Key result
3D neural network detects chronic PE hypoperfusion on CTPA with 0.87 AUC, outperforming density-based thresholds.
Why the study?
Chronic pulmonary embolism is a life-threatening disease easily misdiagnosed on computed tomography.
Does a 3D convolutional neural network improve the detection of hypoperfusion at CT pulmonary angiography compared to a Hounsfield unit threshold method in patients with chronic pulmonary embolism?
Observational (n=50)
Yes
Does a 3D convolutional neural network improve the detection of hypoperfusion at CT pulmonary angiography compared to a Hounsfield unit threshold method in patients with chronic pulmonary embolism?
A 3D convolutional neural network algorithm demonstrated superior performance compared to a standard Hounsfield unit threshold method for detecting hypoperfusion on CT pulmonary angiography in patients with chronic pulmonary embolism.
Answers are grounded in this paper and cite their sources.
Should not yet alter CTPA interpretation in chronic PE; leaves open prospective validation of CNN hypoperfusion detection.
BACKGROUND: Chronic pulmonary embolism (CPE) is a life-threatening disease easily misdiagnosed on computed tomography. We investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed tomography pulmonary angiography (CTPA). METHODS: Preoperative CTPA of 25 patients with CPE and 25 without pulmonary embolism were selected. We applied a 48%-12%-40% training-validation-testing split (12 positive and 12 negative CTPA volumes for training, 3 positives and 3 negatives for validation, 10 positives and 10 negatives for testing). The median number of axial images per CTPA was 335 (min-max, 111-570). Expert manual segmentations were used as training and testing targets. The CNN output was compared to a method in which a Hounsfield unit (HU) threshold was used to detect hypoperfusion. Receiver operating characteristic area under the curve (AUC) and Matthew correlation coefficient (MCC) were calculated with their 95% confidence interval (CI). RESULTS: The predicted segmentations of CNN showed AUC 0.87 (95% CI 0.82-0.91), those of HU-threshold method 0.79 (95% CI 0.74-0.84). The optimal global threshold values were CNN output probability ≥ 0.37 and ≤ -850 HU. Using these values, MCC was 0.46 (95% CI 0.29-0.59) for CNN and 0.35 (95% CI 0.18-0.48) for HU-threshold method (average difference in MCC in the bootstrap samples 0.11 (95% CI 0.05-0.16). A high CNN prediction probability was a strong predictor of CPE. CONCLUSIONS: We proposed a deep learning method for detecting hypoperfusion in CPE from CTPA. This model may help evaluating disease extent and supporting treatment planning.
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Vainio et al. (2021) conducted an observational in Chronic pulmonary embolism (n=50). 3D convolutional neural network (CNN) vs. Naïve HU-threshold method was evaluated on Matthews correlation coefficient (MCC) for hypoperfusion detection (Difference 0.11, 95% CI 0.05-0.16). A 3D convolutional neural network detected hypoperfusion in chronic pulmonary embolism from CT pulmonary angiography with an AUC of 0.87 and MCC of 0.46, outperforming a density-based threshold method.
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