Sentinel lymph node biopsy (SLNB) is critical to breast cancer staging, yet current assessment methods remain heavily dependent on pathology expertise and time-consuming tissue processing. Frozen section analysis can support intraoperative decisions, but it consumes tissue, requires substantial manpower, and does not always deliver ideal diagnostic performance. Conventional paraffin-section pathology is accurate, but it usually cannot provide answers quickly enough to guide surgery in real time. In settings where pathology resources are limited, these constraints can become a serious clinical bottleneck. Due to these challenges, more efficient strategies for intraoperative lymph node assessment are urgently needed.
In a study published (DOI: 10.20892/j.issn.2095-3941.2025.0747)in Cancer Biology & Medicine in March 2026, researchers from Peking University People’s Hospital, Tsinghua University, Capital Medical University, and collaborating institutions evaluated dynamic full-field optical coherence tomography (D-FFOCT) plus deep learning in a prospective dual-center cohort of 155 patients with breast cancer. Using 747 freshly bisected lymph node slides, the team tested whether this label-free optical imaging approach could provide histology-like views of fresh tissue and support faster, more practical nodal assessment during surgery.
D-FFOCT uses light interference to capture high-contrast, subcellular images of fresh tissue without fixation or staining. In this study, the images showed strong concordance with standard hematoxylin and eosin histology and highlighted features associated with nodal metastasis, including disrupted architecture, collagen changes, and malignant cell clusters. The deep learning model, tested at the slide level, achieved 87.88% sensitivity, 91.94% specificity, and an area under the receiver operating characteristic curve of 0.899. When the researchers used a hybrid workflow in which surgeons reviewed model-positive cases, specificity increased to 98.39%, overall accuracy reached 93.63%, and manual workload fell by 75%. Because the method does not consume tissue, it may also leave more material available for subsequent molecular pathology or genetic testing.
The study points to a clinically meaningful shift: instead of waiting for conventional pathology to finish sectioning and staining tissue, surgical teams may be able to obtain near-real-time nodal information from intact fresh samples. In practice, that could support faster intraoperative judgment, reduce unnecessary tissue excision, and potentially lower the risk of delayed decisions that lead to second surgeries. The patient benefit is especially notable in environments where pathology capacity is stretched, because a more automated workflow could help ease reliance on scarce specialist labor while still keeping expert oversight in the loop.
The researchers also note that the method is not yet a complete replacement for pathology. Performance for micrometastases and isolated tumor cells remained limited in this dataset, and the authors call for larger validation studies and continued technical refinement. Despite these limitations, the work highlights a promising direction for intraoperative cancer diagnosis: faster lymph node staging, less tissue consumption, and a workflow better aligned with the needs of precision oncology.
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References
DOI
10.20892/j.issn.2095-3941.2025.0747
Original Source URL
https://doi.org/10.20892/j.issn.2095-3941.2025.0747
Funding information
This work was supported by grants from the National Key Research and Development Program of China (Grant No. 2024YFC3405303), Beijing Natural Science Foundation (Grant No. 7242281 and 7244427), and Research and Development Fund of Peking University People’s Hospital (Grant No. RDZH2024-03 and RDEB2025-25).
About Cancer Biology & Medicine
Cancer Biology & Medicine (CBM) is a peer-reviewed open-access journal sponsored by China Anti-cancer Association (CACA) and Tianjin Medical University Cancer Institute & Hospital. The journal monthly provides innovative and significant information on biological basis of cancer, cancer microenvironment, translational cancer research, and all aspects of clinical cancer research. The journal also publishes significant perspectives on indigenous cancer types in China. The journal is indexed in SCOPUS, MEDLINE and SCI (IF 8.4, 5-year IF 6.7), with all full texts freely visible to clinicians and researchers all over the world (http://www.ncbi.nlm.nih.gov/pmc/journals/2000/).