ArticleScientific reports2018
Label-free quantitative evaluation of breast tissue using Spatial Light Interference Microscopy (SLIM).
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Label-free volumetric refractive-index imaging of fibrillar collagen architecture, assembly, and cell-associated remodeling.bioRxiv : the preprint server for biology · 2026Article
- High-precision label-free virtual H&E staining of 3D holotomography using DAPI-guided conditional diffusion learning.International journal of computer assisted radiology and surgery · 2026Article
- Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining.Nature communications · 2025Article
- Quantitative phase imaging based on holography: trends and new perspectives.Light, science & applications · 2024Review
- Real-time simultaneous refractive index and thickness mapping of sub-cellular biology at the diffraction limit.Communications biology · 2024Article
- Noise-resistant phase imaging with intensity correlation.Science advances · 2023Article
- Article
- Viewing life without labels under optical microscopes.Communications biology · 2023Review
- Multi-contrast digital histopathology of mouse organs using quantitative phase imaging and virtual staining.Biomedical optics express · 2023Article
- Quantification of structural heterogeneity in H&E stained clear cell renal cell carcinoma using refractive index tomography.Biomedical optics express · 2023Article
- Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.Diagnostics (Basel, Switzerland) · 2023Review
- Review
- Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data.Cells · 2022Article
- Monitoring reactivation of latent HIV by label-free gradient light interference microscopy.iScience · 2021Article
- Clear cell renal cell carcinoma detection by multimodal photoacoustic tomography.Photoacoustics · 2021Article
- Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.Nature communications · 2020Article
- Variational Hilbert Quantitative Phase Imaging.Scientific reports · 2020Article
- High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition.Scientific reports · 2020Article
- Label-free colorectal cancer screening using deep learning and spatial light interference microscopy (SLIM).APL photonics · 2020Article
- Quantitative Histopathology of Stained Tissues using Color Spatial Light Interference Microscopy (cSLIM).Scientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most common type of cancer among women worldwide. The standard histopathology of breast tissue, the primary means of disease diagnosis, involves manual microscopic examination of stained tissue by a pathologist. Because this method relies on qualitative information, it can result in inter-observer variation. Furthermore, for difficult cases the pathologist often needs additional markers of malignancy to help in making a diagnosis, a need that can potentially be met by novel microscopy methods. We present a quantitative method for label-free breast tissue evaluation using Spatial Light Interference Microscopy (SLIM). By extracting tissue markers of malignancy based on the nanostructure revealed by the optical path-length, our method provides an objective, label-free and potentially automatable method for breast histopathology. We demonstrated our method by imaging a tissue microarray consisting of 68 different subjects -34 with malignant and 34 with benign tissues. Three-fold cross validation results showed a sensitivity of 94% and specificity of 85% for detecting cancer. Our disease signatures represent intrinsic physical attributes of the sample, independent of staining quality, facilitating classification through machine learning packages since our images do not vary from scan to scan or instrument to instrument.
Indexed as
Identifiers
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.