Metabolic Positron Emission Tomography in Breast Cancer

A PET-Surrogate Signature for the Interrogation of the Metabolic Status of Breast Cancers

Metabolic alterations in cancers can be exploited for diagnostic, prognostic, and therapeutic purposes. This is exemplified by 18F-fluorodeoxyglucose (FDG)-positron emission tomography (FDG-PET), an imaging tool that relies on enhanced glucose uptake by tumors for diagnosis and staging. By performing transcriptomic analysis of breast cancer (BC) samples from patients stratified by FDG-PET, a 54-gene signature (PETsign) is identified that recapitulates FDG uptake. PETsign is independently prognostic of clinical outcome in luminal BCs, the most common and heterogeneous BC molecular subtype, which requires improved stratification criteria to guide therapeutic decision-making. The prognostic power of PETsign is stable across independent BC cohorts and disease stages including the earliest BC stage, arguing that PETsign is an ab initio metabolic signature. Transcriptomic and metabolomic analysis of BC cells reveals that PETsign predicts enhanced glycolytic dependence and reduced reliance on fatty acid oxidation. Moreover, coamplification of PETsign genes occurs frequently in BC arguing for their causal role in pathogenesis. CXCL8 and EGFR signaling pathways feature strongly in PETsign, and their activation in BC cells causes a shift toward a glycolytic phenotype. Thus, PETsign serves as a molecular surrogate for FDG-PET that could inform clinical management strategies for BC patients.

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes

Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings.

Molecular Imaging of Steroid Receptors in Breast Cancer

ABSTRACT

Steroid receptors regulate gene expression for many important physiologic functions and pathologic processes. Receptors for estrogen, progesterone, and androgen have been extensively studied in breast cancer, and their expression provides prognostic information as well as targets for therapy. Noninvasive imaging utilizing positron emission tomography and radiolabeled ligands targeting these receptors can provide valuable insight into predicting treatment efficacy, staging whole-body disease burden, and identifying heterogeneity in receptor expression across different metastatic sites. This review provides an overview of steroid receptor imaging with a focus on breast cancer and radioligands for estrogen, progesterone, and androgen receptors.

Quarter-Century Transformation of Oncology: Positron Emission Tomography for Patients with Breast Cancer

PET radiotracers have become indispensable in the care of patients with breast cancer. 18F-fluorodeoxyglucose has become the preferred method of many oncologists for systemic staging of breast cancer at initial diagnosis, detecting recurrent disease, and for measuring treatment response after therapy. 18F-Sodium Fluoride is valuable for detection of osseous metastases. 18F-fluoroestradiol is now FDA-approved with multiple appropriate clinical uses. There are multiple PET radiotracers in clinical trials, which may add utility of PET imaging for patients with breast cancer in the future. This article will describe the advances during the last quarter century in PET for patients with breast cancer.

Women's Health Update: Growing Role of PET for Patients with Breast Cancer

Positron Emission Tomography (PET) has been growing in usage for patients with breast cancer, due to an increased number of FDA-approved PET radiotracers pertinent to patients with breast cancer as well as increased prospective evidence for the value of these agents. The leading PET radiotracer for patients with breast cancer is 18F-fluorodeoxyglucose (18F-FDG), which measures glucose metabolism. There is prospective evidence for the use of 18F-FDG PET in systemic staging of newly diagnosed locally advanced breast cancer (stages IIB-IIIC), monitoring breast cancer treatment response, and detecting breast cancer recurrence, particularly in no special type (NST) breast cancer. 16α-18F-fluoro-17β-Fluoroestradiol (18F-FES) is a radiolabeled estrogen which evaluates estrogen receptor (ER) accessible for estrogen binding. There is prospective evidence supporting 18F-FES PET as a predictive biomarker for selecting patients with metastatic breast cancer for endocrine therapies. 18F-FES PET has also been shown to be valuable in the evaluation of ER status of lesions which are difficult to biopsy, for evaluation of ER status in lesions that are equivocal on other imaging modalities, and for selecting optimal dosage of novel ER-targeted systemic therapies in early clinical trials. Multiple investigators have suggested 18F-FES PET will have an increasing role for patients with invasive lobular breast cancer (ILC), which is less optimally evaluated by 18F-FDG PET. Sodium 18F-Fluoride (18F-NaF) evaluates bone turnover and has been effective in evaluation of malignancies which commonly metastasize to bone. In patients with metastatic breast cancer, 18F-NaF PET/CT has demonstrated superior sensitivity for osseous metastases than 99mTc-MDP or CT. In addition to these three FDA-approved PET radiotracers, there are multiple novel radiotracers currently in clinical trials with potential to further increase PET usage for patients with breast cancer.

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.