Bibliographic Details
| Title: |
Nomogram established on account of Lasso-Cox regression for predicting recurrence in patients with early-stage hepatocellular carcinoma |
| Authors: |
Qi Wang, Wenying Qiao, Honghai Zhang, Biyu Liu, Jianjun Li, Chaoran Zang, Tingting Mei, Jiasheng Zheng, Yonghong Zhang |
| Source: |
Frontiers in Immunology, Vol 13 (2022) |
| Publisher Information: |
Frontiers Media S.A., 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Immunologic diseases. Allergy |
| Subject Terms: |
nomogram, Lasso-Cox regression, hepatocellular carcinoma, prediction, recurrence, Immunologic diseases. Allergy, RC581-607 |
| More Details: |
PurposeTo investigate the risk factors for recurrence in patients with early-stage hepatocellular carcinoma (HCC) after minimally invasive treatment with curative intent, then to construct a prediction model based on Lasso-Cox regression and visualize the model built.MethodsClinical data were collected from 547 patients that received minimally invasive treatment in our hospital from January 1, 2012, to December 31, 2016. Lasso regression was used to screen risk factors for recurrence. Then we established Cox proportional hazard regression model and random survival forest model including several parameters screened by Lasso regression. An optimal model was selected by comparing the values of C-index, then the model was visualized and the nomogram was finally plotted.ResultsThe variables screened by Lasso regression including age, gender, cirrhosis, tumor number, tumor size, platelet-albumin-bilirubin index (PALBI), and viral load were incorporated in the Cox model and random survival forest model (P |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1664-3224 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fimmu.2022.1019638/full; https://doaj.org/toc/1664-3224 |
| DOI: |
10.3389/fimmu.2022.1019638 |
| Access URL: |
https://doaj.org/article/6420bcfe03964eb3b1c2c9c74dec3685 |
| Accession Number: |
edsdoj.6420bcfe03964eb3b1c2c9c74dec3685 |
| Database: |
Directory of Open Access Journals |
