Enhanced classification performance using deep learning based segmentation for pulmonary embolism detection in CT angiography.

PURPOSE

To develop a deep learning-based algorithm that automatically and accurately classifies patients as either having pulmonary emboli or not in CT pulmonary angiography (CTPA) examinations.

MATERIALS AND METHODS

For model development, 700 CTPA examinations from 652 patients performed at a single institution were used, of which 149 examinations contained 1497 PE traced by radiologists. The nnU-Net deep learning-based segmentation framework was trained using 5-fold cross-validation. To enhance classification, we applied logical rules based on PE volume and probability thresholds. External model evaluation was performed in 770 and 34 CTPAs from two independent datasets.

RESULTS

A total of 1483 CTPA examinations were evaluated. In internal cross-validation and test set, the trained model correctly classified 123 of 128 examinations as positive for PE (sensitivity 96.1 %; 95 % C.I. 91-98 %; P < .05) and 521 of 551 as negative (specificity 94.6 %; 95 % C.I. 92-96 %; P < .05), achieving an area under the receiver operating characteristic (AUROC) of 96.4 % (95 % C.I. 79-99 %; P < .05). In the first external test dataset, the trained model correctly classified 31 of 32 examinations as positive (sensitivity 96.9 %; 95 % C.I. 84-99 %; P < .05) and 2 of 2 as negative (specificity 100 %; 95 % C.I. 34-100 %; P < .05), achieving an AUROC of 98.6 % (95 % C.I. 83-100 %; P < .05). In the second external test dataset, the trained model correctly classified 379 of 385 examinations as positive (sensitivity 98.4 %; 95 % C.I. 97-99 %; P < .05) and 346 of 385 as negative (specificity 89.9 %; 95 % C.I. 86-93 %; P < .05), achieving an AUROC of 98.5 % (95 % C.I. 83-100 %; P < .05).

CONCLUSION

Our automatic pipeline achieved beyond state-of-the-art diagnostic performance of PE in CTPA using nnU-Net for segmentation and volume- and probability-based post-processing for classification.