ArticleComputational and structural biotechnology journal2026
PatternExtract: A Facile, Scalable Pipeline for Point Pattern Generation from Spatial Imaging Data.
Article in Computational and structural biotechnology journal, 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
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spatial point pattern analysis offers a mathematically rigorous framework for characterizing cell distribution in cancer tissue, yet existing pipelines lack robust methods for defining accurate spatial windows that exclude noncellular artifacts. We present PatternExtract, an open-source, multi-platform pipeline for generating biologically accurate spatial point patterns from multiplexed imaging data. The pipeline introduces a 2-kernel tissue segmentation approach that overlays concentric circular kernels on cellular coordinates to construct tissue masks without dependence on proprietary software or fluorescence composite channels, making it compatible with RGB images and coordinate outputs from any cell segmentation tool. Benchmarked on 568 images from 274 diffuse large B cell lymphoma (DLBCL) patients, PatternExtract identified and correctly excluded tissue artifacts in 27% of images, reducing window area by a mean of 9.8 ± 8.2% relative to convex hull methods. Convex hull window misspecification inflated K function AUC by up to 649.6% and produced 100% false rejection of complete spatial randomness under simulation, errors fully corrected by PatternExtract. Cross-cohort validation on an independent 20-patient cohort confirmed pipeline consistency across imaging platforms and staining protocols. Biological application to Ki67
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.