CT perfusion-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease.

Abstract

Purpose:

To build CT perfusion (CTP)-based delta-radiomics models to identify collateral vessel formation after revascularization in patients with moyamoya disease (MMD).

Methods:

Fifty-three MMD patients who underwent CTP and digital subtraction angiography (DSA) examination were retrospectively enrolled. Patients were divided into good and poor groups based on postoperative DSA. CTP parameters, such as mean transit time (MTT), time to drain (TTD), time to maximal plasma concentration (Tmax), and flow extraction product (FE), were obtained. CTP efficacy in evaluating surgical treatment were compared between the good and poor groups. The changes in the relative CTP parameters (ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE) were calculated to evaluate the differences between pre- and postoperative CTP values. CTP parameters were selected to build delta-radiomics models for identifying collateral vessel formation. The identification performance of machine learning classifiers was assessed using area under the receiver operating characteristic curve (AUC).

Results:

Of the 53 patients, 36 (67.9%) and 17 (32.1%) were divided into the good and poor groups, respectively. The postoperative changes of ΔrMTT, ΔrTTD, ΔrTmax, and ΔrFE in the good group were significantly better than the poor group (p < 0.05). Among all CTP parameters in the perfusion improvement evaluation, the ΔrTTD had the largest AUC (0.873). Eleven features were selected from the TTD parameter to build the delta-radiomics model. The classifiers of the support vector machine and k-nearest neighbors showed good diagnostic performance with AUC values of 0.933 and 0.867, respectively.

Conclusion:

The TTD-based delta-radiomics model has the potential to identify collateral vessel formation after the operation.

• Authors: Jizhen Li, Yan Zhang, Di Yin, Hui Shang, Kejian Li, Tianyu Jiao, Caiyun Fang, Yi Cui, Ming Liu, Jun Pan, Qingshi Zeng
• Journal: Frontiers in neuroscience (Front Neurosci) Vol. 16 Pg. 974096 ( 2022) ISSN: 1662-4548 [Print] Switzerland
• PMID: 36033623 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Li, Zhang, Yin, Shang, Li, Jiao, Fang, Cui, Liu, Pan and Zeng.