Introduction: Targeted axillary dissection (TAD) has been proposed as an alternative method for the staging of patients with node-positive breast cancer who undergo neoadjuvant chemotherapy. However, not much is known yet about the false-negative rate (FNR) of the method and the subsequent risk of underestimation of residual axillary disease.
Methods: This study reviews published articles with calculations of false negativity of TAD and potential factors that may influence it.
Results: The FNR of TAD is usually reported as being <10%, but this calculation is usually based on small study populations. Lower FNR is a common finding along with lower N status, while not enough data are available yet for greater axillary involvement. When a marked node is revealed to be a sentinel lymph node (SLN) at surgery after neoadjuvant chemotherapy (NAC), this seems to be another factor that contributes to reliable TAD. With regard to the methods used to mark the positive node before chemotherapy and retrieval at surgery, there is no clear advantage of one over the other. The availability of relevant resources, the costs, and local legislation must all be taken into account for the selection of the optimal strategy.
Conclusion: Although still in its early days, the FNR of TAD can be low, at least in patients with relatively little axillary involvement and when the marked node is the SLN. All reported methods of lymph node marking seem reliable.
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http://dx.doi.org/10.1159/000513037 | DOI Listing |
Radiologie (Heidelb)
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Department of Radiology, The Affiliated Hospital of Wuhan Sports University, 430079, Wuhan, China.
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View Article and Find Full Text PDFHeliyon
January 2025
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AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, and coronavirus. The performance metrics outlined in the comparative table illustrate the significant advancements enabled by AI integration.
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