Image masking using convolutional networks improves performance classification of radiation pneumonitis for non-small cell lung cancer

This study proposes a prediction model for RP grade  ≥ 2 using a convolutional neural network (CNN) model with image cropping. The 3D computed tomography (CT) images cropped in the whole-body, normal lung (nLung), and nLung regions overlapping the region over 20 Gy (nLung∩20 Gy) used in treatment planning were used as the input data. The o utput classifies patients as RP grade <  2 or RP grade ≥ 2. The sensitivity, specificity, accuracy, and area under the curve (AUC) were evaluated using the receiver operating characteristic curve (ROC). The accuracy, specificity, sensitivity, and AUC were 53.9%, 80.0%, 25.5%, and 0.58, respectively, for the whole-body method, and 60.0%, 81.7%, 36.4%, and 0.64, respectively, for the nLung method. For the nLung∩20 Gy method, the accuracy, specificity, sensitivity, and AUC improved to 75.7%, 80.0%, 70.9%, and 0.84, respectively. The CNN model, in which the input image is segmented in the normal lung considering the dose di stribution, can help predict an RP grade ≥ 2 for NSCLC patients after definitive radiotherapy.
Source: Australasian Physical and Engineering Sciences in Medicine - Category: Biomedical Engineering Source Type: research