Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status.

Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application.

Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities.

Artificial intelligence (AI) is the most revolutionizing development in the health care industry in the current decade, with diagnostic imaging having the greatest share in such development. Machine learning and deep learning (DL) are subclasses of AI that show breakthrough performance in image analysis. They have become the state of the art in the field of image classification and recognition.

Preresection Radiologic Assessment and Imaging Features of 156 Pathologically Proven Adrenal Adenomas.

Purpose: The aims of the study were to assess the typical and atypical radiologic features of pathologically proven adrenal adenomas and to determine the relationship between the radiologic and histopathologic classification.

Methods: We retrospectively studied 156 pathologically proven adrenal adenomas in 154 patients from our institutional databases who have computed tomography (CT) and/or magnetic resonance imaging (MRI) examinations before intervention. We determined the histopathologic diagnosis (typical or atypical) using Weiss scoring and classified the adenomas radiologically into typical, atypical, or indeterminate based on lesion size, precontrast CT attenuation, absolute percentage washout, calcification, and necrosis.

Adrenal cortical adenoma: current update, imaging features, atypical findings, and mimics.

Adrenal adenoma is the most common adrenal lesion. Due to its wide prevalence, adrenal adenomas may demonstrate various imaging features. Thus, it is important to identify typical and atypical imaging features of adrenal adenomas and to be able to differentiate atypical adrenal adenomas from potentially malignant lesions.

Systemic therapy for unresectable, mixed hepatocellular-cholangiocarcinoma: treatment of a rare malignancy.

Background: Combined hepatocellular-cholangiocarcinoma (HCC-CC) has a reported incidence of less than 5% of primary hepatic malignancies. The treatment approach to this malignancy is undefined. Our objective of this case series is to provide some insight into chemotherapy and/or targeted therapy in this setting.

Imaging Genomics in Gliomas.

During the last decade, imaging has become the cornerstone for noninvasive diagnosis of different disorders and is currently being used by physicians all over the world. With the emergence of novel advanced imaging techniques that allow microstructural as well as functional tissue characterization along with the extensive work done by The Cancer Genome Atlas focusing on mapping genomic changes in glioblastoma, new correlations have been discovered between alterations at the genomics level and radiological imaging features in cancer patients. This has marked the beginning of a new era in clinical sciences, the era of "imaging genomics," which aims at establishing relationship between radiological imaging features and genomic characteristics of tumors.

Imaging genomics of Glioblastoma: state of the art bridge between genomics and neuroradiology.

Glioblastoma (GBM) is the most common and most aggressive primary malignant tumor of the central nervous system. Recently, researchers concluded that the "one-size-fits-all" approach for treatment of GBM is no longer valid and research should be directed toward more personalized and patient-tailored treatment protocols. Identification of the molecular and genomic pathways underlying GBM is essential for achieving this personalized and targeted therapeutic approach.