate their potential as nodal efficacy biomarkers after NAT. Results Our results demonstrated that three lncRNA sets, lncRNA-AL390243.1, POTEH-AS1, and lncRNA-AC009975.1, were up-regulated in non-apCR tissues. The AUC value was 0.789 (95%CI: 0.703-0.876). The multivariate logistic regression analysis identified the expression of lncRNA-AL390243.1 (OR 5.143; 95% CI: 1.570-16.847), tumor type (OR 0.144; 95% CI: 0.024-0.855), and nodal stage (OR 0.507; 95% CI: 0.289-0.888) as independent predictors for apCR after NAT in HER2+ patients (all p<0.05). Then the three predictors were used to create a predictive nomogram. The AUC value was 0.859 (95%CI: 0.790-0.929). The calibration curve showed a satisfactory fit between predictive and actual observation based on internal validation with a bootstrap resampling frequency of 1000. Patients with higher expression of lncRNA-AL390243.1 had worse survival. LncRNA-AL390243.1 was up-regulated more in the nodal positive subgroup than in the nodal negative subgroup (p=0.0271). Conclusion The lncRNA-AL390243.1, POTEH-AS1, and lncRNA-AC009975.1 were upregulated in non-apCR breast cancer tissues. These three lncRNAs might have the potential to be used as predictive biomarkers of nodal efficacy of HER2+ breast cancer. Further studies are required to illuminate the underlying molecular mechanisms further.
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Affiliation(s)
- Zhao Bi
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Peng-Fei Qiu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yue Zhang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xing-Guo Song
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Peng Chen
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Li Xie
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yong-Sheng Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Xian-Rang Song
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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Bi Z, Chen JJ, Liu PC, Chen P, Wang WL, Liu YB, Wang CJ, Qiu PF, Lv Q, Wu J, Wang YS. Candidates of Genomic Tests in HR+/HER2- Breast Cancer Patients With 1-2 Positive Sentinel Lymph Node Without Axillary Lymph Node Dissection: Analysis From Multicentric Cohorts. Front Oncol 2021; 11:722325. [PMID: 34422668 PMCID: PMC8375498 DOI: 10.3389/fonc.2021.722325] [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: 06/08/2021] [Accepted: 07/20/2021] [Indexed: 02/05/2023] Open
Abstract
Background The genomic tests such as the MammaPrint and Oncotype DX test are being gradually applied for hormone receptor positive/HER-2 negative (HR+/HER2-) breast cancer patients with up to three positive axillary lymph nodes (ALNs). The first results from RxPONDER trial suggested that Oncotype DX could be applied to patients with 1-2 positive sentinel lymph nodes (SLNs) without axillary lymph node dissection (ALND), which constituted 37.4% of the intent-to-treat population. However, there was no distinctive research on how to apply genomic tests precisely to HR+/HER2- patients with 1-2 positive SLNs without ALND. The purpose was to construct a nomogram using the multi-center retrospective data to predict precisely which HR+/HER2- candidates with 1-2 positive SLNs could be subjected to genomic tests (≤ 3 positive lymph nodes). Methods We conducted a retrospective analysis of 18,600 patients with stage I-III breast cancer patients treated with sentinel lymph node biopsy (SLNB) in Shandong Cancer Hospital, Fudan University Shanghai Cancer Center, and West China Hospital. The univariate and multivariate logistic regression analysis was conducted to identify the independent predictive factors of having ≤ 3 positive nodes among patients with 1-2 positive SLNs. A nomogram was developed based on variables in the final model with p<0.05. Calibration of the nomogram was carried out by internal validation using the bootstrap resampling approach and was displayed using a calibration curve. The discrimination of the model was evaluated using the ROC curve. Results Based on the database of the three institutions, a total of 18,600 breast cancer patients were identified undergoing SLNB between May 2010 and 2020. Among the 1817 HR+/HER2- patients with 1-2 positive SLNs undergoing ALND, 84.2% harbored ≤ 3 totals metastatic ALNs. The multivariate logistic regression analysis identified imaging abnormal nodes (OR=0.197, 95%CI: 0.082-0.472), the number of positive SLNs (OR=0.351, 95%CI: 0.266-0.464), the number of negative SLNs (OR=1.639, 95%CI: 1.465-1.833), pathological tumor stage (OR=0.730, 95%CI: 0.552-0.964), and lympho-vascular invasion (OR=0.287, 95%CI: 0.222-0.398) as independent predictors for the proportion of patients with ≤ 3 total metastatic ALNs (all p<0.05). These five predictors were used to create a predictive nomogram. The AUC value was 0.804 (95%CI: 0.681-0.812, p<0.001). The calibration curve showed a satisfactory fit between the predictive and actual observation based on internal validation with a bootstrap resampling frequency of 1000. Conclusion The nomogram based on the multi-centric database showed a good accuracy and could assist the oncologist in determining precisely which HR+/HER2- candidates with 1-2 positive SLNs without ALND could perform genomic tests. In the era of SLNB and precision medicine, the combined application of genomic tests and SLNB could provide patients with a better strategy of dual de-escalation management, including the de-escalation of both surgery and systemic treatment.
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Affiliation(s)
- Zhao Bi
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Jia-Jian Chen
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Peng-Chen Liu
- Department of Breast Surgery, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Peng Chen
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.,Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wei-Li Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Yan-Bing Liu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Chun-Jian Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Peng-Fei Qiu
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Qing Lv
- Department of Breast Surgery, Clinical Research Center for Breast, West China Hospital, Sichuan University, Chengdu, China
| | - Jiong Wu
- Shanghai Cancer Center, Fudan University, Shanghai, China
| | - Yong-Sheng Wang
- Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
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9
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Yang Z, Lan X, Huang Z, Yang Y, Tang Y, Jing H, Wang J, Zhang J, Wang X, Gao J, Wang J, Xuan L, Fang Y, Ying J, Li Y, Huang X, Wang S. Development and external validation of a nomogram to predict four or more positive nodes in breast cancer patients with one to three positive sentinel lymph nodes. Breast 2020; 53:143-151. [PMID: 32823167 PMCID: PMC7451418 DOI: 10.1016/j.breast.2020.08.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/26/2020] [Accepted: 08/04/2020] [Indexed: 11/29/2022] Open
Abstract
Objective To develop a nomogram for predicting the possibility of four or more positive nodes in breast cancer patients with 1–3 positive sentinel lymph nodes (SLN). Materials and methods Retrospective analysis of data of patients from two institutions was conducted. The inclusion criteria were: invasive breast cancer; clinically node negative; received lumpectomy or mastectomy plus SLN biopsy followed by axillary lymph node dissection (ALND); and pathologically confirmed T1-2 tumor, with 1–3 positive SLNs. Patients from one institution formed the training group and patients from the other the validation group. Univariate and multivariate analyses were performed to identify the predictors of four or more positive nodes. These predictors were used to build the nomogram. The area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model. Results Of the 1480 patients (966 patients in the training group, 514 in the validation group), 306 (20.7%) had four or more positive nodes. Multivariate stepwise logistic regression showed number of positive (p < .001) and negative SLN (p < .001), extracapsular extension (p < .001), pT stage (p = .016), and tumor location in outer upper quadrant (p = .031) to be independent predictors of four or more positive nodes. The nomogram was built using these five factors. The AUC was 0.845 in the training group and 0.804 in the validation group. Conclusion The proposed nomogram appears to accurately estimate the likelihood of four or more positive nodes and could help radiation oncologists to decide on use of regional nodal irradiation (RNI) for breast cancer patients with 1–3 positive nodes but no ALND. Five predictors of four or more positive nodes in breast cancer patients were identified. A nomogram was built using these five factors. The nomogram was validated on an external cohort. The proposed nomogram predicts four or more positive nodes with high accuracy. The nomogram can help in decision making on use of regional nodal irradiation.
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Affiliation(s)
- Zhuanbo Yang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaowen Lan
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
| | - Zhou Huang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Radiation Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yong Yang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yu Tang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Hao Jing
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianyang Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianghu Zhang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiang Wang
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jidong Gao
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jing Wang
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lixue Xuan
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yi Fang
- Breast Surgery Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Jianming Ying
- Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yexiong Li
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Xiaobo Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Radiation Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China; Department of Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
| | - Shulian Wang
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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10
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Madekivi V, Boström P, Karlsson A, Aaltonen R, Salminen E. Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer? Acta Oncol 2020; 59:689-695. [PMID: 32148141 DOI: 10.1080/0284186x.2020.1736332] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Background: The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, the accuracy of SLNB to detect nodal stage N2-3 remains debatable. Nomograms can help the decision-making process between axillary treatment options. The aim of this study was to create a new model to predict the nodal stage N2-3 after a positive SLNB using machine learning methods that are rarely seen in nomogram development.Material and methods: Primary breast cancer patients who underwent SLNB and axillary lymph node dissection (ALND) between 2012 and 2017 formed cohorts for nomogram development (training cohort, N = 460) and for nomogram validation (validation cohort, N = 70). A machine learning method known as the gradient boosted trees model (XGBoost) was used to determine the variables associated with nodal stage N2-3 and to create a predictive model. Multivariate logistic regression analysis was used for comparison.Results: The best combination of variables associated with nodal stage N2-3 in XGBoost modeling included tumor size, histological type, multifocality, lymphovascular invasion, percentage of ER positive cells, number of positive sentinel lymph nodes (SLN) and number of positive SLNs multiplied by tumor size. Indicating discrimination, AUC values for the training cohort and the validation cohort were 0.80 (95%CI 0.71-0.89) and 0.80 (95%CI 0.65-0.92) in the XGBoost model and 0.85 (95%CI 0.77-0.93) and 0.75 (95%CI 0.58-0.89) in the logistic regression model, respectively.Conclusions: This machine learning model was able to maintain its discrimination in the validation cohort better than the logistic regression model. This indicates advantages in employing modern artificial intelligence techniques into nomogram development. The nomogram could be used to help identify nodal stage N2-3 in early breast cancer and to select appropriate treatments for patients.
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Affiliation(s)
- V. Madekivi
- Department of Oncology, Turku University Hospital, Turku, Finland
- Faculty of Medicine, University of Turku, Turku, Finland
| | - P. Boström
- Faculty of Medicine, University of Turku, Turku, Finland
- Department of Pathology, Turku University Hospital, Turku, Finland
| | - A. Karlsson
- Faculty of Medicine, University of Turku, Turku, Finland
- Auria Clinical Informatics, Turku, Finland
| | - R. Aaltonen
- Faculty of Medicine, University of Turku, Turku, Finland
- Department of Surgery, Turku University Hospital, Turku, Finland
| | - E. Salminen
- Department of Oncology, Turku University Hospital, Turku, Finland
- Faculty of Medicine, University of Turku, Turku, Finland
- Finnish Nuclear and Radiation Safety, Helsinki, Finland
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11
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Predictors of non-sentinel lymph node metastasis in clinical early stage (cT1-2N0) breast cancer patients with 1-2 metastatic sentinel lymph nodes. Asian J Surg 2019; 43:538-549. [PMID: 31519397 DOI: 10.1016/j.asjsur.2019.07.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 06/23/2019] [Accepted: 07/31/2019] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE The purpose of this study was to determine the risk factors that caused non-sentinel lymph nodes (nonSLNs) metastasis by considering the clinicopathological characteristics of patients who have 1-2 sentinel lymph node (SLN) metastasis in the clinical early stage (T1-2, N0) breast cancer. METHODS The demographic and clinicopathological characteristics of the patients were recorded retrospectively. Among these, age, size of the primary breast tumor, tumor localization and multifocality/multicentricity status, preoperative serum Neutrophil/Lymphocyte rate (NLR), c-erbB2/HER2-neu status, Estrogen Receptor (ER) and Progesterone Receptor (PR) status, primary tumor proliferation index (Ki-67), histopathological grade, molecular subtypes, histopathological subtypes, nipple/areola infiltration, Lymphatic Invasion (LI), Vascular Invasion (VI), Perineural Invasion (PNI), number of metastatic SLN m(SLN), mSLN diameter, SLN Extranodal Extension (ENE) status, and number of metastatic nonSLNs were recorded. RESULTS According to the univariate analysis, the HER2 positivity, Ki-67≥%20, mSLN diameter, LI, VI, PNI, ENE and molecular subtypes were found to be significant. However, the age, tumor localization, multifocality/multicentricity, T stage, ER and PR status, tumor size, histopathological grade and subtypes, nipple/areola infiltration and NLR were not found to be significant. In the multivariate analysis, significant independent predictors in nonSLN metastasis development were found to be HER2 positivity, PNI, mSLN diameter ≥10,5 mm and ENE. CONCLUSION The HER2 positivity, ENE, PNI and mSLN diameter ≥10,5 mm were found to be very strong predictors in nonSLN metastasis development. The findings of this study have the potential to be a guideline for surgeons and oncologists when determining their patients' treatment plan. These components are candidates for inclusion among the clinicopathological factors that may be used in the new nomograms due to their higher sensitivity and specificity.
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12
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Liang Y, Chen X, Tong Y, Zhan W, Zhu Y, Wu J, Huang O, He J, Zhu L, Li Y, Chen W, Shen K. Higher axillary lymph node metastasis burden in breast cancer patients with positive preoperative node biopsy: may not be appropriate to receive sentinel lymph node biopsy in the post-ACOSOG Z0011 trial era. World J Surg Oncol 2019; 17:37. [PMID: 30786903 PMCID: PMC6383227 DOI: 10.1186/s12957-019-1582-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2018] [Accepted: 02/14/2019] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Breast cancer patients with suspicious axillary lymph node (ALN) at ultrasound and positive fine-needle aspiration (FNA) results were required to receive ALN dissection (ALND), which was not certain in the post-ACOSOG Z0011 era. We aim to evaluate the ALN metastasis burden in these patients, thus to illustrate whether they can follow the ACOSOG Z0011 trial procedure. METHODS Clinically, T1-2 N0 breast cancer patients with positive preoperative ALN biopsy (FNA group) or 1-2 positive sentinel nodes (SLNB group) were retrospectively analyzed. ALN metastasis burden was compared between the two groups, which were further analyzed in certain subtypes. An association between clinicopathological factors and ≥ 3 ALN metastasis was also analyzed. RESULTS A total of 388 patients were included: 202 in the FNA group and 186 in the SLNB group. The FNA group had a significantly higher number of positive ALN (5.18 vs. 1.77, P < 0.001) and a larger proportion of patients with ≥ 3 ALN metastasis (58.42% vs. 11.83%, P < 0.001) than the SLNB group, which was not influenced by different tumor size stage and molecular subtypes. ALN metastasis identified by FNA was independently associated with a high rate of ≥ 3 ALN metastasis (OR = 6.98, 95% CI 1.95-25.02, P = 0.003). CONCLUSIONS Patients with positive preoperative ALN biopsy had a higher ALN metastasis burden than patients with 1-2 positive SLNs, which was also the strongest factor associated with ≥ 3 ALN metastasis, indicating that these patients are not appropriate to receive SLNB in the post-ACOSOG Z0011 trial era.
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Affiliation(s)
- Yue Liang
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Xiaosong Chen
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Yiwei Tong
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Weiwei Zhan
- Department of Ultrasound Imaging, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Ying Zhu
- Department of Ultrasound Imaging, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Jiayi Wu
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Ou Huang
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Jianrong He
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Li Zhu
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Yafen Li
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Weiguo Chen
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
| | - Kunwei Shen
- Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, 197 Ruijin Er Road, Shanghai, 200025 China
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13
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Choi HJ, Kim JM, Ryu JM, Kim I, Nam SJ, Yu J, Lee SK, Lee JE, Kim SW. Patterns of Axillary Lymph Node Metastasis in Breast Cancer: A Prospective Single-Center Study. J Breast Cancer 2018; 21:447-452. [PMID: 30607167 PMCID: PMC6310723 DOI: 10.4048/jbc.2018.21.e50] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 09/08/2018] [Indexed: 01/14/2023] Open
Abstract
Purpose The recent trend in breast cancer treatment is to minimize axillary dissection. However, no pattern of axillary metastasis has been precisely established. The purpose of this study was to evaluate the metastatic lymphatic pattern using near-infrared fluorescence imaging with indocyanine green (ICG) in breast cancer with cytologically proven axillary metastasis. Methods This was a prospective single-center study. We evaluated 147 patients with breast cancer involving cytologically proven axillary metastasis, and compared physiological and nonphysiological lymphatic metastasis. Results We performed lymphatic mapping for 64 patients who exhibited level II lymphatic flow on near-infrared fluorescence imaging with ICG, and found that all had axillary metastasis: 51 patients who did not receive neoadjuvant chemotherapy (NAC) and 13 patients post-NAC. Of patients who did not receive NAC, 32 had physiological lymphatic metastasis and 19 had nonphysiological lymphatic metastasis. The risk factors for nonphysiological lymphatic metastasis were age ≥55 years, high Ki-67 index (>20%), and perinodal extension in both univariate and multivariate analysis (p<0.05). Conclusion Patients with identified risk factors in cytologically-proven axillary metastasis who did not receive NAC may have nonphysiological lymphatic metastasis.
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Affiliation(s)
- Hee Jun Choi
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jae-Myung Kim
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jai Min Ryu
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Isaac Kim
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seok Jin Nam
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jonghan Yu
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Se Kyung Lee
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeong Eon Lee
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Seok Won Kim
- Division of Breast Surgery, Department of Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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14
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Shou K, Tang Y, Chen H, Chen S, Zhang L, Zhang A, Fan Q, Yu A, Cheng Z. Diketopyrrolopyrrole-based semiconducting polymer nanoparticles for in vivo second near-infrared window imaging and image-guided tumor surgery. Chem Sci 2018; 9:3105-3110. [PMID: 29732093 PMCID: PMC5914543 DOI: 10.1039/c8sc00206a] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Accepted: 02/05/2018] [Indexed: 12/15/2022] Open
Abstract
A diketopyrrolopyrrole-based semiconducting polymer nanoparticle (PDFT1032) has been developed as a NIR-II (near infrared window II, 1000-1700 nm) fluorescent probe. It shows high photostability, a favorable absorption peak at 809 nm, a large Stokes shift of 223 nm, outstanding biocompatibility and minimal in vivo toxicity. More importantly, the versatile use of PDFT1032 for several important biomedical applications in the NIR-II window has been demonstrated, including the NIR-II optical imaging of tumors on a subcutaneous osteosarcoma model, assessing the vascular embolization therapy of tumors, and NIR-II image-guided orthotopic tumor surgery and sentinel lymph node biopsy (SLNB) with high spatial and temporal resolution. Overall, excellent biocompatibility, favorable hydrophilicity, and desirable chemical and optical properties make the semiconducting polymer nanoparticle PDFT1032 a highly promising NIR-II imaging probe with the potential to be widely applicable in clinical imaging and the surgical treatment of malignancy.
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Affiliation(s)
- Kangquan Shou
- Department of Orthopedics , Zhongnan Hospital of Wuhan University , Wuhan , Hubei 430071 , China .
- Molecular Imaging Program at Stanford (MIPS) , Bio-X Program , Department of Radiology , Canary Center at Stanford for Cancer Early Detection , Stanford University , California 94305-5344 , USA .
| | - Yufu Tang
- Key Laboratory for Organic Electronics and Information Displays , Institute of Advanced Materials (IAM) , Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM) , Nanjing University of Posts & Telecommunications , Nanjing 210023 , China .
| | - Hao Chen
- Molecular Imaging Program at Stanford (MIPS) , Bio-X Program , Department of Radiology , Canary Center at Stanford for Cancer Early Detection , Stanford University , California 94305-5344 , USA .
| | - Si Chen
- Molecular Imaging Program at Stanford (MIPS) , Bio-X Program , Department of Radiology , Canary Center at Stanford for Cancer Early Detection , Stanford University , California 94305-5344 , USA .
| | - Lei Zhang
- Molecular Imaging Program at Stanford (MIPS) , Bio-X Program , Department of Radiology , Canary Center at Stanford for Cancer Early Detection , Stanford University , California 94305-5344 , USA .
| | - Ao Zhang
- CAS Key Laboratory of Receptor Research , Synthetic Organic & Medicinal Chemistry Laboratory (SOMCL) , Shanghai Institute of Materia Medica , Chinese Academy of Sciences , No. 555 Zuchong Road, Pudong New Area , Shanghai , P. R. China 201203
| | - Quli Fan
- Key Laboratory for Organic Electronics and Information Displays , Institute of Advanced Materials (IAM) , Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM) , Nanjing University of Posts & Telecommunications , Nanjing 210023 , China .
| | - Aixi Yu
- Department of Orthopedics , Zhongnan Hospital of Wuhan University , Wuhan , Hubei 430071 , China .
| | - Zhen Cheng
- Molecular Imaging Program at Stanford (MIPS) , Bio-X Program , Department of Radiology , Canary Center at Stanford for Cancer Early Detection , Stanford University , California 94305-5344 , USA .
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