Characterization of cell cycle, inflammation, and oxidative stress signaling role in non-communicable diseases: Insights into genetic variants, microRNAs and pathways.

Non-Communicable Diseases (NCDs) significantly impact global health, contributing to over 70% of premature deaths, as reported by the World Health Organization (WHO). These diseases have complex and multifactorial origins, involving genetic, epigenetic, environmental and lifestyle factors. While Genome-Wide Association Study (GWAS) is widely recognized as a valuable tool for identifying variants associated with complex phenotypes; the multifactorial nature of NCDs necessitates a more comprehensive exploration, encompassing not only the genetic but also the epigenetic aspect. For this purpose, we employed a bioinformatics-multiomics approach to examine the genetic and epigenetic characteristics of NCDs (i.e. colorectal cancer, coronary atherosclerosis, squamous cell lung cancer, psoriasis, type 2 diabetes, and multiple sclerosis), aiming to identify novel biomarkers for diagnosis and prognosis. Leveraging GWAS summary statistics, we pinpointed Single Nucleotide Polymorphisms (SNPs) independently associated with each NCD. Subsequently, we identified genes linked to cell cycle, inflammation and oxidative stress mechanisms, revealing shared genes across multiple diseases, suggesting common functional pathways. From an epigenetic perspective, we identified microRNAs (miRNAs) with regulatory functions targeting these genes of interest. Our findings underscore critical genetic pathways implicated in these diseases. In colorectal cancer, the dysregulation of the "Cytokine Signaling in Immune System" pathway, involving LAMA5 and SMAD7, regulated by Hsa-miR-21-5p, Hsa-miR-103a-3p, and Hsa-miR-195-5p, emerged as pivotal. In coronary atherosclerosis, the pathway associated with "binding of TCF/LEF:CTNNB1 to target gene promoters" displayed noteworthy implications, with the MYC factor controlled by Hsa-miR-16-5p as a potential regulatory factor. Squamous cell lung carcinoma analysis revealed significant pathways such as "PTK6 promotes HIF1A stabilization," regulated by Hsa-let-7b-5p. In psoriasis, the "Endosomal/Vacuolar pathway," involving HLA-C and Hsa-miR-148a-3p and Hsa-miR-148b-3p, was identified as crucial. Type 2 Diabetes implicated the "Regulation of TP53 Expression" pathway, controlled by Hsa-miR-106a-5p and Hsa-miR-106b-5p. In conclusion, our study elucidates the genetic framework and molecular mechanisms underlying NCDs, offering crucial insights into potential genetic/epigenetic biomarkers for diagnosis and prognosis. The specificity of pathways and related miRNAs in different pathologies highlights promising candidates for further clinical validation, with the potential to advance personalized treatments and alleviate the global burden of NCDs.

Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions.

Breast cancer (BC) is a heterogeneous disease, affecting millions of women every year. Early diagnosis is crucial to increasing survival. The clinical workup of BC diagnosis involves diagnostic imaging and bioptic characterization. In recent years, technical advances in image processing allowed for the application of advanced image analysis (radiomics) to clinical data. Furthermore, -omics technologies showed their potential in the characterization of BC. Combining information provided by radiomics with -omics data can be important to personalize diagnostic and therapeutic work up in a clinical context for the benefit of the patient. In this review, we analyzed the recent literature, highlighting innovative approaches to combine imaging and biochemical/biological data, with the aim of identifying recent advances in radiogenomics applied to BC. The results of radiogenomic studies are encouraging approaches in a clinical setting. Despite this, as radiogenomics is an emerging area, the optimal approach has to face technical limitations and needs to be applied to large cohorts including all the expression profiles currently available for BC subtypes (e.g., besides markers from transcriptomics, proteomics and miRNomics, also other non-coding RNA profiles).

Secreted miR-153 Controls Proliferation and Invasion of Higher Gleason Score Prostate Cancer.

Prostate cancer (PC) is a male common neoplasm and is the second leading cause of cancer death in American men. PC is traditionally diagnosed by the evaluation of prostate secreted antigen (PSA) in the blood. Due to the high levels of false positives, digital rectal examination and transrectal ultrasound guided biopsy are necessary in uncertain cases with elevated PSA levels. Nevertheless, the high mortality rate suggests that new PC biomarkers are urgently needed to help clinical diagnosis. In a previous study, we have identified a network of genes, altered in high Gleason Score (GS) PC (GS ≥ 7), being regulated by miR-153. Until now, no publication has explained the mechanism of action of miR-153 in PC. By in vitro studies, we found that the overexpression of miR-153 in high GS cell lines is required to control cell proliferation, migration and invasion rates, targeting Kruppel-like factor 5 (KLF5). Moreover, miR-153 could be secreted by exosomes and microvesicles in the microenvironment and, once entered into the surrounding tissue, could influence cellular growth. Being upregulated in high GS human PC, miR-153 could be proposed as a circulating biomarker for PC diagnosis.

A tri-modal tissue-equivalent anthropomorphic phantom for PET, CT and multi-parametric MRI radiomics.

PURPOSE: Radiomics has emerged as an advanced image processing methodology to define quantitative imaging biomarkers for prognosis and prediction of treatment response and outcome. The development of quantitative imaging biomarkers requires careful analysis to define their accuracy, stability and reproducibility through phantom measurements. Few efforts were devoted to develop realistic anthropomorphic phantoms. In this work, we developed a multimodality image phantom suitable for PET, CT and multiparametric MRI imaging. METHODS: A tissue-equivalent gel-based mixture was designed and tested for compatibility with different imaging modalities. Calibration measurements allowed to assess gel composition to simulate PET, CT and MRI contrasts of oncological lesions. The characterized gel mixture was used to create realistic synthetic lesions (e.g. lesions with irregular shape and non-uniform image contrast), to be inserted in a standard anthropomorphic phantom. In order to show phantom usefulness, issues related to accuracy, stability and reproducibility of radiomic biomarkers were addressed as proofs-of-concept. RESULTS: The procedure for gel preparation was straightforward and the characterized gel mixture allowed to mime simultaneously oncological lesion contrast in CT, PET and MRI imaging. Proofs-of-concept studies suggested that phantom measurements can be customized for specific clinical situations and radiomic protocols. CONCLUSIONS: We developed a strategy to manufacture an anthropomorphic, tissue-equivalent, multimodal phantom to be customized on specific radiomics protocols, for addressing specific methodological issues both in mono and multicentric studies.

Validity of cingulate-precuneus-temporo-parietal hypometabolism for single-subject diagnosis of biomarker-proven atypical variants of Alzheimer's Disease.

The aim of our study was to establish empirically to what extent reduced glucose uptake in the precuneus, posterior cingulate and/or temporo-parietal cortex (PCTP), which is thought to indicate brain amyloidosis in patients with dementia or MCI due to Alzheimer's Disease (AD), permits to distinguish amyloid-positive from amyloid-negative patients with non-classical AD phenotypes at the single-case level. We enrolled 127 neurodegenerative patients with cognitive impairment and a positive (n. 63) or negative (n. 64) amyloid marker (cerebrospinal fluid or amy-PET). Three rating methods of FDG-PET scan were applied: purely qualitative visual interpretation of uptake images (VIUI), and visual reading assisted by a semi-automated and semi-quantitative tool: INLAB, provided by the Italian National Research Council, or Cortex ID Suite, marketed by GE Healthcare. Fourteen scans (11.0%) patients remained unclassified by VIUI or INLAB procedures, therefore, validity values were computed on the remaining 113 cases. The three rating approaches showed good total accuracy (77-78%), good to optimal sensitivity (81-93%), but poorer specificity (62-75%). VIUI showed the highest sensitivity and the lowest specificity, and also the highest proportion of unclassified cases. Cases with asymmetric temporo-parietal hypometabolism and a progressive aphasia or corticobasal clinical profile, in particular, tended to be rated as AD-like, even if biomarkers indicated non-amyloid pathology. Our findings provide formal support to the value of PCTP hypometabolism for single-level diagnosis of amyloid pathophysiology in atypical AD, but also highlight the risk of qualitative assessment to misclassify patients with non-AD PPA or CBS underpinned by asymmetric temporo-parietal hypometabolism.

PET/MRI for Staging the Axilla in Breast Cancer: Current Evidence and the Rationale for SNB vs. PET/MRI Trials.

Axillary surgery in breast cancer (BC) is no longer a therapeutic procedure but has become a purely staging procedure. The progressive improvement in imaging techniques has paved the way to the hypothesis that prognostic information on nodal status deriving from surgery could be obtained with an accurate diagnostic exam. Positron emission tomography/magnetic resonance imaging (PET/MRI) is a relatively new imaging tool and its role in breast cancer patients is still under investigation. We reviewed the available literature on PET/MRI in BC patients. This overview showed that PET/MRI yields a high diagnostic performance for the primary tumor and distant lesions of liver, brain and bone. In particular, the results of PET/MRI in staging the axilla are promising. This provided the rationale for two prospective comparative trials between axillary surgery and PET/MRI that could lead to a further de-escalation of surgical treatment of BC. • SNB vs. PET/MRI 1 trial compares PET/MRI and axillary surgery in staging the axilla of BC patients undergoing primary systemic therapy (PST). • SNB vs. PET/MRI 2 trial compares PET/MRI and sentinel node biopsy (SNB) in staging the axilla of early BC patients who are candidates for upfront surgery. Finally, these ongoing studies will help clarify the role of PET/MRI in BC and establish whether it represents a useful diagnostic tool that could guide, or ideally replace, axillary surgery in the future.

Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer.

OBJECTIVE: The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC). METHODS: In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence. RESULTS: Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87. CONCLUSIONS: Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology.

Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness.

Radiomics allows the extraction quantitative features from imaging, as imaging biomarkers of disease. The objective of this exploratory study is to implement a reproducible radiomic-pipeline for the extraction of a magnetic resonance imaging (MRI) signature for prostate cancer (PCa) aggressiveness. One hundred and two consecutive patients performing preoperative prostate multiparametric magnetic resonance imaging (mpMRI) and radical prostatectomy were enrolled. Multiparametric images, including T2-weighted (T2w), diffusion-weighted and dynamic contrast-enhanced images, were acquired at 1.5 T. Ninety-three imaging features (Ifs) were extracted from segmentation of index lesion. Ifs were ranked based on a stability rank and redundant Ifs were excluded. Using unsupervised hierarchical clustering, patients were grouped on the basis of similar radiomic patterns, whose association with Gleason Grade Group (GGG), extracapsular extension (ECE), and nodal involvement (pN) was tested. Signatures composed by IFs from T2w-images and Apparent Diffusion Coefficient (ADC) maps were tested for the prediction of GGG, ECE, and pN. T2w radiomic pattern was associated with pN, ECE, and GGG (p = 0.027, 0.05, 0.03) and ADC radiomic pattern was associated with GGG (p = 0.004). The best performance was reached by the signature combing IFs from multiparametric images (0.88, 0.89, and 0.84 accuracy for GGG, pN, and ECE). A reliable multiparametric MRI radiomic signature was extracted, potentially able to predict PCa aggressiveness, to be further validated on an independent sample.

AI applications to medical images: From machine learning to deep learning.

PURPOSE: Artificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context. METHODS: A narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections. RESULTS: We first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way. CONCLUSIONS: Biomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practice.

Circulating microRNA: The Potential Novel Diagnostic Biomarkers to Predict Drug Resistance in Temporal Lobe Epilepsy, a Pilot Study.

MicroRNAs (miRNAs) are small noncoding RNAs that have emerged as new potential epigenetic biomarkers. Here, we evaluate the efficacy of six circulating miRNA previously described in the literature as biomarkers for the diagnosis of temporal lobe epilepsy (TLE) and/or as predictive biomarkers to antiepileptic drug response. We measured the differences in serum miRNA levels by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assays in a cohort of 27 patients (14 women and 13 men; mean ± SD age: 43.65 ± 17.07) with TLE compared to 20 healthy controls (HC) matched for sex, age and ethnicity (11 women and 9 men; mean ± SD age: 47.5 ± 9.1). Additionally, patients were classified according to whether they had drug-responsive (n = 17) or drug-resistant (n = 10) TLE. We have investigated any correlations between miRNAs and several electroclinical parameters. Three miRNAs (miR-142, miR-146a, miR-223) were significantly upregulated in patients (expressed as average expression ± SD). In detail, miR-142 expression was 0.40 ± 0.29 versus 0.16 ± 0.10 in TLE patients compared to HC (t-test, p < 0.01), miR-146a expression was 0.15 ± 0.11 versus 0.07 ± 0.04 (t-test, p < 0.05), and miR-223 expression was 6.21 ± 3.65 versus 1.23 ± 0.84 (t-test, p < 0.001). Moreover, results obtained from a logistic regression model showed the good performance of miR-142 and miR-223 in distinguishing drug-sensitive vs. drug-resistant TLE. The results of this pilot study give evidence that miRNAs are suitable targets in TLE and offer the rationale for further confirmation studies in larger epilepsy cohorts.

In Silico Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis.

Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. BACKGROUND: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype. METHODS: We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype. RESULTS: We found six miRNAs correlated with imaging features in Luminal A (miR-1537, -205, -335, -337, -452, and -99a), seven miRNAs (miR-142, -155, -190, -190b, -1910, -3617, and -429) in HER2+, and two miRNAs (miR-135b and -365-2) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone. CONCLUSION: Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.

INTRODUCTION: The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes. OBJECTIVE: The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

Acute Inescapable Stress Rapidly Increases Synaptic Energy Metabolism in Prefrontal Cortex and Alters Working Memory Performance.

Brain energy metabolism actively regulates synaptic transmission and activity. We have previously shown that acute footshock (FS)-stress induces fast and long-lasting functional and morphological changes at excitatory synapses in prefrontal cortex (PFC). Here, we asked whether FS-stress increased energy metabolism in PFC, and modified related cognitive functions. Using positron emission tomography (PET), we found that FS-stress induced a redistribution of glucose metabolism in the brain, with relative decrease of [18F]FDG uptake in ventro-caudal regions and increase in dorso-rostral ones. Absolute [18F]FDG uptake was inversely correlated with serum corticosterone. Increased specific hexokinase activity was also measured in purified PFC synaptosomes (but not in total extract) of FS-stressed rats, which positively correlated with 2-Deoxy [3H] glucose uptake by synaptosomes. In line with increased synaptic energy demand, using an electron microscopy-based stereological approach, we found that acute stress induced a redistribution of mitochondria at excitatory synapses, together with an increase in their volume. The fast functional and metabolic activation of PFC induced by acute stress, was accompanied by rapid and sustained alterations of working memory performance in delayed response to T-maze test. Taken together, the present data suggest that acute stress increases energy consumption at PFC synaptic terminals and alters working memory.

Parameters Influencing PET Imaging Features: A Phantom Study with Irregular and Heterogeneous Synthetic Lesions.

Aim: To evaluate reproducibility and stability of radiomic features as effects of the use of different volume segmentation methods and reconstruction settings. The potential of radiomics in really capturing the presence of heterogeneous tumor uptake and irregular shape was also investigated. Materials and Methods: An anthropomorphic phantom miming real clinical situations including synthetic lesions with irregular shape and nonuniform radiotracer uptake was used. 18F-FDG PET/CT measurements of the phantom were performed including 38 lesions of different shape, size, lesion-to-background ratio, and radiotracer uptake distribution. Different reconstruction parameters and segmentation methods were considered. COVs were calculated to quantify feature variations over the different reconstruction settings. Friedman test was applied to the values of the radiomic features obtained for the considered segmentation approaches. Two sets of test-retest measurement were acquired and the pairwise intraclass correlation coefficient was calculated. Fifty-eight morphological and statistical features were extracted from the segmented lesion volumes. A Mann-Whitney test was used to evaluate significant differences among each feature when calculated from heterogeneous versus homogeneous uptake. The significance of each radiomic feature in terms of capturing heterogeneity was evaluated also by testing correlation with gold standard indexes of heterogeneity and sphericity. Results: The choice of the segmentation method has a strong impact on the stability of radiomic features (less than 20% can be considered stable features). Reconstruction affects the estimate of radiomic features (only 26% are stable). Thirty-one radiomic features (53%) resulted to be reproducible, 11 of them are able to discriminate heterogeneity. Among these, we found a subset of 3 radiomic features strongly correlated with GS heterogeneity index that can be suggested as good features for retrospective evaluations.

A Method for Manufacturing Oncological Phantoms for the Quantification of 18F-FDG PET and DW-MRI Studies.

The aim of this work was to develop a method to manufacture oncological phantoms for quantitation purposes in 18F-FDG PET and DW-MRI studies. Radioactive and diffusion materials were prepared using a mixture of agarose and sucrose radioactive gels. T2 relaxation and diffusion properties of gels at different sucrose concentrations were evaluated. Realistic oncological lesions were created using 3D-printed plastic molds filled with the gel mixture. Once solidified, gels were extracted from molds and immersed in a low-radioactivity gel simulating normal background tissue. A breast cancer phantom was manufactured using the proposed method as an exploratory feasibility study, including several realistic oncological configurations in terms of both radioactivity and diffusion. The phantom was acquired in PET with 18F-FDG, immediately after solidification, and in DW-MRI the following day. Functional volumes characterizing the simulated BC lesions were segmented from PET and DW-MRI images. Measured radioactive uptake and ADC values were compared with gold standards. Phantom preparation was straightforward, and the time schedule was compatible with both PET and MRI measurements. Lesions appeared on 18F-FDG PET and DW-MRI images as expected, without visible artifacts. Lesion functional parameters revealed the phantom's potential for validating quantification methods, in particular for new generation hybrid PET-MRI systems.

Quantitative measurement of 18F-FDG PET/CT uptake reflects the expansion of circulating plasmablasts in IgG4-related disease.

Objective: [18F]Fluorodeoxyglucose (18F-FDG) PET/CT is increasingly used to assess organ involvement and response to treatment in IgG4-related disease (IgG4-RD), but clear correlations between 18F-FDG uptake and disease activity have not been established yet. We aimed to correlate the intensity and distribution of 18F-FDG uptake with validated clinical, serological and immunological parameters of IgG4-RD activity. Methods: Twenty patients with active IgG4-RD underwent a baseline 18F-FDG PET/CT. Ten patients repeated 18F-FDG PET/CT after immunosuppressive treatments. 18F-FDG tissue uptake was measured using the standardized uptake value corrected for the partial volume effect (PVC-SUV) and the total lesion glycolysis (TLG) with (TLGtot+ln) and without (TLGtot-ln) lymph nodes. Disease activity was assessed by means of clinical parameters [IgG4-RD Responder Index (RI)], serological (ESR and CRP) and immunological (serum IgG4 and circulating plasmablasts) biomarkers. The enhanced liver fibrosis score was exploited as a biomarker for fibroblast activation. Results: Thirteen (65%) patients had two or more organs affected by IgG4-RD. All patients had active IgG4-RD as defined by a median IgG4-RD RI value of 9 (range 6-15; normal < 3). Serum IgG4 and plasmablasts were elevated in 85% of patients. Circulating plasmablasts positively correlated with PVC-SUV (P = 0.027), inversely correlated with TLGtot-ln (P = 0.023) and did not correlate with TLGtot+ln (P > 0.05). No statistically significant correlation was found between PVC-SUV or TLG and IgG4-RD RI, ESR, CRP, serum IgG4 or enhanced liver fibrosis score (P > 0.05). Clinical response to immunosuppressive therapies was associated with a consensual reduction of circulating plasmablasts, PVC-SUV, TLGtot+ln and TLGtot-ln values (P < 0.05 for all comparisons). Conclusions: 18F-FDG uptake of IgG4-RD lesions reflects immunological perturbations of the B cell compartment rather than fibroblast activation and extracellular matrix deposition. Conventional biomarkers of disease activity, namely IgG4-RD RI, ESR, CRP and serum IgG4 levels, do not appear to correlate with the radiometabolic activity of IgG4-RD lesions. In light of our results PET/CT represents a reliable instrument for assessing IgG4-RD activity, although lymph-node uptake deserves careful interpretation.

[18F]FDG PET/CT features for the molecular characterization of primary breast tumors.

PURPOSE: The aim of this study was to evaluate the role of imaging features derived from [18F]FDG-PET/CT to provide in vivo characterization of breast cancer (BC). METHODS: Images from 43 patients with a first diagnosis of BC were reviewed. Images were acquired before any treatment. Histological data were derived from pretreatment biopsy or surgical histological specimen; these included tumor type, grade, ER and PgR receptor status, lymphovascular invasion, Ki67 index, HER2 status, and molecular subtype. Standard parameters (SUVmean, TLG, MTV) and advanced imaging features (histogram-based and shape and size features) were evaluated. Univariate analysis, hierarchical clustering analysis, and exact Fisher's test were used for statistical analysis of data. Imaging-derived metrics were reduced evaluating the mutual correlation within group of features as well as the mutual correlation between groups of features to form a signature. RESULTS: A significant correlation was found between some advanced imaging features and the histological type. Different molecular subtypes were characterized by different values of two histogram-based features (median and energy). A significant association was observed between the imaging signature and luminal A and luminal B HER2 negative molecular subtype and also when considering luminal A, luminal B HER2-negative and HER2-positive groups. Similar results were found between the signature and all five molecular subtypes and also when considering the histological types of BC. CONCLUSIONS: Our results suggest a complementary role of standard PET imaging parameters and advanced imaging features for the in vivo biological characterization of BC lesions.

The utility of FDG-PET in the differential diagnosis of Parkinsonism.

INTRODUCTION: Differential diagnosis of parkinsonian disorders can be difficult on clinical grounds, especially in the early stage. Recent advancements in 18-F-fluorodeoxyglucose positron emission tomography (FDG-PET) imaging reveals different patterns of regional glucose metabolism in idiopathic Parkinson's disease (IPD) and atypical parkinsonian syndromes, such as multiple system atrophy (MSA), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), which may help differentiating between these conditions. PURPOSE: To assess the utility of FDG-PET imaging in differential diagnosis of Parkinsonism in clinical practice. METHODS: FDG-PET was performed in 72 patients with parkinsonism (age 34-80 years) referred to our center by movement disorder specialists. FDG-PET diagnosis was obtained by visual assessment of individual scans combined with voxel-based statistical parametric mapping analysis. FDG-PET diagnosis assigned at the time of imaging was compared with the final clinical diagnosis made by the movement disorder specialists after ≥2 years follow-up. RESULTS: FDG-PET findings were consistent with IPD in 27, MSA in 18, PSP in 19 and CBS in 2 patients. The final clinical diagnosis was IPD in 29, MSA in 20, PSP in 21 and CBS in 2 patients. Concordance between the FDG-PET and clinical diagnoses was 92% in the overall sample (IPD 93%, MSA 90%, PSP 91% and CBS 100%). The diagnostic accuracy of FDG-PET was 93% for IPD and MSA and 97% for PSP. CONCLUSION: FDG-PET may help differentiate between IPD, MSA, PSP and CBS among patients presenting with parkinsonian symptoms, which is important for patient counselling and making early decisions about treatment.

FDG PET/CT as theranostic imaging in diagnosis of non-small cell lung cancer.

Objective of this work was to evaluate the role of 18F-fluorodeoxyglucose (FDG) positron-emission tomography features as theranostic imaging biomarkers in non-small cell lung cancer. In a retrospective protocol, 31 stage I-III NSCLC patients were enrolled. Patients underwent FDG PET/CT for staging purposes before surgery and were followed for two years after surgery. PET images were quantitatively analyzed. For the primary lesion, metabolic tumour volume, maximum standardized uptake value (SUV), SUV corrected for partial volume effect, total lesion glycolysis, 14 histogram and four shape-and-size features were extracted as PET imaging features. PET features were correlated with histology and 2-year disease-free survival (DFS). Significant correlations were found between grading, T parameter, N status, pathological stage and different FDG PET features. Histogram-based features "energy" and "kurtosis" resulted to be predictive for DFS. The cut-off value identified for "kurtosis" was able to separate the adenocarcinoma patients with different outcomes. FDG PET features are able to characterize lung cancer lesions, suggesting the possibility of reliable "imaging biopsy", and have a predictive role in adenocarcinoma patients undergoing surgery.