Radiomics aims for ways to personalize patient's treatments based on the extraction of texture features from routine clinical images. However, implementation Radiomics-based models in patient care poses a significant number challenges, since there are multi-step processes model. These include image selection (e.g CT, MRI, PET, or combination these), region interest (ROI) segmentation, texture-f...

In the present issue of the J Nucl Med, Orlhac and colleagues present a study comparing visual assessment of uptake heterogeneity in positron emission tomography (PET) images by experts and a subset of radiomics metrics, namely textural features (TF). They exploited both clinical and simple simulated PET images, going further than previous studies carried out using clinical data only (4–6). Suc...

Abstract Deep learning is a promising method for medical image analysis because it can automatically acquire meaningful representations from raw data. However, technical challenge lies in the difficulty of determining which types internal representation are associated with specific task, feature vectors vary dynamically according to individual inputs. Here, based on magnetic resonance imaging (...

Abstract Introduction Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission provide information regarding the diagnosis, patient outcome, tumor phenotype, gene-protein signatures of diseases. In low-risk group, complete surgical resection is typically sufficient, whereas in high-risk thymoma, adjuvant therapy ...

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In the last few years, biomedical research has been boosted by the technological development of analytical instrumentation generating a large volume of data. Such information has increased in complexity from basic (i.e., blood samples) to extensive sets encompassing many aspects of a subject phenotype, and now rapidly extending into genetic and, more recently, radiomic information. Radiogenomic...

"Personalized oncology" is a multi-disciplinary science, which requires inputs from various streams for optimal patient management. Humongous progress in the treatment modalities available and the increasing need to provide functional information in addition to the morphological data; has led to leaping progress in the field of imaging. Magnetic resonance imaging has undergone tremendous progre...