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

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. In this study, we developed a dual-mode near-infrared multispectral imaging system (DNMIS) to overcome the human visual perceptual limitations and obtain richer sample tissue information by acquiring reflection and transmission images covering visible to NIR-II spectrum range (400–1700 nm). Additionally, we used artificial intelligence (AI) for segmentation of the rich multispectral data. We compared DNMIS with the conventional sampling methods, regular visual inspection and a cabinet X-ray imaging system, using data from 80 breast cancer specimens. 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%). We infer that DNMIS can improve the breast tumor specimen sampling work by helping pathologists avoid missing out tumor foci. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work is supported by the Beijing Fine Inspection Foundation, (Grant No. JJIS2021-011). This study is also supported by Tencent AI Lab, the Fourth Hospital of Hebei Medical University and the West China Hospital, Sichuan University. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee of the Fourth Hospital of Hebei Medical University gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.