Opportunistic skeletal muscle metrics as prognostic tools in metastatic castration-resistant prostate cancer patients candidates to receive Radium-223.

OBJECTIVE: Androgen deprivation therapy alters body composition promoting a significant loss in skeletal muscle (SM) mass through inflammation and oxidative damage. We verified whether SM anthropometric composition and metabolism are associated with unfavourable overall survival (OS) in a retrospective cohort of metastatic castration-resistant prostate cancer (mCRPC) patients submitted to 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (FDG PET/CT) imaging before receiving Radium-223. PATIENTS AND METHODS: Low-dose CT were opportunistically analysed using a cross-sectional approach to calculate SM and adipose tissue areas at the third lumbar vertebra level. Moreover, a 3D computational method was used to extract psoas muscles to evaluate their volume, Hounsfield Units (HU) and FDG retention estimated by the standardized uptake value (SUV). Baseline established clinical, lab and imaging prognosticators were also recorded. RESULTS: SM area predicted OS at univariate analysis. However, this capability was not additive to the power of mean HU and maximum SUV of psoas muscles volume. These factors were thus combined in the Attenuation Metabolic Index (AMI) whose power was tested in a novel uni- and multivariable model. While Prostate-Specific Antigen (PSA), Alkaline Phosphatase (ALP), Lactate Dehydrogenase and Hemoglobin, Metabolic Tumor Volume, Total Lesion Glycolysis and AMI were associated with long-term OS at the univariate analyses, only PSA, ALP and AMI resulted in independent prognosticator at the multivariate analysis. CONCLUSION: The present data suggest that assessing individual 'patients' SM metrics through an opportunistic operator-independent computational analysis of FDG PET/CT imaging provides prognostic insights in mCRPC patients candidates to receive Radium-223.

Myocardial Metabolic Response Predicts Chemotherapy Curative Potential on Hodgkin Lymphoma: A Proof-of-Concept Study.

Genome sharing between cancer and normal tissues might imply a similar susceptibility to chemotherapy toxicity. The present study aimed to investigate whether curative potential of doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) is predicted by the metabolic response of normal tissues in patients with Hodgkin lymphoma (HL). METHODS: According to current guidelines, 86 patients with advanced-stage (IIB-IVB) HL, prospectively enrolled in the HD0607 trial (NCT00795613), underwent 18 F-fluorodeoyglucose PET/CT imaging at diagnosis and, at interim, after two ABVD courses, to decide regimen maintenance or its escalation. In both scans, myocardial FDG uptake was binarized according to its median value. Death and disease relapse were recorded to estimate progression-free survival (PFS) during a follow-up with median duration of 43.8 months (range 6.97-60). RESULTS: Four patients (4.6%) died, while six experienced disease relapse (7%). Complete switch-off of cancer lesions and cardiac lighting predicted a favorable outcome at Kaplan-Mayer analyses. The independent nature and additive predictive value of their risk prediction were confirmed by the multivariate Cox regression analysis. CONCLUSION: Susceptibility of HL lesions to chemotherapy is at least partially determined by factors featuring the host who developed it.

Spinal cord hypermetabolism extends to skeletal muscle in amyotrophic lateral sclerosis: a computational approach to [18F]-fluorodeoxyglucose PET/CT images.

PURPOSE: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease leading to neuromuscular palsy and death. We propose a computational approach to [18F]-fluorodeoxyglucose (FDG) PET/CT images to analyze the structure and metabolic pattern of skeletal muscle in ALS and its relationship with disease aggressiveness. MATERIALS AND METHODS: A computational 3D method was used to extract whole psoas muscle's volumes and average attenuation coefficient (AAC) from CT images obtained by FDG PET/CT performed in 62 ALS patients and healthy controls. Psoas average standardized uptake value (normalized on the liver, N-SUV) and its distribution heterogeneity (defined as N-SUV variation coefficient, VC-SUV) were also extracted. Spinal cord and brain motor cortex FDG uptake were also estimated. RESULTS: As previously described, FDG uptake was significantly higher in the spinal cord and lower in the brain motor cortex, in ALS compared to controls. While psoas AAC was similar in patients and controls, in ALS a significant reduction in psoas volume (3.6 ± 1.02 vs 4.12 ± 1.33 mL/kg; p < 0.01) and increase in psoas N-SUV (0.45 ± 0.19 vs 0.29 ± 0.09; p < 0.001) were observed. Higher heterogeneity of psoas FDG uptake was also documented in ALS (VC-SUV 8 ± 4%, vs 5 ± 2%, respectively, p < 0.001) and significantly predicted overall survival at Kaplan-Meier analysis. VC-SUV prognostic power was confirmed by univariate analysis, while the multivariate Cox regression model identified the spinal cord metabolic activation as the only independent prognostic biomarker. CONCLUSION: The present data suggest the existence of a common mechanism contributing to disease progression through the metabolic impairment of both second motor neuron and its effector.

Overview of radiomics in breast cancer diagnosis and prognostication.

Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication.