1,721,019 research outputs found
Domenico Barone, Un fait décisif
Full text linkThe contribution identifies Domenico Barone as a Neapolitan playwright whom Dideort talks about in his "Le paradoxe du comédien", and from here the question of Barone's relationship with the Italian theater (Goldoni) and the comédie italienne is re-addressed
La scena a rilievo di Domenico Barone di Liveri
No full textDomenico Barone di Liveri, corago, scenografo, commediografo e aristocratico nolano, appassionato di teatro, diventò celebre grazie alla sua attività presso la corte di Napoli, godendo della protezione del sovrano, Carlo di Borbone. Tra il 1735 e il 1757 egli allestì annualmente le sue commedie nella Sala Regia di Palazzo Reale, facendo costruire un originale impianto a scena fissa, tridimensionale e praticabile quasi per intero, per ogni nuova rappresentazione. Le aeree volumetrie delle scene liveriane erano in controtendenza rispetto alla scenografie dei melodrammi barocchi con mutazioni a vista sontuosamente realizzati al Teatro San Carlo; pure la tridimensionalità della scena fissa, la praticablità della scena a rilievo, consentivano al Liveri di ottenere sorprendenti effetti di simultaneità e di gioco d'insieme combinando e concertando il movimento e il ritmo della recitazione. Il saggio investiga sulle esperienze formative che hanno indotto Barone alla scelta della scena a rilievo e propone una visualizzazione delle scena della commedia liveriana 'La Contessa' (1735)
sj-pdf-1-opp-10.1177_1078155220914704 - Supplemental material for Posterior reversible encephalopathy syndrome: A rare neurotoxicity after capecitabine
No full textSupplemental material, sj-pdf-1-opp-10.1177_1078155220914704 for Posterior reversible encephalopathy syndrome: A rare neurotoxicity after capecitabine by Manlio Monti, Domenico Barone, Elena Amadori, Giulia Bartolini, Silvia Ruscelli and Giovanni Luca Frassineti in Journal of Oncology Pharmacy Practice</p
CT perfusion studies of lung cancer: automatic detection of misleading structures and artefacts
No full textPurpose:
The aim of this work is to detect and highlight blood vessels, artefacts, and statistically unreliable blood flow values in CT perfusion (CTp) studies of lung cancer through automatic analysis of the Time-Concentration Curves (TCCs).
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Methods and Materials:
16 patients with primary lung tumour underwent axial CTp, for a total amount of 24 examinations. Blood flow values were computed on fitted data after motion correction, according to the maximum slope method. The average error of the fitted TCC model with respect to the original Hounsfield Unit (HU) values are computed for each voxel and gathered into a histogram. An adaptive parametric threshold was conceived, allowing the automatic selection of voxels in perfusion maps whose model fit error is above the threshold.
This study was approved by the institutional review board.
Results:
Most of the highlighted voxels appeared to be arranged into connected regions, the nature of which is confirmed by two 25-year experienced radiologists operating in a blinded fashion. In particular, these regions resulted to be either physical structures, such as bronchi or vessels, or artefacts coming from reconstruction or residual motion.
Conclusions:
The presence of vessels, bronchi or artefacts in perfusion maps alters the right perception of the perfusion pattern by radiologists, besides jeopardizing results from any subsequent computation or statistical analysis. In addition, the automatic exclusion of these misleading values prevents radiologists from misinterpreting the perfusion maps, possibly leading to wrong clinical considerations, this representing a step forward to clinical utilization of CTp
Radiomics in DW-MRI detects non-clinically significant prostate cancer and reduces overtreatment
No full textPurpose: To assess to what extent radiomic features computed on high b-value DWI sequences (b=2000s/mm2) could reliably detect non-clinically significant (NCS) prostate cancer and reduce overtreatment.
Materials and Methods: This study retrospectively enrolled 25 patients of our institution, randomly extracted from PACS with clinical suspicion of PCa who underwent prostate 3T-mpMRI. 10 patients reported NCS-PCa after TRUS biopsy, with Gleason Score (GS)≤3+3, 15 were CS-PCa. PCa Regions of Interest (ROIs) were outlined in all slices by two experienced radiologists in consensus and reported on the DWI sequences, when needed, where 84 radiomic features with the corresponding ROC curves were computed. In order to prevent overfitting, a one-only feature was selected yielding the highest AUC and p-value<0.001 at the one-tail Wilcoxon rank-sum test.
Results: The dispersion of local skewness (LS) of DWI values is higher for CS-PCa (p-value~10-4) and AUC=0.92 (95%CI, 0.70-0.99). Sensitivity and specificity for NCS were 90% and 87%, respectively (1 FN and 2 FP), with False Omission Rate (FOR) equal to 7%, this representing a very low risk of overtreatment. Moreover, the two FPs have GS=3+4, the CS-PCa group closest to NCS one.
Conclusions: Radiomic features extracted from high b-values DWI sequences allows highlighting non-visible image properties related to complexity of tumour habitat. The higher variability of LS hints at increasing heterogeneity of tumour micro-environment for CS-PCa. In addition, this excellent performance stresses the promising role of DWI-based radiomics in discriminating CS-PCa from NCS-PCa.
Limitations: No clinical parameters were considered for differentiation. However, at most they could improve these results. In addition, the number of patients is limited, but uneven in their characteristics, since not derived from any dedicated study.
Ethics committee approval: IRB approval, written informed consent was waived.
Funding: No funding was received for this work
Texture Analysis of Non-Small Cell Lung Cancer on Unenhanced CT and Blood Flow Maps: a Potential Prognostic Tool
No full textThe presence of tumour heterogeneity makes the clinical oncological practice very challenging, since introduces a
great variability in tumours’ response to available therapies. For this reason, in the last decade, quantifying the salient features
of the intra-tumoural heterogeneity has gained a great attention, also leading to a re-emerging of the texture analysis. Tumour
heterogeneity represents the complex biology of tumour microenvironment, characterised by both spatial and temporal variability,
increased by the presence of chaotic blood vessels within tumour tissue. Computed Tomography (CT) has always been
considered one of the reference technologies for morphological analysis of organs and tissues, permitting to capture the “in
vivo” spatial tumour heterogeneity. The need to also detect hemodynamic tumour features has stimulated the use of CT
perfusion (CTp), a promising functional imaging technique in the oncological field. CTp allows detecting the presence of tumour
abnormal hemodynamic patterns, by analysing the tissue temporal variations occurring after an intravenous administration of
contrast medium. This work presents the extraction of meaningful statistical and texture features from both baseline CT images and
perfusion maps of lung tumours, which could work as prognostic image-based biomarkers
mpMRI detection of suspected prostate cancer with a negative biopsy: can radiomic features help radiologists?
No full textPurpose: To investigate whether DWI-based radiomics features could differentiate patients with a clinical suspicion of PCa and negative TRUS-biopsy that have a positive mpMRI from patients where mpMRIs do not show any evidence.
Materials and Methods: The records of 17 patients undergoing 3T-mpMRI for suspected PCa subsequently not confirmed at TRUS-biopsy were extracted from our institutional database. The ground truth was available for only a few. 7 patients did not reported evidence at mpMRI, while 10 patients showed suspected PCa lesions, contoured in consensus by two radiologists. 84 image-based radiomic features were computed on high b-value DW-MRI sequences of all patients of the two groups. The ROC curve was computed for each feature and the one yielding the highest AUC was selected. Its discrimination power was also assessed via a Wilcoxon rank-sum test (p<0.001).
Results: The mean of local skewness (SL-m), related to local inhomogeneities of DWI values, confirms radiologist reports in 94% of cases, with AUC=0.93 (95% CI, 0.56-1.00), specificity=100% and sensitivity=86% (one false positive only). Median SL-m values in patients with suspected PCa were greater than 30% (p~10-4) with respect to patients showing no evidences at mpMRI.
Conclusions: DWI-based radiomic features strongly support mpMRI evidences in case of suspected, and for some patients clear, PCa although TRUS-biopsy is negative. These outcomes suggest further investigation on the role that these features are extremely promising could have in PCa patient’s stratification.
Limitations: Although it confirmed the mpMRI evidence to be PCa for the few patients where the ground-truth was available, for most of them it was not at our disposal because did not belong to a dedicated study.
Ethics committee approval: IRB approval, written informed consent was waived.
Funding: No funding was received for this work
A novel approach for semi-quantitative assessment of reliability of blood flow values in DCE-CT perfusion
No full textIn the last few years, cancer treatments have improved significantly with the introduction of new therapies aiming at reducing tumour angiogenesis, a process leading to disease progression and metastasis formation. Computed tomography perfusion (CTp) is being emerged as a promising functional technique for assessing tumour response to these new treatments, which yield a reduction of perfusion heterogeneity, occurring long before morphological reduction. However, several factors, such as noise induced by respiratory and physiological involuntary motion, prevent a reliable quantitative assessment, hence the clinical use of CTp. Currently, the assessment strategies rely on global measurements that fail in discriminating between noise and heterogeneity of tumour perfusion, both characterized by a wide value dispersion.
This paper presents a new approach for reliability estimation by introducing a novel local-based index, which is able to discriminate between tumour heterogeneity, featured by locally structured patterns, and noise, characterised by sparse and unstructured values. This index enables a proper comparison between perfusion maps and can replace the parameters based on the global mean, thus improving the overall reliability of CTp studies and favouring the translation into clinical routine
To assess the role of radiomic features from high b-value DWI sequences in the early detection of clinically significant prostate cancer (PCa)
No full textPurpose or Learning Objective
To assess the role of radiomic features from high b-value DWI sequences in the early detection of clinically significant prostate cancer (PCa).
Methods or Background
76 patients are retrospectively enrolled, who undergo multiparametric MRI (mpMRI) and biopsy examination, where they received a Gleason Score (GS)=3+3 representing non-clinically significant PCa (ncsPCa, n=26) or GS≥3+4 meaning clinically significant PCa (csPCa, n=50). PCa Regions of Interest (ROIs) are outlined on DWI at b=2000s/mm2 and eighty-four local first-order radiomic features are extracted. First, the LASSO-based method selects a subset of relevant features, discarding linearly correlated couples. Then, to prevent overfitting, only the couple with the lowest p-value at Wilcoxon rank-sum test is selected. A Support Vector Machine (SVM) is trained on 48 patients, validated through 3-fold Cross Validation and tested on 28 patients. ROC curve and AUC are used to assess the SVM performance, together with specificity, sensitivity, and Positive Predictive Value (PPV).
Results or Findings
The AUC of the ROC curve on the training set is 0.86, with specificity and sensitivity equal to 94% and 77%, respectively, whilst the AUC on the test set is 0.84 with specificity and of 75% and 90%, respectively, and PPV=90%. ncsPCa and csPCa are separated with p=0.007.
Conclusion
The classifier shows a very low probability of overtreatment of ncsPCa while a high PPV strongly improves the performances of clinical mpMRI used in triage pre-biopsy setting.
Limitations
This study has been carried out on a 3T machine
Risk stratification of patients with prostate cancer: promising results with high b-value DWI radiomic features
No full textPurpose: To investigate the potential role of radiomic features computed on high b-value Diffusion Weighted Imaging (DWI) to perform risk stratification of patients with a clinical suspicion of prostate cancer (PCa).
Materials and Methods: 42 patients of our institution, representing 7 risk levels, were retrospectively enrolled in the study and grouped into 4 classes of risk: (a) clinically significant (CS) PCa, split over 4 levels (ISUP=2÷5), (b) non-clinically significant (NCS) PCa (ISUP=1), patients with a negative biopsy and (c) positive mpMRI (NP) or (d) negative mpMRI (NN). After computing radiomic features on DWI b=2000s/mm2, the correlation between radiomic features and risk level was investigated through two steps: (i) Spearman index (ρ), (ii) Kruskal-Wallis and Wilcoxon tests (p<0.05) for multi- and pairwise- comparison of the 4 classes, respectively.
Results: The mean of local coefficient of variation (CVL-m), a measure of local dispersion of DWI values, resulted in the most discriminant radiomic features among the four classes (p~10-6), able to rank the four increasing risk classes with ρ=0.81, with a high pairwise separability (p≤0.026). ρ=0.81 is also achieved when correlating the CVL-m with all the 7 increasing risk level groups.
Conclusions: This study allows performing an early stratification of all 7 PCa risk levels. Increasing values of CVL-m in DWI images describes a higher degree of local heterogeneity, in accordance with tissue over-proliferation and, consequently, increasing level of tumour aggressiveness.
Limitations: The number of patients could be low for a proper stratification of the cohort in 7 classes. However, the excellent results achieved when using CVL-m values to correctly rank all risk levels give CVL-m the most promising role in depicting PCa risk progression.
Ethics committee approval: IRB approval, written informed consent was waived.
Funding: No funding was received for this work
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