Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A
Ying-Hao Xiang1, Huan Mou1, Bo Qu1
1Department of General Surgery, Lichuan People's Hospital, Enshi 445400, Hubei Province, China.
Background:
Although accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult, radiomics is considered an important option for studying prognosis.
Aim:
To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography (CT) image radiomics.
Methods:
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 calculated for each patient using these important predictors.
Results:
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).
Conclusion:
A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.
