Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study

ConclusionsThe models with semantic and texture features provided cross-validated AUCs of 0.85 –0.88 for classification of benign versus cancerous nodules, showing potential in aiding the management of patients.Key Points• Pretest probability of cancer can aid and direct the physician in the diagnosis and management of pulmonary nodules in a cost-effective way.• Semantic features (qualitative features reported by radiologists to characterize lung lesions) and radiomic (e.g., texture) features can be extracted from CT images.• Input of these variables into a model can generate a pretest likelihood of cancer to aid clinical decision and management of pulmonary nodules.
Source: European Radiology - Category: Radiology Source Type: research