Title:A Preoperative Prediction Model for Lymph Node Metastasis in Patients with
Gastric Cancer using a Machine Learning-based Ultrasomics Approach
Volume: 20
Author(s): Weiwei Lin, Qi Zhong, Jingjing Guo, Shanshan Yu, Kunhuang Li, Qingling Shen, Minling Zhuo, EnSheng Xue, Peng Lin and Zhikui Chen*
Affiliation:
- Department of Ultrasound, Fujian Medical University Union Hospital, Fuzhou, China
Keywords:
Gastric cancer, Lymph node metastasis, Ultrasomics, Machine learning, Non-invasive, Radiomics.
Abstract:
Objective:
This study aims to develop an ultrasomics model for predicting lymph node metastasis in patients with gastric cancer (GC).
Methods:
This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was
performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature
normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least
absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC),
sensitivity, specificity, and the corresponding 95% confidence intervals (CIs).
Results:
A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The
patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19
radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate
performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95% CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802,
95% CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and
actual lymph node metastasis status.
Conclusion:
Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering
implications for personalized therapy approaches.
