Improving radiomic modeling for the identification of symptomatic carotid atherosclerotic plaques using deep learning-based 3D super-resolution CT angiography.

Rationale and objectives: Radiomic models based on normal-resolution (NR) computed tomography angiography (CTA) images can fail to distinguish between symptomatic and asymptomatic carotid atherosclerotic plaques. This study aimed to explore the effectiveness of a deep learning-based three-dimensional super-resolution (SR) CTA radiomic model for improved identification of symptomatic carotid atherosclerotic plaques. Materials and methods: A total of 193 patients with carotid atherosclerotic plaques were retrospectively enrolled and allocated into either a symptomatic (n = 123) or an asymptomatic (n = 70) groups. SR CTA images were derived from NR CTA images using deep learning-based three-dimensional SR technology. Handcrafted radiomic features were extracted from both the SR and NR CTA images and three risk models were developed based on manually measured quantitative CTA characteristics and NR and SR radiomic features. Model performances were assessed via receiver operating characteristic, calibration, and decision curve analyses. Results: The SR model exhibited the optimal performance (area under the curve [AUC] 0.820, accuracy 0.802, sensitivity 0.854, F1 score 0.847) in the testing cohort, outperforming the other two models. The calibration curve analyses and Hosmer-Lemeshow test demonstrated that the SR model exhibited the best goodness of fit, and decision curve analysis revealed that SR model had the highest clinical value and potential patient benefits. Conclusions: Deep learning-based three-dimensional SR technology could improve the CTA-based radiomic models in identifying symptomatic carotid plaques, potentially providing more accurate and valuable information to guide clinical decision-making to reduce the risk of ischemic stroke.

Catalytic pyrolysis of black-liquor lignin by co-feeding with different plastics in a fluidized bed reactor.

Catalytic co-pyrolysis of black-liquor lignin and waste plastics (polyethylene, PE; polypropylene PP; polystyrene, PS) was conducted in a fluidized bed. The effects of temperature, plastic to lignin ratio, catalyst and plastic types on product distributions were studied. Both aromatic and olefin yields increased with increasing PE proportion. Petrochemical yield of co-pyrolysis of PE and lignin was LOSA-1 > spent FCC > Gamma-Al2O3 > sand. The petrochemical yield with LOSA-1 is 43.9% which is more than two times of that without catalyst. The feedstock for co-pyrolysis with lignin is polystyrene > polyethylene > polypropylene. Catalytic co-pyrolysis of black-liquor lignin with PS produced the maximum aromatic yield (55.3%), while co-pyrolysis with PE produced the maximum olefin yield (13%).