Development and validation of prognostic nomograms in patients with hepatocellular carcinoma: a population-based study

Zang, Youya; Long, Peiyun; Wang, Ming; Huang, Shan; Chen, Chuang

doi:10.2217/fon-2020-1065

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…AST and ALP have been recognized as independent risk factors for the early recurrence of cHCC-CC. Previous studies on early recurrence of HCC also highlight the independent influence of AST [27,28] and ALP [29,30] as contributing factors. Similarly, MVI has been established as a strong and independent predictor of early postoperative recurrence of cHCC-CC [12,13,15,22], likely because it is associated with the cytokines and proteins secreted by stromal cells in the tumor microenvironment that promote angiogenesis.…”

Section: Discussionmentioning

confidence: 98%

Nomogram based on clinical and preoperative CT features for predicting the early recurrence of combined hepatocellular-cholangiocarcinoma: a multicenter study

Zheng,

Gu,

Huang

et al. 2023

Radiol med

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Purpose To establish and validate a multiparameter prediction model for early recurrence after radical resection in patients diagnosed with combined hepatocellular-cholangiocarcinoma (cHCC-CC). Materials and methods This study reviewed the clinical characteristics and preoperative CT images of 143 cHCC-CC patients who underwent radical resection from three institutions. A total of 110 patients from institution 1 were randomly divided into training set (n = 78) and testing set (n = 32) in the ratio of 7–3. Univariate and multivariate logistic regression analysis were used to construct a nomogram prediction model in the training set, which was internally and externally validated in the testing set and the validation set (n = 33) from institutions 2 and 3. The area under the curve (AUC) of receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration analysis were used to evaluate the model’s performance. Results The combined model demonstrated superior predictive performance compared to the clinical model, the CT model, the pathological model and the clinic-CT model in predicting the early postoperative recurrence. The nomogram based on the combined model included AST, ALP, tumor size, tumor margin, arterial phase peritumoral enhancement, and MVI (Microvascular invasion). The model had AUCs of 0.89 (95% CI 0.81–0.96), 0.85 (95% CI 0.70–0.99), and 0.86 (95% CI 0.72–1.00) in the training, testing, and validation sets, respectively, indicating high predictive power. DCA showed that the combined model had good clinical value and correction effect. Conclusion A nomogram incorporating clinical characteristics and preoperative CT features can be utilized to effectively predict the early postoperative recurrence in patients with cHCC-CC.

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Section: Discussionmentioning

confidence: 98%

Nomogram based on clinical and preoperative CT features for predicting the early recurrence of combined hepatocellular-cholangiocarcinoma: a multicenter study

Zheng,

Gu,

Huang

et al. 2023

Radiol med

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“…Currently, studies have shown that tumor size, tumor number, pathological grading, staging classification, microvascular invasion (MVI), and various biomarkers correlate with patient prognostic outcomes (7)(8)(9)(10). However, this traditional prognostic model, due to its invasive examination, existing comorbidities, and geographical differences in staging classification make its clinical application limited, with the decreased granularity in predicting outcomes, lower accuracy, specificity, and sensitivity, and cannot obtain information on tumor heterogeneity and provide patients with accurate prognostic information to the extent that it affects the clinical decisionmaking treatment process of patients, resulting in poor prognosis and reduced OS (8,(11)(12)(13). In addition, Traditional imaging features, although capable of observing diseaserelated progression, are poorly effective in predicting prognosis.…”

Section: Introductionmentioning

confidence: 99%

Development of a novel combined nomogram model integrating Rad-score, age and ECOG to predict the survival of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization

Liu¹,

Liu²,

Duan³

et al. 2022

J Gastrointest Oncol

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Background: Liver cancer is affecting more and more people's health. Transcatheter arterial chemoembolization (TACE) has become a routine treatment option, but the prognosis of patients is not optimistic. Effectively prediction of prognosis can provide clinicians with an objective basis for patient prognosis and timely adjustment of treatment strategies, thus improving the quality of patient survival.However, the current prediction methods have some limitations. Therefore, this study aims to develop a radiomics nomogram for predicting survival after TACE in patients with advanced hepatocellular carcinoma (HCC). Methods: Seventy advanced HCC patients treated with TACE were enrolled from January 2013 to July 2019. Clinical information included age, sex, and Eastern Cooperative Oncology Group (ECOG) score. Overall survival (OS) was confirmed by postoperative follow-up. Radiomics features were extracted using 3D Slicer (version 4.11.20210226) software, then obtain radiomics signature and calculate radiomics score (Radscore) for each patient. Univariate and multivariate Cox regression were used to analyze the baseline clinical data of patients and establish clinical models. The obtained radiomics signature was incorporated into the clinical model to establish the radiomics nomogram. The predictive performance and calibration ability of the model were assessed by the area under the receiver operating characteristic (ROC) curve (AUC), C-index, and calibration curve.Results: Three significant features were selected from 851 radiomics features by the least absolute shrinkage and selection operator (LASSO) Cox regression model to construct the radiomics signature, and were significantly correlated with overall survival (P<0.001). Rad-score, age, and ECOG score were combined to construct a radiomics nomogram. The AUC, sensitivity, and specificity of the radiomics nomogram were 0.801 (95% CI: 0.693-0.909), 0.822 (95% CI: 0.674-0.915), and 0.720 (95% CI: 0.674-0.915), respectively. The C-index of the radiomics nomogram was 0.700 (95% CI: 0.547-0.853). Calibration curves showed better agreement between the predicted and actual probabilities in the radiomics nomogram among the 3 features. Conclusions:The Rad-score was a strong risk predictor of survival after TACE for HCC patients. The radiomics nomogram might be improved the predictive efficacy of survival after TACE and it may also provide assistance to physicians in making treatment decisions.

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Pre-operative MRI features predict early post-operative recurrence of hepatocellular carcinoma with different degrees of pathological differentiation

Chen

Lin³

et al. 2023

Radiol med

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Purpose To investigate the value of pre-operative gadoxetate disodium (Gd-EOB-DTPA) enhanced MRI predicting early post-operative recurrence (< 2 years) of hepatocellular carcinoma (HCC) with different degrees of pathological differentiation. Methods Retrospective analysis of pre-operative MR imaging features of 177 patients diagnosed as suffering from HCC and that underwent radical resection. Multivariate logistic regression assessment was adopted to assess predictors for HCC recurrence with different degrees of pathological differentiation. The area under the curve (AUC) of receiver operating characteristics (ROC) was utilized to assess the diagnostic efficacy of the predictors. Results Among the 177 patients, 155 (87.5%) were males, 22 (12.5%) were females; the mean age was 49.97 ± 10.71 years. Among the predictors of early post-operative recurrence of highly-differentiated HCC were an unsmooth tumor margin and an incomplete/without tumor capsule (p = 0.037 and 0.033, respectively) whereas those of early post-operative recurrence of moderately-differentiated HCC were incomplete/without tumor capsule, peritumoral enhancement along with peritumoral hypointensity (p = 0.006, 0.046 and 0.004, respectively). The predictors of early post-operative recurrence of poorly-differentiated HCC were peritumoral enhancement, peritumoral hypointensity, and tumor thrombosis (p = 0.033, 0.006 and 0.021, respectively). The AUCs of the multi-predictor diagnosis of early post-operative recurrence of highly-, moderately-, and poorly-differentiated HCC were 0.841, 0.873, and 0.875, respectively. The AUCs of the multi-predictor diagnosis were each higher than for those predicted separately. Conclusions The imaging parameters for predicting early post-operative recurrence of HCC with different degrees of pathological differentiation were different and combining these predictors can improve the diagnostic efficacy of early post-operative HCC recurrence.

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Development and validation of prognostic nomograms in patients with hepatocellular carcinoma: a population-based study

Cited by 3 publications

References 43 publications

Nomogram based on clinical and preoperative CT features for predicting the early recurrence of combined hepatocellular-cholangiocarcinoma: a multicenter study

Nomogram based on clinical and preoperative CT features for predicting the early recurrence of combined hepatocellular-cholangiocarcinoma: a multicenter study

Development of a novel combined nomogram model integrating Rad-score, age and ECOG to predict the survival of patients with hepatocellular carcinoma treated by transcatheter arterial chemoembolization

Pre-operative MRI features predict early post-operative recurrence of hepatocellular carcinoma with different degrees of pathological differentiation

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