Published online Feb 27, 2024. doi: 10.4240/wjgs.v16.i2.345
Peer-review started: December 4, 2023
First decision: December 17, 2023
Revised: January 1, 2024
Accepted: January 29, 2024
Article in press: January 29, 2024
Published online: February 27, 2024
Although accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult, radiomics is considered an important option for studying pro
To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography (CT) image radiomics.
This study included 181 stage II/III gastric cancer patients, 141 from Lichuan People's Hospital, and 40 from the Cancer Imaging Archive (TCIA). Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest (ROI), and approximately 1700 radiomics features were extracted from each ROI. The skeletal muscle index (SMI) and skeletal muscle density (SMD) were measured using CT images from the lower margin of the third lumbar vertebra. Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation, 36 radiomics features were identified as important predictors, and the OS-associated CT image radiomics score (OACRS) was cal
Patients with a high OACRS had a poorer prognosis than those with a low OACRS score (P < 0.05) and those in the TCIA cohort. Univariate and multivariate analyses revealed that OACRS was a risk factor [RR = 3.023 (1.896-4.365), P < 0.001] independent of SMI, SMD, and pathological features. Moreover, OACRS outperformed SMI and SMD and could improve OS prediction (P < 0.05).
A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.
Core Tip: We investigated 141 patients with locally advanced gastric cancer and developed the overall survival-associated computed tomography image radiomics score (OACRS) using machine learning and radiomics. The data revealed that OACRS was associated with overall survival (OS) in these patients, in addition to OS in the Cancer Imaging Archive cohort. Importantly, OACRS could improve the predicted accuracy of OS.