https://www.selleckchem.com/
To establish a machine-learning model to differentiate adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) appearing as pure ground-glass nodules (pGGNs). This retrospective study enrolled 136 patients with histopathologically diagnosed with AIS, MIA, and IAC. All pGGNs were divided randomly into a training and a testing dataset at a ratio of 7 3. Radiomics features were extracted based on the unenhanced computed tomography (CT) images derived from the last preoperative CT examination of each patient. T
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