A Dual-phase Enhanced CT-based Nomogram for Predicting the Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.
[RATIONALE AND OBJECTIVES] Early prediction of response to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC) helps guide treatment decisions and optimize treatment
- p-value p < 0.05
- 95% CI 0.721-0.899
APA
Xiao Z, Chen S, et al. (2026). A Dual-phase Enhanced CT-based Nomogram for Predicting the Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.. Academic radiology, 33(2), 404-414. https://doi.org/10.1016/j.acra.2025.10.060
MLA
Xiao Z, et al.. "A Dual-phase Enhanced CT-based Nomogram for Predicting the Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.." Academic radiology, vol. 33, no. 2, 2026, pp. 404-414.
PMID
41260957
Abstract
[RATIONALE AND OBJECTIVES] Early prediction of response to neoadjuvant chemotherapy (NAC) in patients with locally advanced gastric cancer (LAGC) helps guide treatment decisions and optimize treatment. This study aimed to establish a radiomics nomogram for predicting NAC response in LAGC patients using dual-phase contrast-enhanced CT (CECT) images.
[MATERIALS AND METHODS] This retrospective study recruited 143 patients with LAGC from January 2018 to March 2024. Radiomics features were extracted from arterial phase (AP) and venous phase (VP) CT images, and were used to develop three radiomics models: AP, VP, and a combined AP_VP model. Clinicopathological characteristics were selected via univariate and multivariate logistic regression. A nomogram was then constructed by integrating the AP_VP radiomics signature with clinicopathological characteristics. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. The clinical utility and benefits were further quantified by comparing the nomogram to the clinical model using the net reclassification index (NRI) and integrated discrimination improvement (IDI). Stratified analyses were conducted to explore the model's performance across different patient subgroups.
[RESULTS] The AP_VP model performed well in the radiomics models, with AUCs of 0.810 (95% CI, 0.721-0.899) and 0.745 (95% CI, 0.588-0.903) in the training and validation cohorts, respectively. The clinical model was constructed by cT stage and differentiation, with AUCs of 0.723 (95% CI, 0.619-0.827) and 0.754 (95% CI, 0.591-0.916). The nomogram combining radiomics and clinicopathological characteristics achieved AUCs of 0.845 (95% CI, 0.767-0.924) and 0.829 (95% CI, 0.697-0.96), significantly outperforming the clinical model (DeLong test p < 0.05).
[CONCLUSION] The nomogram, incorporating dual-phase enhanced CT and clinicopathological characteristics, demonstrated satisfactory performance in predicting NAC response in LAGC patients, assisting with individualized treatment.
[MATERIALS AND METHODS] This retrospective study recruited 143 patients with LAGC from January 2018 to March 2024. Radiomics features were extracted from arterial phase (AP) and venous phase (VP) CT images, and were used to develop three radiomics models: AP, VP, and a combined AP_VP model. Clinicopathological characteristics were selected via univariate and multivariate logistic regression. A nomogram was then constructed by integrating the AP_VP radiomics signature with clinicopathological characteristics. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. The clinical utility and benefits were further quantified by comparing the nomogram to the clinical model using the net reclassification index (NRI) and integrated discrimination improvement (IDI). Stratified analyses were conducted to explore the model's performance across different patient subgroups.
[RESULTS] The AP_VP model performed well in the radiomics models, with AUCs of 0.810 (95% CI, 0.721-0.899) and 0.745 (95% CI, 0.588-0.903) in the training and validation cohorts, respectively. The clinical model was constructed by cT stage and differentiation, with AUCs of 0.723 (95% CI, 0.619-0.827) and 0.754 (95% CI, 0.591-0.916). The nomogram combining radiomics and clinicopathological characteristics achieved AUCs of 0.845 (95% CI, 0.767-0.924) and 0.829 (95% CI, 0.697-0.96), significantly outperforming the clinical model (DeLong test p < 0.05).
[CONCLUSION] The nomogram, incorporating dual-phase enhanced CT and clinicopathological characteristics, demonstrated satisfactory performance in predicting NAC response in LAGC patients, assisting with individualized treatment.
MeSH Terms
Humans; Nomograms; Stomach Neoplasms; Female; Male; Neoadjuvant Therapy; Middle Aged; Retrospective Studies; Tomography, X-Ray Computed; Aged; Contrast Media; Adult; Treatment Outcome; Chemotherapy, Adjuvant
같은 제1저자의 인용 많은 논문 (5)
- Efficacy and safety of neoadjuvant treatment of trastuzumab and pyrotinib plus dalpiciclib in HR-negative/HER2-positive breast cancer: an exploratory, open-label phase II study.
- Case Report: Co-existence of gene fusion and variants in myeloid neoplasm with persistent leukocytosis, large splenomegaly, and eosinophilia.
- Mechanism by which Icariin suppresses pulmonary metastasis in triple-negative breast cancer through downregulation of the SPDL1/JAK2/STAT3 signaling pathway.
- Engineered bacterial extracellular vesicles mediate pyroptosis to counteract mA methylation-based immunosuppression after insufficient radiofrequency ablation of hepatocellular carcinoma.
- Hepatocellular Carcinoma Presenting as Bile Duct Tumor Thrombus without Obvious Intrahepatic Mass.