The status of axillary LNM is an important factor affecting the prognosis of BC patients(1, 7). Currently, clinicians mainly rely on mammography, ultrasound, and MRI(8) for the diagnosis of axillary LNM in BC(9). Furthermore, PET/CT has relatively low sensitivity for diagnosing LNM in BC(10). Axillary lymph node biopsy is an invasive procedure that may cause complications such as lymphedema, pain, numbness, limitation of shoulder movement, and nerve injury(11). Therefore, preoperative assessment of axillary lymph nodes using a noninvasive method can avoid unnecessary anterior lymph node biopsy and axillary lymph node dissection.
Recent studies have shown that radiomics have a good predictive power for evaluating LNM various cancers(12, 13). Radiomics features are the product of genotypic and phenotypic influences of tissues that can reflect the biology of tumors(14) and provide reliable potential imaging-based biomarkers for improving diagnosis, optimizing treatment plans, and predicting outcome(15, 16). The current approach of predicting axillary LNM in BC using radiomics evaluates the axillary lymph node images obtained by X-ray mammography, ultrasound, and MRI, of which evaluation of the ultrasound scans are the most frequently used for diagnosis. Mao et al.(17) predicted axillary LNM based on mammography radiomics with an AUC of 0.79; Qiu et al.(18) predicted axillary LNM based on breast ultrasound radiomics with an AUC of 0.759; and Tan et al.(19) predicted axillary LNM based on breast MRI radiomics with an AUC of 0.805. Lee et al.(20) and Gao et al.(21) achieved good predictive results based on breast ultrasound radiomics to evaluate axillary lymph node metastasis.
The application of PET radiomics has not been widely studied in the diagnosis of BC lymph node metastasis; however, it has shown to improve the diagnostic sensitivity for LNM patients with BC(22). As a non-invasive, visual method that can quantify the entire tumor heterogeneity, PET radiomics can reflect the biological characteristics of tumors more objectively and comprehensively by extracting quantifiable image features from the ROI of PET images in high throughput, creating high-dimensional datasets, and mining the features associated with tumors through data mining analysis techniques(23). In previous studies(21, 22), PET imaging-based histology of primary BC was analyzed to predict axillary lymph node status with AUCs of 0.64 and 0.89, respectively, thus showing a large difference in diagnostic efficacy. Therefore, in this study, a comprehensive model (model 3) was constructed to predict axillary LNM based on PET radiomics in addition to the evaluation of the clinical, pathological, ultrasound, and PET/CT parameters. The results showed that model 3 had higher a discrimination and calibration for predicting LNM in BC, with positive improvements in both continuous NRI and IDII, relative to the other two models. Model 3 had a stronger predictive performance as well as a net benefit for more patients.
Previous studies have often predicted LNM by the volume of the primary tumor and its metabolic parameters. For example, studies by De(24) and Song et al.(9, 25) showed that the metabolic activity of the primary tumor obtained by 18-FDG PET/CT in rectal, gastric, and BCs was positively correlated with lymph node metastasis. In contrast, SUVmax, SUVmean, SD, and MTV did not significantly correlate with axillary LNM the present study. Another study(22) showed that data on pathological classification, molecular subtypes, and immunohistochemistry were not associated with axillary lymph node metastasis, and the present study was similar to these results.
Limitations of this study are that it was a retrospective single-center study with possible selection bias; patients with multifocal lesions, bilateral lesions, and occult lesions were excluded because it was difficult to identify lesions that would lead to lymph node metastasis; and only internal validation was performed due to the volume of data, which needs to be expanded for external validation.