Objectives To investigate the predictive value of habitat imaging based on spectral CT-derived iodine maps (IMs) for pathologic response to neoadjuvant therapy (NAT) in gastric cancer (GC). Materials and methods This retrospective, two-center study included 151 patients with pathologically confirmed GC who underwent NAT followed by gastrectomy between July 2022 and June 2025. All patients underwent dual-phase, contrast-enhanced, dual-layer spectral CT scans before NAT. Based on tumor regression grade, patients were categorized as responders or non-responders. In venous-phase IMs, tumor voxels in the entire lesion were clustered into distinct habitats using the k-means algorithm. The volume fraction of each habitat and an intratumoral heterogeneity score (ITHscore) were calculated. Univariate analysis and logistic regression analyses determined predictive parameters among clinicopathologic and IM-based variables. A weighted logistic regression model for responders was developed and validated using fivefold cross-validation and an external test set. Results Among the 151 patients, 54 (35.8%) were responders. Three distinct perfusion habitats (high, middle, and low) were identified. Responders exhibited a higher volume fraction of the low-perfusion habitat and a lower ITHscore (both p < 0.05). By integrating these two metrics, patients were stratified into four perfusion subtypes, with response rates ranging from 75.0% to 17.4% (p < 0.001). The predictive model combining perfusion subtype and Lauren classification achieved an average area under the curve (AUC) of 0.794 (0.751–0.836) in internal cross-validation and 0.782 (0.605–0.909) in the external test set. Conclusion IM-derived habitat imaging can distinguish distinct perfusion subtypes, suggesting a promising approach for predicting pathologic response to NAT in GC. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Accurately predicting the efficacy of neoadjuvant therapy prior to treatment in gastric cancer remains challenging due to the lack of reliable biomarkers. <Emphasis Type="BoldItalic">Findings</Emphasis> Perfusion subtypes identified through iodine map-based habitat imaging effectively predict tumor regression in gastric cancer following neoadjuvant therapy. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> Iodine map-based habitat imaging is a promising biomarker for noninvasively predicting pathologic response to neoadjuvant therapy in patients with gastric cancer, thereby enabling more personalized treatment strategies for this population. Graphical Abstract

Spectral CT-based habitat analysis for predicting pathologic response to neoadjuvant therapy in gastric cancer

期刊论文
发表日期：

2026年7月4日

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Yaru You,
Mengchen Yuan,
Xiaofei Yang,
Yusong Chen,
Jinjin Dang,
Xuejun Chen,
Jing Li,
Jianbo Gao

摘要

Objectives

To investigate the predictive value of habitat imaging based on spectral CT-derived iodine maps (IMs) for pathologic response to neoadjuvant therapy (NAT) in gastric cancer (GC).

Materials and methods

This retrospective, two-center study included 151 patients with pathologically confirmed GC who underwent NAT followed by gastrectomy between July 2022 and June 2025. All patients underwent dual-phase, contrast-enhanced, dual-layer spectral CT scans before NAT. Based on tumor regression grade, patients were categorized as responders or non-responders. In venous-phase IMs, tumor voxels in the entire lesion were clustered into distinct habitats using the k-means algorithm. The volume fraction of each habitat and an intratumoral heterogeneity score (ITHscore) were calculated. Univariate analysis and logistic regression analyses determined predictive parameters among clinicopathologic and IM-based variables. A weighted logistic regression model for responders was developed and validated using fivefold cross-validation and an external test set.

Results

Among the 151 patients, 54 (35.8%) were responders. Three distinct perfusion habitats (high, middle, and low) were identified. Responders exhibited a higher volume fraction of the low-perfusion habitat and a lower ITHscore (both p < 0.05). By integrating these two metrics, patients were stratified into four perfusion subtypes, with response rates ranging from 75.0% to 17.4% (p < 0.001). The predictive model combining perfusion subtype and Lauren classification achieved an average area under the curve (AUC) of 0.794 (0.751–0.836) in internal cross-validation and 0.782 (0.605–0.909) in the external test set.

Conclusion

IM-derived habitat imaging can distinguish distinct perfusion subtypes, suggesting a promising approach for predicting pathologic response to NAT in GC.

Key Points

Question Accurately predicting the efficacy of neoadjuvant therapy prior to treatment in gastric cancer remains challenging due to the lack of reliable biomarkers.

Findings Perfusion subtypes identified through iodine map-based habitat imaging effectively predict tumor regression in gastric cancer following neoadjuvant therapy.

Clinical relevance Iodine map-based habitat imaging is a promising biomarker for noninvasively predicting pathologic response to neoadjuvant therapy in patients with gastric cancer, thereby enabling more personalized treatment strategies for this population.

Graphical Abstract