Radiomics of lung ventilation/perfusion tomographic imaging in pulmonary embolism diagnosis.

Purpose: The aim of this study was to develop a machine learning model (named V/P-mics) to identify pulmonary embolism based on lung ventilation/perfusion single-photon emission tomography (V/P-SPECT) images.

Methods: We retrospectively collected the data of 260 patients from one hospital who underwent V/P-SPECT. Patients were randomly assigned to training and testing groups in a 7:3 ratio. We created an internal further validation group using data of an additional 35 patients from the same hospital, and an external further validation group using data of 30 patients from another hospital. We constructed 35 models and selected one for further optimization. The generalizability of V/P-mics was proven by comparing the area under the curve (AUC) of the testing group, internal and external further validation groups. The diagnostic accuracy and efficiency of V/P-mics was compared with that of nuclear physicians.

Results: V/P-mics showed excellent generalizability, with no statistical difference in AUC among the testing, internal further validation, and external further validation groups (0.938 vs. 0.923 vs. 0.990, all P values > 0.05). The AUC of V/P-mics was close to that of the senior physician (0.923 vs. 0.975, P = 0.332), but significantly higher than the junior physician (0.923 vs. 0.725, P = 0.050). Furthermore, V/P-mics significantly shortened the diagnosis time as compared to the junior physician (100 ± 16 s vs. 240 ± 37 s, P = 0.001).

Conclusion: The V/P-mics had good discrimination and generalizability and significantly shortened the diagnosis time for patients with pulmonary embolism. Of note, the model showed excellent interpretability.