Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know.

Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.

The forgotten appearance of metastatic melanoma in the small bowel.

BACKGROUND: Melanoma is the most aggressive form of skin cancer, with a tendency to metastasise to any organ of the human body. While the most common body organs affected include liver, lungs, brain and soft tissues, spread to the gastrointestinal tract is not uncommon. In the bowel, it can present with a multitude of imaging appearances, more rarely as an aneurysmal dilatation. This appearance is classically associated with lymphoma, but it has more rarely been associated with other forms of malignancy. CASE PRESENTATION: We report a case series of three patients with aneurysmal dilatation in the small bowel (SB) confirmed to be due to metastatic melanoma (MM). All patients had non-specific symptoms; most times being attributed initially to causes other than melanoma. On CT the identified aneurysmal SB dilatations were diagnosed as presumed lymphoma in all cases. In two cases, the aneurysmal dilatation was the first presentation of metastatic disease and in two of the cases more than one site of the gastrointestinal tract was concomitantly involved. All patients underwent surgical resection with histological confirmation of MM. CONCLUSIONS: Recognition of unusual SB presentation of MM, such as aneurysmal SB dilatation, is important to expedite diagnosis, provide appropriate treatment, and consequently improve quality of life and likely survival of these patients. As the most common cancer to metastasise to the SB and as a known imaging mimicker, MM should remain in any radiologist's differential diagnosis for SB lesions with aneurysmal dilatation.

Robustness of radiomic features in CT images with different slice thickness, comparing liver tumour and muscle.

Radiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and further validation of feature robustness with respect to imaging acquisition parameters. In this paper, the robustness of radiomic features extracted from computed tomography (CT) images is evaluated for liver tumour and muscle, comparing the values of the features in images reconstructed with two different slice thicknesses of 2.0 mm and 5.0 mm. Novel approaches are presented to address the intrinsic dependencies of texture radiomic features, choosing the optimal number of grey levels and correcting for the dependency on volume. With the optimal values and corrections, feature values are compared across thicknesses to identify reproducible features. Normalisation using muscle regions is also described as an alternative approach. With either method, a large fraction of features (75-90%) was found to be highly robust (< 25% difference). The analyses were performed on a homogeneous CT dataset of 43 patients with hepatocellular carcinoma, and consistent results were obtained for both tumour and muscle tissue. Finally, recommended guidelines are included for radiomic studies using variable slice thickness.