ArticleScientific reports2026
A statistically rigorous multi-scale texture analysis framework for 3D spheroid characterization: temporal autocorrelation correction and molecular validation.
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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional tumor spheroids represent dynamic biomodeling systems exhibiting emergent collective behaviors-spontaneous reorganization, migration dynamics, and epithelial-mesenchymal transition observable through label-free time-lapse microscopy. However, optical opacity from light scattering limits fluorescence imaging depth, preventing single-cell resolution deep within spheroid volumes and necessitating ensemble-level morphological analysis rather than exhaustive single-cell profiling, while destructive molecular endpoint assays provide only discrete temporal snapshots, missing continuous dynamic changes defining biological processes. We present a validated computational framework for statistically rigorous ensemble morphological profiling, integrating multi-scale texture analysis (gray-level co-occurrence matrices, wavelet decomposition, Gabor filtering; 37 features spanning orientations and scales), global standardization eliminating scale artifacts while preserving biological signal, and autocorrelation-informed block bootstrap resampling addressing temporal dependence. Proof-of-concept analysis using representative A549 and H1299 lung cancer spheroids (n = 1 per condition, 48 hourly observations) demonstrates three framework capabilities. First, global standardization normalized features spanning 24 orders of magnitude in variance while preserving biological discrimination (16.8-fold Fisher score difference between cell lines). Second, temporal autocorrelation analysis revealed 4-hour median decorrelation lags, addressed through 5-h block bootstrap resampling enabling valid statistical inference. Third, external validation using Cancer Dependency Map RNA-sequencing (1699 cell lines) demonstrated that texture discrimination (16.8-fold) corresponded quantitatively to independent molecular differences (14.6-fold VIM/CDH1 ratio), achieving concordance within 15%. This framework enables statistically valid, biologically grounded morphological profiling for continuous monitoring applications including drug screening, organoid development tracking, and biomanufacturing quality control where ensemble dynamics complement discrete molecular measurements.
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.