| Autor: |
Xijun Luo, Hui Deng, Fei Xie, Liyan Wang, Junjie Liang, Xianjun Zhu, Tao Li, Xingkui Tang, Weixiong Liang, Zhiming Xiang, Jialin He |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Translational Oncology, Vol 47, Iss , Pp 101997- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1936-5233 |
| DOI: |
10.1016/j.tranon.2024.101997 |
| Popis: |
The liver is the most common organ for the formation of colorectal cancer metastasis. Non-invasive prognostication of colorectal cancer liver metastasis (CRLM) may better inform clinicians for decision-making. Contrast-enhanced computed tomography images of 180 CRLM cases were included in the final analyses. Radiomics features, including shape, first-order, wavelet, and texture, were extracted with Pyradiomics, followed by feature engineering by penalized Cox regression. Radiomics signatures were constructed for disease-free survival (DFS) by both elastic net (EN) and random survival forest (RSF) algorithms. The prognostic potential of the radiomics signatures was demonstrated by Kaplan-Meier curves and multivariate Cox regression. 11 radiomics features were selected for prognostic modelling for the EN algorithm, with 835 features for the RSF algorithm. Survival heatmap indicates a negative correlation between EN or RSF risk scores and DFS. Radiomics signature by EN algorithm successfully separates DFS of high-risk and low-risk cases in the training dataset (log-rank test: p < 0.01, hazard ratio: 1.45 (1.07–1.96), p < 0.01) and test dataset (hazard ratio: 1.89 (1.17–3.04), p < 0.05). RSF algorithm shows a better prognostic implication potential for DFS in the training dataset (log-rank test: p < 0.001, hazard ratio: 2.54 (1.80–3.61), p < 0.0001) and test dataset (log-rank test: p < 0.05, hazard ratio: 1.84 (1.15–2.96), p < 0.05). Radiomics features have the potential for the prediction of DFS in CRLM cases. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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