Intraoperative evaluation of metastatic SLNs with NIRF imaging assisted by artificial intelligence in breast cancers.

BACKGROUND

Near-infrared fluorescence (NIRF) imaging with indocyanine green (ICG) is widely employed for sentinel lymph node (SLN) biopsy in breast cancer but cannot assess SLN metastatic status. Current intraoperative assessment relies on frozen section (FS) analysis, which is time-consuming, causes tissue loss, and suffers from limited sensitivity with a high false-negative rate (FNR).

MATERIALS AND

METHODS

Preclinical data included fluorescence images from 4T1-Luc and MDA-MB-231-Luc mouse lymph node metastasis models. Clinical data comprised 35 breast cancer patients in a prospective clinical trial (NCT05623280). Intraoperative ICG-based NIRF imaging was performed, and four convolutional neural networks (Vgg19, Efficientnet, Resnet, and Densenet) were evaluated.

RESULTS

In the mouse test set, the proposed approach achieved an area under the receiver operating characteristic curve (AUC) of 0.799 (95% confidence interval [CI]: 0.787-0.810) for cohort 1 and 0.804 (95% CI: 0.793-0.816) for cohort 2. In the clinical cohort comprising 35 patients and 114 excised SLNs (16 metastatic and 98 non-metastatic), it demonstrated robust performance in detecting metastatic SLNs, with an AUC of 0.898 (95% CI: 0.892-0.903). The LymphNet approach, which aggregates predictions from multiple SLN images, yielded an FNR of 18.75%, comparable to FS analysis (FNR: 13.5-31.3%). LymphNet avoids tissue loss and streamlines the workflow, with the model's prediction process requiring less than 10 s. For the first time, we observed a marked reduction or complete absence of fluorescence in metastatic SLNs infiltrated by breast cancer cells.

CONCLUSION

This study establishes a novel, efficient tool for intraoperative SLN metastatic assessment.