|ECR 2019 / C-2065|
|Towards radiologist-level malignancy detection on chest CT scans: a comparative study of the performance of convolutional neural networks and four thoracic radiologists|
The deep learning system shows better performance than experienced radiologists, individually and in aggregate, in predicting the presence of malignant nodules on the 96 CT scans obtained from the NLST dataset. The difference in the interpretation of radiologists were not found to be statistically significant.
Clinically, as low-dose CT scans are non-contrast scans, the classically described contrast enhancement characteristics for diagnosing malignant nodules cannot be used to assess the risk of malignancy in these cases.
The availability of a highly sensitive nodule characterization tool will improve the early cancer detection rates. Radiologists aided by deep learning solutions for malignancy have the potential to identify lung cancer earlier as well as reduce unnecessary biopsies.
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ECR 2019 / C-3685
Evaluation of deep learning software tool for CT based lung nodule growth assessment
ECR 2019 / C-2695
Variability of coronary plaque burden evaluation by low-dose coronary CTA with iterative model reconstruction