ArticleScientific reports2026
Rapid autofluorescence based 3D optical imaging of the pancreatic cancer milieu at mesoscopic scale - stain-free volumetric segmentation.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although major advances have been made in the field of mesoscopic imaging and associated tissue clearing protocols, these applications are greatly challenged when applied to imaging of pancreatic ductal adenocarcinoma (PDAC) tissue. Most importantly, penetration of labelling agents, typically antibodies, can be drastically reduced from the characteristically dense PDAC stroma. To circumvent this issue, we present a method by which machine learning assisted segmentation is applied to resolve the 3D PDAC microarchitecture from autofluorescence (AF) based light-sheet fluorescence microscopy (LSFM) scans. Hereby, PDAC tissue features could be studied in 3D space without the need for labelling or sectioning. In this proof of principle study, we applied this imaging pipeline on surgical specimens from five PDAC patients and normal pancreatic tissue, generating mosaics of cm
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.