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Wang J, Di W, Shi K, Wang S, Jiang Y, Xu W, Zhong Z, Pan H, Xie H, Zhou W, Zhao M, Wang S. Axilla View of Mammography in Preoperative Axillary Lymph Node Evaluation of Breast Cancer Patients: A Pilot Study. Clin Breast Cancer 2024; 24:e51-e60. [PMID: 37925360 DOI: 10.1016/j.clbc.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/23/2023] [Accepted: 10/15/2023] [Indexed: 11/06/2023]
Abstract
PURPOSE This study aimed to explore a novel position of mammography named axilla view in axillary lymph node (ALN) evaluation in breast cancer. PATIENTS AND METHODS Patients were prospectively enrolled and scheduled for mammography before surgery. Investigated imaging patterns included mediolateral oblique (2D-MLO) and axilla view (2D-axilla) of mammography, and axilla view of digital breast tomosynthesis (3D-axilla). The correlation of ALN numbers between imaging and pathology was analyzed. Diagnostic performance was analyzed via AUC. RESULTS 75 patients were included. A larger and clearer axillary region was displayed in axilla view. The total number of ALNs detected under 2D/3D-axilla view was significantly higher than that under 2D-MLO view (4.6 vs. 2.5, P < .001; 5.6 vs. 4.6, P = .034). Correlations between number of positive ALNs detected under 2D/3D-axilla view and pathologically confirmed metastatic ALNs were stronger than 2D-MLO view (Pearson correlation coefficients: 0.7084,0.7044 and 0.4744). The proportion of cases with ≥5 positive ALNs detected under 3D-axilla view was significantly higher than that under 2D-MLO (38.2% vs. 14.7%, P = .028). The overweight and obese group showed a higher AUC value than the underweight and lean group in ALN evaluation, although not significantly (2D-MLO: 0.7643 vs. 0.6458, P = .2656; 2D-axilla: 0.8083 vs. 0.6586, P = .1522; 3D-axilla: 0.8045 vs. 0.6615, P = .1874). This difference was more pronounced in axilla view. CONCLUSION Axilla view exhibited advantages over conventional MLO view in the extent of axilla displayed by mammography in breast cancer. Further studies with larger sample sizes are needed.
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Affiliation(s)
- Ji Wang
- Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Wenyang Di
- Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Ke Shi
- Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Siqi Wang
- Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Yunshan Jiang
- Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Weiwei Xu
- Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Zhaoyun Zhong
- Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Hong Pan
- Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Hui Xie
- Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wenbin Zhou
- Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Meng Zhao
- Department of Radiology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China.
| | - Shui Wang
- Department of Breast Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, China.
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Cheng J, Ren C, Liu G, Shui R, Zhang Y, Li J, Shao Z. Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer. Cancers (Basel) 2022; 14:cancers14040950. [PMID: 35205699 PMCID: PMC8870230 DOI: 10.3390/cancers14040950] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/26/2022] [Accepted: 01/31/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Accurate clinical axillary evaluation plays an important role in the diagnosis of and treatment planning for breast cancer (BC). This study aimed to develop a machine learning model integrating dedicated breast PET and clinical characteristics for prediction of axillary lymph node status in cT1-2N0-1M0 BC non-invasively. The performance of this integrating model in identifying pN0 and pN1 with the AUC was 0.94. We achieved an NPV of 96.88% in the cN0 and PPV of 92.73% in the cN1 subgroup. The higher true positive and true negative rate could delineate clinical subtypes and apply more precise treatment for patients with early-stage BC. Abstract Purpose of the Report: Accurate clinical axillary evaluation plays an important role in the diagnosis and treatment planning for early-stage breast cancer (BC). This study aimed to develop a scalable, non-invasive and robust machine learning model for predicting of the pathological node status using dedicated-PET integrating the clinical characteristics in early-stage BC. Materials and Methods: A total of 420 BC patients confirmed by postoperative pathology were retrospectively analyzed. 18F-fluorodeoxyglucose (18F-FDG) Mammi-PET, ultrasound, physical examination, Lymph-PET, and clinical characteristics were analyzed. The least absolute shrinkage and selection operator (LASSO) regression analysis were used in developing prediction models. The characteristic curve (ROC) of the area under receiver-operator (AUC) and DeLong test were used to evaluate and compare the performance of the models. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results: A total of 290 patients were enrolled in this study. The AUC of the integrated model diagnosed performance was 0.94 (95% confidence interval (CI), 0.91–0.97) in the training set (n = 203) and 0.93 (95% CI, 0.88–0.99) in the validation set (n = 87) (both p < 0.05). In clinical N0 subgroup, the negative predictive value reached 96.88%, and in clinical N1 subgroup, the positive predictive value reached 92.73%. Conclusions: The use of a machine learning integrated model can greatly improve the true positive and true negative rate of identifying clinical axillary lymph node status in early-stage BC.
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Affiliation(s)
- Jingyi Cheng
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; (J.C.); (Y.Z.)
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai 201321, China
| | - Caiyue Ren
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Shanghai 201321, China;
| | - Guangyu Liu
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China;
- Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
| | - Ruohong Shui
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China;
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Yingjian Zhang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China; (J.C.); (Y.Z.)
- Department of Nuclear Medicine, Shanghai Proton and Heavy Ion Center, Fudan University Cancer Hospital, Shanghai 201321, China
| | - Junjie Li
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China;
- Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
- Correspondence: (J.L.); (Z.S.); Tel.: +86-021-64175590 (ext. 88809) (J.L. & Z.S.); Fax: +86-021-64176650 (J.L. & Z.S.)
| | - Zhimin Shao
- Department of Breast Surgery, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China;
- Key Laboratory of Breast Cancer in Shanghai, Fudan University Shanghai Cancer Center, Fudan University, Shanghai 200032, China
- Correspondence: (J.L.); (Z.S.); Tel.: +86-021-64175590 (ext. 88809) (J.L. & Z.S.); Fax: +86-021-64176650 (J.L. & Z.S.)
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