Gene's expression underpinning the divergent predictive value of [18F]F-fluorodeoxyglucose and prostate-specific membrane antigen positron emission tomography in primary prostate cancer: a bioinformatic and experimental study.

BACKGROUND: Positron Emission Tomography (PET) imaging with Prostate-Specific Membrane Antigen (PSMA) and Fluorodeoxyglucose (FDG) represent promising biomarkers for risk-stratification of Prostate Cancer (PCa). We verified whether the expression of genes encoding for PSMA and enzymes regulating FDG cellular uptake are independent and additive prognosticators in PCa. METHODS: mRNA expression of genes involved in glucose metabolism and PSMA regulation obtained from primary PCa specimens were retrieved from open-source databases and analyzed using an integrative bioinformatics approach. Machine Learning (ML) techniques were used to create predictive Progression-Free Survival (PFS) models. Cellular models of primary PCa with different aggressiveness were used to compare [18F]F-PSMA-1007 and [18F]F-FDG uptake kinetics in vitro. Confocal microscopy, immunofluorescence staining, and quantification analyses were performed to assess the intracellular and cellular membrane PSMA expression. RESULTS: ML analyses identified a predictive functional network involving four glucose metabolism-related genes: ALDOB, CTH, PARP2, and SLC2A4. By contrast, FOLH1 expression (encoding for PSMA) did not provide any additive predictive value to the model. At a cellular level, the increase in proliferation rate and migratory potential by primary PCa cells was associated with enhanced FDG uptake and decreased PSMA retention (paralleled by the preferential intracellular localization). CONCLUSIONS: The overexpression of a functional network involving four glucose metabolism-related genes identifies a higher risk of disease progression since the earliest phases of PCa, in agreement with the acknowledged prognostic value of FDG PET imaging. By contrast, the prognostic value of PSMA PET imaging is independent of the expression of its encoding gene FOLH1. Instead, it is influenced by the protein docking to the cell membrane, regulating its accessibility to tracer binding.

Nodal staging with MRI after neoadjuvant chemo-radiotherapy for locally advanced rectal cancer: a fast and reliable method.

OBJECTIVES: In patients with locally advanced rectal carcinoma (LARC), negative nodal status after neoadjuvant chemoradiotherapy (nCRT) may allow for rectum-sparing protocols rather than total mesorectal excision; however, current MRI criteria for nodal staging have suboptimal accuracy. The aim of this study was to compare the diagnostic accuracy of different MRI dimensional criteria for nodal staging after nCRT in patients with LARC. MATERIALS AND METHODS: Patients who underwent MRI after nCRT for LARC followed by surgery were retrospectively included and divided into a training and a validation cohort of 100 and 39 patients, respectively. Short-, long-, and cranial-caudal axes and volume of the largest mesorectal node and nodal status based on European Society of Gastrointestinal Radiology consensus guidelines (i.e., ESGAR method) were assessed by two radiologists independently. Inter-reader agreement was assessed in the training cohort. Histopathology was the reference standard. ROC curves and the best cut-off were calculated, and accuracies compared with the McNemar test. RESULTS: The study population included 139 patients (median age 62 years [IQR 55-72], 94 men). Inter-reader agreement was high for long axis (κ = 0.81), volume (κ = 0.85), and ESGAR method (κ = 0.88) and low for short axis (κ = 0.11). Accuracy was similar (p > 0.05) for long axis, volume, and ESGAR method both in the training (71%, 74%, and 65%, respectively) and in the validation (83%, 78%, and 75%, respectively) cohorts. CONCLUSION: Accuracy of the measurement of long axis and volume of the largest lymph node is not inferior to the ESGAR method for nodal staging after nCRT in LARC. CLINICAL RELEVANCE STATEMENT: In MRI restaging of rectal cancer, measurement of the long axis or volume of largest mesorectal lymph node after preoperative chemoradiotherapy is a faster and reliable alternative to ESGAR criteria for nodal staging. KEY POINTS: • Current MRI criteria for nodal staging in locally advanced rectal cancer after chemo-radiotherapy have suboptimal accuracy and are time-consuming. • Measurement of long axis or volume of the largest mesorectal lymph node on MRI showed good accuracy for assessment of loco-regional nodal status in locally advanced rectal cancer. • MRI measurement of the long axis and volume of largest mesorectal lymph node after chemo-radiotherapy could be a faster and reliable alternative to ESGAR criteria for nodal staging.

Blockchain in radiology research and clinical practice: current trends and future directions.

Blockchain usage in healthcare, in radiology, in particular, is at its very early infancy. Only a few research applications have been tested, however, blockchain technology is widely known outside healthcare and widely adopted, especially in Finance, since 2009 at least. Learning by history, radiology is a potential ideal scenario to apply this technology. Blockchain could have the potential to increase radiological data value in both clinical and research settings for the patient digital record, radiological reports, privacy control, quantitative image analysis, cybersecurity, radiomics and artificial intelligence.Up-to-date experiences using blockchain in radiology are still limited, but radiologists should be aware of the emergence of this technology and follow its next developments. We present here the potentials of some applications of blockchain in radiology.

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.

MRI T2-weighted sequences-based texture analysis (TA) as a predictor of response to neoadjuvant chemo-radiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).

PURPOSE: To determine whether MRI T2-weighted sequences-based texture analysis (TA) can predict histopathological tumor regression grade (TRG) in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemo-radiotherapy (nCRT). METHODS: Data on patients undergoing curative-intent surgery for LARC were collected. Patients with a complete pathological response, or TRG1 according to Mandard's system were classified as responders, while patients with TRG ≥ 2 were classified as non-responders. Tumor TA was performed on each patient's paraxial T2w MRI in both pre- and post-nCRT scans, in order to extract histograms, gray-level co-occurrence matrix (GLCM) and run-length matrix (RLM) texture parameters. For features that showed a significant difference between the two groups, a receiver operating characteristic (ROC) curve was drawn. RESULTS: Overall, 62 patients with LARC, treated with nCRT and resective surgery at our institution between 2013 and 2019 were identified. Only post-nCRT GLCM maximum probability showed a significant difference between the two groups (2909 ± 4479 in responders vs. 6515 ± 8990 in non- responders; p = 0.039); at the ROC curve, Youden index showed a sensitivity of 14% and a specificity of 100% for this parameter. CONCLUSIONS: MRI T2-weighted sequences-based TA was not effective in predicting pathological complete response to nCRT in patients with LARC. Further studies are needed to thoroughly investigate the potential of MRI TA in this setting.

Interplay between spinal cord and cerebral cortex metabolism in amyotrophic lateral sclerosis.

We recently reported the potential of Hough transform in delineating spinal cord metabolism by 18F-fluorodeoxyglucose PET/CT scanning in amyotrophic lateral sclerosis. The present study aimed to verify the relationship between spinal cord and brain metabolism in 44 prospectively recruited patients affected by amyotrophic lateral sclerosis submitted to 18F-fluorodeoxyglucose brain and whole-body PET/CT. Patients were studied to highlight the presence of brain hypo- or hypermetabolism with respect to healthy controls, and multiple regression analysis was performed to evaluate the correlation between spinal cord and brain metabolism. Our results confirmed higher 18F-fluorodeoxyglucose uptake in both cervical and dorsal spinal cord in patients with amyotrophic lateral sclerosis with respect to controls. This finding was paralleled by the opposite pattern in the brain cortex that showed a generalized reduction in tracer uptake. This hypometabolism was particularly evident in wide regions of the frontal-dorsolateral cortex while it did not involve the midbrain. Bulbar and spinal disease onset was associated with similar degree of metabolic activation in the spinal cord. However, among spinal onset patients, upper limb presentation was associated with a more pronounced metabolic activation of cervical segment. Obtained data suggest a differential neuro-pathological state or temporal sequence in disease progression.

Allogeneic cell transplant expands bone marrow distribution by colonizing previously abandoned areas: an FDG PET/CT analysis.

Mechanisms of hematopoietic reconstitution after bone marrow (BM) transplantation remain largely unknown. We applied a computational quantification software application to hybrid 18F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images to assess activity and distribution of the hematopoietic system throughout the whole skeleton of recently transplanted patients. Thirty-four patients underwent PET/CT 30 days after either adult stem cell transplantation (allogeneic cell transplantation [ACT]; n = 18) or cord blood transplantation (CBT; n = 16). Our software automatically recognized compact bone volume and trabecular bone volume (IBV) in CT slices. Within IBV, coregistered PET data were extracted to identify the active BM (ABM) from the inactive tissue. Patients were compared with 34 matched controls chosen among a published normalcy database. Whole body ABM increased in ACT and CBT when compared with controls (12.4 ± 3 and 12.8 ± 6.8 vs 8.1 ± 2.6 mL/kg of ideal body weight [IBW], P < .001). In long bones, ABM increased three- and sixfold in CBT and ACT, respectively, compared with controls (0.9 ± 0.9 and 1.7 ± 2.5 vs 0.3 ± 0.3 mL/kg IBW, P < .01). These data document an unexpected distribution of transplanted BM into previously abandoned BM sites.

A PET/CT approach to spinal cord metabolism in amyotrophic lateral sclerosis.

PURPOSE: In amyotrophic lateral sclerosis, functional alterations within the brain have been intensively assessed, while progression of lower motor neuron damage has scarcely been defined. The aim of the present study was to develop a computational method to systematically evaluate spinal cord metabolism as a tool to monitor disease mechanisms. METHODS: A new computational three-dimensional method to extract the spinal cord from (18)F-FDG PET/CT images was evaluated in 30 patients with spinal onset amyotrophic lateral sclerosis and 30 controls. The algorithm identified the skeleton on the CT images by using an extension of the Hough transform and then extracted the spinal canal and the spinal cord. In these regions, (18)F-FDG standardized uptake values were measured to estimate the metabolic activity of the spinal canal and cord. Measurements were performed in the cervical and dorsal spine and normalized to the corresponding value in the liver. RESULTS: Uptake of (18)F-FDG in the spinal cord was significantly higher in patients than in controls (p < 0.05). By contrast, no significant differences were observed in spinal cord and spinal canal volumes between the two groups. (18)F-FDG uptake was completely independent of age, gender, degree of functional impairment, disease duration and riluzole treatment. Kaplan-Meier analysis showed a higher mortality rate in patients with standardized uptake values above the fifth decile at the 3-year follow-up evaluation (log-rank test, p < 0.01). The independence of this value was confirmed by multivariate Cox analysis. CONCLUSION: Our computational three-dimensional method enabled the evaluation of spinal cord metabolism and volume and might represent a potential new window onto the pathophysiology of amyotrophic lateral sclerosis.

Spatiotemporal dynamics in understanding hand-object interactions.

It is generally accepted that visual perception results from the activation of a feed-forward hierarchy of areas, leading to increasingly complex representations. Here we present evidence for a fundamental role of backward projections to the occipito-temporal region for understanding conceptual object properties. The evidence is based on two studies. In the first study, using high-density EEG, we showed that during the observation of how objects are used there is an early activation of occipital and temporal areas, subsequently reaching the pole of the temporal lobe, and a late reactivation of the visual areas. In the second study, using transcranial magnetic stimulation over the occipital lobe, we showed a clear impairment in the accuracy of recognition of how objects are used during both early activation and, most importantly, late occipital reactivation. These findings represent strong neurophysiological evidence that a top-down mechanism is fundamental for understanding conceptual object properties, and suggest that a similar mechanism might be also present for other higher-order cognitive functions.

Interaction Between Words and Symbolic Gestures as Revealed By N400.

What happens if you see a person pronouncing the word "go" after having gestured "stop"? Differently from iconic gestures, that must necessarily be accompanied by verbal language in order to be unambiguously understood, symbolic gestures are so conventionalized that they can be effortlessly understood in the absence of speech. Previous studies proposed that gesture and speech belong to a unique communication system. From an electrophysiological perspective the N400 modulation was considered the main variable indexing the interplay between two stimuli. However, while many studies tested this effect between iconic gestures and speech, little is known about the capability of an emblem to modulate the neural response to subsequently presented words. Using high-density EEG, the present study aimed at evaluating the presence of an N400 effect and its spatiotemporal dynamics, in terms of cortical activations, when emblems primed the observation of words. Participants were presented with symbolic gestures followed by a semantically congruent or incongruent verb. A N400 modulation was detected, showing larger negativity when gesture and words were incongruent. The source localization during N400 time window evidenced the activation of different portions of temporal cortex according to the gesture and word congruence. Our data provide further evidence of how the observation of an emblem influences verbal language perception, and of how this interplay is mainly instanced by different portions of the temporal cortex.

Adult advanced chronic lymphocytic leukemia: computational analysis of whole-body CT documents a bone structure alteration.

PURPOSE: To assess the presence of alteration of bone structure and bone marrow metabolism in adult patients who were suspected of having advanced chronic lymphocytic leukemia (ACLL) by using a computational prognostic model that was based on computational analysis of positron emission tomography (PET)/computed tomography (CT) images. MATERIALS AND METHODS: In this retrospective study, all patients signed written informed consent as a requisite to undergo PET/CT examination. However, due to its observational nature, approval from the ethical committee was not deemed necessary. Twenty-two previously untreated chronic lymphocytic leukemia patients underwent PET/CT for disease progression. PET/CT images were analyzed by using dedicated software, capable of recognizing an external 2-pixel bone ring whose Hounsfield coefficient served as cutoff to recognize trabecular and compact bone. PET/CT data from 22 age- and sex-matched control subjects were used as comparison. All data are reported as means ± standard deviations. The Student t test, log-rank, or Cox proportional hazards model were used as appropriate, considering a difference with a P value of less than .05 as significant. RESULTS: Trabecular bone was expanded in ACLL patients and occupied a larger fraction of the skeleton with respect to control subjects (mean, 39% ± 5 [standard deviation] vs 31% ± 7; ie, 32 of 81 mL/kg of ideal body weight vs 27 of 86 mL/kg of ideal body weight, respectively; P < .001). After stratification according to median value, patients with a ratio of trabecular to skeletal bone volume of more than 37.3% showed an actuarial 2-year survival of 18%, compared with 82% for those with a ratio of less than 37.3% (P < .001), independent from age, sex, biological markers, and disease duration. CONCLUSION: These data suggest that computational assessment of skeletal alterations might represent a new window for prediction of the clinical course of the disease.

Cognitive abilities in siblings of children with autism spectrum disorders.

The aim of the present study was to assess the cognitive profiles of children with autistic spectrum disorder and of their healthy siblings (Siblings). With the term cognitive profile, we indicate the relationship extant among the values of verbal and performance subtests of the Wechsler Intelligence Scale. The conducted statistical analyses indicated that, although siblings showed a normal intelligent quotient and did not differ in this aspect from typically developing group, their cognitive profile was amazingly similar to that of their relatives affected by autism. A k-means clustering analysis on the values of single subtests further confirmed this result, showing a clear separation between typically developing children on the one side, and autistics and their siblings on the other. We suggest that the common cognitive profile observed in autistic children and their siblings could represent a marker of liability to autism and, thus, a possible intermediate phenotype of this syndrome.

Prediction of candidemia with machine learning techniques: state of the art.

In this narrative review, we discuss studies assessing the use of machine learning (ML) models for the early diagnosis of candidemia, focusing on employed models and the related implications. There are currently few studies evaluating ML techniques for the early diagnosis of candidemia as a prediction task based on clinical and laboratory features. The use of ML tools holds promise to provide highly accurate and real-time support to clinicians for relevant therapeutic decisions at the bedside of patients with suspected candidemia. However, further research is needed in terms of sample size, data quality, recognition of biases and interpretation of model outputs by clinicians to better understand if and how these techniques could be safely adopted in daily clinical practice.

Candida is a type of fungus that can cause fatal infections. To confirm the presence of the infection, doctors may search for the fungus in the blood. Here, we discuss if computer systems can help to identify infection more easily and more rapidly.

Explainable and Interpretable Machine Learning for Antimicrobial Stewardship: Opportunities and Challenges.

There is growing interest in exploiting the advances in artificial intelligence and machine learning (ML) for improving and monitoring antimicrobial prescriptions in line with antimicrobial stewardship principles. Against this background, the concepts of interpretability and explainability are becoming increasingly essential to understanding how ML algorithms could predict antimicrobial resistance or recommend specific therapeutic agents, to avoid unintended biases related to the "black box" nature of complex models. In this commentary, we review and discuss some relevant topics on the use of ML algorithms for antimicrobial stewardship interventions, highlighting opportunities and challenges, with particular attention paid to interpretability and explainability of employed models. As in other fields of medicine, the exponential growth of artificial intelligence and ML indicates the potential for improving the efficacy of antimicrobial stewardship interventions, at least in part by reducing time-consuming tasks for overwhelmed health care personnel. Improving our knowledge about how complex ML models work could help to achieve crucial advances in promoting the appropriate use of antimicrobials, as well as in preventing antimicrobial resistance selection and dissemination.

Artificial intelligence and prescription of antibiotic therapy: present and future.

INTRODUCTION: In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression to deep neural networks and large language models, have been explored in the literature to support decisions regarding antibiotic prescription. AREAS COVERED: In this narrative review, we discuss promises and challenges of the application of ML-based clinical decision support systems (ML-CDSSs) for antibiotic prescription. A search was conducted in PubMed up to April 2024. EXPERT OPINION: Prescribing antibiotics is a complex process involving various dynamic phases. In each of these phases, the support of ML-CDSSs has shown the potential, and also the actual ability in some studies, to favorably impacting relevant clinical outcomes. Nonetheless, before widely exploiting this massive potential, there are still crucial challenges ahead that are being intensively investigated, pertaining to the transparency of training data, the definition of the sufficient degree of prediction explanations when predictions are obtained through black box models, and the legal and ethical framework for decision responsibility whenever an antibiotic prescription is supported by ML-CDSSs.

Data-driven analysis of regional brain metabolism in behavioral frontotemporal dementia and late-onset primary psychiatric diseases with frontal lobe syndrome: A PET/MRI study.

Late-onset primary psychiatric disease (PPD) and behavioral frontotemporal dementia (bvFTD) present with a similar frontal lobe syndrome. We compare brain glucose metabolism in bvFTD and late-onset PPD and investigate the metabolic correlates of cognitive and behavioral disturbances through FDG-PET/MRI. We studied 37 bvFTD and 20 late-onset PPD with a mean clinical follow-up of three years. At baseline evaluation, metabolism of the dorsolateral, ventrolateral, orbitofrontal regions and caudate could classify the patients with a diagnostic accuracy of 91% (95% CI: 0.81-0.98%). 45% of PPD showed low-grade hypometabolism in the anterior cingulate and/or parietal regions. Frontal lobe metabolism was normal in 32% of genetic bvFTD and bvFTD with motor neuron signs. Hypometabolism of the frontal and caudate regions could help in distinguishing bvFTD from PPD, except in cases with motor neuron signs and/or genetic bvFTD for which brain metabolism may be less informative.

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study.

BACKGROUND: The burden of Parkinson Disease (PD) represents a key public health issue and it is essential to develop innovative and cost-effective approaches to promote sustainable diagnostic and therapeutic interventions. In this perspective the adoption of a P3 (predictive, preventive and personalized) medicine approach seems to be pivotal. The NeuroArtP3 (NET-2018-12366666) is a four-year multi-site project co-funded by the Italian Ministry of Health, bringing together clinical and computational centers operating in the field of neurology, including PD. OBJECTIVE: The core objectives of the project are: i) to harmonize the collection of data across the participating centers, ii) to structure standardized disease-specific datasets and iii) to advance knowledge on disease's trajectories through machine learning analysis. METHODS: The 4-years study combines two consecutive research components: i) a multi-center retrospective observational phase; ii) a multi-center prospective observational phase. The retrospective phase aims at collecting data of the patients admitted at the participating clinical centers. Whereas the prospective phase aims at collecting the same variables of the retrospective study in newly diagnosed patients who will be enrolled at the same centers. RESULTS: The participating clinical centers are the Provincial Health Services (APSS) of Trento (Italy) as the center responsible for the PD study and the IRCCS San Martino Hospital of Genoa (Italy) as the promoter center of the NeuroartP3 project. The computational centers responsible for data analysis are the Bruno Kessler Foundation of Trento (Italy) with TrentinoSalute4.0 -Competence Center for Digital Health of the Province of Trento (Italy) and the LISCOMPlab University of Genoa (Italy). CONCLUSIONS: The work behind this observational study protocol shows how it is possible and viable to systematize data collection procedures in order to feed research and to advance the implementation of a P3 approach into the clinical practice through the use of AI models.

CT Texture Analysis of Adrenal Pheochromocytomas: A Pilot Study.

Radiomics is a promising research field that combines big data analysis (from tissue texture analysis) with clinical questions. We studied the application of CT texture analysis in adrenal pheochromocytomas (PCCs) to define the correlation between the extracted features and the secretory pattern, the histopathological data, and the natural history of the disease. A total of 17 patients affected by surgically removed PCCs were retrospectively enrolled. Before surgery, all patients underwent contrast-enhanced CT and complete endocrine evaluation (catecholamine secretion and genetic evaluation). The pheochromocytoma adrenal gland scaled score (PASS) was determined upon histopathological examination. After a resampling of all CT images, the PCCs were delineated using LifeX software in all three phases (unenhanced, arterial, and venous), and 58 texture parameters were extracted for each volume of interest. Using the Mann-Whitney test, the correlations between the hormonal hypersecretion, the malignancy score of the lesion (PASS > 4), and texture parameters were studied. The parameters DISCRETIZED_HUpeak and GLZLM_GLNU in the unenhanced phase and GLZLM_SZE, CONVENTIONAL_HUmean, CONVENTIONAL_HUQ3, DISCRETIZED_HUmean, DISCRETIZED_AUC_CSH, GLRLM_HGRE, and GLZLM_SZHGE in the venous phase were able to differentiate secreting PCCs (p < 0.01), and the parameters GLZLM_GLNU in the unenhanced phase and GLRLM_GLNU and GLRLM_RLNU in the venous differentiated tumors with low and high PASS. CT texture analysis of adrenal PCCs can be a useful tool for the early identification of secreting or malignant tumors.

Semi-Quantification of Myocardial Uptake of Bone-Seeking Agents in Suspected Cardiac Amyloidosis.

INTRODUCTION: Bone scintigraphy has emerged as a key tool for non-invasive etiologic diagnosis of transthyretin (ATTR) cardiac amyloidosis (CA). We focused on a new semi-quantification method (on planar imaging) that could complement the qualitative/visual Perugini scoring system, especially when SPET/CT is not available. MATERIAL AND METHODS: We retrospectively/qualitatively evaluated 8674 consecutive, planar 99mTc-biphosphonate scintigraphies (performed for non-cardiac reasons), identifying 68 (0.78%) individuals (mean age 79 ± 7 years, range 62-100 years; female/male ratio 16/52) presenting myocardial uptake. Due to the retrospective nature of the study, no SPET/CT, pathologic or genetic confirmation was obtained. The Perugini scoring system was determined (in patients presenting cardiac uptake) and compared with three newly proposed semi-quantitative indices. We took 349 consecutive bone scintigraphies, qualitatively absent of any cardiac/pulmonary uptake, as "healthy controls" (HC). RESULTS: The heart-to-thigh ratio (RHT) and lung-to-thigh ratio (RLT) indices were significantly higher in patients than in HCs (p ≤ 0.0001). There were statistically significant differences for RHT in HCs vs. patients with qualitative Perugini scores of 1 or >1 (with p ranging from ≤0.001 to ≤0.0001). ROC curves showed that RHT outperformed the other indices and was more accurate in both male and female groups. Furthermore, in the male population, RHT accurately distinguished HCs and patients with scores of 1 (less likely affected by ATTR) from patients with qualitative scores >1 (more likely affected by ATTR) with an AUC of 99% (sensitivity: 95%; specificity: 97%). CONCLUSION: The proposed semi-quantitative RHT index can accurately/semi-quantitatively distinguish between HCs and subjects probably affected by CA (Perugini scores from 1 to 3), and could be particularly useful when no SPET/CT data are available (such as in retrospective studies and data mining). Furthermore, RHT can semi-quantitatively predict, with very high accuracy, subjects in the male population more likely to be affected by ATTR. The present study, although using a very large sample, is however retrospective, monocentric, and therefore the generalizability of the results should be proved by an accurate external validation. ADVANCES IN KNOWLEDGE: The proposed heart-to-thigh ratio (RHT) can distinguish healthy controls and subjects that are probably affected by cardiac amyloidosis in a simple and more reproducible way, as compared to standard qualitative/visual evaluation.

Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions.

Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) 'AverageIntensity', (2) 'Autocorrelation', (3) 'Contrast', (4) 'Compactness', (5) 'StandardDeviation', (6) 'MeanAbsoluteDeviation' and (7) 'InterquartileRange'. AUC values of 0.86 (95% C.I. 0.73-0.94) for the training set and 0.73 (95% C.I. 0.54-0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28-0.57) for the training set and 0.4 (0.23-0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.