Application of fluorescence endoscopy with methylene blue dye and indocyanine green dual-tracer method in sentinel lymph node biopsy for women with breast cancer.

Background: Indocyanine green (ICG) allows for the real-time visualization of lymphatic drainage and provides favorable performance for sentinel lymph node (SLN) mapping. However, the limited ability of tissue penetration of the near-infrared fluorescence of ICG may lead to the failure of lymph node detection in the traditional open approach of sentinel lymph node biopsy (SLNB) for breast cancer, especially in overweight or obese patients. To accurately and quickly detect SLNs, we applied fluorescence endoscopy with a dual-tracer method using ICG and methylene blue dye (MBD) in SLNB for breast cancer. We conducted this study to assess the feasibility and application value of this method in minimally invasive surgery. Methods: A total of 117 patients who received dual-tracer injection of ICG and MBD prior to endoscopic SLNB from November 2020 to September 2021 were examined in this study. The number of SLNs identified, the SLN identification rate, the time to identify the first SLN, the procedure duration, and the postoperative morbidity were analyzed. Results: Biopsied SLNs could be identified in 116 patients (99.15%) with an average number of 5.12±1.87 per patient. Blue-stained SLNs were found in 99 patients (84.62%) and fluorescent SLNs in 112 patients (95.73%). A total of 34 patients (29.06%) had positive SLNs. In 6 cases (5.13%), the positive SLNs were only stained with ICG fluorescence. In 1 case (0.85%), the positive SLNs were only blue-stained with no fluorescence staining. The mean durations for the identification of the first SLN and endoscopic SLNB were 7.14±6.31 and 37.75±16.94 min, respectively. Upper-limb lymphoedema was observed 5 cases (4.27%) during a median follow-up period of 10 months. Conclusions: The fluorescence endoscopy method assisted by dual tracer facilitates SLN detection with a comparatively short procedure duration and low complication rate. This approach could serve as a new method for SLNB for patients with breast cancer.

Prototypical multiple instance learning for predicting lymph node metastasis of breast cancer from whole-slide pathological images.

Computerized identification of lymph node metastasis of breast cancer (BCLNM) from whole-slide pathological images (WSIs) can largely benefit therapy decision and prognosis analysis. Besides the general challenges of computational pathology, like extra-high resolution, very expensive fine-grained annotation, etc., two particular difficulties with this task lie in (1) modeling the significant inter-tumoral heterogeneity in BCLNM pathological images, and (2) identifying micro-metastases, i.e., metastasized tumors with tiny foci. Towards this end, this paper presents a novel weakly supervised method, termed as Prototypical Multiple Instance Learning (PMIL), to learn to predict BCLNM from WSIs with slide-level class labels only. PMIL introduces the well-established vocabulary-based multiple instance learning (MIL) paradigm into computational pathology, which is characterized by utilizing the so-called prototypes to model pathological data and construct WSI features. PMIL mainly consists of two innovatively designed modules, i.e., the prototype discovery module which acquires prototypes from training data by unsupervised clustering, and the prototype-based slide embedding module which builds WSI features by matching constitutive patches against the prototypes. Relative to existing MIL methods for WSI classification, PMIL has two substantial merits: (1) being more explicit and interpretable in modeling the inter-tumoral heterogeneity in BCLNM pathological images, and (2) being more effective in identifying micro-metastases. Evaluation is conducted on two datasets, i.e., the public Camelyon16 dataset and the Zbraln dataset created by ourselves. PMIL achieves an AUC of 88.2% on Camelyon16 and 98.4% on Zbraln (at 40x magnification factor), which consistently outperforms other compared methods. Comprehensive analysis will also be carried out to further reveal the effectiveness and merits of the proposed method.

Three-dimensional visual technique based on CT lymphography data combined with methylene blue in endoscopic sentinel lymph node biopsy for breast cancer.

BACKGROUND: The combined application of blue dye and radioisotopes is currently the primary mapping technique used for sentinel lymph node biopsy (SLNB) in breast cancer patients. However, radiocolloid techniques have not been widely adopted, especially in developing countries, given the strict restrictions on radioactive materials. Consequently, we carried out a retrospective study to evaluate the feasibility and accuracy of three-dimensional visualization technique (3DVT) based on computed tomography-lymphography (CT-LG) in endoscopic sentinel lymph node biopsy (ESLNB) for breast cancer. METHODS: From September 2018 to June 2020, 389 patients who underwent surgical treatment of breast cancer in our department were included in this study. The CT-LG data of these patients were reconstructed into digital 3D models and imported into Smart Vision Works V1.0 to locate the sentinel lymph node (SLN) and for visual simulation surgery. ESLNB and endoscopic axillary lymph node dissection were carried out based on this new technique; the accuracy and clinical value of 3DVT in ESLNB were analyzed. RESULTS: The reconstructed 3D models clearly displayed all the structures of breast and axilla, which favors the intraoperative detection of SLNs. The identification rate of biopsied SLNs was 100% (389/389). The accuracy, sensitivity, and false-negative rate were 93.83% (365/389), 93.43% (128/137), and 6.57% (9/137), respectively. Upper limb lymphedema occurred in one patient 3 months after surgery during the 12-month follow-up period. CONCLUSIONS: Our 3DVT based on CT-LG data combined with methylene blue in ESLNB ensures a high identification rate of SLNs with low false-negative rates. It, therefore, has the potential to serve as a new method for SLN biopsy in breast cancer cases.