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. 2023 Oct 10;309(1):e222904. doi: 10.1148/radiol.222904

Figure 1:

Diagram shows an overview of the major approaches to indeterminate pulmonary nodule classification and characterization of early lung cancer. A lung nodule detected on a CT scan may be annotated using semantic terms, undergo segmentation for extraction of radiomic features, or serve as input to a deep learning engine. The outputs of each analysis pathway are then submitted to some form of classifier to produce an output providing a probability of lung cancer or prediction of the histologic characteristics and/or genetic makeup of a known lung cancer. LUL = left upper lobe.

Diagram shows an overview of the major approaches to indeterminate pulmonary nodule classification and characterization of early lung cancer. A lung nodule detected on a CT scan may be annotated using semantic terms, undergo segmentation for extraction of radiomic features, or serve as input to a deep learning engine. The outputs of each analysis pathway are then submitted to some form of classifier to produce an output providing a probability of lung cancer or prediction of the histologic characteristics and/or genetic makeup of a known lung cancer. LUL = left upper lobe.