Title:Application of Machine-learning based on Radiomics Features in Differential
Diagnosis of Superficial Lymphadenopathy
Volume: 20
Author(s): Shuyi LYU, Meiwu Zhang, Lifen Yang, Baisong Zhang, Libo Gao, Liu Yang and Yan Zhang*
Affiliation:
- Department of Interventional Therapy, Ningbo NO.2 Hospital, China
- Department of Ultrasound, Zhenhai Hospital of Traditional Chinese Medicine, Ningbo, China
Keywords:
Lymphadenopathy, Ultrasound, Radiomics, Machine learning, Benign lymph node, Lymphoma, Metastatic lymph node.
Abstract:
Objective:
The accurate diagnosis of superficial lymphadenopathy is challenging. We aim to explore a non-invasive and accurate machine-learning method
for distinguishing benign lymph nodes, lymphoma, and metastatic lymph nodes.
Methods:
The clinical data and ultrasound images of 160 patients with superficial lymphadenopathy (58 benign lymph nodes, 62 lymphoma, 40 metastatic
lymph nodes) admitted to our hospital from January 2020 to November 2022 were retrospectively studied. Patients were randomly divided into a
training set and test set according to the ratio of 6:4. Firstly, the radiomics features of each lymph node were extracted, and then a series of
statistical methods were used to avoid over-fitting. Then, the gradient boosting machine(GBM) was used to build the model. The area under
receiver(AUC) operating characteristic curve, precision, recall rate and F1 value were calculated to evaluate the effectiveness of the model.
Results:
Ten robust features were selected to build the model. The AUC values of benign lymph nodes, lymphoma and metastatic lymph nodes in the
training set were 1.00, 0.98 and 0.99, and the AUC values of the test set were 0.96, 0.84 and 0.90, respectively.
Conclusion:
It was a reliable and non-invasive method for the differential diagnosis of lymphadenopathy based on the model constructed by machine learning.
