ArticleBiomedical optics express2016
Classification and analysis of human ovarian tissue using full field optical coherence tomography.
Article in Biomedical optics express, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The clinical usefulness of optical coherence tomography during cancer interventions.Journal of cancer research and clinical oncology · 2018Pooled it
- Microscopic assessment of lymph node status in gynecological malignancies using full-field optical coherence tomography.Scientific reports · 2025Article
- Article
- Depth-Resolved Attenuation Mapping of the Vaginal Wall under Prolapse and after Laser Treatment Using Cross-Polarization Optical Coherence Tomography: A Pilot Study.Diagnostics (Basel, Switzerland) · 2023Article
- Enhanced 3D visualization of human fallopian tube morphology using a miniature optical coherence tomography catheter.Biomedical optics express · 2023Article
- Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis.Journal of biophotonics · 2023Review
- Methods and applications of full-field optical coherence tomography: a review.Journal of biomedical optics · 2022Review
- MRI-based radiomics model can improve the predictive performance of postlaminar optic nerve invasion in retinoblastoma.The British journal of radiology · 2022Article
- Ovarian cancer detection using optical coherence tomography and convolutional neural networks.Neural computing & applications · 2022Article
- A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring.Sensors (Basel, Switzerland) · 2021Review
- Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.PloS one · 2021Article
- High-resolution microscopy for imaging cancer pathobiology.Current pathobiology reports · 2019Article
- Evaluation of segmentation algorithms for optical coherence tomography images of ovarian tissue.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Quantitative multispectralBiomedical optics express · 2018Article
- Preliminary human application of optical coherence tomography for quantification and localization of primordial follicles aimed at effective ovarian tissue transplantation.Journal of assisted reproduction and genetics · 2018Article
- Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study.Journal of medical imaging (Bellingham, Wash.) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In this study, a full field optical coherence tomography (FFOCT) system was used to analyze and classify normal and malignant human ovarian tissue. 14 ovarian tissue samples (7 normal, 7 malignant) were imaged with the FFOCT system and five features were extracted by analyzing the normalized image histogram from 56 FFOCT images, based on the differences in the morphology of the normal and malignant tissue samples. A generalized linear model (GLM) classifier was trained using 36 images, and sensitivity of 95.3% and specificity of 91.1% was obtained. 20 images were used to test the model, and a sensitivity of 91.6% and specificity of 87.7% was obtained.
Indexed as
Identifiers
What Socratic 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 Socratic graph.