Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas
Menée à partir de l'analyse des clichés radiologiques de 290 patients ayant subi une tomographie numérique sans rehaussement de contraste et présentant des nodules pulmonaires suspects (âge moyen : 68 ans), cette étude met en évidence l'intérêt des caractéristiques radiomiques des régions intra- et périnodulaires pour différencier un adénocarcinome du poumon non à petites cellules d'un granulome bénin
Perinodular and intranodular radiomic features corresponding to texture and shape (radiomics) were evaluated to distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT.
Purpose : To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT.
Materials and Methods : For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18–92 years; 125 men [mean age, 67 years; range, 18–90 years] and 165 women [mean age, 68 years; range, 33–92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on axial CT images by a radiologist with manual annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists.
Results : Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively.
Conclusion : Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT.
Radiology , résumé, 2017