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Objective <p>Radiogenomics promises noninvasive tumor profiling; however, the extent to which imaging morphology reflects tumor lineage <i>versus</i> host-organ milieu remains unclear. This study aimed to quantify the relative influence of tumor type and anatomical environment on contrast-enhanced computed tomography (CT) radiomic phenotypes.</p> Materials and methods <p>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 <i>t</i>-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.</p> Results <p>Six morphological clusters were identified in the discovery set (silhouette = 0.44). Morphology aligned more strongly with environment (mean ARI = 0.37) but poorly with lineage (mean ARI = 0.04; <i>p</i> &lt; 0.010); this pattern held externally. In solid organ metastases, environment dominance was even stronger (mean ARI = 0.60 <i>versus</i> 0.05; <i>p</i> &lt; 0.010). Intensity and texture drove the morphological association with anatomical environment (ARI = 0.64–0.56) more than shape (ARI = 0.06). When the periphery of the tumor was eroded, the same patterns were observed, implicating the tumor core.</p> Conclusion <p>Across organs and tumor types, tumor morphological phenotype on CT imaging is largely driven by a host tissue-related environmental “imprint” rather than the primary tumor site.</p> Relevance statement <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.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>In a large, multicenter cohort, tumors exhibited distinct morphological clustering.</p> </ItemContent> <ItemContent> <p>These clusters did not align with primary tumor sites (ARI = 0.04).</p> </ItemContent> <ItemContent> <p>Stronger associations emerged between morphological clusters and the local anatomical environment (ARI = 0.37).</p> </ItemContent> <ItemContent> <p>Stratification by lesion type revealed even stronger associations between local anatomical context and solid organ metastases (ARI = 0.60).</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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Tumor morphology on CT radiomics is largely driven by the local anatomical environment, not the primary tumor type

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月12日

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  • Sajjad Rostami,
  • Corentin Guérendel,
  • Marleen Soliman,
  • Hannah W. Stutterheim,
  • Olga Maxouri,
  • Diana Ivonne Rodríguez Sánchez,
  • Stephan Ursprung,
  • Nino Boveradze,
  • George Agrotis,
  • Kalina Chupetlovska,
  • Francesca Castagnoli,
  • Federica Landolfi,
  • Eun Kyoung Hong,
  • Andrea Delli Pizzi,
  • Nicolo Gennaro,
  • Mohamed A. Abdelatty,
  • Warissara Jutidamrongphan,
  • Liliana Petrychenko,
  • Peter Matkulcik,
  • Alba Salgado-Parente,
  • Francesco Marcello Arico,
  • Sean Benson,
  • Petur Snaebjornsson,
  • Zuhir Bodalal,
  • Regina G. H. Beets-Tan

摘要

Objective

Radiogenomics promises noninvasive tumor profiling; however, the extent to which imaging morphology reflects tumor lineage versus host-organ milieu remains unclear. This study aimed to quantify the relative influence of tumor type and anatomical environment on contrast-enhanced computed tomography (CT) radiomic phenotypes.

Materials and methods

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 t-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.

Results

Six morphological clusters were identified in the discovery set (silhouette = 0.44). Morphology aligned more strongly with environment (mean ARI = 0.37) but poorly with lineage (mean ARI = 0.04; p < 0.010); this pattern held externally. In solid organ metastases, environment dominance was even stronger (mean ARI = 0.60 versus 0.05; p < 0.010). Intensity and texture drove the morphological association with anatomical environment (ARI = 0.64–0.56) more than shape (ARI = 0.06). When the periphery of the tumor was eroded, the same patterns were observed, implicating the tumor core.

Conclusion

Across organs and tumor types, tumor morphological phenotype on CT imaging is largely driven by a host tissue-related environmental “imprint” rather than the primary tumor site.

Relevance statement

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.

Key Points

In a large, multicenter cohort, tumors exhibited distinct morphological clustering.

These clusters did not align with primary tumor sites (ARI = 0.04).

Stronger associations emerged between morphological clusters and the local anatomical environment (ARI = 0.37).

Stratification by lesion type revealed even stronger associations between local anatomical context and solid organ metastases (ARI = 0.60).

Graphical Abstract