Predicting the risk of axillary lymph node metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features and the use of nomograms. A prospective single-center observational study
Creators
- 1. Department of Breast Surgery, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 107 Yanjiang West Road, 510120, Guangzhou (China)
- 2. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510120, Guangzhou (China)
- 3. Department of Pathology, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou (China)
- 4. Diagnostic Department, Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou (China)
- 5. Artificial Intelligence Laboratory, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong (China)
Description
The purpose of this study was to establish two preoperative nomograms to evaluate the risk for axillary lymph node (ALN) metastasis in early breast cancer patients based on ultrasonographic-clinicopathologic features. We prospectively evaluated 593 consecutive female participants who were diagnosed with cTNM breast cancer between March 2018 and May 2019 at Sun Yat-Sen Memorial Hospital. The participants were randomly classified into training and validation sets in a 4:1 ratio for the development and validation of the nomograms, respectively. Multivariate logistic regression analysis was performed to identify independent predictors of ALN status. We developed Nomogram A and Nomogram B to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2), respectively. A total of 528 participants were evaluated in the final analyses. Multivariable analysis revealed that the number of suspicious lymph nodes, long axis, short-to-long axis ratio, cortical thickness, tumor location, and histological grade were independent predictors of ALN status. The AUCs of nomogram A in the training and validation groups were 0.83 and 0.78, respectively. The AUCs of nomogram B in the training and validation groups were 0.87 and 0.87, respectively. Both nomograms were well-calibrated. We developed two preoperative nomograms that can be used to predict ALN metastasis (presence vs. absence) and the number of metastatic ALNs (≤ 2 vs. > 2) in early breast cancer patients. Both nomograms are useful tools that will help clinicians predict the risk of ALN metastasis and facilitate therapy decision-making about axillary surgery. We developed two preoperative nomograms to predict axillary lymph node status based on ultrasonographic-clinicopathologic features. Nomogram A was used to predict axillary lymph node metastasis (presence vs. absence). The AUCs in the training and validation groups were 0.83 and 0.78, respectively. Nomogram B was used to estimate the number of metastatic lymph nodes (≤ 2 vs. > 2). The AUCs in the training and validation group were 0.87 and 0.87, respectively. Our nomograms may help clinicians weigh the risks and benefits of axillary surgery more appropriately.
Availability note (English)
Available from: http://dx.doi.org/10.1007/s00330-022-08855-8Additional details
Identifiers
Publishing Information
- Journal Title
- European Radiology (Internet)
- Journal Volume
- 32
- Journal Issue
- 12
- Journal Page Range
- p. 8200-8212
- ISSN
- 1432-1084
- CODEN
- EURAE3
INIS
- Country of Publication
- Germany
- Country of Input or Organization
- Germany
- INIS RN
- 54010533
- Subject category
- S62: RADIOLOGY AND NUCLEAR MEDICINE;
- Descriptors DEI
- CARCINOMAS; DATA COMPILATION; DECISION MAKING; DIAGNOSIS; HEALTH HAZARDS; IMAGE PROCESSING; LYMPH NODES; MAMMARY GLANDS; METASTASES; MULTIVARIATE ANALYSIS; NOMOGRAMS; REGRESSION ANALYSIS; SURGERY; THERAPY; THICKNESS; TRAINING; ULTRASONOGRAPHY; VALIDATION
- Descriptors DEC
- BODY; DATA; DATA PROCESSING; DIAGNOSTIC TECHNIQUES; DIAGRAMS; DIMENSIONS; DISEASES; EDUCATION; GLANDS; HAZARDS; INFORMATION; LYMPHATIC SYSTEM; MATHEMATICS; MEDICINE; NEOPLASMS; ORGANS; PROCESSING; STATISTICS; TESTING