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This study aimed to quantify the relative influence of tumor type and anatomical environment on contrast-enhanced computed tomography (CT) radiomic phenotypes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      A discovery cohort of 1,598 patients (10,485 lesions) and an external validation cohort of 2,440 patients (6,597 lesions) underwent portal-venous-phase CT. After manual segmentation, lesion-level radiomic features were standardized and embedded using\n                      <jats:italic>t<\/jats:italic>\n                      -distributed stochastic neighbor embedding. Bayesian-optimized agglomerative clustering defined morphology-based groups. Concordance with the primary tumor site (lineage) and anatomical environment was quantified using bootstrapped adjusted Rand indices (ARI); the silhouette score assessed clustering quality. Feature-class (shape, intensity, texture) and mask-erosion experiments probed mechanistic drivers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Six morphological clusters were identified in the discovery set (silhouette = 0.44). Morphology aligned more strongly with environment (mean ARI\u2009=\u20090.37) but poorly with lineage (mean ARI\u2009=\u20090.04;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.010); this pattern held externally. In solid organ metastases, environment dominance was even stronger (mean ARI\u2009=\u20090.60\n                      <jats:italic>versus<\/jats:italic>\n                      0.05;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.010). Intensity and texture drove the morphological association with anatomical environment (ARI\u2009=\u20090.64\u20130.56) more than shape (ARI\u2009=\u20090.06). When the periphery of the tumor was eroded, the same patterns were observed, implicating the tumor core.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Across organs and tumor types, tumor morphological phenotype on CT imaging is largely driven by a host tissue-related environmental \u201cimprint\u201d rather than the primary tumor site.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>Context-aware modeling is essential for reliable radiomic biomarkers and could motivate a two-step AI pipeline that first identifies the organ habitat and refines lineage-specific predictions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>In a large, multicenter cohort, tumors exhibited distinct morphological clustering.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>These clusters did not align with primary tumor sites (ARI\u2009=\u20090.04).<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Stronger associations emerged between morphological clusters and the local anatomical environment (ARI\u2009=\u20090.37).<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>Stratification by lesion type revealed even stronger associations between local anatomical context and solid organ metastases (ARI\u2009=\u20090.60).<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-026-00691-5","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T09:50:44Z","timestamp":1773309044000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Tumor morphology on CT radiomics is largely driven by the local anatomical environment, not the primary tumor 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