Local Density Random Walk Model Preprocessing for Local Shear Wave Viscoelastographic Estimation

Shear wave (SW) elastography is an ultrasound technique that provides quantitative tissue elasticity and viscosity measurements by imaging the tissue response to an applied excitation. In particular, the phase difference method allows for local viscoelasticity estimation through the dispersion curve using the phases from acquired signals at two laterally-spaced pixels in SW elastography. However, this method is sensitive to measurement noise in the SW particle displacement signals. To address this, we propose adopting the local density random walk model to fit the measured noisy SW particle displacement signals. Local elasticity and viscosity are then estimated from the phase difference of the fit signals at two neighboring pixels. Elasticity and viscosity were estimated in six customized phantoms by the phase difference method with and without the proposed preprocessing by model fitting. A decreased interquartile range was obtained by model fitting in both the elasticity and viscosity estimates across all phantoms. In addition, the Levene test indicated a significant difference (p-value < 0.05) between the original and proposed methods in the variance of the estimated elasticity and viscosity. These findings highlight the reduced variability observed in pixel-based viscoelastic estimations with the proposed method, indicating its enhanced robustness

The key drivers of born-sustainable businesses: Evidence from the italian fashion industry

Environmental pollution has become one of the most pressing preoccupations for governments, policymakers, and consumers. For this reason, many companies make constant efforts to comply with international laws and standards on ethics, social responsibility, and environmental protection. Fashion companies are among the main producers of pollution because their manufacturing processes result in highly negative outcomes for the environment. In recent years, numerous fashion industries have been transforming their production policies to be sustainable, while others are already born as sustainable businesses. Based on Resource-Based View (RBV) theory and Natural Resource-Based View theory (NRBV), this paper aims at understanding how internal and external factors stimulate born-sustainable businesses operating in the fashion sector, adopting a multiple case study methodology. Our analysis shows that culture, entrepreneurial orientation of the founders, and the proximity of the suppliers among the internal factors, combined with the increase of green consumers as an external factor, foster the creation of green businesses. At the same time, neither current legislation nor the dynamism and competitiveness of markets have influenced the choice of the companies’ founders to start a business based on green production logic. These results reveal the centrality of the founders’ sensitivity toward green strategies to create a sustainable business. The findings have practical implications because they could support regulatory institutions to introduce some incentives that more clearly encourages companies that choose to adopt sustainable business models from the founding, by acting to the internal and external key factors that drive born-sustainable businesses. This study also provides an extension of the existing literature on sustainable born companies, offering researchers useful information on internal and internal factors that promote the adoption of green policies in the fashion industry

The scaffold protein p140Cap limits ERBB2-mediated breast cancer progression interfering with Rac GTPase-controlled circuitries.

The docking protein p140Cap negatively regulates tumour cell features. Its relevance on breast cancer patient survival, as well as its ability to counteract relevant cancer signalling pathways, are not fully understood. Here we report that in patients with ERBB2-amplified breast cancer, a p140Cap-positive status associates with a significantly lower probability of developing a distant event, and a clear difference in survival. p140Cap dampens ERBB2- positive tumour cell progression, impairing tumour onset and growth in the NeuT mouse model, and counteracting epithelial mesenchymal transition, resulting in decreased metastasis formation. One major mechanism is the ability of p140Cap to interfere with ERBB2- dependent activation of Rac GTPase-controlled circuitries. Our findings point to a specific role of p140Cap in curbing the aggressiveness of ERBB2-amplified breast cancers and suggest that, due to its ability to impinge on specific molecular pathways, p140Cap may represent a predictive biomarker of response to targeted anti-ERBB2 therapies

Contrast-ultrasound dispersion imaging for renal cell carcinoma diagnostics

Cost-effective screening methods for Renal Cell Carcinoma (RCC) are still lacking. Angiogenesis is a recognized hallmark of cancer growth, leading to distinguishable perfusion patterns in tumors from those in normal tissue. This establishes the basis for diagnostic imaging solutions by dynamic contrast-enhanced ultrasound (DCE-US). In the past years, we have developed contrast-ultrasound dispersion imaging (CUDI) techniques to quantify prostate DCE-US acquisitions, obtaining promising results for prostate cancer localization. In this pilot study, we investigated for the first time its feasibility for RCC localization. DCE-US acquisitions of the kidney in 5 patients were used to perform CUDI analysis. With the obtained CUDI parameters and the delineated tumor and parenchyma regions, we performed pixel-based classification, from which the highest area under the receiver-operating-characteristic curve (AUC) = 0.96 was obtained for an individual patient, and an average AUC = 0.68 was obtained for the full patient dataset, showing the potential of CUDI for solid RCC localization. Further validation in a larger dataset and evaluation of the compatibility of point-of-care diagnosis are required.</p

Model-Based Approaches for Breath-to-breath Estimation of Patient Effort during Mechanical Ventilation

Patient inspiratory effort during invasive mechanical ventilation is the voluntary breathing effort made by the patient while receiving partial assistance from a ventilator. Monitoring the effort is crucial to prevent lung and diaphragm injuries, optimize ventilation, and for weaning. Currently, invasive sensors are used to monitor the patient's effort or techniques that only give an intermittent estimate of the effort. Several noninvasive model-based techniques to estimate patient effort are available in the literature which could potentially give a breath-to-breath estimate of the effort without the use of invasive sensors. However, most techniques are not clinically validated and a systematic comparison of techniques is lacking. We tested and compared five different model-based estimation techniques for estimating patient effort noninvasively, including a new method that uses a more detailed model than is currently available in the literature. First, the techniques were tested on simulated data, after this, they were tested on clinical data recorded in the intensive care unit. The results indicated that least square fitting and fitting of an extended equation of motion performed well in both tests. However, techniques that estimated both the respiratory mechanics parameters and the patient's inspiratory effort on the same breath were not able to give reliable estimates. More research needs to be done to verify that the estimates correspond to the measurements of invasive sensors.</p

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Objectives: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. Methods: After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). Results: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value &lt; 0.05) using the Gradient Boosting classifier. Conclusions: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.</p

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Objectives: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. Methods: After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). Results: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value &lt; 0.05) using the Gradient Boosting classifier. Conclusions: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.</p

2D Contrast-Ultrasound Dispersion Imaging of Angiogenesis in Adenomyosis:First Experimental Measurements

Adenomyosis, a benign yet serious uterine condition, is highly prevalent but difficult to diagnose and assess using conventional B-mode ultrasound imaging. As the formation of adenomyosis involves angiogenesis and influences local microvascular dispersion, monitoring microvascularity could improve adenomyosis diagnostics. Hence, contrast-enhanced ultrasound (CEUS), a non-invasive imaging technique, is explored as an alternative to B-mode as it allows visualizing microvascularity. The goal is to investigate the potential of characterizing adenomyosis by quantifying surrogate measures of contrast dispersion, using the contrast-ultrasound dispersion imaging (CUDI) frame-work based on uterine CEUS. CUDI models the passage of an ultrasound-contrast-agent (UCA) bolus through the uterine tissue as a convective dispersion process and expresses UCA dispersion kinetics through temporal fitting (κ) and spatiotemporal similarity analysis. Before applying CUDI on clinical data, the speckle size is regularized to yield isotropy and depth-independence. In addition, the relationship between measured acoustic intensities and underlying UCA concentrations is determined to be linear (R2=0.94) for SonoVue TM UCA concentrations of 0.15-1.00 mg/L, allowing for the direct application of the proposed convective-dispersion modeling. After regularization, 2D CEUS acquisitions of one adenomyotic and one healthy uterus under-went CUDI analysis. The adenomyotic myometrium presented heterogeneously higher values of κ and spatiotemporal similarity, for the healthy uterus the values were homogeneous and lower. Since κ and spatiotemporal similarity are inversely proportional to the level of local dispersion, the results indicate the presence of angiogenesis in adenomyotic tissue, supported by the observations of heterogeneously decreased local dispersion