Radiomics Based on Single-Phase CTA for Distinguishing Left Atrial Appendage Thrombus from Circulatory Stasis in Patients with Atrial Fibrillation before Ablation.
Xue LiYuyan CaiXiaoyi ChenYue MingWenzhang HeJing LiuHuaxia PuXinyue ChenLi-Qing PengPublished in: Diagnostics (Basel, Switzerland) (2023)
Differentiation of left atrial appendage thrombus (LAAT) and left atrial appendage (LAA) circulatory stasis is difficult when based only on single-phase computed tomography angiography (CTA) in routine clinical practice. Radiomics provides a promising tool for their identification. We retrospectively enrolled 204 (training set: 144; test set: 60) atrial fibrillation patients before ablation, including 102 LAAT and 102 circulatory stasis patients. Radiomics software was used to segment whole LAA on single-phase CTA images and extract features. Models were built and compared via a multivariable logistic regression algorithm and area under of the receiver operating characteristic curves (AUCs), respectively. For the radiomics model, radiomics clinical model, radiomics radiological model, and combined model, the AUCs were 0.82, 0.86, 0.90, 0.93 and 0.82, 0.82, 0.84, 0.85 in the training set and the test set, respectively ( p < 0.05). One clinical feature (rheumatic heart disease) and four radiological features (transverse diameter of left atrium, volume of left atrium, location of LAA, shape of LAA) were added to the combined model. The combined model exhibited excellent differential diagnostic performances between LAAT and circulatory stasis without increasing extra radiation exposure. The single-phase, CTA-based radiomics analysis shows potential as an effective tool for accurately detecting LAAT in patients with atrial fibrillation before ablation.
Keyphrases
- left atrial appendage
- atrial fibrillation
- catheter ablation
- lymph node metastasis
- end stage renal disease
- contrast enhanced
- clinical practice
- ejection fraction
- deep learning
- chronic kidney disease
- extracorporeal membrane oxygenation
- newly diagnosed
- heart failure
- machine learning
- prognostic factors
- left atrial
- peritoneal dialysis
- magnetic resonance imaging
- oxidative stress
- magnetic resonance
- computed tomography
- pulmonary hypertension
- inferior vena cava
- vena cava
- pulmonary artery
- pulmonary arterial hypertension
- pulmonary embolism
- acute coronary syndrome
- convolutional neural network