European journal of radiology · 2025
Predicting tumor response to TACE plus lenvatinib and PD-1 inhibitors for unresectable HCC: A multicenter observational study.
СтатьяМногоцентровое исследованиеНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Deng Li-Wei, Xie Qing-Yun, Peng Bo, Zhao Yong, Liu Bin, Feng Shi-Feng, Liu Dong-Xu, Sun Yan-Yuan, Wang Hai-Qing, Liao Chang-Li, Wang Yan-Ling, Liu Jun-Feng, Zhang Chi, Chen Yan, Xu Guo-Hui, Liu Le, Cao Lei, Wei Guo, Ren Yi, Yang Xue-Gang
Аннотация
OBJECTIVES: Preoperatively identifying patients with unresectable hepatocellular carcinoma (uHCC) who are likely to achieve an objective response to the treatment regimen of transarterial chemoembolization (TACE) plus lenvatinib and programmed death-1 inhibitors (TLP) remains challenging. We aimed to develop and validate a predictive model for tumor response to TLP treatment in patients with uHCC.
MATERIALS AND METHODS: Patients with uHCC who received TLP treatment were divided into training (n = 107), internal validation (n = 46), and external validation (n = 52) cohorts. A nomogram model was developed based on the clinical variables of the training cohort using multivariate logistic regression. The performance of this nomogram model was evaluated using the area under the curve (AUC) and calibration curves, and its performance was compared with that of other predictive models.
RESULTS: The Eastern Cooperative Oncology Group performance status, albumin-bilirubin grade, platelet-to-lymphocyte ratio, tumor distribution, and total bilirubin were identified as independent predictors of objective response. These variables were incorporated to develop the EAPTT model. The AUCs of the EAPTT model were 0.84, 0.90, and 0.85 in the training, internal validation, and external validation cohorts, respectively-statistical analysis via the DeLong test showed that these AUCs were significantly higher than those of the other seven predictive models. Stratification of patients into objective responders and non-responders via the EAPTT model revealed statistically significant progression-free survival and overall survival differences between the two groups.
CONCLUSION: The EAPTT model may enable precise stratification of the efficacy of patients with uHCC receiving TLP treatment, serving to assist in identifying the optimal candidates.
MATERIALS AND METHODS: Patients with uHCC who received TLP treatment were divided into training (n = 107), internal validation (n = 46), and external validation (n = 52) cohorts. A nomogram model was developed based on the clinical variables of the training cohort using multivariate logistic regression. The performance of this nomogram model was evaluated using the area under the curve (AUC) and calibration curves, and its performance was compared with that of other predictive models.
RESULTS: The Eastern Cooperative Oncology Group performance status, albumin-bilirubin grade, platelet-to-lymphocyte ratio, tumor distribution, and total bilirubin were identified as independent predictors of objective response. These variables were incorporated to develop the EAPTT model. The AUCs of the EAPTT model were 0.84, 0.90, and 0.85 in the training, internal validation, and external validation cohorts, respectively-statistical analysis via the DeLong test showed that these AUCs were significantly higher than those of the other seven predictive models. Stratification of patients into objective responders and non-responders via the EAPTT model revealed statistically significant progression-free survival and overall survival differences between the two groups.
CONCLUSION: The EAPTT model may enable precise stratification of the efficacy of patients with uHCC receiving TLP treatment, serving to assist in identifying the optimal candidates.