# Prognostic potential of radiomics evaluation of lung artery thrombus for pulmonary embolism patients

## TL;DR

This study explores how radiomics features from CT scans of lung artery thrombus can predict mortality and troponin levels in pulmonary embolism patients, offering potential for improved prognosis and personalized treatment.

## Contribution

The novel contribution is the evaluation of radiomics features as a prognostic tool for pulmonary embolism patients using machine learning methods.

## Key findings

- Radiomics features achieved an accuracy of 0.967 in predicting patient outcomes.
- ReliefF, Logistic Regression, and CART Classification were top-performing feature selection methods.
- Firstorder, Shape, and GLCM radiomics features were most frequently selected for analysis.

## Abstract

This study evaluates radiomics correlation with mortality and suitability as prognostic indicator for troponin for pulmonary embolism to enhance prognostic accuracy and guide personalized treatment strategies with the help of machine learning. We conducted an initial study focusing on texture information of the arterial thrombus.

Computed tomography (CT) of the lung from 86 patients with pulmonary embolism was used. As target variables, we used patients 30-day mortality and troponin results. Each arterial thrombus was manually segmented. After the extraction of their radiomics features and the reduction via correlation analysis and 12 feature selection methods, these and the target variables were given to 12 different classification methods to record the accuracies (Acc.), F1-scores (F1) and ROC curve areas under the curve (AUC) for comparison and evaluation.

The resulting accuracy achieved was 0.967, the F1-score 0.973 for class 0 and 0.967 for class 1 and the AUC around 0.9686. The feature selection methods which resulted in the highest results were ReliefF (RF), Logistic Regression (LOR) and CART Classification (CARTC). For the classification methods, Support Vector Machines (SVM), eXtreme Gradiant Boosting (XGB) and Ensemble Bagged Trees (EBT) lead to the highest results. Firstorder, Shape and gray-level co-occurrence matrix (GLCM) were the most selected radiomics feature classes.

Within this study, we conducted radiomics feature extraction within a medical image data analysis pipeline with subsequent correlation analysis and training of classifiers for patients with pulmonary lung embolism. We could show that the radiomics features correlated with patient’s morphology as well as troponin range with an accuracy of 0.967 and 0.9302, respectively, yield high potential for prognosis and treatment strategy of pulmonary embolism patients in the future.