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1.
Curr Oncol ; 28(6): 5318-5331, 2021 Dec 12.
Artigo em Inglês | MEDLINE | ID: mdl-34940083

RESUMO

BACKGROUND/AIM: Nowadays, Machine Learning (ML) algorithms have demonstrated remarkable progress in image-recognition tasks and could be useful for the new concept of precision medicine in order to help physicians in the choice of therapeutic strategies for brain tumours. Previous data suggest that, in the central nervous system (CNS) tumours, amino acid PET may more accurately demarcate the active disease than paramagnetic enhanced MRI, which is currently the standard method of evaluation in brain tumours and helps in the assessment of disease grading, as a fundamental basis for proper clinical patient management. The aim of this study is to evaluate the feasibility of ML on 11[C]-MET PET/CT scan images and to propose a radiomics workflow using a machine-learning method to create a predictive model capable of discriminating between low-grade and high-grade CNS tumours. MATERIALS AND METHODS: In this retrospective study, fifty-six patients affected by a primary brain tumour who underwent 11[C]-MET PET/CT were selected from January 2016 to December 2019. Pathological examination was available in all patients to confirm the diagnosis and grading of disease. PET/CT acquisition was performed after 10 min from the administration of 11C-Methionine (401-610 MBq) for a time acquisition of 15 min. 11[C]-MET PET/CT images were acquired using two scanners (24 patients on a Siemens scan and 32 patients on a GE scan). Then, LIFEx software was used to delineate brain tumours using two different semi-automatic and user-independent segmentation approaches and to extract 44 radiomics features for each segmentation. A novel mixed descriptive-inferential sequential approach was used to identify a subset of relevant features that correlate with the grading of disease confirmed by pathological examination and clinical outcome. Finally, a machine learning model based on discriminant analysis was used in the evaluation of grading prediction (low grade CNS vs. high-grade CNS) of 11[C]-MET PET/CT. RESULTS: The proposed machine learning model based on (i) two semi-automatic and user-independent segmentation processes, (ii) an innovative feature selection and reduction process, and (iii) the discriminant analysis, showed good performance in the prediction of tumour grade when the volumetric segmentation was used for feature extraction. In this case, the proposed model obtained an accuracy of ~85% (AUC ~79%) in the subgroup of patients who underwent Siemens tomography scans, of 80.51% (AUC 65.73%) in patients who underwent GE tomography scans, and of 70.31% (AUC 64.13%) in the whole patients' dataset (Siemens and GE scans). CONCLUSIONS: This preliminary study on the use of an ML model demonstrated to be feasible and able to select radiomics features of 11[C]-MET PET with potential value in prediction of grading of disease. Further studies are needed to improve radiomics algorithms to personalize predictive and prognostic models and potentially support the medical decision process.

2.
J Imaging ; 7(11)2021 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-34821853

RESUMO

The cytotoxic activity of T cells and Natural Killer cells is usually measured with the chromium release assay (CRA), which involves the use of 51Chromium (51Cr), a radioactive substance dangerous to the operator and expensive to handle and dismiss. The accuracy of the measurements depends on how well the target cells incorporate 51Cr during labelling which, in turn, depends on cellular division. Due to bystander metabolism, the target cells spontaneously release 51Cr, producing a high background noise. Alternative radioactive-free methods have been developed. Here, we compare a bioluminescence (BLI)-based and a carboxyfluorescein succinimidyl ester (CFSE)-based cytotoxicity assay to the standard radioactive CRA. In the first assay, the target cells stably express the enzyme luciferase, and vitality is measured by photon emission upon the addition of the substrate d-luciferin. In the second one, the target cells are labelled with CFSE, and the signal is detected by Flow Cytometry. We used these two protocols to measure cytotoxicity induced by treatment with NK cells. The cytotoxicity of NK cells was determined by adding increasing doses of human NK cells. The results obtained with the BLI method were consistent with those obtained with the CRA- or CFSE-based assays 4 hours after adding the NK cells. Most importantly, with the BLI assay, the kinetic of NK cells' killing was thoroughly traced with multiple time point measurements, in contrast with the single time point measurement the other two methods allow, which unveiled additional information on NK cell killing pathways.

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(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.

5.
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.

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.
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
10.
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
11.
Curr Probl Diagn Radiol ; 50(2): 175-185, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-31761413

RESUMO

PURPOSE: To determine the diagnostic performance of texture analysis of prostate MRI for the diagnosis of prostate cancer among Prostate Imaging Reporting and Data System (PI-RADS) 3 lesions. MATERIALS AND METHODS: Forty-three patients with at least 1 PI-RADS 3 lesion on prostate MRI performed between June 2016 and January 2019 were retrospectively included. Reference standard was pathological analysis of radical prostatectomy specimens or MRI-targeted biopsies. Texture analysis extraction of target lesions was performed on axial T2-weighted images and apparent diffusion coefficient (ADC) maps using a radiomic software. Lesions were categorized as prostate cancer (Gleason score [GS] ≥ 6), and no prostate cancer. Statistical analysis was performed using the generalized linear model (GLM) regression and the discriminant analysis (DA). AUROC with 95% confidence intervals were calculated to assess the diagnostic performance of standalone features and predictive models for the diagnosis of prostate cancer (GS ≥ 6) and clinically-significant prostate cancer (GS ≥ 7). RESULTS: The analysis of 46 PI-RADS 3 lesions (ie, 27 [58.7%] no prostate cancers; 19 [41.3%] prostate cancers) revealed 9 and 6 independent texture parameters significantly correlated with the final histopathological results on T2-weighted and ADC maps images, respectively. The resulting GLM and DA predictive models for the diagnosis of prostate cancer yielded an AUROC of 0.775 and 0.779 on T2-weighted images or 0.815 and 0.821 on ADC maps images. For the diagnosis of clinically-significant prostate cancer, the resulting GLM and DA predictive models for the diagnosis of prostate cancer yielded an AUROC of 0.769 and 0.817 on T2-weighted images or 0.749 and 0.744 on ADC maps images. CONCLUSION: Texture analysis of PI-RADS 3 lesions on T2-weighted and ADC maps images helps identifying prostate cancer. The good diagnostic performance of the combination of multiple radiomic features for the diagnosis of prostate cancer may help predicting lesions where aggressive management may be warranted.


Assuntos
Imageamento por Ressonância Magnética , Neoplasias da Próstata , Humanos , Masculino , Gradação de Tumores , Neoplasias da Próstata/diagnóstico por imagem , Estudos Retrospectivos
12.
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
13.
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
14.
Artigo em Inglês | MEDLINE | ID: mdl-32726569

RESUMO

Betamethasone is a glucocorticoid authorised in cattle for the treatment of metabolic and inflammatory diseases, but, in Europe, it is illegally employed to improve productive performances. LC-MS/MS is the official control method of veterinary drugs residues in food of animal origin. An experimental study was developed to evaluate the feasibility of proton magnetic resonance spectroscopy (1H-MRS) as a potential alternative approach to detect the presence of betamethasone residues. Eight rat liver samples were collected 24 h post-betamethasone-treatment from experimental and control animals and were analysed by 1H-MRS using a 7-Tesla MRI scanner. 1H-MR reference spectra both of the Bentelan formulation used for treatment, and of three solutions of betamethasone in dimethyl sulphoxide (DMSO) at 5, 10 and 100 mM, respectively, were acquired to fit analyte-peaks in the liver samples spectra. Betamethasone-peaks were found only in the 100 mM betamethasone in DMSO solution spectrum. Betamethasone residues were not detected in any of the tissue samples analysed, probably related to the low concentration of injected drug. These findings allow us to establish, for the first time in the literature, the detection limit (in the range 10-100 mM) of betamethasone for the 7-Tesla MRI scanner used here. Given this very-low sensitivity, we conclude that the evaluated 1H-MR spectroscopy approach is not suitable for the detection of betamethasone residues in edible tissues, since the maximum residue limit imposed by Commission Regulation (EC) 37/2010 for betamethasone in the liver, and metabolic concentrations required to be detected in animal samples from livestock, are far below the detection limit we found.


Assuntos
Betametasona/análise , Resíduos de Drogas/análise , Fígado/química , Animais , Masculino , Espectroscopia de Prótons por Ressonância Magnética/instrumentação , Ratos , Ratos Wistar
15.
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.

16.
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.

17.
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
18.
Recenti Prog Med ; 111(3): 130-135, 2020 03.
Artigo em Italiano | MEDLINE | ID: mdl-32157259

RESUMO

Radiomics is a new frontier of medicine based on the extraction of quantitative data from radiological images which can not be seen by radiologist's naked eye and on the use of these data for the creation of clinical decision support systems. The long-term goal of radiomics is to improve the non-invasive diagnosis of focal and diffuse diseases of different organs by understanding links between extracted quantitative imaging data and the underlying molecular and pathological characteristics of lesions. In the last decade, several studies have highlighted the enormous potential of radiomics in both tumoral and non-tumoral diseases of many organs and systems including brain, lung, breast, gastrointestinal and genitourinary tracts. The enormous potential of radiomics needs to be pursued with the methodological rigor of scientific research and by integrating radiological data with other medical disciplines, in order to improve personalized patient management.


Assuntos
Inteligência Artificial , Sistemas de Apoio a Decisões Clínicas , Diagnóstico por Imagem/métodos , Humanos , Neoplasias/diagnóstico por imagem , Medicina de Precisão/métodos
19.
J Imaging ; 6(11)2020 Oct 23.
Artigo em Inglês | MEDLINE | ID: mdl-34460557

RESUMO

In order to tackle three-dimensional tumor volume reconstruction from Positron Emission Tomography (PET) images, most of the existing algorithms rely on the segmentation of independent PET slices. To exploit cross-slice information, typically overlooked in these 2D implementations, I present an algorithm capable of achieving the volume reconstruction directly in 3D, by leveraging an active surface algorithm. The evolution of such surface performs the segmentation of the whole stack of slices simultaneously and can handle changes in topology. Furthermore, no artificial stop condition is required, as the active surface will naturally converge to a stable topology. In addition, I include a machine learning component to enhance the accuracy of the segmentation process. The latter consists of a forcing term based on classification results from a discriminant analysis algorithm, which is included directly in the mathematical formulation of the energy function driving surface evolution. It is worth noting that the training of such a component requires minimal data compared to more involved deep learning methods. Only eight patients (i.e., two lung, four head and neck, and two brain cancers) were used for training and testing the machine learning component, while fifty patients (i.e., 10 lung, 25 head and neck, and 15 brain cancers) were used to test the full 3D reconstruction algorithm. Performance evaluation is based on the same dataset of patients discussed in my previous work, where the segmentation was performed using the 2D active contour. The results confirm that the active surface algorithm is superior to the active contour algorithm, outperforming the earlier approach on all the investigated anatomical districts with a dice similarity coefficient of 90.47 ± 2.36% for lung cancer, 88.30 ± 2.89% for head and neck cancer, and 90.29 ± 2.52% for brain cancer. Based on the reported results, it can be claimed that the migration into a 3D system yielded a practical benefit justifying the effort to rewrite an existing 2D system for PET imaging segmentation.

20.
J Imaging ; 6(11)2020 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-34460569

RESUMO

BACKGROUND: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. METHODS: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources' requirements. RESULTS: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. CONCLUSIONS: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.

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