Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas.
Margarita KirienkoGaia NinattiLuca CozziEmanuele VoulazNicolò GennaroIsabella BarajonFrancesca RicciCarmello Carlo-StellaPaolo ZucaliMartina SolliniLuca BalzariniArturo ChitiPublished in: La Radiologia medica (2020)
We developed and validated a CT-based radiomic model able to differentiate mediastinal masses on non-contrast-enhanced images, as thymic neoplasms or lymphoma. The proposed method was not affected by image postprocessing. Therefore, the present image-derived method has the potential to noninvasively support diagnosis in patients with prevascular mediastinal masses with major impact on management of asymptomatic cases.
Keyphrases
- contrast enhanced
- computed tomography
- deep learning
- diffusion weighted
- magnetic resonance imaging
- magnetic resonance
- lymph node
- dual energy
- diffusion weighted imaging
- ultrasound guided
- positron emission tomography
- convolutional neural network
- image quality
- diffuse large b cell lymphoma
- machine learning
- optical coherence tomography