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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\u2009=\u20091 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.<\/jats:p>","DOI":"10.1038\/s41598-026-51722-5","type":"journal-article","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T03:57:21Z","timestamp":1778299041000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A statistically rigorous multi-scale texture analysis framework for 3D spheroid characterization: temporal autocorrelation correction and molecular validation"],"prefix":"10.1038","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4601-3806","authenticated-orcid":false,"given":"Daniel G.","family":"Regassa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marat S.","family":"Babaev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evgeniya Y.","family":"Shabalina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp Y.","family":"Maximov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena V.","family":"Petersen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"51722_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.jbiotec.2010.01.012","volume":"148","author":"F Hirschhaeuser","year":"2010","unstructured":"Hirschhaeuser, F. et al. 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Baseline comparison demonstrating superiority of multi-scale texture features over mean pixel intensity for detecting treatment response. Fisher discriminant scores (F, higher values indicate better separation between untreated and treated conditions) were calculated for mean pixel intensity (a single first-order statistic representing average brightness across tissue pixels) and the best-performing texture feature for each cell line using identical methodology (Eq.\u00a0\n                      \n                      ). Mean intensity achieves minimal discrimination for both cell lines (A549: F\u2009=\u20090.10, Cohen\u2019s d\u2009=\u20090.45; H1299: F\u2009=\u20090.03, Cohen\u2019s d\u2009=\u20090.22), failing to capture morphological changes induced by capecitabine treatment. Best texture features achieve substantially higher discrimination (A549: F\u2009=\u20090.46, detail_d1_variance; H1299: F\u2009=\u20097.72, detail_d2_variance), representing 4.5-fold and approximately 300-fold improvement in discrimination capacity respectively. The extreme improvement for H1299 reflects near-zero intensity discrimination (F\u2009\u2248\u20090.03) rather than implying 300-fold greater biological signal. Notably, mean intensity yields biologically incorrect conclusions: mean intensity suggests A549 exhibits greater treatment response than H1299 (F\u2009=\u20090.10\u2009>\u2009F\u2009=\u20090.03), contradicting independent molecular characterization from the Cancer Dependency Map where H1299\u2019s mesenchymal phenotype (VIM\/CDH1 expression ratio\u2009=\u20091,438) compared to A549\u2019s epithelial phenotype (VIM\/CDH1\u2009=\u200998) predicts substantially greater morphological plasticity. Texture features correctly identify H1299 as more treatment-responsive, with the 16.8-fold texture discrimination ratio closely matching the 14.6-fold molecular marker ratio (15% agreement).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"21252"}}