Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology
Open accessAbstract
Recent advances in label-free virtual histology promise a new era for real-time molecular diagnosis in the operating room and during biopsy procedures. To take full advantage of the rich, multidimensional information provided by these technologies, reproducible and reliable computational tools that could facilitate the diagnosis are in great demand. In this study, we developed a deep-learning-based framework to recognize cancer versus normal human breast tissue from real-time label-free virtual histology images, with a tile-level AUC (area under receiver operating curve) of 95% and slide-level AUC of 100% on unseen samples. Furthermore, models trained on a high-quality laboratory-generated dataset can generalize to independent datasets acquired from a portable intraoperative version of the imaging technology with a physics-based adapted design. Classification activation maps and final feature visualization revealed discriminative patterns, such as tumor cells and tumor-associated vesicles, that are highly associated with cancer status. These results demonstrate that through the combination of real-time virtual histopathology and a deep-learning framework, accurate real-time diagnosis could be achieved in point-of-procedure clinical applications.
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Several studies have shown potential for AI to facilitate intraoperative cancer diagnosis using label-free and cryosectioned tissue samples.
“several studies have demonstrated the potential of using AI to facilitate intraoperative diagnosis (e.g., during surgical resection) using label-free (40,41) and cryosectioned (42,43) tissue samples.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [40]
You and colleagues presented an approach combining intraoperative microscopy with deep learning to classify malignant versus benign breast tissue.
“You and colleagues (40) have presented an approach that combines intraoperative microscopy with a deep learning model to classify malignant versus benign breast tissue.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [40]
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- Authors
- You, S., Sun, Y., Yang, L., Park, J., Tu, H., Marjanovic, M., Sinha, S., & Boppart, S. A.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- University of Notre Dame (US)
- Corresponding author
- Sinha, Saurabh
- Boppart, Stephen A.
- Venue
- npj Precision Oncology
- Published
- 2019-12-17
- Volume
- 3
- Issue
- 1
- Pages
- 33-33
- ISSN
- 2397-768X (ISSN-L)
- PMC
- PMC6917773
- OpenAlex
- W2995528452
- Cited by
- 46
- References
- 46
- Citations by year
- 2026: 6 · 2025: 1 · 2024: 9 · 2023: 9 · 2022: 8 · 2021: 11 · 2020: 2
- FWCI
- 2.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Precision Oncology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | Center for Information Technology (R01 EB023232)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- intraoperative diagnosis, breast cancer, label-free imaging, deep learning classification, cancer cell detection, feature visualization
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