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Background <p>Perineural invasion (PNI) is a key prognostic determinant in gastric adenocarcinoma but remains radiologically occult on conventional CT.</p> Purpose <p>To develop and validate an integrated model combining dual-energy CT (DECT)–derived functional spectral slopes and subregional habitat signatures for the preoperative prediction of PNI.</p> Materials and methods <p>In this retrospective study, 175 patients (median age, 66 years; interquartile range, 58–73 years; 131 men) with gastric adenocarcinoma who underwent preoperative DECT between 2023 and 2025 were evaluated. Patients were randomized into a training cohort (<i>n</i> = 122) and an internal test cohort (<i>n</i> = 53). Tumor subregions were parcellated into three biologically distinct habitats using voxel-wise clustering. Stable habitat features (ICC ≥ 0.75), three-phase spectral slopes, and a Habitat-Graph Neural Network (H-GNN)–derived Topological Interaction Score (TIS)—capturing spatial adjacency relationships between habitat subregions—were collectively submitted as candidate predictors to LASSO regression for construction of the final Spectral-Habitat Signature. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Clinical utility was assessed via decision curve analysis.</p> Results <p>PNI was pathologically confirmed in 46.9% (82 of 175) of patients. The integrated Spectral-Habitat model achieved an AUC of 0.832 (95% CI: 0.760, 0.898) in the training cohort and 0.758 (95% CI: 0.666, 0.845) in the internal test cohort. A stratified 5-fold cross-validation demonstrated the stability of the framework, yielding a mean AUC of 0.765 (SD, 0.084). The combined model significantly outperformed the extracellular volume (ECV) model (AUC, 0.758 vs. 0.544; <i>P</i> &lt; .001). Equilibrium-phase spectral slope emerged as a decisive functional biomarker. Model calibration was excellent in the test cohort (<i>P</i> = .560, Hosmer-Lemeshow test). Decision curve analysis demonstrated a substantial net clinical benefit across threshold probabilities of 10%–80%.</p> Conclusion <p>In this retrospective single-center study, an integrated model leveraging functional spectral kinetics and subregional spatial heterogeneity showed potential for the preoperative stratification of occult perineural invasion. However, these preliminary findings derived from a limited sample size require further validation in larger, multi-institutional prospective cohorts before clinical implementation in surgical planning.</p> Graphical abstract <p>Scientific workflow of the integrated functional-spatial model. The diagnostic pipeline consists of four integrated phases. (I) Preoperative data acquisition: Dual-energy CT (DECT) data are obtained and segmented using 3D whole-tumor volumetric regions of interest (VOIs). (II) Multi-modal feature extraction: Subregional spatial phenotypes are delineated via unsupervised 3D habitat parcellation (Innovation C), and functional kinetics are quantified via 3D mean spectral slope (<i>λHU</i>) calculation (Innovation B). (III) Topological modeling: A Habitat-Graph Neural Network (H-GNN) is employed as a topological feature extractor to decode high-order spatial adjacency relationships between microenvironmental niches. (IV) Internal validation: The components are integrated into a finalized Spectral-Habitat signature (Innovation A), yielding a validated AUC of 0.758 in the internal test cohort to assist in preoperative PNI risk stratification.</p> <p></p>

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Integrated dual-energy CT spectral kinetics and subregional habitat phenotyping for preoperative prediction of perineural invasion in gastric adenocarcinoma

  • 期刊论文

  • 发表日期：

    2026年7月13日

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  • Chong Wang,
  • Zhen Wang,
  • Xiaohan Liu,
  • Chen Wu,
  • Xiaolong Wang,
  • Shang Jin,
  • He Zhang,
  • Yixin Xu,
  • Aiyun Sun,
  • Tao Ding,
  • Kai Xu,
  • Yankai Meng

摘要

Background

Perineural invasion (PNI) is a key prognostic determinant in gastric adenocarcinoma but remains radiologically occult on conventional CT.

Purpose

To develop and validate an integrated model combining dual-energy CT (DECT)–derived functional spectral slopes and subregional habitat signatures for the preoperative prediction of PNI.

Materials and methods

In this retrospective study, 175 patients (median age, 66 years; interquartile range, 58–73 years; 131 men) with gastric adenocarcinoma who underwent preoperative DECT between 2023 and 2025 were evaluated. Patients were randomized into a training cohort (n = 122) and an internal test cohort (n = 53). Tumor subregions were parcellated into three biologically distinct habitats using voxel-wise clustering. Stable habitat features (ICC ≥ 0.75), three-phase spectral slopes, and a Habitat-Graph Neural Network (H-GNN)–derived Topological Interaction Score (TIS)—capturing spatial adjacency relationships between habitat subregions—were collectively submitted as candidate predictors to LASSO regression for construction of the final Spectral-Habitat Signature. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). Clinical utility was assessed via decision curve analysis.

Results

PNI was pathologically confirmed in 46.9% (82 of 175) of patients. The integrated Spectral-Habitat model achieved an AUC of 0.832 (95% CI: 0.760, 0.898) in the training cohort and 0.758 (95% CI: 0.666, 0.845) in the internal test cohort. A stratified 5-fold cross-validation demonstrated the stability of the framework, yielding a mean AUC of 0.765 (SD, 0.084). The combined model significantly outperformed the extracellular volume (ECV) model (AUC, 0.758 vs. 0.544; P < .001). Equilibrium-phase spectral slope emerged as a decisive functional biomarker. Model calibration was excellent in the test cohort (P = .560, Hosmer-Lemeshow test). Decision curve analysis demonstrated a substantial net clinical benefit across threshold probabilities of 10%–80%.

Conclusion

In this retrospective single-center study, an integrated model leveraging functional spectral kinetics and subregional spatial heterogeneity showed potential for the preoperative stratification of occult perineural invasion. However, these preliminary findings derived from a limited sample size require further validation in larger, multi-institutional prospective cohorts before clinical implementation in surgical planning.

Graphical abstract

Scientific workflow of the integrated functional-spatial model. The diagnostic pipeline consists of four integrated phases. (I) Preoperative data acquisition: Dual-energy CT (DECT) data are obtained and segmented using 3D whole-tumor volumetric regions of interest (VOIs). (II) Multi-modal feature extraction: Subregional spatial phenotypes are delineated via unsupervised 3D habitat parcellation (Innovation C), and functional kinetics are quantified via 3D mean spectral slope (λHU) calculation (Innovation B). (III) Topological modeling: A Habitat-Graph Neural Network (H-GNN) is employed as a topological feature extractor to decode high-order spatial adjacency relationships between microenvironmental niches. (IV) Internal validation: The components are integrated into a finalized Spectral-Habitat signature (Innovation A), yielding a validated AUC of 0.758 in the internal test cohort to assist in preoperative PNI risk stratification.