ArticleNPJ precision oncology2019
Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology.
Article in NPJ precision oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 44 citations in OpenAlex.
- Recent Advances in Modeling and Prediction of Blood Glucose in Type 1 Diabetes.Delaware journal of public health · 2026Article
- Effective transfer of tumor annotations from hematoxylin and eosin to fluorescence images of breast and lung tissues.Journal of biomedical optics · 2026Article
- Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms.Biophotonics discovery · 2025Article
- Deep and dynamic metabolic and structural imaging in living tissues.Science advances · 2024Article
- Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy.Light, science & applications · 2024Review
- Inspiring a convergent engineering approach to measure and model the tissue microenvironment.Heliyon · 2024Review
- Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.Cancer discovery · 2024Review
- In vivo organoid growth monitoring by stimulated Raman histology.Npj imaging · 2024Article
- Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis.Scientific reports · 2023Article
- Label-free biomedical optical imaging.Nature photonics · 2023Article
- A deep learning-based stripe self-correction method for stitched microscopic images.Nature communications · 2023Article
- Toward next-generation endoscopes integrating biomimetic video systems, nonlinear optical microscopy, and deep learning.Biophysics reviews · 2023Review
- Automatic diagnosis and classification of breast surgical samples with dynamic full-field OCT and machine learning.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Biological insights and novel biomarker discovery through deep learning approaches in breast cancer histopathology.NPJ breast cancer · 2023Review
- Learned end-to-end high-resolution lensless fiber imaging towards real-time cancer diagnosis.Scientific reports · 2022Article
- Automated classification of otitis media with OCT: augmenting pediatric image datasets with gold-standard animal model data.Biomedical optics express · 2022Article
- Label-free multimodal nonlinear optical imaging of needle biopsy cores for intraoperative cancer diagnosis.Journal of biomedical optics · 2022Article
- Electrical Impedance-Based Characterization of Hepatic Tissue with Early-Stage Fibrosis.Biosensors · 2022Article
- Biopsy-free in vivo virtual histology of skin using deep learning.Light, science & applications · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
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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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.