Emily A. Aherne

Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.

A key advantage of cardiac magnetic resonance (CMR) imaging over other cardiac imaging modalities is the ability to perform detailed tissue characterization. CMR techniques continue to evolve, with advanced imaging sequences being developed to provide a reproducible, quantitative method of tissue interrogation. The T 1 mapping technique, a pixel‐by‐pixel method of quantifying T 1 relaxation time of soft tissues, has been shown to be promising for characterization of diseased myocardium in a wide variety of cardiomyopathies. In this review, we describe the basic principles and common techniques for T 1 mapping and its use for native T 1 , postcontrast T 1 , and extracellular volume mapping. We will review a wide range of clinical applications of the technique that can be used for identification and quantification of myocardial edema, fibrosis, and infiltrative diseases with illustrative clinical examples. In addition, we will explore the current limitations of the technique and describe some areas of ongoing development. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:1336–1356.

OBJECTIVE: The purpose of this article is to discuss the role of the radiologist in the treatment of peritoneal cancer, with focus placed on advanced treatment options and selection of patients with resectable disease for whom complete cytoreduction can be achieved. CONCLUSION: Peritoneal cancers traditionally have been associated with significant morbidity and universal mortality; however, the management of such cancers has evolved substantially. Advanced treatment options, including cytoreductive surgery and intraperitoneal chemotherapy, are associated with significantly improved long-term patient survival. To ensure that patients benefit from aggressive multimodality treatments, the radiologist plays a pivotal role in the multidisciplinary team to ensure careful patient selection, identifying individuals with resectable disease for whom complete cytoreduction can be achieved.