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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. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.

Abstract: 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. Machine learning deals with the extraction of the important characteristic features from images, whereas DL uses neural networks to solve such problems with better performance. In this review, we discuss the current applications of machine learning and DL in the field of diagnostic radiology.Deep learning applications can be divided into medical imaging analysis and applications beyond analysis. In the field of medical imaging analysis, deep convolutional neural networks are used for image classification, lesion detection, and segmentation. Also used are recurrent neural networks when extracting information from electronic medical records and to augment the use of convolutional neural networks in the field of image classification. Generative adversarial networks have been explicitly used in generating high-resolution computed tomography and magnetic resonance images and to map computed tomography images from the corresponding magnetic resonance imaging. Beyond image analysis, DL can be used for quality control, workflow organization, and reporting.In this article, we review the most current AI models used in medical imaging research, providing a brief explanation of the various models described in the literature within the past 5 years. Emphasis is placed on the various DL models, as they are the most state-of-art in imaging analysis., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

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. The κ statistic was used to assess the agreement between radiologists. The Fisher exact test was used to compare the radiologic and pathological classifications., Results: In consensus, there were 83 typical, 42 atypical, and 31 indeterminate adrenal lesions. Logistic regression model showed that radiologically atypical adenoma was significantly associated with larger size, lobulated shape, higher unenhanced CT attenuation, heterogeneous appearance, nonfunctioning status, absolute percentage washout of less than 60%, and a signal intensity index of less than 16.5%.Pathologically, 147 adenomas were pathologically typical (Weiss 0), and 9 adenomas were pathologically atypical (Weiss 1-2). Radiologically, there was substantial agreement between both readers, with Cohen κ at 0.71. Approximately 98% of radiologically typical adenomas were pathologically typical. Only 17% of radiologically atypical adenomas were pathologically atypical. All radiologically indeterminate adenomas were pathologically typical. However, some of the radiologically indeterminate and typical adenomas still had an atypical component on pathologic analysis, such as necrosis, nuclear atypia, or oncocytic features., Conclusions: Radiologically atypical lesion was significantly associated with larger size and higher unenhanced CT attenuation. Approximately 27% of the cases demonstrated atypical features on imaging. Most radiologically atypical adrenal adenomas are pathologically typical.

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. In this article, we will discuss the diagnostic approach, typical and atypical imaging features of adrenal adenomas, as well as other lesions that mimic adrenal adenomas.

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., Methods: Pathologic and radiographic review confirmed seven combined HCC-CC patients during a 5-year time frame [2009-2014]. Data points were demographics, chemotherapy and/or targeted therapy given in the first and second-line setting, localized treatment if given, first radiographic result, progression-free survival (PFS), and overall survival (OS)., Results: Seven patients were identified. Front-line treatment showed a median PFS of 3.4 months. Total median OS was 8.3 months. Regimens given included gemcitabine alone +/- bevacizumab, gemcitabine + platinum (GP) +/- bevacizumab, and sorafenib. Front-line treatment with these regimens showed progressive disease in 71% (5 patients) on first radiographic scan with all patients who received sorafenib front-line progressing at that restaging. Disease-control (complete response + partial response + stable disease) was seen in 29% of patients (2 patients) with 1 patient receiving GP and 1 patient receiving gemcitabine + bevacizumab. Of note, 2 patients that received GP +/- bevacizumab in the second-line setting had disease control on first radiographic scan., Conclusions: Our retrospective review speaks to the rarity of this malignancy and challenges that are associated with its diagnosis and treatment. GP +/- bevacizumab showed disease control in first or second-line treatment in 3 patients. Treatment with this regimen in this rare malignancy subgroup warrants further investigation., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare.

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. This article reviews the fundamentals of imaging genomics in glioma, its role in noninvasive genomic detection, and its future potential in personalized treatment planning.

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. Imaging genomics represents a new era as a noninvasive surrogate for genomic and molecular profile identification. This article discusses the basics of imaging genomics of GBM, its role in treatment decision-making, and its future potential in noninvasive genomic identification., (Copyright © 2015 Elsevier Inc. All rights reserved.)