The CT delta-radiomics based machine learning approach in evaluating multiple primary lung adenocarcinoma.

ABSTRACT:

Object

To evaluate the difference between multiple primary lung adenocarcinoma (MPLA) and solitary primary lung adenocarcinoma (SPLA) by delta-radiomics based machine learning algorithms in CT images.

Methods

A total of 1094 patients containing 268 MPLAs and 826 SPLAs were recruited for this retrospective study between 2014 to 2020. After the segmentation of volume of interest, the radiomic features were automatically calculated. The patients were categorized into the training set and testing set by a random proportion of 7:3. After feature selection, the relevant classifiers were constructed by the machine learning algorithms of Bayes, forest, k-nearest neighbor, logistic regression, support vector machine, and decision tree. The relative standard deviation (RSD) was calcul

SUBMITTER: Ma Y 

PROVIDER: S-EPMC9440600 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature