Observational studyInternational journal of surgery (London, England)2026
Intraoperative evaluation of metastatic SLNs with NIRF imaging assisted by artificial intelligence in breast cancers.
Observational study in International journal of surgery (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05623280 (Artificial Intelligence Analysis of Fluorescence Image to Intraoperatively Detect Metastatic Sentinel Lymph Node in Patients With Breast Cancer.), which is not on this map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Artificial Intelligence Analysis of Fluorescence Image to Intraoperatively Detect Metastatic Sentinel Lymph Node in Patients With Breast Cancer.
Who cites it
1 citing paper in PubMed.
- TROP2-targeted NIR-II fluorescence imaging for visualizing surgical margins and metastatic sentinel lymph nodes in breast cancers.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNear-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
methodsPreclinical 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.
resultsIn 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.
conclusionThis study establishes a novel, efficient tool for intraoperative SLN metastatic assessment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.