{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T06:03:51Z","timestamp":1789625031613,"version":"build-2803163510"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The spatial tumor immune microenvironment (TIME) determines therapy response in colorectal cancer liver metastases (CRLM). To overcome biopsy sampling bias, this study investigates multimodal macroscopic image fractal dynamics to non-invasively map immune physical barriers and metabolic states, constructing a multi-scale immune-radiomics joint score (IRJS) for robust prognostic stratification and exploratory assessment of treatment resistance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      This dual-center observational study enrolled 270 CRLM patients with dual-modality imaging (contrast-enhanced magnetic resonance imaging, CE-MRI, 18F-fluorodeoxyglucose (FDG) positron emission tomography-computed-tomography,\n                      <jats:sup>18<\/jats:sup>\n                      F-FDG PET\/CT) and digital spatial pathology. Patients were spatiotemporally divided into training (n=150), prospective temporal validation (n=45), and external validation (n=75) cohorts. Structural (FD_MRI) and metabolic (FD_PET) fractal dimensions were combined with the systemic immune-inflammation index (SII) and\n                      <jats:italic>KRAS<\/jats:italic>\n                      status to build the IRJS using XGBoost, utilizing the SHAP framework to evaluate mapping mechanisms.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      FD_MRI positively correlated with \u03b1-SMA\n                      <jats:sup>+<\/jats:sup>\n                      pro-fibrotic stromal density (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.684), which may reflect the mechanical barrier excluding CD8\n                      <jats:sup>+<\/jats:sup>\n                      T cells. FD_PET showed a significant association with core hypoxia (HIF-1\u03b1) and systemic immune exhaustion. IRJS robustly identified the immune-inflamed phenotype across training, temporal, and external validation cohorts (AUCs: 0.921, 0.886, 0.862, respectively). The IRJS-identified inflamed phenotype was an independent protective factor for overall survival (HR\u00a0=\u00a00.31,\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt;\u00a00.001). Furthermore, IRJS demonstrated significant clinical net reclassification improvement (NRI\u00a0=\u00a00.384,\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt;\u00a00.001) and net clinical benefit in decision curve analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Dual-modal fractal dynamics provide non-invasive surrogate markers for mechanical constraints and metabolic exhaustion in CRLM. The IRJS model enables robust spatiotemporal assessment of immune evasion, offering multidisciplinary teams a potential framework to explore non-invasive biopsy decisions, prognostic stratification, and provide exploratory insights into potential immunotherapy resistance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1889571","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T05:33:27Z","timestamp":1789623207000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Mapping the physical topology of the tumor immune microenvironment: macroscopic image fractals as surrogate markers for immune exclusion phenotype and treatment resistance in colorectal cancer liver metastases"],"prefix":"10.3389","volume":"17","author":[{"given":"Guo","family":"Ziyi","sequence":"first","affiliation":[{"name":"Department of Nuclear Medicine, Jinzhou Medical 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