Techniques for sentinel node biopsy in breast cancer

Application of artificial intelligence in ultrasound imaging for predicting lymph node metastasis in breast cancer: A meta-analysis

BACKGROUND

This study aims to comprehensively evaluate the accuracy and effectiveness of ultrasound imaging based on artificial intelligence algorithms in predicting lymph node metastasis in breast cancer patients through a meta-analysis.

METHODS

We systematically searched PubMed, Embase, and Cochrane Library for literature published up to May 2023. The search terms included artificial intelligence, ultrasound, breast cancer, and lymph node. Studies meeting the inclusion criteria were selected, and data were extracted for analysis. The main evaluation indicators included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and area under the curve (AUC). The heterogeneity was assessed using the Cochrane Q test combined with the I^2 statistic expressing the percentage of total effect variation that can be attributed to the effect variation between studies, as recommended by the Cochrane Handbook for heterogeneity quantification. A threshold p-value of 0.10 was considered to compensate for the low power of the Q test. Sensitivity analysis was performed to assess the stability of individual studies, and publication bias was determined with funnel plots. Additionally, fagan plots were used to assess clinical utility.

RESULTS

Ten studies involving 4726 breast cancer patients were included in the meta-analysis. The results showed that ultrasound imaging based on artificial intelligence algorithms had high accuracy and effectiveness in predicting lymph node metastasis in breast cancer patients. The pooled sensitivity was 0.88 (95% CI: 0.81-0.93; P < 0.001; I2 = 84.68), specificity was 0.75 (95% CI: 0.66-0.83; P < 0.001; I2 = 91.11), and AUC was 0.89 (95% CI: 0.86-0.91). The positive likelihood ratio was 3.5 (95% CI: 2.6-4.8), negative likelihood ratio was 0.16 (95% CI: 0.10-0.26), and diagnostic odds ratio was 23 (95% CI: 13-40). However, the combined sensitivity of ultrasound imaging based on non-AI algorithms for predicting lymph node metastasis in breast cancer patients was 0.78 (95%CI: 0.63-0.88), the specificity was 0.76 (95%CI: 0.63-0.86), and the AUC was 0.84 (95%CI: 0.80-0.87). The positive likelihood ratio was 3.3 (95% CI: 1.9-5.6), the negative likelihood ratio was 0.29 (95% CI: 0.15-0.54), and the diagnostic odds ratio was 11 (95% CI: 4-33). Due to limited sample size (n = 2), meta-analysis was not conducted for the outcome of predicting lymph node metastasis burden.

CONCLUSION

Ultrasound imaging based on artificial intelligence algorithms holds promise in predicting lymph node metastasis in breast cancer patients, demonstrating high accuracy and effectiveness. The application of this technology helps in the diagnosis and treatment decisions for breast cancer patients and is expected to become an important tool in future clinical practice.

New techniques versus standard mapping for sentinel lymph node biopsy in breast cancer: a systematic review and meta-analysis

New tracers for sentinel lymph node biopsy (SLNB), as indocyanine green (ICG), superparamagnetic iron oxide (SPIO) and micro bubbles, have been recently introduced in clinical practice showing promising but variable results. We reviewed the available evidence comparing these new techniques with the standard tracers to evaluate their safety. To identify all available studies, a systematic search was performed in all electronic databases. Data regarding sample size, mean number of SLN harvested for patient, number of metastatic SLN and SLN identification rate of all studies were extracted. No significant differences were found in terms of SLNs identification rates between SPIO, RI and BD but with a higher identification rate with the use of ICG. No significant differences were also found for the number of metastatic lymph nodes identified between SPIO, RI and BD and the mean number of SLNs identified between SPIO and ICG versus conventional tracers. A statistically significant differences in favor of ICG was reported for the comparison between ICG and conventional tracers for the number of metastatic lymph nodes identified. Our meta-analysis demonstrates that the use of both ICG and SPIO for the pre-operative mapping of sentinel lymph nodes in breast cancer treatment is adequately effective.

Meta-Analysis of the Diagnostic Value of Tracer Staining Technology Based on Nanocarbon Suspension in Sentinel Lymph Node Biopsy of Breast Cancer

Objective. To evaluate the diagnostic value of the nanometer carbon suspension tracer staining technique in sentinel lymph node biopsy of breast cancer is the objective of this study. Methods. The PubMed, Embase, Cochrane Library (Central), and Web of Science (SCI Expanded), and Chinese databases (CNKI, VIP, Wan Fang, and CBM) were systematically searched for studies on the diagnostic value of nanocarbon suspension in sentinel lymph node biopsy of breast cancer. Two reviewers independently assessed the methodological quality of each study using the QUADAS-2 tool. The extracted valid data were calculated using Meta-Disc1.4 software and tested for heterogeneity. STATA14.0 software was selected for sensitivity analysis of the included studies, and publication bias was assessed using Deeks’ forest plot asymmetry test. Results. A total of 10 studies were obtained. The pooled data were as follows: sensitivity, 0.92 (0.88~0.95); specificity, 0.99 (0.98~1.00); positive likelihood ratio, 69.24 (30.34~158.02); negative likelihood ratio, 0.09 (0.06~0.13); and the combined diagnostic odds ratio, 747.40 (285.77~1954.76), $\text{AUC}=0.9881$ . Nanocarbon suspension tracers have an accuracy rate of 98.81% in the diagnosis of sentinel lymph nodes in breast cancer. Conclusion. Tracer staining technology based on nanocarbon suspension can accurately assess the status of lymph nodes in sentinel lymph node biopsy of breast cancer and has good stability and operability, which is worthy of clinical promotion.