Dataset Information

A novel nomogram to predict survival of patients with hepatocellular carcinoma after transarterial chemoembolization: a tool for retreatment decision making.

ABSTRACT:

Background

There is still no standardized policy regarding how to identify patients who are not benefiting from transarterial chemoembolization (TACE). We aimed to establish and validate a nomogram model to predict the survival rate of hepatocellular carcinoma (HCC) patients after TACE.

Methods

A total of 578 HCC patients undergoing initial TACE at the First Affiliated Hospital of Wenzhou Medical University were retrospectively recruited to the study. The patients were randomly divided into 2 cohorts: a training cohort (n=405) and a validation cohort (n=173). To develop the nomogram, Cox regression analyses were used to identify independent risk factors. The performances of the nomogram were assessed by concordance index (C-index), calibration curves, and decision curve analysis (DCA), and were compared to 4 developed prognostic models.

Results

We used 5 independent risk factors including postoperative albumin-bilirubin (ALBI) grade, tumor diameter, number of tumors, portal vein invasion, and tumor response to develop the nomogram. Calibration curves showed consistency between the nomogram and the actual observation. The C-index of the nomogram was 0.753 [95% confidence interval (CI): 0.722, 0.784], which was higher than the other prognostic models (P<0.001). The DCA showed that the nomogram had the highest net benefit among the models. According to predicted survival risk, the nomogram could divide patients into 3 groups (P<0.001). All the results were verified in the validation cohort.

Conclusions

This study developed and validated a nomogram model for HCC patients undergoing TACE, which could predict the survival rate and provide support for further treatment strategies.

SUBMITTER: Li J

PROVIDER: S-EPMC9929754 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel nomogram to predict survival of patients with hepatocellular carcinoma after transarterial chemoembolization: a tool for retreatment decision making.

Li Jie J Li Chengjun C Zhu Guoqing G Yang Junhui J Zhang Yunjie Y Yu Zhijie Z Xia Jinglin J

Annals of translational medicine 20230113 2

<h4>Background</h4>There is still no standardized policy regarding how to identify patients who are not benefiting from transarterial chemoembolization (TACE). We aimed to establish and validate a nomogram model to predict the survival rate of hepatocellular carcinoma (HCC) patients after TACE.<h4>Methods</h4>A total of 578 HCC patients undergoing initial TACE at the First Affiliated Hospital of Wenzhou Medical University were retrospectively recruited to the study. The patients were randomly di ...[more]

PMID: 36819596

Similar Datasets

Project description:(1) Background: Conventional transarterial chemoembolization (cTACE) is the mainstay treatment for patients with Barcelona Clinic Liver Cancer (BCLC) B-stage hepatocellular carcinoma (HCC). However, BCLC B-stage patients treated with cTACE represent a prognostically heterogeneous population. We aim to develop and validate a lipiodol-deposition-based nomogram for predicting the long-term survival of BCLC B-stage HCC patients after sequential cTACE. (2) Methods: In this retrospective study, 229 intermediate-stage HCC patients from two hospitals were separately allocated to a training cohort (n = 142) and a validation cohort (n = 87); these patients underwent repeated TACE (≥4 TACE sessions) between May 2010 and May 2017. Lipiodol deposition was assessed by semiautomatic volumetric measurement with multidetector computed tomography (MDCT) before cTACE and was characterized by two ordinal levels: ≤50% (low) and >50% (high). A clinical lipiodol deposition nomogram was constructed based on independent risk factors identified by univariate and multivariate Cox regression analyses, and the optimal cutoff points were obtained. Prediction models were assessed by time-dependent receiver-operating characteristic curves, calibration curves, and decision curve analysis. (3) Results: The median number of TACE sessions was five (range, 4-7) in both cohorts. Before the TACE-3 sessions, the newly constructed nomogram based on lipiodol deposition achieved desirable diagnostic performance in the training and validation cohorts with AUCs of 0.72 (95% CI, 0.69-0.74) and 0.71 (95% CI, 0.68-0.73), respectively, and demonstrated higher predictive ability compared with previously published prognostic models (all p < 0.05). The prognostic nomogram obtained good clinical usefulness in predicting the patient outcomes after TACE. (4) Conclusions: Based on each pre-TACE lipiodol deposition, two sessions are recommended before abandoning cTACE or combining treatment for patients with intermediate-stage HCC. Furthermore, the nomogram based on pre-TACE-3 lipiodol deposition can be used to predict the prognoses of patients with BCLC B-stage HCC.

Project description:Patients with HCC receiving TACE have various clinical outcomes. Several prognostic models have been proposed to predict clinical outcomes for patients with hepatocellular carcinomas (HCC) undergoing transarterial chemoembolization (TACE), but establishing an accurate prognostic model remains necessary. We aimed to develop a radiomics signature from pretreatment CT to establish a combined radiomics-clinic (CRC) model to predict survival for these patients. We compared this CRC model to the existing prognostic models in predicting patient survival. This retrospective study included multicenter data from 162 treatment-naïve patients with unresectable HCC undergoing TACE as an initial treatment from January 2007 and March 2017. We randomly allocated patients to a training cohort (n = 108) and a testing cohort (n = 54). Radiomics features were extracted from intra- and peritumoral regions on both the arterial phase and portal venous phase CT images. A radiomics signature (Rad-signature) for survival was constructed using the least absolute shrinkage and selection operator method in the training cohort. We used univariate and multivariate Cox regressions to identify associations between the Rad- signature and clinical factors of survival. From these, a CRC model was developed, validated, and further compared with previously published prognostic models including four-and-seven criteria, six-and-twelve score, hepatoma arterial-embolization prognostic scores, and albumin-bilirubin grade. The CRC model incorporated two variables: The Rad-signature (composed of features extracted from intra- and peritumoral regions on the arterial phase and portal venous phase) and tumor number. The CRC model performed better than the other seven well-recognized prognostic models, with concordance indices of 0.73 [95% confidence interval (CI) 0.68-0.79] and 0.70 [95% CI 0.62-0.82] in the training and testing cohorts, respectively. Among the seven models tested, the six-and-12 score and four-and-seven criteria performed better than the other models, with C-indices of 0.64 [95% CI 0.58-0.70] and 0.65 [95% CI 0.55-0.75] in the testing cohort, respectively. The CT radiomics signature represents an independent biomarker of survival in patients with HCC undergoing TACE, and the CRC model displayed improved predictive performance.

Dataset Information

A novel nomogram to predict survival of patients with hepatocellular carcinoma after transarterial chemoembolization: a tool for retreatment decision making.

Background

Methods

Results

Conclusions

Publications

A novel nomogram to predict survival of patients with hepatocellular carcinoma after transarterial chemoembolization: a tool for retreatment decision making.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets