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1.
BMC Neurol ; 21(1): 449, 2021 Nov 16.
Artigo em Inglês | MEDLINE | ID: mdl-34784880

RESUMO

BACKGROUND: Tuberculous meningitis (TBM) is an important disease leading to morbidity, disability and mortality that primarily affects children and immune-depressed patients. Specific neuromarkers predicting outcomes, severity and inflammatory response are still lacking. In recent years an increasing number of evidences show a possible role for infective agents in developing neurodegenerative diseases. METHODS: We retrospectively included 13 HIV-negative patients presenting with TBM and we compared them with two control groups: one of patients with a confirmed diagnosis of AD, and one of those with syphilis where lumbar punctures excluded central nervous system involvement. Lumbar punctures were performed for clinical reasons and CSF biomarkers were routinely available: we analyzed blood brain barrier permeability (CSF to serum albumin ratio, "CSAR"), intrathecal IgG synthesis, (CSF to serum IgG ratio), inflammation (neopterin), amyloid deposition (Aß1-42), neuronal damage (T-tau, P-tau, 14.3.3) and astrocytosis (S-100 ß). RESULTS: TBM patients were 83 % male and 67 % Caucasian with a median age of 51 years (24.5-63.5 IQR). Apart from altered CSAR (median value 18.4, 17.1-30.9 IQR), neopterin (14.3 ng/ml, 9.7-18.8) and IgG ratios (15.4, 7.9-24.9), patients showed very low levels of Aß1-42 in their CSF (348.5 pg/mL,125-532.2), even lower compared to AD and controls [603 pg/mL (IQR 528-797) and 978 (IQR 789-1178)]. Protein 14.3.3 tested altered in 38.5 % cases. T-tau, P-tau and S100Beta were in the range of normality. Altered low level of Aß1-42 correlated over time with classical TBM findings and altered neuromarkers. CONCLUSIONS: CSF Biomarkers from patients with TBM were compatible with inflammation, blood brain barrier damage and impairment in amyloid-beta metabolism. Amyloid-beta could be tested as a prognostic markers, backing the routine use of available neuromarkers. To our knowledge this is the first case showing such low levels of Aß1-42 in TBM; its accumulation, drove by neuroinflammation related to infections, can be central in understanding neurodegenerative diseases.


Assuntos
Doença de Alzheimer , Tuberculose Meníngea , Peptídeos beta-Amiloides , Biomarcadores , Criança , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fragmentos de Peptídeos , Estudos Retrospectivos , Proteínas tau
2.
Dement Neurocogn Disord ; 20(4): 89-98, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34795772

RESUMO

Background and Purpose: The aim of this study was to describe the variations in the speech range profile (SRP) of patients affected by cognitive decline. Methods: We collected the data of patients managed for suspected voice and speech disorders, and suspected cognitive impairment. Patients underwent an Ear Nose and Throat evaluation and Mini-Mental State Examination (MMSE). To obtain SRP, we asked the patients to read 18 sentences twice, at their most comfortable pitch and loudness as they would do in daily conversation, and recorded their voice on to computer software. Results: The study included 61 patients. The relationship between the MMSE score and SRP parameters was established. Increased severity of the MMSE score resulted in a statistically significant reduction in the average values of the semitones to the phonetogram, and the medium and maximum sound pressure levels (p<0.001). The maximum predictivity of MMSE was based on the highly significant values of semitones (p<0.001) and the maximum sound pressure levels (p=0.010). Conclusions: The differences in SRP between the various groups were analyzed. Specifically, the SRP value decreased with increasing severity of cognitive decline. SRP was useful in highlighting the relationship between all cognitive declines tested and speech.

3.
Br J Radiol ; 94(1128): 20210340, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34591597

RESUMO

OBJECTIVE: To investigate whether MRI-based texture analysis improves diagnostic performance for the diagnosis of parotid gland tumors compared to conventional radiological approach. METHODS: Patients with parotid gland tumors who underwent salivary glands MRI between 2008 and 2019 were retrospectively selected. MRI analysis included a qualitative assessment by two radiologists (one of which subspecialized on head and neck imaging), and texture analysis on various sequences. Diagnostic performances including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of qualitative features, radiologists' diagnosis, and radiomic models were evaluated. RESULTS: Final study cohort included 57 patients with 74 tumors (27 pleomorphic adenomas, 40 Warthin tumors, 8 malignant tumors). Sensitivity, specificity, and AUROC for the diagnosis of malignancy were 75%, 97% and 0.860 for non-subspecialized radiologist, 100%, 94% and 0.970 for subspecialized radiologist and 57.2%, 93.4%, and 0.927 using a MRI radiomics model obtained combining texture analysis on various MRI sequences. Sensitivity, specificity, and AUROC for the differential diagnosis between pleomorphic adenoma and Warthin tumors were 81.5%, 70%, and 0.757 for non-subspecialized radiologist, 81.5%, 95% and 0.882 for subspecialized radiologist and 70.8%, 82.5%, and 0.808 using a MRI radiomics model based on texture analysis of T2 weighted sequence. A combined radiomics model obtained with all MRI sequences yielded a sensitivity of 91.5% for the diagnosis of pleomorphic adenoma. CONCLUSION: MRI qualitative radiologist assessment outperforms radiomic analysis for the diagnosis of malignancy. MRI predictive radiomics models improves the diagnostic performance of non-subspecialized radiologist for the differential diagnosis between pleomorphic adenoma and Warthin tumor, achieving similar performance to the subspecialized radiologist. ADVANCES IN KNOWLEDGE: Radiologists outperform radiomic analysis for the diagnosis of malignant parotid gland tumors, with some MRI qualitative features such as ill-defined margins, perineural spread, invasion of adjacent structures and enlarged lymph nodes being highly specific for malignancy. A radiomic model based on texture analysis of T2 weighted images yields higher specificity for the diagnosis of pleomorphic adenoma compared to a radiologist non-subspecialized in head and neck radiology, thus minimizing false-positive pleomorphic adenoma diagnosis rate and reducing unnecessary surgical complications.

4.
J Imaging ; 7(8)2021 Aug 04.
Artigo em Inglês | MEDLINE | ID: mdl-34460767

RESUMO

BACKGROUND: In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based prediction model. In this context, we present a customized deep learning approach aimed at addressing the real-time, and fully automated identification and segmentation of COVID-19 infected regions in computed tomography images. METHODS: In a previous study, we adopted ENET, originally used for image segmentation tasks in self-driving cars, for whole parenchyma segmentation in patients with idiopathic pulmonary fibrosis which has several similarities to COVID-19 disease. To automatically identify and segment COVID-19 infected areas, a customized ENET, namely C-ENET, was implemented and its performance compared to the original ENET and some state-of-the-art deep learning architectures. RESULTS: The experimental results demonstrate the effectiveness of our approach. Considering the performance obtained in terms of similarity of the result of the segmentation to the gold standard (dice similarity coefficient ~75%), our proposed methodology can be used for the identification and delineation of COVID-19 infected areas without any supervision of a radiologist, in order to obtain a volume of interest independent from the user. CONCLUSIONS: We demonstrated that the proposed customized deep learning model can be applied to rapidly identify, and segment COVID-19 infected regions to subsequently extract useful information for assessing disease severity through radiomics analyses.

5.
J Imaging ; 7(2)2021 Feb 11.
Artigo em Inglês | MEDLINE | ID: mdl-34460633

RESUMO

Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.

6.
Artigo em Inglês | MEDLINE | ID: mdl-34315623

RESUMO

PURPOSE: Aim of this study was to evaluate a fully automated deep learning network named Efficient Neural Network (ENet) for segmentation of prostate gland with median lobe enlargement compared to manual segmentation. MATERIALS AND METHODS: One-hundred-three patients with median lobe enlargement on prostate MRI were retrospectively included. Ellipsoid formula, manual segmentation and automatic segmentation were used for prostate volume estimation using T2 weighted MRI images. ENet was used for automatic segmentation; it is a deep learning network developed for fast inference and high accuracy in augmented reality and automotive scenarios. Student t-test was performed to compare prostate volumes obtained with ellipsoid formula, manual segmentation, and automated segmentation. To provide an evaluation of the similarity or difference to manual segmentation, sensitivity, positive predictive value (PPV), dice similarity coefficient (DSC), volume overlap error (VOE), and volumetric difference (VD) were calculated. RESULTS: Differences between prostate volume obtained from ellipsoid formula versus manual segmentation and versus automatic segmentation were statistically significant (P < 0.049318 and P < 0.034305, respectively), while no statistical difference was found between volume obtained from manual versus automatic segmentation (P = 0.438045). The performance of ENet versus manual segmentations was good providing a sensitivity of 93.51%, a PPV of 87.93%, a DSC of 90.38%, a VOE of 17.32% and a VD of 6.85%. CONCLUSION: The presence of median lobe enlargement may lead to MRI volume overestimation when using the ellipsoid formula so that a segmentation method is recommended. ENet volume estimation showed great accuracy in evaluation of prostate volume similar to that of manual segmentation.

7.
Biomed Eng Lett ; 11(1): 15-24, 2021 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-33747600

RESUMO

Diagnosis of ascending thoracic aortic aneurysm (ATAA) is based on the measurement of the maximum aortic diameter, but size is not a good predictor of the risk of adverse events. There is growing interest in the development of novel image-derived risk strategies to improve patient risk management towards a highly individualized level. In this study, the feasibility and efficacy of deep learning for the automatic segmentation of ATAAs was investigated using UNet, ENet, and ERFNet techniques. Specifically, CT angiography done on 72 patients with ATAAs and different valve morphology (i.e., tricuspid aortic valve, TAV, and bicuspid aortic valve, BAV) were semi-automatically segmented with Mimics software (Materialize NV, Leuven, Belgium), and then used for training of the tested deep learning models. The segmentation performance in terms of accuracy and time inference were compared using several parameters. All deep learning models reported a dice score higher than 88%, suggesting a good agreement between predicted and manual ATAA segmentation. We found that the ENet and UNet are more accurate than ERFNet, with the ENet much faster than UNet. This study demonstrated that deep learning models can rapidly segment and quantify the 3D geometry of ATAAs with high accuracy, thereby facilitating the expansion into clinical workflow of personalized approach to the management of patients with ATAAs.

8.
Appl Sci (Basel) ; 11(2)2021 Jan 02.
Artigo em Inglês | MEDLINE | ID: mdl-33680505

RESUMO

Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.

9.
Int J Mol Sci ; 22(3)2021 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-33525692

RESUMO

Flavonoids display a broad range of structures and are responsible for the major organoleptic characteristics of plant-derived foods and beverages. Recent data showed their activity, and in particular of luteolin-7-O-glucoside (LUT-7G), in reduction of oxidative stress and inflammatory mechanisms in different physiological systems. In this paper, we tried to elucidate how LUT-7G could exert both antioxidant and anti-inflammatory effects in endothelial cells cultured in vitro. Here, we showed that LUT-7G is able to inhibit the STAT3 pathway, to have an antiproliferative action, and an important antioxidant property in HUVEC cells. These properties are exerted by the flavone in endothelial through the transcriptional repression of a number of inflammatory cytokines and their receptors, and by the inhibition of ROS generation. ROS and STAT3 activation has been correlated with the production of oxysterols and other hydroxylated fatty acids, and they have been recognized important as players of atherogenesis and cardiocirculatory system diseases. The analysis of the general production pathway of these hydroxylated species, showed a strong decrease of cholesterol hydroxylated species such as 7-alpha-hydroxicholesterol, 7-beta-hydroxicholesterol by the treatment with LUT-7G. This confirms the anti-inflammatory properties of LUT-7G also in the endothelial district, showing for the first time the molecular pathway that verify previous postulated cardiovascular benefits of this flavone.


Assuntos
Anti-Inflamatórios/farmacologia , Antioxidantes/farmacologia , Flavonas/farmacologia , Glucosídeos/farmacologia , Queratinócitos/citologia , Sialiltransferases/metabolismo , Linhagem Celular , Proliferação de Células , Células Endoteliais/química , Células Endoteliais/citologia , Células Endoteliais/efeitos dos fármacos , Ácidos Graxos/metabolismo , Células Endoteliais da Veia Umbilical Humana , Humanos , Hidroxilação , Queratinócitos/química , Queratinócitos/efeitos dos fármacos , Metabolômica , Oxisteróis/metabolismo , Espécies Reativas de Oxigênio/metabolismo , Transdução de Sinais/efeitos dos fármacos
10.
J Magn Reson Imaging ; 54(2): 452-459, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-33634932

RESUMO

BACKGROUND: Prostate volume, as determined by magnetic resonance imaging (MRI), is a useful biomarker both for distinguishing between benign and malignant pathology and can be used either alone or combined with other parameters such as prostate-specific antigen. PURPOSE: This study compared different deep learning methods for whole-gland and zonal prostate segmentation. STUDY TYPE: Retrospective. POPULATION: A total of 204 patients (train/test = 99/105) from the PROSTATEx public dataset. FIELD STRENGTH/SEQUENCE: A 3 T, TSE T2 -weighted. ASSESSMENT: Four operators performed manual segmentation of the whole-gland, central zone + anterior stroma + transition zone (TZ), and peripheral zone (PZ). U-net, efficient neural network (ENet), and efficient residual factorized ConvNet (ERFNet) were trained and tuned on the training data through 5-fold cross-validation to segment the whole gland and TZ separately, while PZ automated masks were obtained by the subtraction of the first two. STATISTICAL TESTS: Networks were evaluated on the test set using various accuracy metrics, including the Dice similarity coefficient (DSC). Model DSC was compared in both the training and test sets using the analysis of variance test (ANOVA) and post hoc tests. Parameter number, disk size, training, and inference times determined network computational complexity and were also used to assess the model performance differences. A P < 0.05 was selected to indicate the statistical significance. RESULTS: The best DSC (P < 0.05) in the test set was achieved by ENet: 91% ± 4% for the whole gland, 87% ± 5% for the TZ, and 71% ± 8% for the PZ. U-net and ERFNet obtained, respectively, 88% ± 6% and 87% ± 6% for the whole gland, 86% ± 7% and 84% ± 7% for the TZ, and 70% ± 8% and 65 ± 8% for the PZ. Training and inference time were lowest for ENet. DATA CONCLUSION: Deep learning networks can accurately segment the prostate using T2 -weighted images. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.


Assuntos
Aprendizado Profundo , Neoplasias da Próstata , Humanos , Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Masculino , Neoplasias da Próstata/diagnóstico por imagem , Estudos Retrospectivos
11.
Eur Radiol ; 31(7): 4595-4605, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-33443602

RESUMO

OBJECTIVE: The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging. MATERIAL AND METHODS: Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been implemented for features reduction and selection, while Discriminant analysis (DA) was used as a method for features classification in a whole sample and sub-groups for primary tumor or local relapse (T), nodal disease (N), and metastatic disease (M). RESULTS: In the whole group, 2 feature (HISTO_Entropy_log10; HISTO_Energy_Uniformity) results were able to discriminate the occurrence of disease progression at follow-up, obtaining the best performance in DA classification (sensitivity 47.1%, specificity 76.5%, positive predictive value (PPV) 46.7%, and accuracy 67.6%). In the sub-group analysis, the best performance in DA classification for T was obtained by selecting 3 features (SUVmin; SHAPE_Sphericity; GLCM_Correlation) with a sensitivity of 91.6%, specificity 84.1%, PPV 79.1%, and accuracy 87%; for N by selecting 2 features (HISTO = _Energy Uniformity; GLZLM_SZLGE) with a sensitivity of 68.1%, specificity 91.4%, PPV 83%, and accuracy 82.6%; and for M by selecting 2 features (HISTO_Entropy_log10 - HISTO_Entropy_log2) with a sensitivity 64.4%, specificity 74.6%, PPV 40.6%, and accuracy 72.5%. CONCLUSION: This machine learning model demonstrated to be feasible and useful to select Cho-PET features for T, N, and M with valuable association with high-risk PCa patients' outcomes. KEY POINTS: • Artificial intelligence applications are feasible and useful to select Cho-PET features. • Our model demonstrated the presence of specific features for T, N, and M with valuable association with high-risk PCa patients' outcomes. • Further prospective studies are necessary to confirm our results and to develop the application of artificial intelligence in PET imaging of PCa.


Assuntos
Tomografia por Emissão de Pósitrons combinada à Tomografia Computadorizada , Neoplasias da Próstata , Inteligência Artificial , Colina/análogos & derivados , Humanos , Aprendizado de Máquina , Masculino , Recidiva Local de Neoplasia , Estudos Prospectivos , Neoplasias da Próstata/diagnóstico por imagem
12.
Eur Arch Otorhinolaryngol ; 278(3): 741-748, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33068169

RESUMO

PURPOSE: This study aims to understand the factors contributing to the severity of oropharyngeal dysphagia and its persistence in the sub-acute phase of stroke. METHODS: We retrospectively collected the data of all the patients suffering from a stroke in the last year. The severity of stroke was reported according to the NIHSS score. All the patients were evaluated with the Dysphagia Risk Score and with a FEES. We classified the Dysphagia Risk Score and FEES results using the PAS score and ASHA-NOMS levels. The data were analysed statistically with ANOVA test, Student's t test and Pearson's correlation coefficient. RESULTS: A series of 54 patients were evaluated. The ANOVA test did not find any difference in the mean score of Dysphagia Risk Score, PAS and ASHA-NOMS when compared with the brain area of stroke. An NIHSS at hospital admission (stroke unit) of more than 12 was predictive of ASHA-NOMS score 1-4 after 60 days (p < 0.05). A PAS score between 6 and 8 at first FEES evaluation was predictive of poor (1-4) ASHA-NOMS score after 60 days (p < 0.01). A moderate positive linear correlation was found between NIHSS score and both PAS (r 0.65) and Dysphagia Risk Score (r 0.50); a moderate negative linear correlation was recorded between NIHSS and ASHA-NOMS (r - 0.66) scores. CONCLUSION: In the sub-acute phase of stroke, the predictive factors of persistent dysphagia are not linked to the damaged neuroanatomical region and others factors such as NIHSS value and high PAS score seem more useful.


Assuntos
Transtornos de Deglutição , Acidente Vascular Cerebral , Transtornos de Deglutição/diagnóstico , Transtornos de Deglutição/epidemiologia , Transtornos de Deglutição/etiologia , Humanos , Estudos Retrospectivos , Fatores de Risco , Índice de Gravidade de Doença , Acidente Vascular Cerebral/complicações , Acidente Vascular Cerebral/diagnóstico
13.
Curr Radiopharm ; 14(3): 209-219, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-32564769

RESUMO

In medical imaging, Artificial Intelligence is described as the ability of a system to properly interpret and learn from external data, acquiring knowledge to achieve specific goals and tasks through flexible adaptation. The number of possible applications of Artificial Intelligence is also huge in clinical medicine and cardiovascular diseases. To describe for the first time in literature, the main results of articles about Artificial Intelligence potential for clinical applications in molecular imaging techniques, and to describe its advancements in cardiovascular diseases assessed with nuclear medicine imaging modalities. A comprehensive search strategy was used based on SCOPUS and PubMed databases. From all studies published in English, we selected the most relevant articles that evaluated the technological insights of AI in nuclear cardiology applications. Artificial Intelligence may improve patient care in many different fields, from the semi-automatization of the medical work, through the technical aspect of image preparation, interpretation, the calculation of additional factors based on data obtained during scanning, to the prognostic prediction and risk-- group selection. Myocardial implementation of Artificial Intelligence algorithms in nuclear cardiology can improve and facilitate the diagnostic and predictive process, and global patient care. Building large databases containing clinical and image data is a first but essential step to create and train automated diagnostic/prognostic models able to help the clinicians to make unbiased and faster decisions for precision healthcare.


Assuntos
Inteligência Artificial , Cardiologia/tendências , Doenças Cardiovasculares/diagnóstico por imagem , Imagem Molecular/tendências , Humanos , Medicina Nuclear/tendências , Prognóstico
14.
Hypertension ; 77(2): 729-738, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33356396

RESUMO

As novel drug treatments for diabetes have shown favorable cardiovascular effects, interest has mounted with regard to their possible vascular actions, particularly in relation to visceral adipose tissue perfusion and remodeling in obesity. The present study tested the vasorelaxing effect of the SGLT2 (sodium-glucose transporter type 2) inhibitor canagliflozin in arteries from visceral adipose tissue of either nonobese or obese humans and investigated the underlying mechanisms. Also, the vasorelaxing effect of canagliflozin and the GLP-1 (glucagon-like peptide 1) agonist liraglutide were compared in arteries from obese patients. To these purposes, small arteries (116-734 µm) isolated from visceral adipose tissue were studied ex vivo in a wire myograph. Canagliflozin elicited a higher concentration-dependent vasorelaxation in arterioles from obese than nonobese individuals (P=0.02). The vasorelaxing response to canagliflozin was not modified (P=0.93) by inhibition of nitric oxide synthase (L-NAME) or prostacyclin (indomethacin), or by H2O2 scavenging (catalase); also, canagliflozin-induced relaxation was similar (P=0.23) in endothelium-intact or -denuded arteries precontracted with high potassium concentration, thereby excluding an involvement of endothelium-derived hyperpolarizing factors. The vasorelaxing response to canagliflozin was similar to that elicited by the Na+/H+ exchanger 1 inhibitor BIX (P=0.67), but greater than that to the Na+/Ca++ exchanger inhibitor SEA 0400 (P=0.001), hinting a role of Na+/H+ exchanger inhibition in canagliflozin-induced relaxation. In arterioles from obese patients, the vasorelaxing response to canagliflozin was greater than that to liraglutide (P=0.004). These findings demonstrate that canagliflozin induces endothelium-independent vasorelaxation in arterioles from human visceral adipose tissue, thereby suggesting that SGLT2 inhibition might favorably impact the processes linking visceral adipose burden to vascular disease in obesity.


Assuntos
Arteríolas/efeitos dos fármacos , Canagliflozina/farmacologia , Gordura Intra-Abdominal/efeitos dos fármacos , Obesidade/fisiopatologia , Inibidores do Transportador 2 de Sódio-Glicose/farmacologia , Vasodilatação/efeitos dos fármacos , Endotélio Vascular/efeitos dos fármacos , Inibidores Enzimáticos/farmacologia , Humanos , Hipoglicemiantes/farmacologia , Gordura Intra-Abdominal/fisiopatologia , Liraglutida/farmacologia , NG-Nitroarginina Metil Éster/farmacologia , Óxido Nítrico Sintase/antagonistas & inibidores , Vasodilatação/fisiologia
15.
BMC Bioinformatics ; 21(Suppl 8): 325, 2020 Sep 16.
Artigo em Inglês | MEDLINE | ID: mdl-32938360

RESUMO

BACKGROUND: Positron Emission Tomography (PET) is increasingly utilized in radiomics studies for treatment evaluation purposes. Nevertheless, lesion volume identification in PET images is a critical and still challenging step in the process of radiomics, due to the low spatial resolution and high noise level of PET images. Currently, the biological target volume (BTV) is manually contoured by nuclear physicians, with a time expensive and operator-dependent procedure. This study aims to obtain BTVs from cerebral metastases in patients who underwent L-[11C]methionine (11C-MET) PET, using a fully automatic procedure and to use these BTVs to extract radiomics features to stratify between patients who respond to treatment or not. For these purposes, 31 brain metastases, for predictive evaluation, and 25 ones, for follow-up evaluation after treatment, were delineated using the proposed method. Successively, 11C-MET PET studies and related volumetric segmentations were used to extract 108 features to investigate the potential application of radiomics analysis in patients with brain metastases. A novel statistical system has been implemented for feature reduction and selection, while discriminant analysis was used as a method for feature classification. RESULTS: For predictive evaluation, 3 features (asphericity, low-intensity run emphasis, and complexity) were able to discriminate between responder and non-responder patients, after feature reduction and selection. Best performance in patient discrimination was obtained using the combination of the three selected features (sensitivity 81.23%, specificity 73.97%, and accuracy 78.27%) compared to the use of all features. Secondly, for follow-up evaluation, 8 features (SUVmean, SULpeak, SUVmin, SULpeak prod-surface-area, SUVmean prod-sphericity, surface mean SUV 3, SULpeak prod-sphericity, and second angular moment) were selected with optimal performance in discriminant analysis classification (sensitivity 86.28%, specificity 87.75%, and accuracy 86.57%) outperforming the use of all features. CONCLUSIONS: The proposed system is able i) to extract 108 features for each automatically segmented lesion and ii) to select a sub-panel of 11C-MET PET features (3 and 8 in the case of predictive and follow-up evaluation), with valuable association with patient outcome. We believe that our model can be useful to improve treatment response and prognosis evaluation, potentially allowing the personalization of cancer treatment plans.


Assuntos
Neoplasias Encefálicas/diagnóstico por imagem , Tomografia por Emissão de Pósitrons/métodos , Neoplasias Encefálicas/secundário , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Metástase Neoplásica , Prognóstico
16.
Artigo em Inglês | MEDLINE | ID: mdl-32543166

RESUMO

BACKGROUND: Radiomic features are increasingly utilized to evaluate tumor heterogeneity in PET imaging but to date its role has not been investigated for Cho-PET in prostate cancer. The potential application of radiomics features analysis using a machine-learning radiomics algorithm was evaluated to select 18F-Cho PET/CT imaging features to predict disease progression in PCa. METHODS: We retrospectively analyzed high-risk PCa patients who underwent restaging 18F-Cho PET/CT from November 2013 to May 2018. 18F-Cho PET/CT studies and related structures containing volumetric segmentations were imported in the "CGITA" toolbox to extract imaging features from each lesion. A Machine-learning model has been adapted using NCA for feature selection, while DA was used as a method for feature classification and performance analysis. RESULTS: 106 imaging features were extracted for 46 lesions for a total of 4876 features analyzed. No significant differences between the training and validating sets in terms of age, sex, PSA values, lesion location and size (p > 0.05) were demonstrated by the machine-learning model. Thirteen features were able to discriminate FU disease status after NCA selection. Best performance in DA classification was obtained using the combination of the 13 selected features (sensitivity 74%, specificity 58% and accuracy 66%) compared to the use of all features (sensitivity 40%, specificity 52%, and accuracy 51%). Per-site performance of the 13 selected features in DA classification were as follow: T= sensitivity 63%, specificity 83%, accuracy 71%; N= sensitivity 87%, specificity 91% of and accuracy 90%; bone-M= sensitivity 33%, specificity 77% and accuracy 66%. CONCLUSIONS: An artificial intelligence model demonstrated to be feasible and able to select a panel of 18F-Cho PET/CT features with valuable association with PCa patients' outcome.

17.
Am J Otolaryngol ; 41(4): 102501, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32409161

RESUMO

PURPOSE: To evaluate the prevalence of oropharyngeal dysphagia in elderly patients suffering from minimal or mild cognitive decline. PATIENTS AND METHODS: We retrospectively collected the data of patients suffering from mild cognitive impairment or mild dementia and were undergoing management for suspected oropharyngeal dysphagia, in our department. All our patients were subjected to Mini Mental State Examination test, MD Anderson dysphagia inventory and caregiver mealtime and dysphagia questionnaire. We performed a mealtime observation study and endoscopic evaluation of swallowing in all our patients. Following evaluation, we then analysed the data statistically. RESULTS: Out of 708 patients who visited us for cognitive decline and suspected oropharyngeal dysphagia in the last two years, 52 patients were confirming to the inclusion criteria of this study. Classification of oropharyngeal dysphagia patients according to ASHA-NOMS scale showed that 32.7% of patients presented with grade 4 of dysphagia followed by another 32.7% with grade 5 and 30.8% presented with grade 6. Only 3.8% of our patients were considered normal (grade 7 of ASHA-NOMS scale). MD Anderson dysphagia inventory could collected swallowing alterations in only 23.1% of the cases. The caregiver mealtime and dysphagia questionnaire showed acceptable caregivers patient management in 53.8% of patients. CONCLUSION: Our study underscores the fact that oropharyngeal dysphagia is present in many cases of mild cognitive decline. While patients understate their swallowing problems, the caregivers are not competent enough to manage this situation in a great percentage of cases. Only a mealtime observation by a speech-language pathologist along with FEES is able to identify the true prevalence of the condition.


Assuntos
Doença de Alzheimer/epidemiologia , Disfunção Cognitiva/epidemiologia , Transtornos de Deglutição/epidemiologia , Demência/epidemiologia , Idoso , Idoso de 80 Anos ou mais , Doença de Alzheimer/fisiopatologia , Disfunção Cognitiva/fisiopatologia , Comorbidade , Deglutição , Transtornos de Deglutição/fisiopatologia , Demência/fisiopatologia , Humanos , Estudos Retrospectivos
18.
Diagnostics (Basel) ; 10(5)2020 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-32429182

RESUMO

BACKGROUND: Our study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests. METHODS: We consider thirty-two patients retrospectively for whom both HRCT examinations and spirometry tests were available. First, we analyse the HRCT histogram to extract quantitative lung fibrosis features. Next, we evaluate the relationship between pulmonary function and the HRCT features at selected HU thresholds, namely -200 HU, 0 HU, and +200 HU. We model the relationship using a Poisson approximation to identify the measure with the highest log-likelihood. RESULTS: Our Poisson models reveal no difference at the -200 and 0 HU thresholds. However, inferential conclusions change at the +200 HU threshold. Among the HRCT features considered, the percentage of normally attenuated lung at -200 HU shows the most significant diagnostic utility. CONCLUSIONS: The percentage of normally attenuated lung can be used together with qualitative HRCT assessment and pulmonary function tests to enhance the idiopathic pulmonary fibrosis (IPF) diagnostic process.

19.
Comput Biol Med ; 120: 103701, 2020 05.
Artigo em Inglês | MEDLINE | ID: mdl-32217282

RESUMO

Delineation of tumours in Positron Emission Tomography (PET) plays a crucial role in accurate diagnosis and radiotherapy treatment planning. In this context, it is of outmost importance to devise efficient and operator-independent segmentation algorithms capable of reconstructing the tumour three-dimensional (3D) shape. In previous work, we proposed a system for 3D tumour delineation on PET data (expressed in terms of Standardized Uptake Value - SUV), based on a two-step approach. Step 1 identified the slice enclosing the maximum SUV and generated a rough contour surrounding it. Such contour was then used to initialize step 2, where the 3D shape of the tumour was obtained by separately segmenting 2D PET slices, leveraging the slice-by-slice marching approach. Additionally, we combined active contours and machine learning components to improve performance. Despite its success, the slice marching approach poses unnecessary limitations that are naturally removed by performing the segmentation directly in 3D. In this paper, we migrate our system into 3D. In particular, the segmentation in step 2 is now performed by evolving an active surface directly in the 3D space. The key points of such an advancement are that it performs the shape reconstruction on the whole stack of slices simultaneously, naturally leveraging cross-slice information that could not be exploited before. Additionally, it does not require any specific stopping condition, as the active surface naturally reaches a stable topology once convergence is achieved. Performance of this fully 3D approach is evaluated on the same dataset discussed in our previous work, which comprises fifty PET scans of lung, head and neck, and brain tumours. The results have confirmed that a benefit is indeed achieved in practice for all investigated anatomical districts, both quantitatively, through a set of commonly used quality indicators (dice similarity coefficient >87.66%, Hausdorff distance < 1.48 voxel and Mahalanobis distance < 0.82 voxel), and qualitatively in terms of Likert score (>3 in 54% of the tumours).


Assuntos
Algoritmos , Neoplasias Encefálicas , Neoplasias Encefálicas/diagnóstico por imagem , Humanos , Imageamento Tridimensional , Aprendizado de Máquina , Tomografia por Emissão de Pósitrons
20.
J Thorac Imaging ; 35(2): 115-122, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31913257

RESUMO

PURPOSE: The purpose of this study was to investigate histogram-based quantitative high-resolution computed tomography (HRCT) indexes in the assessment of lung cancer (LC) development in idiopathic pulmonary fibrosis (IPF) patients. MATERIALS AND METHODS: From IPF databases of 2 national respiratory centers, we retrospectively studied patients with and without LC development-respectively, divided into Group A (n=16) and Group B (n=33). The extent of fibrotic disease was quantified on baseline and follow-up HRCT examinations using kurtosis, skewness, percentage of high attenuation area (HAA%), and percentage of fibrotic area (FA%). These indexes were compared between the 2 groups using the Mann-Whitney U test. In the prediction of LC development, receiver operating characteristic analysis was performed to assess threshold values of HRCT indexes. RESULTS: At baseline, no difference was reported among groups for kurtosis, skewness, HAA%, and FA%, with P-values of 0.0881, 0.0606, 0.0578, and 0.0606, respectively. On follow-up, significant differences were reported, with P-values of 0.0174 for kurtosis, 0.0313 for skewness, 0.0297 for HAA%, and 0.0407 for FA%.On baseline HRCT, in the prediction of LC development, receiver operating characteristic analysis reported sensibility and specificity of 87.5% and 45.45% for kurtosis, 68.75% and 63.64% for skewness, 81.25% and 54.55% for FA%, and 75% and 60.61% for HAA%. CONCLUSIONS: LC development is associated with progression of fibrosis; at baseline, FA% and HAA% reported more convenient sensitivity/specificity ratios in the prediction of LC development.


Assuntos
Fibrose Pulmonar Idiopática/complicações , Fibrose Pulmonar Idiopática/diagnóstico por imagem , Neoplasias Pulmonares/complicações , Neoplasias Pulmonares/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Idoso , Idoso de 80 Anos ou mais , Progressão da Doença , Feminino , Humanos , Pulmão/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Estudos Retrospectivos , Sensibilidade e Especificidade
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