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Background AD is the most frequent neurodegenerative disease, severely impacting our society. Early diagnosis and prognosis are challenging tasks in the management of AD patients. New method We implemented a machine-learning classifier for the automatic early diagnosis and prognosis of AD by means of features extracted, selected and optimized from structural MRI brain images. The classifier was designed to perform multi-label automatic classification into the following four classes: HC, ncMCI, cMCI, and AD. Results From our analyses, it emerged that MMSE and hippocampus-related measures must be included as primary measures in automatic-classification systems for both the early diagnosis and the prognosis of AD. The voting scheme mainly based on the binary-classification performances on the different four groups is the best choice to model the multi-label decision function for AD, when compared with a simple majority-vote scheme or with a scheme aimed at discriminating patients with high vs low risk of conversion to AD and therapy addressing. Comparison with existing method(s) The accuracies of our binary classifications were higher than or comparable to previously published methods. An improvement is needed on the approach we used to combine binary-classification outputs to obtain the final multi-label classification. Conclusions The performance of multi-label automatic-classification systems strongly depends on the choice of the voting scheme used for combining binary-classification labels. [ABSTRACT FROM AUTHOR]

Monte Carlo (MC) methods provide ideal data sets to assess reconstruction and correction techniques in emission tomography (ET). Although several ET-dedicated MC codes are available, their use is hindered by the heavy computation burden required for high statistics simulations as well as by the need to adapt the code to the purpose of the individual user. In this work a publicly accessible database of MC-simulated ET data sets (the MC-ET database) was created and published on an Internet web site (), in order to provide MC-simulated data ready to be downloaded and used by researchers at different sites with similar evaluation purposes. At present, the MC-ET database provides direct access to MC-simulated raw data of unscattered, scattered and total events: (a) obtained by different MC codes, (b) relative to different radioactive sources, from simple geometrical phantoms to studies of normal and pathological subjects and (c) derived from different SPECT and PET scanners. The main features of the MC-ET data sets are: (a) validation by comparison with measured data, (b) classification according to pre-defined database characteristics, (c) common-use file format and (d) easy and free access and download. [ABSTRACT FROM AUTHOR]

The aim of this work was to assess lesion detectability and quantification in whole body oncological 18F-FDG studies performed by a state-of-the-art integrated Positron Emission Tomograph/Computed tomography (PET/CT) system. Lesion detectability and quantification were assessed by a Monte Carlo (MC) simulation approach as a function of different physical factors (e.g., attenuation and scatter), image counting statistics, lesion size and position, lesion-to-background radioactivity concentration ratio (L/B), and reconstruction algorithms. The results of this work brought to a number of conclusions. • The MC code PET-electron gamma shower (EGS) was accurate in simulating the physical response of the considered PET/CT scanner (>90%). • PET -EGS and patient-derived phantoms can be used in simulating 18F-FDG PET oncological studies. • Counting statistics is a dominant factor in lesion detectability. • Correction for scatter (from both inside and outside the field of view) is needed to improve lesion detectability. • Iterative reconstruction and attenuation correction must be used to interpret clinical images. • Re-binning algorithms are appropriate for whole-body oncological data. • A MU-based method for correction of partial volume effect is feasible. For the considered PET/CT system, limits in lesion detectability were determined in situations comparable to those of real oncological studies: at a L/B = 3 for lesions of 12 mm diameter and at a L/B = 4 for lesions of 8 mm diameter. [ABSTRACT FROM AUTHOR]

Focuses on a model developed by Monte Carlo methods to estimate noise components in three-dimensional positron emission tomography. Quantitative accuracy of the developed noise model; Optimization of signal-to-noise response; Axial distributions of scatter-to-true and random-to-true ratios for the brain, abdomen and whole body phantoms.

Provides information on a study which used PET-EGS, a Monte Carlo (MC) software package designed to simulate positron-emission tomography (PET) clinical studies, in order to assess approaches to scatter correction in three-dimensional PET. Details on scatter correction and MC methods; How the study was conducted; Results and conclusion.

A letter to the editor is presented commenting on statistical parametric mapping for the in-vivo examination of brain functional parameters in neurological diseases.

Highlights: [•] The algorithm allows individual differential diagnosis of PD and PSP by means of MR images. [•] The algorithm does not require a priori hypotheses of where useful information may be coded in the images. [•] Classification accuracy was significantly higher compared to other published methods. [•] The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP. [ABSTRACT FROM AUTHOR]

To develop and evaluate the performance of a radiomics and machine learning model applied to ultrasound (US) images in predicting the risk of malignancy of a uterine mesenchymal lesion. Single-center retrospective evaluation of consecutive patients who underwent surgery for a malignant uterine mesenchymal lesion (sarcoma) and a control group of patients operated on for a benign uterine mesenchymal lesion (myoma). Radiomics was applied to US preoperative images according to the International Biomarker Standardization Initiative guidelines to create, validate and test a classification model for the differential diagnosis of myometrial tumors. The TRACE4 radiomic platform was used thus obtaining a full-automatic radiomic workflow. Definitive histology was considered as gold standard. Accuracy, sensitivity, specificity, AUC and standard deviation of the created classification model were defined. A total of 70 women with uterine mesenchymal lesions were recruited (20 with histological diagnosis of sarcoma and 50 myomas). Three hundred and nineteen radiomics IBSI-compliant features were extracted and 308 radiomics features were found stable. Different machine learning classifiers were created and the best classification system showed Accuracy 0.85 ± 0.01, Sensitivity 0.80 ± 0.01, Specificity 0.87 ± 0.01, AUC 0.86 ± 0.03. Radiomics applied to US images shows a great potential in differential diagnosis of mesenchymal tumors, thus representing an interesting decision support tool for the gynecologist oncologist in an area often characterized by uncertainty. [Display omitted] • No imaging technique can effectively discriminate between sarcoma and myoma. • Inadequate surgery in case of occult sarcoma will worsen the patient's prognosis. • Radiomics allows to correlate radiological images to the tissue pathophysiology. • Radiomics applied to US images represents an effective diagnostic support tool. [ABSTRACT FROM AUTHOR]

Objective. The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. Method. In a multicenter study, a phantom with spheres of different diameters (6.5–57.4 mm) was filled with 18F-FDG at different target-to-background ratios (TBR: 2.5–70) and scanned for different acquisition periods (2–5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS. Results. The goodness of the model fit was high (R2: 0.74–0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation. Conclusions. Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to evaluate the predictive role of pre-therapy fluorodeoxyglucose (FDG) uptake parameters of primary tumour in head and neck cancer (HNC) patients undergoing intensity-modulated radiotherapy (IMRT) with simultaneous integrated boost (SIB) on FDG-positive volume—positron emission tomography (PET) gross tumour volume (PET-GTV). Methods: This retrospective study included 19 patients (15 men and 4 women, mean age 59.2 years, range 23–81 years) diagnosed with HNC between 2005 and 2011. Of 19 patients, 15 (79 %) had stage III–IV. All patients underwent FDG PET/CT before treatment. Metabolic indexes of primary tumour, including metabolic tumour volume (MTV), maximum and mean standardized uptake value (SUV max, SUV mean) and total lesion glycolysis (TLG) were considered. Partial volume effect correction (PVC) was performed for SUV mean and TLG estimation. Correlations between PET/CT parameters and 2-year disease-free survival (DFS), local recurrence-free survival (LRFS) and distant metastasis-free survival (DMFS) were assessed. Median patient follow-up was 19.2 months (range 4–24 months). Results: MTV, TLG and PVC-TLG predicting patients’ outcome with respect to all the considered local and distant disease control endpoints (LRFS, DMFS and DFS) were 32.4 cc, 469.8 g and 547.3 g, respectively. SUV mean and PVC-SUV mean cut-off values predictive of LRFS and DFS were 10.8 and 13.3, respectively. PVC was able to compensate errors up to 25 % in the primary HNC tumour uptake. Moreover, PVC enhanced the statistical significance of the results. Conclusion: FDG PET/CT uptake parameters are predictors of patients’ outcome and can potentially identify patients with higher risk of treatment failure that could benefit from more aggressive approaches. Application of PVC is recommended for accurate measurement of PET parameters. [ABSTRACT FROM AUTHOR]

We have developed, optimized, and validated a method for partial volume effect (PVE) correction of oncological lesions in positron emission tomography (PET) clinical studies, based on recovery coefficients (RC) and on PET measurements of lesion-tobackground ratio (L/Bm) and of lesion metabolic volume. An operator-independent technique, based on an optimised threshold of the maximum lesion uptake, allows to define an isocontour around the lesion on PET images in order to measure both lesion radioactivity uptake and lesion metabolic volume. RC are experimentally derived from PET measurements of hot spheres in hot background, miming oncological lesions. RC were obtained as a function of PET measured sphere-to-background ratio and PET measured sphere metabolic volume, both resulting from the threshold-isocontour technique. PVE correction of lesions of a diameter ranging from 10 mm to 40 mm and for measured L/Bm from 2 to 30 was performed using measured RC curves tailored at answering the need to quantify a large variety of real oncological lesions by means of PET. Validation of the PVE correction method resulted to be accurate (>89%) in clinical realistic conditions for lesion diameter > 1 cm, recovering >76% of radioactivity for lesion diameter < 1 cm. Results from patient studies showed that the proposed PVE correction method is suitable and feasible and has an impact on a clinical environment. [ABSTRACT FROM AUTHOR]

Epigenetic silencing of tumour suppressor genes is an important mechanism involved in cell transformation and tumour progression. The Set and RING-finger-associated domain-containing protein UHRF1 might be an important link between different epigenetic pathways. Here, we report that UHRF1 is frequently overexpressed in human prostate tumours and has an important role in prostate cancer pathogenesis and progression. Analysis of human prostate cancer samples by microarrays and immunohistochemistry showed increased expression of UHRF1 in about half of the cases. Moreover, UHRF1 expression was associated with reduced overall survival after prostatectomy in patients with organ-confined prostate tumours (P<0.0001). UHRF1 expression was negatively correlated with several tumour suppressor genes and positively with the histone methyltransferase (HMT) EZH2 both in prostate tumours and cell lines. UHRF1 knockdown reduced proliferation, clonogenic capability and anchorage-independent growth of prostate cancer cells. Depletion of UHRF1 resulted in reactivation of several tumour suppressor genes. Gene reactivation upon UHRF1 depletion was associated with changes in histone H3K9 methylation, acetylation and DNA methylation, and impaired binding of the H3K9 HMT Suv39H1 to the promoter of silenced genes. Co-immunoprecipitation experiments showed direct interaction between UHRF1 and Suv39H1. Our data support the notion that UHRF1, along with Suv39H1 and DNA methyltransferases, contributes to epigenetic gene silencing in prostate tumours. This could represent a parallel and convergent pathway to the H3K27 methylation catalyzed by EZH2 to synergistically promote inactivation of tumour suppressor genes. Deregulated expression of UHRF1 is involved in the prostate cancer pathogenesis and might represent a useful marker to distinguish indolent cancer from those at high risk of lethal progression. [ABSTRACT FROM AUTHOR]

In everyday life, the successful monitoring of behavior requirescontinuous updating of the effectiveness of motor acts; one crucial stepis becoming aware of the movements one is performing. We studied theanatomical distribution of lesions in right-brain-damaged hemiplegicpatients, who obstinately denied their motor impairment, claiming thatthey could move their paralyzed limbs. Denial was associated withlesions in areas related to the programming of motor acts, particularlyBrodmann's premotor areas 6 and 44, motor area 4, and the somatosensorycortex. This association suggests that monitoring systems may beimplemented within the same cortical network that is responsible for theprimary function that has to be monitored. [ABSTRACT FROM AUTHOR]

Abstract. In this paper a clustering technique is proposed for attenuation correction (AC) in positron emission tomography (PET). The method is unsupervised and adaptive with respect to counting statistics in the transmission (TR) images. The technique allows the classification of pre- or post-injection TR images into main tissue components in terms of attenuation coefficients. The classified TR images are then forward projected to generate new TR sinograms to be used for AC in the reconstruction of the corresponding emission (EM) data. The technique has been tested on phantoms and clinical data of brain, heart and whole-body PET studies. The method allows: (a) reduction of noise propagation from TR into EM images, (b) reduction of TR scanning to a few minutes (3 min) with maintenance of the quantitative accuracy (within 6%) of longer acquisition scans (15-20 min), (c) reduction of the radiation dose to the patient, (d) performance of quantitative whole-body studies. [ABSTRACT FROM AUTHOR]

We have developed, optimized, and validated a method for partial volume effect (PVE) correction of oncological lesions in positron emission tomography (PET) clinical studies, based on recovery coefficients (RC) and on PET measurements of lesion-to-background ratio (L/B m) and of lesion metabolic volume. An operator-independent technique, based on an optimised threshold of the maximum lesion uptake, allows to define an isocontour around the lesion on PET images in order to measure both lesion radioactivity uptake and lesion metabolic volume. RC are experimentally derived from PET measurements of hot spheres in hot background, miming oncological lesions. RC were obtained as a function of PET measured sphere-to-background ratio and PET measured sphere metabolic volume, both resulting from the threshold-isocontour technique. PVE correction of lesions of a diameter ranging from 10 mm to 40 mm and for measured L/B m from 2 to 30 was performed using measured RC curves tailored at answering the need to quantify a large variety of real oncological lesions by means of PET. Validation of the PVE correction method resulted to be accurate (>89%) in clinical realistic conditions for lesion diameter > 1 cm, recovering >76% of radioactivity for lesion diameter < 1 cm. Results from patient studies showed that the proposed PVE correction method is suitable and feasible and has an impact on a clinical environment. [ABSTRACT FROM AUTHOR]

Abstract: The diagnostic methodologies used for the radiotherapy planning have undergone great developments in the last 30 years. Since the 1980s, after the introduction of the CT scanner, the modality for the planning moved beyond the planar 2D assessment to approach a real and more realistic volumetric 3D definition. Consequently the dose distribution, previously obtained by means of an overly simple approximation, became increasingly complex, better tailoring the true shape of the tumour. The final therapeutic improvement has been obtained by a parallel increase in the complexity of the irradiating units: the Linacs for therapy have, in fact, been equipped with a full accessory set capable to modulate the fluence (IMRT) and to check the correct target position continuously during the therapy session (IMRT–IGRT). The multimodal diagnostic approach, which integrates diagnostic information, from images of the patient taken with CT, NMR, PET and US, further improves the data for a biological and topological optimization of the radiotherapy plan and consequently of the dose distribution in the Planning Target Volume. Proteomic and genomic analysis will be the next step in tumour diagnosis. These methods will provide the planners with further information, for a true personalization of the treatment regimen and the assessment of the predictive essays for each tumour and each patient. [Copyright &y& Elsevier]