European journal of radiology · 2025
Longitudinal changes in tumor morphology and body composition following neoadjuvant chemotherapy predict progression-free survival in adenocarcinoma of the esophagogastric junction: A multi-center study.
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Luo Chenglong, Mu Wanling, Zhang Haojie, Meng Xinhua, Zhang Youxin, Mu Yusai, Yuan Mengchen, Zhou Yue, Li Liming, Ding Changmao, Chen Xuejun, Li Ming, Li Jing, Gao Jianbo
Аннотация
OBJECTIVES: This study aimed to investigate the predictive value of longitudinal changes in tumor morphology and body composition during neoadjuvant chemotherapy (NAC) for progression-free survival (PFS) following radical resection of adenocarcinoma of the esophagogastric junction (AEG).
METHODS: This retrospective study included 258 AEG patients receiving NAC at three hospitals. Clinical and pathological data were collected. Tumor morphological and body composition parameters were quantitatively assessed on venous phase CT images at pre-treatment (Pre) and post-treatment (Post) time points. The two measurements were compared and the reduction rate (Δ%) was calculated. Multivariate Cox regression analysis was used to identify independent predictors of PFS in AEG patients and a nomogram model was developed. The incremental predictive value of tumor morphological and body composition parameters was evaluated using the concordance index (C-index), net reclassification improvement, and integrated discrimination improvement. The goodness-of-fit of models was assessed via the Akaike information criterion and χ2 likelihood ratio test. The performance of the nomogram was evaluated by the area under the time-dependent receiver operating characteristic (tdROC) curve, calibration curves, and decision curve analysis. High-risk and low-risk subgroup analyses were performed according to nomogram scores.
RESULTS: Multivariate Cox regression analysis showed that ypTNM staging, Post-tumor volume, and Δ%-skeletal muscle index (SMI) were independent predictors of PFS. The nomogram incorporating these predictors demonstrated significantly superior discrimination over ypTNM staging alone in both the training cohort (C-index: 0.744; 95 % CI: 0.670-0.790; P = 0.004) and an external validation cohort (C-index: 0.738; 95 % CI: 0.615-0.807; P = 0.024). tdROC analysis showed that the nomogram achieved area under the curve (AUC) values of 0.815 and 0.791 for predicting 1- and 2-year PFS, respectively, in the training cohort. These findings were corroborated in the external validation cohort, with corresponding AUCs of 0.761 and 0.746 for 1- and 2-year PFS, respectively. Moreover, according to the score of the nomogram, patients can be effectively divided into low-risk and high-risk groups.
CONCLUSION: The nomogram, incorporating ypTNM staging, Post-tumor volume, and Δ%-SMI, demonstrated robust performance in predicting PFS in AEG patients. This model significantly outperformed traditional ypTNM staging alone and may help guide personalized postoperative monitoring strategies.
METHODS: This retrospective study included 258 AEG patients receiving NAC at three hospitals. Clinical and pathological data were collected. Tumor morphological and body composition parameters were quantitatively assessed on venous phase CT images at pre-treatment (Pre) and post-treatment (Post) time points. The two measurements were compared and the reduction rate (Δ%) was calculated. Multivariate Cox regression analysis was used to identify independent predictors of PFS in AEG patients and a nomogram model was developed. The incremental predictive value of tumor morphological and body composition parameters was evaluated using the concordance index (C-index), net reclassification improvement, and integrated discrimination improvement. The goodness-of-fit of models was assessed via the Akaike information criterion and χ2 likelihood ratio test. The performance of the nomogram was evaluated by the area under the time-dependent receiver operating characteristic (tdROC) curve, calibration curves, and decision curve analysis. High-risk and low-risk subgroup analyses were performed according to nomogram scores.
RESULTS: Multivariate Cox regression analysis showed that ypTNM staging, Post-tumor volume, and Δ%-skeletal muscle index (SMI) were independent predictors of PFS. The nomogram incorporating these predictors demonstrated significantly superior discrimination over ypTNM staging alone in both the training cohort (C-index: 0.744; 95 % CI: 0.670-0.790; P = 0.004) and an external validation cohort (C-index: 0.738; 95 % CI: 0.615-0.807; P = 0.024). tdROC analysis showed that the nomogram achieved area under the curve (AUC) values of 0.815 and 0.791 for predicting 1- and 2-year PFS, respectively, in the training cohort. These findings were corroborated in the external validation cohort, with corresponding AUCs of 0.761 and 0.746 for 1- and 2-year PFS, respectively. Moreover, according to the score of the nomogram, patients can be effectively divided into low-risk and high-risk groups.
CONCLUSION: The nomogram, incorporating ypTNM staging, Post-tumor volume, and Δ%-SMI, demonstrated robust performance in predicting PFS in AEG patients. This model significantly outperformed traditional ypTNM staging alone and may help guide personalized postoperative monitoring strategies.