{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:56:57Z","timestamp":1792083417450,"version":"4.4.0"},"reference-count":22,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To determine whether a CT radiomics\u2010defined poor\u2010prognosis phenotype in NSCLC is associated with reproducible transcriptomic programs and distinct transcription factor (TF) activity patterns.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective radiogenomic study used public datasets from The Cancer Imaging Archive. A prognostic radiomics model was developed from pretreatment CT in 402 patients from the NSCLC\u2010Radiomics collection and evaluated in an independent cohort of 110 patients from the NSCLC Radiogenomics collection. Seven tumor\u2010centered intra\u2010 and peritumoral regions were analyzed. Features were extracted from original, wavelet\u2010, and Laplacian\u2010of\u2010Gaussian\u2010filtered CT images. After preprocessing and training\u2010cohort standardization, a LASSO\u2010penalized Cox model was developed. The locked model retained eight features, all from the GTV+10\u2010mm region, and was applied to the internal and external validation cohorts without refitting. The training\u2010cohort median RadScore was used unchanged for Kaplan\u2013Meier visualization. Matched transcriptomic data were analyzed using preranked gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), target\u2010gene\u2010based TF activity inference, TF co\u2010activity networks, exploratory regulator prioritization, unsupervised TF\u2010state clustering, and LINCS\u2010based perturbational analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The radiomics model achieved Harrell concordance indices of 0.785 (95% confidence interval [CI], 0.767\u20130.793), 0.819 (95% CI, 0.788\u20130.833), and 0.785 (95% CI, 0.752\u20130.802) in the training, internal validation, and external validation cohorts, respectively. The high\u2010risk phenotype showed exploratory enrichment of proliferative and stress\u2010adaptive programs, led by HALLMARK_E2F_TARGETS (normalized enrichment score [NES]\u00a0=\u00a01.696, false discovery rate [FDR]\u00a0=\u00a00.045), with additional enrichment of MYC targets and inflammatory response. HALLMARK_HYPOXIA showed a positive directional trend but did not meet the prespecified exploratory threshold. GSVA provided complementary sample\u2010level evidence for MYC targets, hypoxia, E2F targets, DNA repair, and cell\u2010cycle activity. Eleven of 13 evaluated TFs met the prespecified FDR threshold, including YAP1, MYC, HIF1A, E2F3, STAT3, KLF4, E2F1, and TP53. Integrated analysis prioritized HIF1A, STAT3, TP53, YAP1, and E2F1\/E2F3 as candidate regulatory nodes. Unsupervised clustering of all 110 radiogenomic patients, performed independently of radiomics risk labels, identified three exploratory TF\u2010defined states: Hypoxia_Metabolic (n\u00a0=\u00a069), Cell_Cycle_E2F (n\u00a0=\u00a031), and Hippo_Mechanosensing (n\u00a0=\u00a010). LINCS\u2010based analysis generated regulatory\u2010state\u2010associated compound hypotheses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A CT radiomics\u2010defined poor\u2010prognosis phenotype in NSCLC is associated with coherent transcriptomic programs and an interpretable TF regulatory architecture. This framework links routine imaging phenotypes to latent tumor\u2010control programs and supports regulatory\u2010state\u2010informed therapeutic hypothesis generation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70694","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T17:27:36Z","timestamp":1791307656000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiogenomic inference of transcription factor regulatory architecture associated with a high\u2010risk CT phenotype in Non\u2010Small Cell Lung Cancer"],"prefix":"10.1002","volume":"53","author":[{"given":"Daisuke","family":"Kawahara","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Graduate School of Biomedical and Health Sciences Hiroshima University  Hiroshima Hiroshima 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