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Updated: Jul 15, 2026

Gene Regulation and Targeted Therapy in Gastric Cancer Peritoneal Metastasis: Radiological Findings from Dual Energy CT and PET/CT

Gene Regulation and Targeted Therapy in Gastric Cancer Peritoneal Metastasis: Radiological Findings from Dual Energy CT and PET/CT

Published on: January 22, 2018

Treatment-specific CT Radiomics Models To Predict Response To Neoadjuvant Therapy And Explore Individualized

Jiaxin Liu¹, Xujie Gao¹, Tingting Ma²

¹Department of Radiology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin, China (J.L., X.G., X.L., L.Y., L.W., J.X., Z.Y.); Tianjin's Clinical Research Center for Cancer, Tianjin, China (J.L., X.G., T.M., X.L., L.Y., L.W., X.D., J.X., Z.Y.); State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin, China (J.L., X.G., T.M., X.L., L.Y., L.W., X.D., J.X., Z.Y.); Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China (J.L., X.G., T.M., X.L., L.Y., L.W., X.D., J.X., Z.Y.); Tianjin Key Laboratory of Digestive Cancer, Tianjin, China (J.L., X.G., T.M., X.L., L.Y., L.W., X.D., J.X., Z.Y.).

Academic Radiology

|July 13, 2026

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Summary

Treatment-specific radiomics models accurately predict pathological response to neoadjuvant therapy (NAT) in gastric cancer (GC). A dual-score framework aids in selecting the best NAT for individual patients, improving treatment outcomes.

Area of Science:

Oncology
Radiology
Medical Imaging

Background:

Gastric cancer (GC) treatment response prediction is crucial for effective neoadjuvant therapy (NAT).
Radiomics analysis of pretreatment computed tomography (CT) shows promise for predicting treatment outcomes.
Developing accurate models for diverse NAT regimens is essential for personalized medicine.

Purpose of the Study:

To develop and compare general and treatment-specific radiomics models using pretreatment CT for predicting pathological response to NAT in gastric cancer (GC).
To explore a dual-score framework for guiding individualized treatment selection based on radiomics predictions.
To assess the performance of treatment-specific models versus general models in predicting response to neoadjuvant chemotherapy (NAC) and neoadjuvant immunochemotherapy (NAIC).

Keywords:

Gastric cancer Individualized treatment Machine learning Neoadjuvant therapy Radiomics

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Main Methods:

Retrospective analysis of 405 GC patients undergoing NAT followed by gastrectomy.
Extraction of radiomics features from portal venous-phase CT images.
Construction and comparison of general and treatment-specific radiomics models using machine learning classifiers.

Main Results:

Treatment-specific radiomics models (NAC-specific: AUC 0.770, NAIC-specific: AUC 0.753) outperformed the general model (AUC 0.679) in predicting pathological response.
In a temporal test cohort, the NAIC-specific model showed superior performance (AUC 0.707 vs 0.626).
Patients receiving model-recommended treatments exhibited significantly higher pathological response rates.

Conclusions:

Treatment-specific radiomics models offer superior prediction accuracy for pathological response to NAT in GC compared to general models.
The developed dual-score framework presents a promising approach for individualized treatment selection in GC patients.
Radiomics holds potential for optimizing neoadjuvant treatment strategies in gastric cancer.