AI-Powered Biomolecular-Specific and Label-Free Multispectral Imaging Rapidly Detects Malignant Neoplasm in Surgically Excised Breast Tissue Specimens

Rishikesh Pandey, David Fournier, Gary Root, Machele Riccio, Aditya Shirvalkar, Gianfranco Zamora, Noel Daigneault, Michael Sapack, Minghao Zhong, Malini Harigopal

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Context.-Repeated surgery is necessary for 20% to 40% of breast conservation surgeries owing to the unavailability of any adjunctive, accurate, and objective tool in the surgeon's hand for real-time margin assessment to achieve the desired balance of oncologic and cosmetic outcomes. Objective.-To assess the feasibility of using a multispectral autofluorescence imaging device for discriminating malignant neoplasm from normal breast tissue in pathology as a critical step in the development of a device for intraoperative use, and to demonstrate the device's utility for use in processing and prioritizing specimens during frozen section and in the pathology grossing room. Design.-We performed a preliminary assessment of our device, called the TumorMAP system, on 172 fresh tissue blocks from 115 patients obtained from lumpectomy specimens at the time of initial gross examination and compared the device results with gold standard pathology evaluation. Results.-The preliminary results demonstrate the potential of our device in detecting breast cancer in fresh tissue samples with a sensitivity of 82%, a specificity of 91%, a positive predictive value of 84%, and a negative predictive value of 89%. Conclusions.-Our results suggest that the TumorMAP system is suitable for the detection of malignant neoplasm in freshly excised breast specimens and has the potential to evaluate resection margins in real time.

Original languageEnglish (US)
Pages (from-to)1298-1306
Number of pages9
JournalArchives of Pathology and Laboratory Medicine
Volume147
Issue number11
DOIs
StatePublished - Nov 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 College of American Pathologists. All rights reserved.

PubMed: MeSH publication types

  • Journal Article

Fingerprint

Dive into the research topics of 'AI-Powered Biomolecular-Specific and Label-Free Multispectral Imaging Rapidly Detects Malignant Neoplasm in Surgically Excised Breast Tissue Specimens'. Together they form a unique fingerprint.

Cite this