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Primary research
Zhi yun Yang
Capital Medical University Affiliated Beijing Ditan Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2965-2339
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Purpose
Hepatocellular carcinoma (HCC) patients with alpha-fetoprotein (AFP)-negative (<8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of HCC patients with AFP-negative.
Materials and Methods
A total of 410 AFP-negative patients with clinical diagnosed with HCC as a primary cohort; 148 AFP-negative HCC patients as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by univariate and multivariate Cox hazard analysis were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort.
Results
The C-index of nomogram1was 0.708 (95%CI: 0.673-0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606-0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690-0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691-0.813; AUC: 0.784, 95%CI: 0.709-0.847) and good calibration. The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively.
Conclusions
Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-negative HCC. This model could help AFP-negative HCC facilitate a personalized prognostic evaluation.
Keywords
hepatocellular carcinoma, nomogram, prognosis, LASSO Cox regression
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Primary research
Zhi yun Yang
Capital Medical University Affiliated Beijing Ditan Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2965-2339
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 01 Aug, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Purpose
Hepatocellular carcinoma (HCC) patients with alpha-fetoprotein (AFP)-negative (<8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of HCC patients with AFP-negative.
Materials and Methods
A total of 410 AFP-negative patients with clinical diagnosed with HCC as a primary cohort; 148 AFP-negative HCC patients as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by univariate and multivariate Cox hazard analysis were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort.
Results
The C-index of nomogram1was 0.708 (95%CI: 0.673-0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606-0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690-0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691-0.813; AUC: 0.784, 95%CI: 0.709-0.847) and good calibration. The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively.
Conclusions
Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-negative HCC. This model could help AFP-negative HCC facilitate a personalized prognostic evaluation.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
The full text of this article is available to read as a PDF.
This is a list of supplementary files associated with this preprint. Click to download.
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