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Published in: Journal of Translational Medicine 1/2019

Open Access 01-12-2019 | Metastasis | Research

Pretreatment risk management of a novel nomogram model for prediction of thoracoabdominal extrahepatic metastasis in primary hepatic carcinoma

Authors: Jia Hu, Ting Wang, Kun-He Zhang, Yi-Ping Jiang, Song Xu, Si-Hai Chen, Yu-Ting He, Hai-Liang Yuan, Yu-Qi Wang

Published in: Journal of Translational Medicine | Issue 1/2019

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Abstract

Background

Extrahepatic metastasis is the independent risk factor of poor survival of primary hepatic carcinoma (PHC), and most occurs in the chest and abdomen. Currently, there is still no available method to predict thoracoabdominal extrahepatic metastasis in PHC. In this study, a novel nomogram model was developed and validated for prediction of thoracoabdominal extrahepatic metastasis in PHC, thereby conducted individualized risk management for pretreatment different risk population.

Methods

The nomogram model was developed in a primary study that consisted of 330 consecutive pretreatment patients with PHC. Large-scale datasets were extracted from clinical practice. The nomogram was based on the predictors optimized by data dimension reduction through Lasso regression. The prediction performance was measured by the area under the receiver operating characteristic (AUROC), and calibrated to decrease the overfit bias. Individualized risk management was conducted by weighing the net benefit of different risk population via decision curve analysis. The prediction performance was internally and independently validated, respectively. An independent-validation study using a separate set of 107 consecutive patients.

Results

Four predictors from 55 high-dimensional clinical datasets, including size, portal vein tumor thrombus, infection, and carbohydrate antigen 125, were incorporated to develop a nomogram model. The nomogram demonstrated valuable prediction performance with AUROC of 0.830 (0.803 in internal-validation, and 0.773 in independent-validation, respectively), and fine calibration. Individual risk probability was visually scored. Weighing the net benefit, threshold probability was classified for three-independent risk population, which was < 19.9%, 19.9–71.8% and > 71.8%, respectively. According to this classification, pretreatment risk management was based on a treatment-flowchart for individualized clinical decision-making.

Conclusions

The proposed nomogram is a useful tool for pretreatment risk management of thoracoabdominal extrahepatic metastasis in PHC for the first time, and may handily facilitate timely individualized clinical decision-making for different risk population.
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Metadata
Title
Pretreatment risk management of a novel nomogram model for prediction of thoracoabdominal extrahepatic metastasis in primary hepatic carcinoma
Authors
Jia Hu
Ting Wang
Kun-He Zhang
Yi-Ping Jiang
Song Xu
Si-Hai Chen
Yu-Ting He
Hai-Liang Yuan
Yu-Qi Wang
Publication date
01-12-2019
Publisher
BioMed Central
Keyword
Metastasis
Published in
Journal of Translational Medicine / Issue 1/2019
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-019-1861-z

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