Low-dose CT images show examples of screen-detected pulmonary nodules (IMAGE)
Caption
Low-dose CT images show examples of screen-detected pulmonary nodules (arrows) where the deep learning algorithm provides a more accurate malignancy risk estimation than the Pan-Canadian Early Detection of Lung Cancer (PanCan) model on axial (top), coronal (middle), and sagittal (bottom) planes. (A) Image shows a 9.7-mm malignant nodule (arrows) with a high deep learning risk score (32.3%) and low PanCan risk score (3.2%) in a 74-year-old male participant diagnosed with squamous cell carcinoma. (B) Image shows a 6.8-mm malignant nodule (arrows) with a high deep learning risk score (15.9%) and low PanCan risk score (1.2%) in a 71-year-old male participant diagnosed with adenocarcinoma. (C) Image shows a 19- mm benign nodule (arrows) with a low deep learning risk score (4.7%) and high PanCan risk score (32.7%) in a 50-year-old female participant. Additional PanCan input features used in the model were retrieved from original trial records, as follows: (A) negative for family history of lung cancer, negative for emphysema, negative for spiculation, negative for upper lobe location, nodule count: four; (B) negative for family history of lung cancer, negative for emphysema, negative for spiculation, negative for upper lobe location, nodule count: two; (C) negative for family history of lung cancer, positive for emphysema, negative for spiculation, positive for upper lobe location, nodule count: one.
Credit
Radiological Society of North America (RSNA)
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