336P - Dual-mode near-infrared multispectral imaging system equipped with deep learning models improves the identification of cancer foci in breast cancer specimens

Background

For surgically resected breast cancer samples, it is challenging to perform specimen sampling by visual inspection, especially when the tumor bed shrinks after neoadjuvant therapy in breast cancer.

Methods

We developed a dual-mode near-infrared multispectral imaging system (DNMIS) to obtain richer sample tissue information by acquiring reflection and transmission images covering visible to NIR-II spectrum range (400–1700 nm). Additionally, artificial intelligence (AI) was used to segment the rich multispectral data. 80 breast cancer samples were collected to verify the advantage of DNMIS in assisting pathologists to identify tumor beds.

Results

DNMIS demonstrated better tissue contrast and eliminated the interference of surgical inks on the breast tissue surface, helping pathologists find the tumor area which is easy to be overlooked with visual inspection. Statistically, AI-powered DNMIS provided a higher tumor sensitivity (95.9% vs visual inspection 88.4% and X-rays 92.8%), especially for breast samples after neoadjuvant therapy (90.3% vs visual inspection 68.6% and X-rays 81.8%).

Conclusions

We infer that DNMIS can improve the breast tumor specimen sampling work by helping pathologists avoid missing out tumor foci.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.