Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features

ABSTRACT:

Background

Internal tumor motion is commonly predicted using external respiratory signals. However, the internal/external correlation is complex and patient-specific. The purpose of this study was to develop various models based on the radiomic features of computed tomography (CT) images to predict the accuracy of tumor motion tracking using external surrogates and to find accurate and reliable tracking algorithms.

Methods

Images obtained from a total of 108 and 71 patients pathologically diagnosed with lung and liver cancers, respectively, were examined. Real-time position monitoring motion was fitted to tumor motion, and samples with fitting errors greater than 2 mm were considered positive. Radiomic features were extracted from internal target volumes of average intens

SUBMITTER: Li G 

PROVIDER: S-EPMC10006135 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature