Predictive modeling of perioperative patient deterioration: combining unanticipated ICU admissions and mortality for improved risk prediction.

OBJECTIVE: This paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes. MATERIALS AND METHODS: With less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost. RESULTS: During evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction. DISCUSSION: The study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies. CONCLUSIONS: This work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.

Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis?

In this multicenter, retrospective study, we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients, including 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast-enhanced MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists was evaluated using Cohen's Kappa. On a per-patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI was 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2), these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI, and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen's Kappa = 0.4250, p = 0.004). The results of this study show the potential of MRDI to improve inter-observer variability and the detection of csPCa.

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 < 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.

Ultrasound Viscoelastography by Acoustic Radiation Force: A State-of-the-Art Review.

Ultrasound elastography (USE) is a promising tool for tissue characterization as several diseases result in alterations of tissue structure and composition, which manifest as changes in tissue mechanical properties. By imaging the tissue response to an applied mechanical excitation, USE mimics the manual palpation performed by clinicians to sense the tissue elasticity for diagnostic purposes. Next to elasticity, viscosity has recently been investigated as an additional, relevant, diagnostic biomarker. Moreover, since biological tissues are inherently viscoelastic, accounting for viscosity in the tissue characterization process enhances the accuracy of the elasticity estimation. Recently, methods exploiting different acquisition and processing techniques have been proposed to perform ultrasound viscoelastography. After introducing the physics describing viscoelasticity, a comprehensive overview of the currently available USE acquisition techniques is provided, followed by a structured review of the existing viscoelasticity estimators classified according to the employed processing technique. These estimators are further reviewed from a clinical usage perspective, and current outstanding challenges are discussed.

Use of the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) questionnaires for clinical decision-making and psychological referral in ophthalmic care: a multicentre observational study.

OBJECTIVES: The aim of this study was to evaluate the influence of anxiety and depression on clinician decision-making in patients suffering from chronic eye disease in ophthalmological clinical practice. DESIGN AND SETTING: This multicentre observational study, in collaboration with the WHO, included ophthalmologists and their patients affected by chronic eye disease. States of anxiety and depression were screened with specific questionnaires, the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7), self-administered by patients before the visit. In the present analysis, we report data from three major eye care centres in Italy between 2021 and 2022. PRIMARY AND SECONDARY OUTCOMES: To assess self-reported changes in ophthalmologists' clinical approach (communication style and their clinical-therapeutic strategies) and decisions after knowing questionnaire scores (primary aim), and to analyse the PHQ-9 and GAD-7 scores in patients with chronic eye diseases (secondary aim). RESULTS: 41 ophthalmologists and 359 patients were included. The results from PHQ-9 and GAD-7 scores showed critical depression and anxiety status scores (PHQ-9 ≥5 and GAD-7 ≥10) in 258 patients. In 74% of cases, no actions were taken by the ophthalmologists based on these scores; in 26% of cases, they changed their clinical approach; and in 14% of cases, they referred the patients for psychological/psychiatric evaluation. CONCLUSIONS: States of anxiety and depression affect many patients with chronic eye conditions and need to be detected and managed early to improve patients' well-being. Providing ophthalmologists with knowledge of their patients' psychological conditions can change the clinical management and attitude towards referral for a psychological evaluation. Further studies are needed to expand our knowledge of how to raise awareness among ophthalmologists regarding multimorbidity of patients suffering from chronic eye diseases in order to achieve better clinical outcomes.

Quantification of extravasation and binding of PSMA-targeted nanobubbles by modelling the second-wave phenomenon.

PURPOSE: With about ten-fold smaller diameter than MBs, nanobubbles (NBs) were developed as new-generation ultrasound contrast agents (UCA) able to extravasate and target specific receptors expressed on extravascular cancer cells, such as the prostate-specific membrane antigen (PSMA). It has been shown that PSMA-targeted NBs (PSMA-NBs) can bind to specific prostate cancer (PCa) cells and exhibit a prolonged retention effect (PRE), observable by NB-based CEUS (NB-CEUS). However, previous analyses of PRE were mainly limited to the semi-quantitative assessment of the time-intensity curve (TIC) in an entire tumor ROI, possibly losing information on tumor spatial heterogeneity and local characteristics. When analyzing the pixel-level TICs of free NB-based CEUS, we observed a unique second-wave phenomenon: The first pass of the NB wave (bolus) is usually accompanied by a second wave in the time range of 3 to 15 min after the bolus injection. Such a phenomenon was shown to be potentially valuable in supporting the diagnostics of cancerous lesions. PROCEDURES: Seven male athymic nude mice were included and implanted with a tumor expressing PSMA (PSMA+) and tumors not expressing PSMA (PSMA-) on two flanks. Using either free NBs or PSMA-NBs, the characteristics of pixel-level TICs were estimated by a specialized model accounting for the two-wave phenomenon, compared with a conventional model describing only one wave. The estimated parameters by the two models were presented as parametric maps to visualize the PRE of PSMA-NBs in a dual-tumor mouse model. The effectiveness of the two models were also assessed by comparing the estimated parameters in the PSMA+ and PSMA- tumors through Mann-Whitney U test and quartile difference. RESULTS: Two parameters, the peak time and residual factor of the second wave, by the second-wave model were significantly different between PSMA+ and PSMA- tumors when using PSMA-NBs. Compared with the TICs of free NBs, TICs of PSMA-NBs present higher peak intensity and a more delayed second wave, especially in the PSMA+ tumor. CONCLUSIONS: The estimation of parametric maps allows the estimation and visualization of specific binding of PSMA-NBs in PCa. The incorporation of the second-wave phenomenon enrich our understanding of NB kinetics in vivo and can possibly contribute to improved diagnostics of PCa in the future.

Spatiotemporal analysis of contrast-enhanced ultrasound for differentiating between malignant and benign breast lesions.

OBJECTIVES: The aim of this study was to apply spatiotemporal analysis of contrast-enhanced ultrasound (CEUS) loops to quantify the enhancement heterogeneity for improving the differentiation between benign and malignant breast lesions. MATERIALS AND METHODS: This retrospective study included 120 women (age range, 18-82 years; mean, 52 years) scheduled for ultrasound-guided biopsy. With the aid of brightness-mode images, the border of each breast lesion was delineated in the CEUS images. Based on visual evaluation and quantitative metrics, the breast lesions were categorized into four grades of different levels of contrast enhancement. Grade-1 (hyper-enhanced) and grade-2 (partly-enhanced) breast lesions were included in the analysis. Four parameters reflecting enhancement heterogeneity were estimated by spatiotemporal analysis of neighboring time-intensity curves (TICs). By setting the threshold on mean parameter, the diagnostic performance of the four parameters for differentiating benign and malignant lesions was evaluated. RESULTS: Sixty-four of the 120 patients were categorized as grade 1 or 2 and used for estimating the four parameters. At the pixel level, mutual information and conditional entropy present significantly different values between the benign and malignant lesions (p < 0.001 in patients of grade 1, p = 0.002 in patients of grade 1 or 2). For the classification of breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893 in patients of grade 1, AUC = 0.848 in patients of grade 1 or 2). CONCLUSIONS: The proposed spatiotemporal analysis for assessing the enhancement heterogeneity shows promising results to aid in the diagnosis of breast cancer by CEUS. CLINICAL RELEVANCE STATEMENT: The proposed spatiotemporal method can be developed as a standardized software to automatically quantify the enhancement heterogeneity of breast cancer on CEUS, possibly leading to the improved diagnostic accuracy of differentiation between benign and malignant lesions. KEY POINTS: • Advanced spatiotemporal analysis of ultrasound contrast-enhanced loops for aiding the differentiation of malignant or benign breast lesions. • Four parameters reflecting the enhancement heterogeneity were estimated in the hyper- and partly-enhanced breast lesions by analyzing the neighboring pixel-level time-intensity curves. • For the classification of hyper-enhanced breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893).

The Effect of Varied Microperimetric Biofeedback Training in Central Vision Loss: A Randomized Trial.

SIGNIFICANCE: This investigation reports for the first time the effects of different microperimetric biofeedback strategies in visually impaired subjects with central field loss. PURPOSE: This study aimed to evaluate the effects of two MP-3 microperimeter biofeedback strategies on the visual performance of subjects with central vision loss. Moreover, changes between the groups were compared to provide indications of practice with biofeedback stimulation in subjects with central vision loss. METHODS: Using simple randomization, 19 participants were trained according to two different biofeedback stimulation approaches using the MP-3 microperimeter. Patients were assigned to two different groups: subjects trained for 2 days a week (group A) and 3 days a week (group B). The patients in each group were randomized to perform a total of 10 or 15 sessions. RESULTS: Fixation stability increased from 4.5 ± 2.8 to 2.3 ± 2.2° 2 and from 8.2 ± 6.9 to 1.4 ± 1° 2 after 2 and 3 weekly biofeedback training sessions, respectively ( P < .05). Biofeedback training induced a significant improvement of 40.7 and 29.4% in reading speed for groups A and B, respectively ( P < .05). A comparison of two weekly biofeedback training sessions with three weekly biofeedback sessions demonstrated greater fixation stability in group B ( P < .05). CONCLUSIONS: This study concludes that a biofeedback intervention is effective in enhancing oculomotor control in patients with central vision loss. In our study, a more intensive biofeedback strategy seemed to produce significantly better results in terms of functional vision parameters.

Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors.

BACKGROUND AND OBJECTIVES: Currently, no evidence-based criteria exist for decision making in the post anesthesia care unit (PACU). This could be valuable for the allocation of postoperative patients to the appropriate level of care and beneficial for patient outcomes such as unanticipated intensive care unit (ICU) admissions. The aim is to assess whether the inclusion of intra- and postoperative factors improves the prediction of postoperative patient deterioration and unanticipated ICU admissions. METHODS: A retrospective observational cohort study was performed between January 2013 and December 2017 in a tertiary Dutch hospital. All patients undergoing surgery in the study period were selected. Cardiothoracic surgeries, obstetric surgeries, catheterization lab procedures, electroconvulsive therapy, day care procedures, intravenous line interventions and patients under the age of 18 years were excluded. The primary outcome was unanticipated ICU admission. RESULTS: An unanticipated ICU admission complicated the recovery of 223 (0.9%) patients. These patients had higher hospital mortality rates (13.9% versus 0.2%, p<0.001). Multivariable analysis resulted in predictors of unanticipated ICU admissions consisting of age, body mass index, general anesthesia in combination with epidural anesthesia, preoperative score, diabetes, administration of vasopressors, erythrocytes, duration of surgery and post anesthesia care unit stay, and vital parameters such as heart rate and oxygen saturation. The receiver operating characteristic curve of this model resulted in an area under the curve of 0.86 (95% CI 0.83-0.88). CONCLUSIONS: The prediction of unanticipated ICU admissions from electronic medical record data improved when the intra- and early postoperative factors were combined with preoperative patient factors. This emphasizes the need for clinical decision support tools in post anesthesia care units with regard to postoperative patient allocation.

A multidisciplinary approach to inherited retinal dystrophies from diagnosis to initial care: a narrative review with inputs from clinical practice.

BACKGROUND: Non-syndromic inherited retinal dystrophies (IRDs) such as retinitis pigmentosa or Leber congenital amaurosis generally manifest between early childhood and late adolescence, imposing profound long-term impacts as a result of vision impairment or blindness. IRDs are highly heterogeneous, with often overlapping symptoms among different IRDs, and achieving a definite diagnosis is challenging. This narrative review provides a clinical overview of the non-syndromic generalized photoreceptor dystrophies, particularly retinitis pigmentosa and Leber congenital amaurosis. The clinical investigations and genetic testing needed to establish a diagnosis are outlined, and current management approaches are discussed, focusing on the importance of the involvement of an interdisciplinary team from diagnosis and initial care to long-term follow-up and support. RESULTS: The effective management of IRDs requires a multidisciplinary, and ideally interdisciplinary, team of experts knowledgeable about IRDs, with experienced professionals from fields as diverse as ophthalmology, neuropsychiatry, psychology, neurology, genetics, orthoptics, developmental therapy, typhlology, occupational therapy, otolaryngology, and orientation and mobility specialties. Accurate clinical diagnosis encompasses a range of objective and subjective assessments as a prerequisite for the genetic testing essential in establishing an accurate diagnosis necessary for the effective management of IRDs, particularly in the era of gene therapies. Improvements in genome sequencing techniques, such as next-generation sequencing, have greatly facilitated the complex process of determining IRD-causing gene variants and establishing a molecular diagnosis. Genetic counseling is essential to help the individual and their family understand the condition, the potential risk for offspring, and the implications of a diagnosis on visual prognosis and treatment options. Psychological support for patients and caregivers is important at all stages of diagnosis, care, and rehabilitation and is an essential part of the multidisciplinary approach to managing IRDs. Effective communication throughout is essential, and the patient and caregivers' needs and expectations must be acknowledged and discussed. CONCLUSION: As IRDs can present at an early age, clinicians need to be aware of the clinical signs suggesting visual impairment and follow up with multidisciplinary support for timely diagnoses to facilitate appropriate therapeutic or rehabilitation intervention to minimize vision loss.

Radiogenomics Analysis Linking Multiparametric MRI and Transcriptomics in Prostate Cancer.

Prostate cancer (PCa) is a highly prevalent cancer type with a heterogeneous prognosis. An accurate assessment of tumor aggressiveness can pave the way for tailored treatment strategies, potentially leading to better outcomes. While tumor aggressiveness is typically assessed based on invasive methods (e.g., biopsy), radiogenomics, combining diagnostic imaging with genomic information can help uncover aggressive (imaging) phenotypes, which in turn can provide non-invasive advice on individualized treatment regimens. In this study, we carried out a parallel analysis on both imaging and transcriptomics data in order to identify features associated with clinically significant PCa (defined as an ISUP grade ≥ 3), subsequently evaluating the correlation between them. Textural imaging features were extracted from multi-parametric MRI sequences (T2W, DWI, and DCE) and combined with DCE-derived parametric pharmacokinetic maps obtained using magnetic resonance dispersion imaging (MRDI). A transcriptomic analysis was performed to derive functional features on transcription factors (TFs), and pathway activity from RNA sequencing data, here referred to as transcriptomic features. For both the imaging and transcriptomic features, different machine learning models were separately trained and optimized to classify tumors in either clinically insignificant or significant PCa. These models were validated in an independent cohort and model performance was used to isolate a subset of relevant imaging and transcriptomic features to be further investigated. A final set of 31 imaging features was correlated to 33 transcriptomic features obtained on the same tumors. Five significant correlations (p < 0.05) were found, of which, three had moderate strength (|r| ≥ 0.5). The strongest significant correlations were seen between a perfusion-based imaging feature-MRDI A median-and the activities of the TFs STAT6 (-0.64) and TFAP2A (-0.50). A higher-order T2W textural feature was also significantly correlated to the activity of the TF STAT6 (-0.58). STAT6 plays an important role in controlling cell proliferation and migration. Loss of the AP2alpha protein expression, quantified by TFAP2A, has been strongly associated with aggressiveness and progression in PCa. According to our findings, a combination of texture features extracted from T2W and DCE, as well as perfusion-based pharmacokinetic features, can be considered for the prediction of clinically significant PCa, with the pharmacokinetic MRDI A feature being the most correlated with the underlying transcriptomic information. These results highlight a link between quantitative imaging features and the underlying transcriptomic landscape of prostate tumors.

New Hemodynamic Parameters in Peri-Operative and Critical Care-Challenges in Translation.

Hemodynamic monitoring technologies are evolving continuously-a large number of bedside monitoring options are becoming available in the clinic. Methods such as echocardiography, electrical bioimpedance, and calibrated/uncalibrated analysis of pulse contours are becoming increasingly common. This is leading to a decline in the use of highly invasive monitoring and allowing for safer, more accurate, and continuous measurements. The new devices mainly aim to monitor the well-known hemodynamic variables (e.g., novel pulse contour, bioreactance methods are aimed at measuring widely-used variables such as blood pressure, cardiac output). Even though hemodynamic monitoring is now safer and more accurate, a number of issues remain due to the limited amount of information available for diagnosis and treatment. Extensive work is being carried out in order to allow for more hemodynamic parameters to be measured in the clinic. In this review, we identify and discuss the main sensing strategies aimed at obtaining a more complete picture of the hemodynamic status of a patient, namely: (i) measurement of the circulatory system response to a defined stimulus; (ii) measurement of the microcirculation; (iii) technologies for assessing dynamic vascular mechanisms; and (iv) machine learning methods. By analyzing these four main research strategies, we aim to convey the key aspects, challenges, and clinical value of measuring novel hemodynamic parameters in critical care.

Efficacy and Patients' Satisfaction with the ORCAM MyEye Device Among Visually Impaired People: A Multicenter Study.

To evaluate usability of and satisfaction with OrCam MyEye, a finger-size wearable assistive technology device for visually impaired during real-world tasks. This prospective multicenter study was conducted on visually impaired people recruited from 5 vision rehabilitation centers. Patients performed real-world tasks such as near and distance reading, money handling, colour identification and face recognition in 2 different scenarios: without using any low vision aid and with OrCam. System Usability Scale (SUS), Patient's Global Impression of Change (PGIC), the Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0) and the Psychosocial Impact of Assistive Devices Scale (PIADS) were administered after the use of the OrCam device. Among the 100 participants, use of OrCam MyEye device improved many daily-living tasks (F = 1.67, P < .05), and in particular reading and face recognition. Multivariate logistic regression showed that age and visual field defect explained 89% of the variation in efficacy of the device. Nearly half (45%) of the participants indicated a positive rating with the SUS. The PGIC rates showed a minimal improvement with a mean score of 4.2 (SD:1.8). The most highlighted parameter with the QUEST 2.0 test was "ease of use" in 58% (48 subjects). The PIADS indicator showed that the device positively impacted on the daily-living tasks of users (r2 = 0.72, P < .05). Regression modelling demonstrated a good relation between the questionnaires scores and demographic, disease and visual factors (P < .05). OrCam MyEye allowed visually impaired people to read, handle money and face recognition independently. This device may offer to these subjects to be independent.

Automated detection and classification of patient-ventilator asynchrony by means of machine learning and simulated data.

BACKGROUND AND OBJECTIVE: Mechanical ventilation is a lifesaving treatment for critically ill patients in an Intensive Care Unit (ICU) or during surgery. However, one potential harm of mechanical ventilation is related to patient-ventilator asynchrony (PVA). PVA can cause discomfort to the patient, damage to the lungs, and an increase in the length of stay in the ICU and on the ventilator. Therefore, automated detection algorithms are being developed to detect and classify PVAs, with the goal of optimizing mechanical ventilation. However, the development of these algorithms often requires large labeled datasets; these are generally difficult to obtain, as their collection and labeling is a time-consuming and labor-intensive task, which needs to be performed by clinical experts. METHODS: In this work, we aimed to develop a computer algorithm for the automatic detection and classification of PVA. The algorithm employs a neural network for the detection of the breath of the patient. The development of the algorithm was aided by simulations from a recently published model of the patient-ventilator interaction. RESULTS: The proposed method was effective, providing an algorithm with reliable detection and classification results of over 90% accuracy. Besides presenting a detection and classification algorithm for a variety of PVAs, here we show that using simulated data in combination with clinical data increases the variability in the training dataset, leading to a gain in performance and generalizability. CONCLUSIONS: In the future, these algorithms can be utilized to gain a better understanding of the clinical impact of PVAs and help clinicians to better monitor their ventilation strategies.

Pharmacokinetic Modeling of the Second-Wave Phenomenon in Nanobubble-Based Contrast-Enhanced Ultrasound.

With a typical 100-500 nm diameter, nanobubbles are a promising new-generation ultrasound contrast agent that paves ways for several applications, such as efficient drug delivery, molecular imaging, and assessment of vascular permeability. Due to their unique physical properties, nanobubbles exhibit distinct in vivo pharmacokinetics. We have shown that the first pass of the nanobubble bolus is usually accompanied by the appearance of a second bolus (wave) within a time range of about 15 minutes. Such phenomenon, to the best of our knowledge, has never been observed with conventional microbubbles and smaller molecular contrast agents used in MRI and CT. In a previous study, we showed the potential of this phenomenon in supporting cancer diagnosis. This study focuses on developing a new compartmental pharmacokinetic model that can be used to interpret the second-wave phenomenon. With this model, we can analyze more in-depth the roles of several physiological factors affecting the characteristics of the second-wave phenomenon.

Posturographic Analysis in Patients Affected by Central and Peripheral Visual Impairment.

Although vision loss is known to affect equilibrium maintenance, postural control in patients affected by low vision has been poorly investigated. We evaluated postural stability and the ability to use visual, proprioceptive and vestibular information in different low vision patterns. Ten adults with normal vision (NC), fourteen adults affected by central visual impairment (CLV) and eight adults affected by peripheral visual impairment (PLV) were enrolled in our study. Patients underwent visual, vestibular and postural evaluation (bedside examination, Computed Dynamic Posturograophy). Motor Control Tests were performed to analyze automatic postural adaptive responses elicited by unexpected postural disturbances. Clinical evaluations did not show abnormality in all patients. In the Sensory Organization Test, CLV and PLV patients performed more poorly in conditions 3-6 and 3-4, as compared to NC subjects. The condition 5 score was significantly lower in the CLV group with respect to the PLV patients. Composite equilibrium scores demonstrated significant differences between low-vision subjects vs. NC subjects. No differences were found for somatosensorial contribution. Visual afferences showed lower values in all visually impaired subjects, while vestibular contribution was lower in the CLV patients as compared to the NC and PLV patients. MCT latencies were significantly worse in the CLV subjects. In the low-vision patients, postural control was modified with a specific pattern of strategy adaptation. Different modulations of postural control and different adaptive responses seemed to characterize CLV patients as compared to PLV subjects.

On the value of MRI for improved understanding of cuff-based oscillometric measurements.

Blood pressure (BP) is a key parameter in critical care and in cardiovascular disease management. BP is typically measured via cuff-based oscillometry. This method is highly inaccurate in hypo- and hypertensive patients. Improvements are difficult to achieve because oscillometry is not yet fully understood; many assumptions and uncertainties exist in models describing the process by which arterial pulsations become expressed within the cuff signal. As a result, it is also difficult to estimate other parameters via the cuff such as arterial stiffness, cardiac output and pulse wave velocity (PWV)-BP calibration. Many research modalities have been employed to study oscillometry (ultrasound, computer simulations, ex-vivo studies, measurement of PWV, mechanical analysis). However, uncertainties remain; additional investigation modalities are needed. In this study, we explore the extent to which MRI can help investigate oscillometric assumptions. Four healthy volunteers underwent a number of MRI scans of the upper arm during cuff inflation. It is found that MRI provides a novel perspective over oscillometry; the artery, surrounding tissue, veins and the cuff can be simultaneously observed along the entire length of the upper arm. Several existing assumptions are challenged: tissue compression is not isotropic, arterial transmural pressure is not uniform along the length of the cuff and propagation of arterial pulsations through tissue is likely impacted by patient-specific characteristics (vasculature position and tissue composition). Clinical Relevance- The cuff interaction with the vasculature is extremely complex; existing models are oversimplified. MRI is a valuable tool for further development of cuff-based physiological measurements.

Pharmacokinetic modeling of PSMA-targeted nanobubbles for quantification of extravasation and binding in mice models of prostate cancer.

PURPOSE: Contrast-enhanced ultrasound (CEUS) by injection of microbubbles (MBs) has shown promise as a cost-effective imaging modality for prostate cancer (PCa) detection. More recently, nanobubbles (NBs) have been proposed as novel ultrasound contrast agents. Unlike MBs, which are intravascular ultrasound contrast agents, the smaller diameter of NBs allows them to cross the vessel wall and target specific receptors on cancer cells such as the prostate-specific membrane antigen (PSMA). It has been demonstrated that PSMA-targeted NBs can bind to the receptors of PCa cells and show a prolonged retention effect in dual-tumor mice models. However, the analysis of the prolonged retention effect has so far been limited to qualitative or semi-quantitative approaches. METHODS: This work introduces two pharmacokinetics models for quantitative analysis of time-intensity curves (TICs) obtained from the CEUS loops. The first model is based on describing the vascular input by the modified local density random walk (mLDRW) model and independently interprets TICs from each tumor lesion. Differently, the second model is based on the reference-tissue model, previously proposed in the context of nuclear imaging, and describes the binding kinetics of an indicator in a target tissue by using a reference tissue where binding does not occur. RESULTS: Our results show that four estimated parameters, ß, ß / λ $\beta /\lambda $ , ß + / ß - ${\beta }_ + /{\beta }_ - $ , for the mLDRW-input model, and γ for the reference-based model, were significantly different (p-value <0.05) between free NBs and PSMA-NBs. These parameters estimated by the two models demonstrate different behaviors between PSMA-targeted and free NBs. CONCLUSIONS: These promising results encourage further quantitative analysis of targeted NBs for improved cancer diagnostics and characterization.

A Comprehensive Motion Compensation Method for In-Plane and Out-of-Plane Motion in Dynamic Contrast-Enhanced Ultrasound of Focal Liver Lesions.

Contrast-enhanced ultrasound (CEUS) acquisitions of focal liver lesions are affected by motion, which has an impact on contrast signal quantification. We therefore developed and tested, in a large patient cohort, a motion compensation algorithm called the Iterative Local Search Algorithm (ILSA), which can correct for both periodic and non-periodic in-plane motion and can reject frames with out-of-plane motion. CEUS cines of 183 focal liver lesions in 155 patients from three hospitals were used to develop and test ILSA. Performance was evaluated through quantitative metrics, including the root mean square error and R2 in fitting time-intensity curves and standard deviation value of B-mode intensities, computed across cine frames), and qualitative evaluation, including B-mode mean intensity projection images and parametric perfusion imaging. The median root mean square error significantly decreased from 0.032 to 0.024 (p < 0.001). Median R2 significantly increased from 0.88 to 0.93 (p < 0.001). The median standard deviation value of B-mode intensities significantly decreased from 6.2 to 5.0 (p < 0.001). B-Mode mean intensity projection images revealed improved spatial resolution. Parametric perfusion imaging also exhibited improved spatial detail and better differentiation between lesion and background liver parenchyma. ILSA can compensate for all types of motion encountered during liver CEUS, potentially improving contrast signal quantification of focal liver lesions.

The unique second wave phenomenon in contrast enhanced ultrasound imaging with nanobubbles.

Investigation of nanobubble (NB) pharmacokinetics in contrast-enhanced ultrasound (CEUS) at the pixel level shows a unique phenomenon where the first pass of the contrast agent bolus is accompanied by a second wave. This effect has not been previously observed in CEUS with microbubbles. The objective of this study was to investigate this second-wave phenomenon and its potential clinical applications. Seven mice with a total of fourteen subcutaneously-implanted tumors were included in the experiments. After injecting a bolus of NBs, the NB-CEUS images were acquired to record the time-intensity curves (TICs) at each pixel. These TICs are fitted to a pharmacokinetic model which we designed to describe the observed second-wave phenomenon. The estimated model parameters are presented as parametric maps to visualize the characteristics of tumor lesions. Histological analysis was also conducted in one mouse to compare the molecular features of tumor tissue with the obtained parametric maps. The second-wave phenomenon is evidently shown in a series of pixel-based TICs extracted from either tumor or tissues. The value of two model parameters, the ratio of the peak intensities of the second over the first wave, and the decay rate of the wash-out process present large differences between malignant tumor and normal tissue (0.04 < Jessen-Shannon divergence < 0.08). The occurrence of a second wave is a unique phenomenon that we have observed in NB-CEUS imaging of both mouse tumor and tissue. As the characteristics of the second wave are different between tumor and tissue, this phenomenon has the potential to support the diagnosis of cancerous lesions.