Obesity and diabetes: newer concepts in imaging

Radiomics Improves Cancer Screening and Early Detection

Imaging is a key technology in the early detection of cancers, including X-ray mammography, low-dose CT for lung cancer, or optical imaging for skin, esophageal, or colorectal cancers. Historically, imaging information in early detection schema was assessed qualitatively. However, the last decade has seen increased development of computerized tools that convert images into quantitative mineable data (radiomics), and their subsequent analyses with artificial intelligence (AI). These tools are improving diagnostic accuracy of early lesions to define risk and classify malignant/aggressive from benign/indolent disease. The first section of this review will briefly describe the various imaging modalities and their use as primary or secondary screens in an early detection pipeline. The second section will describe specific use cases to illustrate the breadth of imaging modalities as well as the benefits of quantitative image analytics. These will include optical (skin cancer), X-ray CT (pancreatic and lung cancer), X-ray mammography (breast cancer), multiparametric MRI (breast and prostate cancer), PET (pancreatic cancer), and ultrasound elastography (liver cancer). Finally, we will discuss the inexorable improvements in radiomics to build more robust classifier models and the significant limitations to this development, including access to well-annotated databases, and biological descriptors of the imaged feature data.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Molecular imaging of diabetes and diabetic complications: Beyond pancreatic β-cell targeting

Diabetes is a chronic non-communicable disease affecting over 400 million people worldwide. Diabetic patients are at a high risk of various complications, such as cardiovascular, renal, and other diseases. The pathogenesis of diabetes (both type 1 and type 2 diabetes) is associated with a functional impairment of pancreatic β-cells. Consequently, most efforts to manage and prevent diabetes have focused on preserving β-cells and their function. Advances in imaging techniques, such as magnetic resonance imaging, magnetic resonance spectroscopy, positron emission tomography, and single-photon-emission computed tomography, have enabled noninvasive and quantitative detection and characterization of the population and function of β-cells in vivo. These advantages aid in defining and monitoring the progress of diabetes and determining the efficacy of anti-diabetic therapies. Beyond β-cell targeting, molecular imaging of biomarkers associated with the development of diabetes, e.g., lymphocyte infiltration, insulitis, and metabolic changes, may also be a promising strategy for early detection of diabetes, monitoring its progression, and occurrence of complications, as well as facilitating exploration of new therapeutic interventions. Moreover, molecular imaging of glucose uptake, production and excretion in specified tissues is critical for understanding the pathogenesis of diabetes. In the current review, we summarize and discuss recent advances in noninvasive imaging technologies for imaging of biomarkers beyond β-cells for early diagnosis of diabetes, investigation of glucose metabolism, and precise diagnosis and monitoring of diabetic complications for better management of diabetic patients.

Validation of a DIXON-based fat quantification technique for the measurement of visceral fat using a CT-based reference standard

PURPOSE

The purpose of the study is to determine whether a novel semi-automated DIXON-based fat quantification algorithm can reliably quantify visceral fat using a CT-based reference standard.

METHODS

This was an IRB-approved retrospective cohort study of 27 subjects who underwent abdominopelvic CT within 7 days of proton density fat fraction (PDFF) mapping on a 1.5T MRI. Cross-sectional visceral fat area per slice (cm²) was measured in blinded fashion in each modality at intervertebral disc levels from T12 to L4. CT estimates were obtained using a previously published semi-automated computational image processing system that sums pixels with attenuation - 205 to - 51 HU. MR estimates were obtained using two novel semi-automated DIXON-based fat quantification algorithms that measure visceral fat area by spatially regularizing non-uniform fat-only signal intensity or de-speckling PDFF 2D images and summing pixels with PDFF ≥ 50%. Pearson's correlations and Bland-Altman analyses were performed.

RESULTS

Visceral fat area per slice ranged from 9.2 to 429.8 cm² for MR and from 1.6 to 405.5 cm² for CT. There was a strong correlation between CT and MR methods in measured visceral fat area across all studied vertebral body levels (r = 0.97; n = 101 observations); the least (r = 0.93) correlation was at T12. Bland-Altman analysis revealed a bias of 31.7 cm² (95% CI [- 27.1]-90.4 cm²), indicating modestly higher visceral fat assessed by MR.

CONCLUSION

MR- and CT-based visceral fat quantification are highly correlated and have good cross-modality reliability, indicating that visceral fat quantification by either method can yield a stable and reliable biomarker.

Diagnostic imaging in the management of patients with metabolic syndrome

Metabolic syndrome (MetS) is the constellation of metabolic risk factors that might foster development of type 2 diabetes and cardiovascular disease. Abdominal obesity and insulin resistance play a prominent role among all metabolic traits of MetS. Because intervention including weight loss can reduce these morbidity and mortality in MetS, early detection of the severity and complications of MetS could be useful. Recent advances in imaging modalities have provided significant insight into the development and progression of abdominal obesity and insulin resistance, as well as target organ injuries. The purpose of this review is to summarize advances in diagnostic imaging modalities in MetS that can be applied for evaluating each components and target organs. This may help in early detection, monitoring target organ injury, and in turn developing novel therapeutic target to alleviate and avert them.

Challenges of imaging for cancer in patients with diabetes and obesity

A growing body of evidence supports a connection among diabetes (predominantly type 2), obesity, and cancer. Multiple meta-analyses of epidemiological data show that people with diabetes are at increased risk of developing a variety of different cancers and suffer from an increased rate of perioperative complications and cancer mortality. Computed tomography (CT) has played an important role in diagnosis and staging of cancer. Positron emission tomography is complementary to CT in the diagnosis, staging, and evaluation of treatment response for many types of cancer. Because of generally poor clinical outcome of cancers when they are detected in late stages, more research is now focused on stratifying risk to allow personalized screening of at-risk patients and cancer detection at an earlier stage. In this review, we summarize basic noninvasive imaging techniques currently in use to detect cancer with emphasis on the challenges of imaging for early cancer detection in obese patients with diabetes.