# CT-based multi-regional radiomics model for predicting contrast medium extravasation in patients with tumors: A case-control study

## TL;DR

This study creates a radiomics model using non-contrast CT scans to better predict contrast medium extravasation in tumor patients compared to traditional clinical models.

## Contribution

The novel contribution is a multi-regional radiomics model using non-contrast CT images to predict contrast medium extravasation in tumor patients.

## Key findings

- The radiomics model outperformed the clinical model in predicting contrast medium extravasation across multiple cohorts.
- An integrated model combining radiomics and clinical factors achieved higher accuracy (AUC up to 0.945) in predicting extravasation.
- Calibration and decision curve analysis confirmed the model's high accuracy and clinical utility.

## Abstract

To develop a non-contrast CT based multi-regional radiomics model for predicting contrast medium (CM) extravasation in patients with tumors.

A retrospective analysis of non-contrast CT scans from 282 tumor patients across two medical centers led to the development of a radiomics model, using 157 patients for training, 68 for validation, and 57 from an external center as an independent test cohort. The different volumes of interest from right common carotid artery/right internal jugular vein, right subclavian artery/vein and thoracic aorta were delineated. Radiomics features from the training cohort were used to calculate radiomics scores (Rad scores) and develop radiomics model. Non-contrast CT radiomics features were combined with clinical factors to develop an integrated model. A nomogram was created to visually represent the integration of radiomic signatures and clinical factors. The model’s predictive performance and clinical utility were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA), respectively. Calibration curves were also used to assess the concordance between the model-predicted probabilities and the observed event probabilities.

Thirteen radiomics features were selected to determine the Rad score. The radiomic model outperformed the clinical model in the training, validation, and external test cohorts, achieving a greater area under the ROC curve (AUC) with values of 0.877, 0.866, 0.828 compared to the clinical model’s 0.852, 0.806, 0.740. The combined model yielded better AUC of 0.945, 0.911, and 0.869 in the respective cohorts. The nomogram identified females, the elderly, individuals with hypertension, long term chemotherapy, radiomic signatures as independent risk factors for CM extravasation in patients with tumors. Calibration and DCA validated the high accuracy and clinical utility of this model.

Radiomics models based on multi-regional non-contrast CT image offered improved prediction of CM extravasation compared with clinical model alone.