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.

Radiomics and Artificial Intelligence for Outcome Prediction in Multiple Myeloma Patients Undergoing Autologous Transplantation: A Feasibility Study with CT Data.

Multiple myeloma is a plasma cell dyscrasia characterized by focal and non-focal bone lesions. Radiomic techniques extract morphological information from computerized tomography images and exploit them for stratification and risk prediction purposes. However, few papers so far have applied radiomics to multiple myeloma. A retrospective study approved by the institutional review board: n = 51 transplanted patients and n = 33 (64%) with focal lesion analyzed via an open-source toolbox that extracted 109 radiomics features. We also applied a dedicated tool for computing 24 features describing the whole skeleton asset. The redundancy reduction was realized via correlation and principal component analysis. Fuzzy clustering (FC) and Hough transform filtering (HTF) allowed for patient stratification, with effectiveness assessed by four skill scores. The highest sensitivity and critical success index (CSI) were obtained representing each patient, with 17 focal features selected via correlation with the 24 features describing the overall skeletal asset. These scores were higher than the ones associated with a standard cytogenetic classification. The Mann-Whitney U-test showed that three among the 17 imaging descriptors passed the null hypothesis. This AI-based interpretation of radiomics features stratified relapsed and non-relapsed MM patients, showing some potentiality for the determination of the prognostic image-based biomarkers in disease follow-up.

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.

Quantitative Imaging and Radiomics in Multiple Myeloma: A Potential Opportunity?

Multiple Myeloma (MM) is the second most common type of hematological disease and, although it is rare among patients under 40 years of age, its incidence rises in elderly subjects. MM manifestations are usually identified through hyperCalcemia, Renal failure, Anaemia, and lytic Bone lesions (CRAB). In particular, the extent of the bone disease is negatively related to a decreased quality of life in patients and, in general, bone disease in MM increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially computerized tomography (CT) and Magnetic Resonance Imaging (MRI), is becoming crucial from the clinical viewpoint to separate asymptomatic from symptomatic MM patients and the detection of focal lytic lesions in these imaging data is becoming relevant even when no clinical symptoms are present. Therefore, radiology is pivotal in the staging and accurate management of patients with MM even in early phases of the disease. In this review, we describe the opportunities offered by quantitative imaging and radiomics in multiple myeloma. At the present time there is still high variability in the choice between various imaging methods to study MM patients and high variability in image interpretation with suboptimal agreement among readers even in tertiary centers. Therefore, the potential of medical imaging for patients affected by MM is still to be completely unveiled. In the coming years, new insights to study MM with medical imaging will derive from artificial intelligence (AI) and radiomics usage in different bone lesions and from the wide implementations of quantitative methods to report CT and MRI. Eventually, medical imaging data can be integrated with the patient's outcomes with the purpose of finding radiological biomarkers for predicting the prognostic flow and therapeutic response of the disease.

Development and definition of a simplified scoring system in patients with multiple myeloma undergoing stem cells transplantation on standard computed tomography: myeloma spine and bone damage score (MSBDS).

BACKGROUND: In clinical practice, there is the need to optimize imaging usage in MM patients. Accordingly, the aim of this paper was to develop a simple computed tomography (CT) scoring method for MM, able to shorten and simplify the interpretation time with good intra- and inter-reader reliability. This method, named MSBDS (Myeloma Spine and Bone Damage Score) was developed with the final aim to use standard total-body CT in the routine practice of MM centres as a complement of standard evaluations in patients undergoing stem cells transplantation. METHODS: We used a widely accepted consensus formation method and literature research during three structured face-to-face meetings specifically designed to combine opinions from a group of experts with proven experience in multiple myeloma care and/or musculoskeletal CT to facilitate the consensus on the field of study topics and the contents of the MSBDS score. Seven practical requisites for the MSBDS score were agreed. A total of 70 MM patients (mean age, 60 years ±9.2 [standard deviation]; range, 35-70 years) undergoing total-body CT was included to develop MSBDS scores. Patients data were already stored in the Radiological database for other Research studies IRB approved (054/2019). Readers to test the MSDMS were radiologists and clinicians involved in MM care or expert in bone damage scores with different level of experience in musculoskeletal and total body CT. Readers were blinded to the clinical data of the patients. RESULTS: The MSBDS scores based on the consensus work described above and literature analysis was finalized. MSBDS is based on an additive scale with assessment of a total body CT with the bone window one time and includes indicators of structural bone damage and instability or fracture risk. The total score is given by the sum of item scores for abnormalities detected. Its values range from 0 (minimum) to values > 10 where 10 is represented by high-risk patients. In high-risk patients immediate surgical or radiation oncologist consultation is suggested. CONCLUSIONS: The MSBDS descriptive criteria are easy, highly reproducible and can be considered as a strong base for harmonizing total body CT interpretation in multiple myeloma patients undergoing stem cell transplantation.

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.

Fee Payment System for Nursing Performance: An operational proposal through the use of I.C.A. (Indexes of Complexity of Assistance) methodology.

BACKGROUND AND AIM OF THE STUDY: The objective of this study is to demonstrate the feasibility of a possible fee system based on the performance of the nursing function, validating the theoretical and methodological assumption of an economic analysis for nursing. METHOD: The I.C.A. Methodology (Indexes of Complexity of Assistance) was chosen as a system able to produce the reading, in an economic sense, of nursing performance to a sufficiently accurate degree, by its use of "assistance settings" and "nursing assistance plans". For the planning of assistance, the Nursing Interventions Classification (NIC) was used, as it is a validated and shared language. For the cost of each single operation/intervention and recovery, the "weight intervention" algorithm of the I.C.A. methodology was used. This research project was carried out in the form of a pilot study which investigated a sample of 30 patients, and a multi-center cross-sectional and observational retrospective study conducted on a sample of 135 patients coming from three Region of Liguria Hospitals.   Results: The study was concerned with 165 cases. It highlighted 65 D.R.G.s, of which 17 were selected, containing comprehensively 61 cases. The results obtained confirm that it is possible to use this proposed approach to calculate the direct and indirect costs of nursing activity, and that it is also possible to compare it to the present D.R.G. system. CONCLUSIONS: It is necessary to create a multidisciplinary payment system for a patient's care. This must be able to monitor the whole treatment process, and therefore all of the activities carried out for the patient's benefit. The present D.R.G. system is not able to register the care provided by nursing services.