Prognostication of colorectal cancer liver metastasis by CE-based radiomics and machine learning.
Transl Oncol
; 47: 101997, 2024 Sep.
Article
in En
| MEDLINE
| ID: mdl-38889522
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Transl Oncol
Year:
2024
Document type:
Article