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Sae-Lim C, Wu WP, Chang MC, Lai HW, Chen ST, Chou CT, Liao CY, Huang HI, Chen ST, Chen DR, Hung CL. Reliability of predicting low-burden (≤ 2) positive axillary lymph nodes indicating sentinel lymph node biopsy in primary operable breast cancer - a retrospective comparative study with PET/CT and breast MRI. World J Surg Oncol 2024; 22:12. [PMID: 38183069 PMCID: PMC10770957 DOI: 10.1186/s12957-023-03297-y] [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: 07/03/2023] [Accepted: 12/26/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND Sentinel lymph node biopsy (SLNB) is the standard of care for axillary staging in early breast cancer patients with low-burden axillary metastasis (≤ 2 positive nodes). This study aimed to determine the diagnostic performances of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and breast magnetic resonance imaging in detecting axillary lymph node (ALN) metastases and the reliability to predict ALN burden. METHODS A total of 275 patients with primary operable breast cancer receiving preoperative PET/CT and upfront surgery from January 2001 to December 2022 in a single institution were enrolled. A total of 244 (88.7%) of them also received breast MRI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of PET/CT and breast MRI were assessed. The predictive values to determine ALN burden were evaluated using radio-histopathological concordance. RESULTS PET/CT demonstrated a sensitivity of 53.4%, specificity of 82.1%, PPV of 65.5%, NPV of 73.5%, and accuracy of 70.9% for detecting ALN metastasis, and the corresponding values for MRI were 71.8%, 67.8%, 56%, 80.8%, and 69.2%, respectively. Combining PET/CT and MRI showed a significantly higher PPV than MRI (72.7% vs 56% for MRI alone, p = 0.037) and a significantly higher NPV than PET/CT (84% vs 73.5% for PET/CT alone, p = 0.041). For predicting low-burden axillary metastasis (1-2 positive nodes), the PPVs were 35.9% for PET/CT, 36.7% for MRI, and 55% for combined PET/CT and MRI. Regarding patients with 0-2 positive ALNs in imaging, who were indicated for SLNB, the predictive correctness was 96.1% for combined PET/CT and MRI, 95.7% for MRI alone, and 88.6% for PET/CT alone. CONCLUSIONS PET/CT and breast MRI exhibit high predictive values for identifying low-burden axillary metastasis in patients with operable breast cancer with ≦ 2 positive ALNs on imaging.
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Affiliation(s)
- Chayanee Sae-Lim
- Department of Surgery, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Wen-Pei Wu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan
- Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Ming-Che Chang
- Department of Nuclear Medicine, Changhua Christian Hospital, Changhua, Taiwan
| | - Hung-Wen Lai
- Division of General Surgery, Changhua Christian Hospital, Changhua, Taiwan.
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua, Taiwan.
- Endoscopic and Oncoplastic Breast Surgery Center, Changhua Christian Hospital, 135 Nanxiao Street, Changhua, 500, Taiwan.
- Minimally Invasive Surgery Research Center, Changhua Christian Hospital, Changhua, Taiwan.
- Division of Breast Surgery, Yuanlin Christian Hospital, Yuanlin, Taiwan.
- Kaohsiung Medical University, Kaohsiung, Taiwan.
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan.
- Department of Surgery, National Yang Ming Chiao Tung University, Taipei, Taiwan.
| | - Shu-Tian Chen
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi Branch, Chiayi, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
| | - Chen-Te Chou
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan
- Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Chiung-Ying Liao
- Department of Radiology, Changhua Christian Hospital, Changhua, Taiwan
| | - Hsin-I Huang
- Department of Information Management, National Sun Yat-Sen University, Kaohsiung, Taiwan
| | - Shou-Tung Chen
- Division of General Surgery, Changhua Christian Hospital, Changhua, Taiwan
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Dar-Ren Chen
- Division of General Surgery, Changhua Christian Hospital, Changhua, Taiwan
- Comprehensive Breast Cancer Center, Changhua Christian Hospital, Changhua, Taiwan
| | - Che-Lun Hung
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, 11221, Taiwan
- Department of Computer Science and Communication Engineering, Providence University, Taichung, Taiwan
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