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1. Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity

Author

Julien Guiot, Nathalie Maes, Marie Winandy, Monique Henket, Benoit Ernst, Marie Thys, Anne-Noelle Frix, Philippe Morimont, Anne-Françoise Rousseau, Perrine Canivet, Renaud Louis, Benoît Misset, Paul Meunier, Jean-Paul Charbonnier, and Bernard Lambermont

Subjects
SARS-CoV-2, CT scan analysis, artificial intelligence, mechanical ventilation risk, severity of hospital stay prediction, COVID-19, Medicine (General), R5-920
Abstract

The pandemic of COVID-19 led to a dramatic situation in hospitals, where staff had to deal with a huge number of patients in respiratory distress. To alleviate the workload of radiologists, we implemented an artificial intelligence (AI) - based analysis named CACOVID-CT, to automatically assess disease severity on chest CT scans obtained from those patients. We retrospectively studied CT scans obtained from 476 patients admitted at the University Hospital of Liege with a COVID-19 disease. We quantified the percentage of COVID-19 affected lung area (% AA) and the CT severity score (total CT-SS). These quantitative measurements were used to investigate the overall prognosis and patient outcome: hospital length of stay (LOS), ICU admission, ICU LOS, mechanical ventilation, and in-hospital death. Both CT-SS and % AA were highly correlated with the hospital LOS, the risk of ICU admission, the risk of mechanical ventilation and the risk of in-hospital death. Thus, CAD4COVID-CT analysis proved to be a useful tool in detecting patients with higher hospitalization severity risk. It will help for management of the patients flow. The software measured the extent of lung damage with great efficiency, thus relieving the workload of radiologists.

Published
2022
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2. Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning

Author

Turkey Refaee, Zohaib Salahuddin, Anne-Noelle Frix, Chenggong Yan, Guangyao Wu, Henry C. Woodruff, Hester Gietema, Paul Meunier, Renaud Louis, Julien Guiot, Philippe Lambin, Precision Medicine, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Beeldvorming, and MUMC+: DA BV Medisch Specialisten Radiologie (9)

Subjects
interstitial lung disease, radiomics, IMAGES, SURVIVAL, computed tomography, General Medicine, artificial intelligence (AI), idiopathic pulmonary fibrosis, interpretability
Abstract

PurposeTo develop handcrafted radiomics (HCR) and deep learning (DL) based automated diagnostic tools that can differentiate between idiopathic pulmonary fibrosis (IPF) and non-IPF interstitial lung diseases (ILDs) in patients using high-resolution computed tomography (HRCT) scans.Material and MethodsIn this retrospective study, 474 HRCT scans were included (mean age, 64.10 years ± 9.57 [SD]). Five-fold cross-validation was performed on 365 HRCT scans. Furthermore, an external dataset comprising 109 patients was used as a test set. An HCR model, a DL model, and an ensemble of HCR and DL model were developed. A virtual in-silico trial was conducted with two radiologists and one pulmonologist on the same external test set for performance comparison. The performance was compared using DeLong method and McNemar test. Shapley Additive exPlanations (SHAP) plots and Grad-CAM heatmaps were used for the post-hoc interpretability of HCR and DL models, respectively.ResultsIn five-fold cross-validation, the HCR model, DL model, and the ensemble of HCR and DL models achieved accuracies of 76.2 ± 6.8, 77.9 ± 4.6, and 85.2 ± 2.7%, respectively. For the diagnosis of IPF and non-IPF ILDs on the external test set, the HCR, DL, and the ensemble of HCR and DL models achieved accuracies of 76.1, 77.9, and 85.3%, respectively. The ensemble model outperformed the diagnostic performance of clinicians who achieved a mean accuracy of 66.3 ± 6.7% (p < 0.05) during the in-silico trial. The area under the receiver operating characteristic curve (AUC) for the ensemble model on the test set was 0.917 which was significantly higher than the HCR model (0.817, p = 0.02) and the DL model (0.823, p = 0.005). The agreement between HCR and DL models was 61.4%, and the accuracy and specificity for the predictions when both the models agree were 93 and 97%, respectively. SHAP analysis showed the texture features as the most important features for IPF diagnosis and Grad-CAM showed that the model focused on the clinically relevant part of the image.ConclusionDeep learning and HCR models can complement each other and serve as useful clinical aids for the diagnosis of IPF and non-IPF ILDs.

Published
2022

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