Virtual histology imaging of lymph nodes via dynamic full-field optical coherence tomography and deep learning to differentiate metastasis.

OBJECTIVE

The current pathological diagnosis of lymph node metastasis is time-consuming, labor-intensive, and dependent on sectioning of paraffin blocks. Herein, in a prospective cohort of patients with breast cancer, we validated dynamic full-field optical coherence tomography (D-FFOCT), a virtual pathology tool integrating deep learning for nodal metastasis detection, and offering rapid and label-free histologic approximations of fresh tissues.

METHODS

In a prospective dual-center cohort of 155 patients with breast cancer, 747 freshly bisected lymph node slides were obtained via D-FFOCT. Surgeons interpreted each slide with histopathology as the gold standard. A deep learning model was trained on 28,911 patches (corresponding to 590 slides) and tested on 7,736 patches (corresponding to 157 slides). The results were mapped to the slide level for potential intraoperative evaluation.

RESULTS

D-FFOCT strongly correlated with hematoxylin and eosin (H&E)-stained histological images. Surgeons achieved 97.10% specificity in nodal diagnosis with D-FFOCT. The performance of the artificial intelligence (AI) model was not inferior to that of human experts and had a sensitivity/specificity of 87.88%/91.94% and an area under the receiver operating characteristic curve of 0.899 at the slide level. The human-AI collaborative system reduced labor requirements by 75% and increased the specificity by 6.5%, to 98.39%.

CONCLUSIONS

D-FFOCT has excellent potential as a tool for assessing lymph node metastatic status without tissue preparation or consumption. The integration of D-FFOCT with deep learning decreases labor demands and maintains high accuracy, thereby enabling streamlined nodal prediction independent of routine pathology procedures.