Computational Decision Support for PE Diagnosis based on Ventilation Perfusion Ratio.

Abstract

AIM The aim of this study is to investigate whether computer-aided, semi-automated 3D lung lobe quantification can support decision-making on PE diagnosis based on the ventilation-perfusion ratio in clinical practice. METHODS A study cohort of 100 patients (39 male, 61 female, age 64.8±15.8 years) underwent ventilation/perfusion single photon emission computed tomography (V/Q-SPECT/CT) to exclude acute PE on SPECT/CT OPTIMA NM/CT 640 (GE Healthcare). Two 3D lung lobe quantification software tools (Q. Lung: Xeleris 4.0, GE Healthcare and LLQ: Hermes Hybrid 3D Lung Lobar Quantification, Hermes Medical Solutions) were used to evaluate the numerical lobar ventilation/perfusion ratio (VQR) and lobar volume/perfusion ratio (VPR). A test of linearity and equivalence of the two 3D software tools was performed using Pearson, Spearman, quadratic weighted kappa and the mean squared deviation for VPR/VQR. An algorithm was developed that identified PE candidates using ROC analysis. The agreement between the PE findings of an experienced nuclear medicine expert and the calculated PE candidates was represented by the magnitude of the YOUDEN index (J) and the size of the area under the receiver operating curve (AUC). RESULTS Both 3D software tools showed good comparability. The YOUDEN index for QLUNG(VPR/VQR)/LLQ(VPR/VQR) was in the range from 0.2 to 0.5. The mean AUC averaged over all lung lobes for QLUNG(VPR) was 0.66, CI95%: ±14.0%, for QLUNG(VQR) 0.66, CI95%: ±13.3%, for LLQ(VPR) 0.64, CI95%: ±14.7% and for LLQ(VQR) 0.65, CI95%: ±13.1%. CONCLUSION This study reveals that 3D software tools are feasible for numerical PE detection. The clinical decision can be supported by using a numerical algorithm based on ROC analysis.