ArticleJournal of medical imaging (Bellingham, Wash.)2019
Calibration of fluorescence imaging for tumor surgical margin delineation: multistep registration of fluorescence and histological images.
Article in Journal of medical imaging (Bellingham, Wash.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Protein scaffolds: antibody alternatives for cancer diagnosis and therapy.RSC chemical biology · 2022Review
- Off-Peak Near-Infrared-II (NIR-II) Bioimaging of an Immunoconjugate Having Peak Fluorescence Emission in the NIR-I Spectral Region for Improving Tumor Margin Delineation.ACS applied bio materials · 2020Article
- Fluorescence Spectroscopy Study of Protoporphyrin IX in Optical Tissue Simulating Liquid Phantoms.Materials (Basel, Switzerland) · 2020Article
- Intensity-based registration of bright-field and second-harmonic generation images of histopathology tissue sections.Biomedical optics express · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Although a greater extent of tumor resection is important for patients' survival, complete tumor removal, especially tumor margins, remains challenging due to the lack of sensitivity and specificity of current surgical guidance techniques at the margins. Intraoperative fluorescence imaging with targeted fluorophores is promising for tumor margin delineation. To verify the tumor margins detected by the fluorescence images, it is necessary to register fluorescence with histological images, which provide the ground truth for tumor regions. However, current registration methods compare fluorescence images to a single-layer histological slide, which is selected subjectively and represents a single plane of the three-dimensional tumor. A multistep pipeline is established to correlate fluorescence images to stacked histological images, including fluorescence calibration and multistep registration. Multiple histological slices are integrated as a two-dimensional (2-D) tumor map using optical attenuation model and average intensity projection. A BLZ-100-labeled medulloblastoma mouse model is used to test the whole framework. On average, the synthesized 2-D tumor map outperforms the selected best slide as ground truth [Dice similarity coefficient (DSC): 0.582 versus 0.398, with significant differences; mean area under the curve (AUC) of the receiver operating characteristic curve: 88% versus 85.5%] and the randomly selected slide as ground truth (DSC: 0.582 versus 0.396 with significant differences; mean AUC: 88% versus 84.1% with significant differences), which indicates our pipeline is reliable and can be applied to investigate targeted fluorescence probes in tumor margin detection. Following this proposed pipeline, BLZ-100 shows enhancement in both tumor cores and tumor margins (mean target-to-background ratio:
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.