An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection

BACKGROUND AND OBJECTIVE

Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise. Simulating the lung-ventilator interactions has been proposed to obtain large labeled datasets to train machine learning classifiers. However, the obtained data lacks the influence of different hardware, of servo-controlled algorithms, and different sources of noise. Here, we propose VentGAN, an adversarial learning approach to improve simulated data by learning the ventilator fingerprints from unlabeled clinical data.

METHODS

In VentGAN, the loss functions are designed to add characteristics of clinical waveforms to the generated results, while preserving the labels of the simulated waveforms. To validate VentGAN, we compare the performance for detection and classification of PVAs when training a previously developed machine learning algorithm with the original simulated data and with the data generated by VentGAN. Testing is performed on independent clinical data labeled by experts. The McNemar test is applied to evaluate statistical differences in the obtained classification accuracy.

RESULTS

VentGAN significantly improves the classification accuracy for late cycling, early cycling and normal breaths (p< 0.01); no significant difference in accuracy was observed for delayed inspirations (p = 0.2), while the accuracy decreased for ineffective efforts (p< 0.01).

CONCLUSIONS

Generation of realistic synthetic data with labels by the proposed framework is feasible and represents a promising avenue for improving training of machine learning models.

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.

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.

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.

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.

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.

Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilation

There is a clinical need for monitoring inspiratory effort to prevent lung- and diaphragm injury in patients who receive supportive mechanical ventilation in an Intensive Care Unit. Different pressure-based techniques are available to estimate this inspiratory effort at the bedside, but the accuracy of their effort estimation is uncertain since they are all based on a simplified linear model of the respiratory system, which omits gas compressibility of air, and the viscoelasticity and nonlinearities of the respiratory system. The aim of this in-silico study was to provide an overview of the pressure-based estimation techniques and to evaluate their accuracy using a more sophisticated model of the respiratory system and ventilator. The influence of the following parameters on the accuracy of the pressure-based estimation techniques was evaluated using the in-silico model: 1) the patient's respiratory mechanics 2) PEEP and the inspiratory pressure of the ventilator 3) gas compressibility of air 4) viscoelasticity of the respiratory system 5) the strength of the inspiratory effort. The best-performing technique in terms of accuracy was the whole breath occlusion. The average error and maximum error were the lowest for all patient archetypes. We found that the error was related to the expansion of gas in the breathing set and lungs and respiratory compliance. However, concerns exist that other factors not included in the model, such as a changed muscle-force relation during an occlusion, might influence the true accuracy. The estimation techniques based on the esophageal pressure showed an error related to the viscoelastic element in the model which leads to a higher error than the occlusion. The error of the esophageal pressure-based techniques is therefore highly dependent on the pathology of the patient and the settings of the ventilator and might change over time while a patient recovers or becomes more ill.

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.

3D printing and 3D bioprinting technology in medicine: ethical and legal issues

Abstract

There has been a surge of interest in new technologies in medicine because of their promising clinical applications. Extensive research on additive manufacturing and its applications in the medical field has been carried out with good results and very high expectations. Due to their disruptive nature and potential, 3D printing and even more 3D bioprinting raise many ethical and safety concerns that need to be adequately addressed to provide good regulation before entering clinical practice. This article aims to highlight the general ethical concerns associated with the use of additive manufacturing in medicine and the lack of current international regulatory directives to guide these experiments. Transparency about how these new medical devices are regulated and approved is a fundamental requirement to promote and improve public trust, efficiency, safety and quality.

The number of risk factors not at target is associated with cardiovascular risk in a type 2 diabetic population with albuminuria in primary cardiovascular prevention. Post-hoc analysis of the NID-2 trial

Background

Nephropathy in Diabetes type 2 (NID-2) study is an open-label cluster randomized clinical trial that demonstrated that multifactorial intensive treatment reduces Major Adverse Cardiac Events (MACEs) and overall mortality versus standard of care in type 2 diabetic subjects with albuminuria and no history of cardiovascular disease. Aim of the present post-hoc analysis of NID- 2 study is to evaluate whether the number of risk factors on target associates with patient outcomes.

Methods

Intervention phase lasted four years and subsequent follow up for survival lasted 10 years. To the aim of this post-hoc analysis, the whole population has been divided into 3 risk groups: 0–1 risk factor (absent/low); 2–3 risk factors (intermediate); 4 risk factors (high). Primary endpoint was a composite of fatal and non-fatal MACEs, the secondary endpoint was all-cause death at the end of the follow-up phase.

Results

Absent/low risk group included 166 patients (52.4%), intermediate risk group 128 (40.4%) and high-risk group 23 (7.3%). Cox model showed a significant higher risk of MACE and death in the high-risk group after adjustment for confounding variables, including treatment arm (HR 1.91, 95% CI 1.04–3.52, P = 0.038 and 1.96, 95%CI 1.02–3.8, P = 0,045, respectively, vs absent/low risk group).

Conclusions

This post-hoc analysis of the NID-2 trial indicates that the increase in the number of risk factors at target correlates with better cardiovascular-free survival in patients with type 2 diabetes at high CV risk.

Clinical Trial Registration

ClinicalTrials.gov number, NCT00535925. https://clinicaltrials.gov/ct2/show/NCT00535925

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01674-7.

Death occurred due to undiagnosed systemic amyloidosis: a case report

Amyloidosis is a disorder related to errors in protein folding. We present a clinical case of systemic amyloidosis manifesting as hypotension, tachycardia, pain, weight loss, asthenia, anorexia, dysphagia, and mood deflection in a 49-year-old-year-old woman with a previous clinical history of articular and muscular pain, correlated to suspected seronegative arthritis. The blood test revealed kidney insufficiency, an electrocardiogram identified low voltages of the peripheral leads and T waves anomalies. A serum protein electrophoresis revealed the presence of high levels of monoclonal kappa free chains. The woman started to have a sense of suffocation, and after one week she was found dead in her bed. After the autopsy, the results of Congo red staining of the myocardium were characteristic of amyloid. According to the autoptic and the histological examination, death occurred due to acute cardiac and respiratory arrest secondary to amyloid cardiomyopathy in a patient with undiagnosed systemic amyloidosis.

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.

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, β,β / λ $\beta /\lambda $ ,β + / β - ${\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.

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 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.

Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound

This work proposes an interpretable radiomics approach to differentiate between malignant and benign focal liver lesions (FLLs) on contrast-enhanced ultrasound (CEUS). Although CEUS has shown promise for differential FLLs diagnosis, current clinical assessment is performed only by qualitative analysis of the contrast enhancement patterns. Quantitative analysis is often hampered by the unavoidable presence of motion artifacts and by the complex, spatiotemporal nature of liver contrast enhancement, consisting of multiple, overlapping vascular phases. To fully exploit the wealth of information in CEUS, while coping with these challenges, here we propose combining features extracted by the temporal and spatiotemporal analysis in the arterial phase enhancement with spatial features extracted by texture analysis at different time points. Using the extracted features as input, several machine learning classifiers are optimized to achieve semiautomatic FLLs characterization, for which there is no need for motion compensation and the only manual input required is the location of a suspicious lesion. Clinical validation on 87 FLLs from 72 patients at risk for hepatocellular carcinoma (HCC) showed promising performance, achieving a balanced accuracy of 0.84 in the distinction between benign and malignant lesions. Analysis of feature relevance demonstrates that a combination of spatiotemporal and texture features is needed to achieve the best performance. Interpretation of the most relevant features suggests that aspects related to microvascular perfusion and the microvascular architecture, together with the spatial enhancement characteristics at wash-in and peak enhancement, are important to aid the accurate characterization of FLLs.

Acoustic characterization of tissue-mimicking materials for ultrasound perfusion imaging research

Materials with well-characterized acoustic properties are of great interest for the development of tissue-mimicking phantoms with designed (micro)vasculature networks. These represent a useful means for controlled in-vitro experiments to validate perfusion imaging methods such as Doppler and contrast-enhanced ultrasound (CEUS) imaging. In this work, acoustic properties of seven tissue-mimicking phantom materials at different concentrations of their compounds and five phantom case materials are characterized and compared at room temperature. The goal of this research is to determine the most suitable phantom and case material for ultrasound perfusion imaging experiments. The measurements show a wide range in speed of sound varying from 1057 to 1616 m/s, acoustic impedance varying from 1.09 to 1.71 × 10⁶ kg/m²s, and attenuation coefficients varying from 0.1 to 22.18 dB/cm at frequencies varying from 1 MHz to 6 MHz for different phantom materials. The nonlinearity parameter B/A varies from 6.1 to 12.3 for most phantom materials. This work also reports the speed of sound, acoustic impedance and attenuation coefficient for case materials. According to our results, polyacrylamide (PAA) and polymethylpentene (TPX) are the optimal materials for phantoms and their cases, respectively. To demonstrate the performance of the optimal materials, we performed power Doppler ultrasound imaging of a perfusable phantom, and CEUS imaging of that phantom and a perfusion system. The obtained results can assist researchers in the selection of the most suited materials for in-vitro studies with ultrasound imaging.

Radiomic combination of spatial and temporal features extracted from DCE-MRI for prostate cancer detection. Annu Int Conf IEEE Eng Med Biol Soc 2021;2021:3153-3156. [PMID: 34891910 DOI: 10.1109/embc46164.2021.9630015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Multi-parametric MRI is part of the standard prostate cancer (PCa) diagnostic protocol. Recent imaging guidelines (PI-RADS v2) downgraded the value of Dynamic Contrast-Enhanced (DCE)-MRI in the diagnosis of PCa. A purely qualitative analysis of the DCE-MRI time series, as it is generally done by radiologists, might indeed overlook information on the microvascular architecture and function. In this study, we investigate the discriminative power of quantitative imaging features derived from texture and pharmacokinetic analysis of DCE-MRI. In 605 regions of interest (benign and malignant tissue) delineated in 80 patients, we found through independent cross-validation that a subset of quantitative spatial and temporal features extracted from DCE-MRI and incorporated in machine learning classifiers obtains a good diagnostic performance (AUC = 0.80-0.86) in distinguishing malignant from benign regions.Clinical Relevance- These findings highlight the underlying potential of quantitative DCE-derived radiomic features in identifying PCa by MRI.

A Model-based Approach to Generating Annotated Pressure Support Waveforms

During pressure support ventilation, every breath is triggered by the patient. Mismatches between the patient and the ventilator are called asynchronies. It has been reported that large numbers of asynchronies may be harmful and may lead to increased mortality. Automatic asynchrony detection and classification, with subsequent feedback to clinicians, will improve lung ventilation and, possibly, patient outcome. Machine learning techniques have been used to detect asynchronies. However, large, diverse and high-quality training and verification data sets are needed. In this work, we propose a model for generating a large, realistic, labeled, synthetic dataset for training and testing machine learning algorithms to detect a wide variety of asynchrony types. Next to a morphological evaluation of the obtained waveforms, validation of the proposed model includes a test with a machine learning algorithm trained on clinical data.

Modulation of pulse travel and blood flow during cuff inflation- An experimental case study

The blood pressure (BP) cuff can be used to modulate blood flow and propagation of pressure pulse along the artery. In our previous work, we researched methods to adapt cuff modulation techniques for pulse transit time vs. BP calibration and for measurement of other hemodynamic indices of potential interest to critical care, such as arterial compliance. A model characterized the response of the vasculature located directly under the cuff, but assumed that no significant changes occur in the distal vasculature.This study has been tailored to gain insights into the response of distal BP and pulse transit time to cuff inflation. Invasive BP data collected downstream from the cuff demonstrates that highly dynamic processes occur in the distal arm during cuff inflation. Mean arterial pressure increases in the distal artery by up to 20 mmHg, leading to a decrease in pulse transit time of up to 20 ms. Clinical Relevance: Such significant changes need to be taken into account in order to improve non-invasive BP estimations and to enable inference of other hemodynamic parameters from vasculature response to cuff inflation. A simple model is developed in order to reproduce the observed behaviors. The lumped-parameter model demonstrates opportunities for cuff modulation measurements which can reveal information on parameters such as systemic resistance, distal arterial, venous compliances and artery-vein interaction.

Modulation of Pulse Propagation and Blood Flow via Cuff Inflation-New Distal Insights

In standard critical care practice, cuff sphygmomanometry is widely used for intermittent blood pressure (BP) measurements. However, cuff devices offer ample possibility of modulating blood flow and pulse propagation along the artery. We explore underutilized arrangements of sensors involving cuff devices which could be of use in critical care to reveal additional information on compensatory mechanisms. In our previous work, we analyzed the response of the vasculature to occlusion perturbations by means of observations obtained non-invasively. In this study, our aim is to (1) acquire additional insights by means of invasive measurements and (2) based on these insights, further develop cuff-based measurement strategies. Invasive BP experimental data is collected downstream from the cuff in two patients monitored in the OR. It is found that highly dynamic processes occur in the distal arm during cuff inflation. Mean arterial pressure increases in the distal artery by 20 mmHg, leading to a decrease in pulse transit time by 20 ms. Previous characterizations neglected such distal vasculature effects. A model is developed to reproduce the observed behaviors and to provide a possible explanation of the factors that influence the distal arm mechanisms. We apply the new findings to further develop measurement strategies aimed at acquiring information on pulse arrival time vs. BP calibration, artery compliance, peripheral resistance, artery-vein interaction.

Biofeedback stimulation in the visually impaired: a systematic review of literature

BACKGROUND

It is estimated that approximately 1.3 billion people live with some form of distance or near visual impairment. Numerous studies have been carried out to evaluate the effects of biofeedback (BF) and establish if it could be a useful tool in vision rehabilitation for various eye diseases.

OBJECTIVE

This systematic review aimed: 1) to examine the current evidence of BF efficacy for the rehabilitation of the visually impaired and 2) to describe methodological variations used in previous BF studies to provide recommendations for vision rehabilitation interventions.

METHODS

A systematic review was conducted in the Medline, PubMed, Cochrane Library and Web of Science databases to collect documents published between January 2000 and May 2020. Of the 1,960 studies identified, 43 met the criteria for inclusion. The following information was collected from each study: sample size, control group, any eye disease, apparatus used, frequency and number of sessions of BF, main outcomes of training and whether a follow-up was conducted. The first group included studies published as scientific articles in peer-reviewed journals. The second group included abstracts of studies presented at peer-reviewed conferences. Publications were also grouped according to the eye disease treated.

RESULTS

25 articles and 18 peer-reviewed conference abstracts (PRCAs) were included in this review. BF stimulation is a commonly used technique for the treatment of visual impairment caused by macular disease. Most BF studies evaluate the effect of training on the preferred retinal locus (PRL), particularly with regard to fixation location and stability. Across these studies, participants who received BF intervention improved fixation stability and reading speed. High variability in the number of sessions and the duration of BF training was found. Most studies did not use a control group.

CONCLUSIONS

The findings of this review present evidence for biofeedback treatment in vision rehabilitation, with improved oculomotor abilities. Currently, it is not possible to formulate evidence-based recommendations for a standard training procedure due to the poor quality of existing randomised controlled trials. High-quality studies are needed to develop standard protocols for a range of eye diseases.

Role of post mortem CT (PMCT) in high energy traumatic deaths

BACKGROUND

Post Mortem Computed Tomography (PMCT) is being increasingly implemented in forensic field and could be an adjuvant to classic autopsies. In this study we evaluated the feasibility of complementation of conventional autopsy in trauma victims with PMCT.

MATERIALS AND METHODS

A total of 21 subjects, who had sustained various types of blunt high-energy trauma, were selected from the casuistry of the Section of Legal Medicine at University of Pisa: before autopsy, a PMCT examination (Toshiba Aquilion 16 CT scanner) was performed, and after the acquisition of the raw images, MPR and VR reconstructions were performed with dedicated software.

RESULTS

PMCT is more sensitive than conventional autopsy in detecting skeletal injuries, whilst autopsy constitutes the method of choice for the detection of thoracic and abdominal visceral injuries.

CONCLUSIONS

PMCT should be considered a useful tool in addition to conventional autopsy in evaluating trauma victims: it detects further bone fractures in body parts difficult to investigate during autopsy (i.e. posterior regions), facilitating the pathologist in the reconstruction of events and in determining the cause of death.

Blood flow patterns estimation in the left ventricle with low-rate 2D and 3D dynamic contrast-enhanced ultrasound

BACKGROUND AND OBJECTIVE

Left ventricle (LV) dysfunction always occurs at early heart-failure stages, producing variations in the LV flow patterns. Cardiac diagnostics may therefore benefit from flow-pattern analysis. Several visualization tools have been proposed that require ultrafast ultrasound acquisitions. However, ultrafast ultrasound is not standard in clinical scanners. Meanwhile techniques that can handle low frame rates are still lacking. As a result, the clinical translation of these techniques remains limited, especially for 3D acquisitions where the volume rates are intrinsically low.

METHODS

To overcome these limitations, we propose a novel technique for the estimation of LV blood velocity and relative-pressure fields from dynamic contrast-enhanced ultrasound (DCE-US) at low frame rates. Different from other methods, our method is based on the time-delays between time-intensity curves measured at neighbor pixels in the DCE-US loops. Using Navier-Stokes equation, we regularize the obtained velocity fields and derive relative-pressure estimates. Blood flow patterns were characterized with regard to their vorticity, relative-pressure changes (dp/dt) in the LV outflow tract, and viscous energy loss, as these reflect the ejection efficiency.

RESULTS

We evaluated the proposed method on 18 patients (9 responders and 9 non-responders) who underwent cardiac resynchronization therapy (CRT). After CRT, the responder group evidenced a significant (p<0.05) increase in vorticity and peak dp/dt, and a non-significant decrease in viscous energy loss. No significant difference was found in the non-responder group. Relative feature variation before and after CRT evidenced a significant difference (p<0.05) between responders and non-responders for vorticity and peak dp/dt. Finally, the method feasibility is also shown with 3D DCE-US.

CONCLUSIONS

Using the proposed method, adequate visualization and quantification of blood flow patterns are successfully enabled based on low-rate DCE-US of the LV, facilitating the clinical adoption of the method using standard ultrasound scanners. The clinical value of the method in the context of CRT is also shown.

A modelling framework for assessment of arterial compliance by fusion of oscillometry and pulse wave velocity information

BACKGROUND AND OBJECTIVES

Measurement of arterial compliance is recognized as important for clinical use and for enabling better understanding of circulatory system regulation mechanisms. Estimation of arterial compliance involves either a direct measure of the ratio between arterial volume and pressure changes or an inference from the pulse wave velocity (PWV). In this study we demonstrate an approach to assess arterial compliance by fusion of these two information sources. The approach is based on combining oscillometry as used for blood pressure inference and PWV measurements based on ECG/PPG. Enabling reliable arterial compliance measurements will contribute to the understanding of regulation mechanisms of the arterial tree, possibly establishing arterial compliance as a key measure relevant in hemodynamic monitoring.

METHODS

A measurement strategy, a physiological model, and a framework based on Bayesian principles are developed for measuring changes in arterial compliance based on combining oscillometry and PWV data. A simulation framework is used to study and validate the algorithm and measurement principle in detail, motivated by previous experimental findings.

RESULTS

Simulations demonstrate the possibility of inferring arterial compliance via fusion of simultaneously acquired volume/pressure relationships and PWV data. In addition, the simulation framework demonstrates how Bayesian principles can be used to handle low signal - to - noise ratio and partial information loss.

CONCLUSIONS

The developed simulation framework shows the feasibility of the proposed approach for assessment of arterial compliance by combining multiple data sources. This represents a first step towards integration of arterial compliance measurements in hemodynamic monitoring using existing clinical technology. The Bayesian approach is of particular relevance for such patient monitoring settings, where measurements are repeated frequently, context is relevant, and data is affected by artefacts. In addition, the simulation framework is necessary for future clinical-study design, in order to determine device specifications and the extent to which noise affects the inference process.

Method for measurement of arterial compliance by fusion of oscillometry and pulse wave velocity

Up until now estimation of arterial compliance has been performed either by analysis of arterial pressure changes with respect to volume changes or by inference based on pulse wave velocity (PWV). In this study we demonstrate the possibility of an approach to assess arterial compliance by fusing the two information sources namely the pressure/volume relationship obtained from oscillography and PWV data. The goal is to assess arterial properties easily and robustly, enhancing current hemodynamic monitoring. The approach requires as input signals: an electrocardiogram (ECG), a photo- plethysmogram (PPG) and the arterial oscillation as measured during non-invasive blood pressure measurements based on oscillometry with a cuff. These signals are fused by an algorithm using Bayesian principles underpinned by a physiological model. In our simulations, we demonstrate the feasibility to infer arterial compliance by our proposed strategy. A very first measurement on a healthy volunteer supports our findings from the simulation.Clinical Relevance- Arterial compliance/stiffness is recognized as a key hemodynamic parameter, which is not easily accessible and not a standard parameter currently. The presented method and obtained results are encouraging for future research in this area.

A machine learning method for automatic detection and classification of patient-ventilator asynchrony

Patients suffering from respiratory failure are often put on assisted mechanical ventilation. Patient-ventilator asynchrony (PVA) can occur during mechanical ventilation, which cause damage to the lungs and has been linked to increased mortality in the intensive care unit. In current clinical practice PVA is still detected using visual inspection of the air pressure, flow, and volume curves, which is time-consuming and sensitive to subjective interpretation. Correct detection of the patient respiratory efforts is needed to properly asses the type of asynchrony. Therefore, we propose a method for automatic detection of the patient respiratory efforts using a one-dimensional convolution neural network. The proposed method was able to detect patient efforts with a sensitivity and precision of 98.6% and 97.3% for the inspiratory efforts, and 97.7% and 97.2% for the expiratory efforts. Besides allowing detection of PVA, combining the estimated timestamps of patient's inspiratory and expiratory efforts with the timings of the mechanical ventilator further allows for classification of the asynchrony type. In the future, the proposed method could support clinical decision making by informing clinicians on the quality of ventilation and providing actionable feedback for properly adjusting the ventilator settings.

Pharmacokinetic Modeling of Targeted Ultrasound Contrast Agents for Quantitative Assessment of Anti-Angiogenic Therapy: a Longitudinal Case-Control Study in Colon Cancer

PURPOSE

To evaluate quantitative and semi-quantitative ultrasound molecular imaging (USMI) for antiangiogenic therapy monitoring in human colon cancer xenografts in mice.

PROCEDURES

Colon cancer was established in 17 mice by injection of LS174T (N_r = 9) or CT26 (N_n = 8) cancer cells to simulate clinical responders and non-responders, respectively. Antiangiogenic treatment (bevacizumab; N_rt = N_nt = 5) or control treatment (saline; N_rc = 4, N_nc = 3) was administered at days 0, 3, and 7. Three-dimensional USMI was performed by injection at days 0, 1, 3, 7, and 10 of microbubbles targeted to the vascular endothelial growth factor receptor 2 (VEGFR2). Microbubble binding rate (k_b), estimated by first-pass binding model fitting, and semi-quantitative parameters late enhancement (LE) and differential targeted enhancement (dTE) were compared at each day to evaluate their ability to assess and predict the response to therapy. Correlation analysis with the ex-vivo immunohistological quantification of VEGFR2 expression and the percentage blood vessel area was also performed.

RESULTS

Significant changes in the USMI parameters during treatment were observed only in the responders treated with bevacizumab (p-value < 0.05). Prediction of the response to therapy as early as 1 day after treatment was achieved by the quantitative parameter k_b (p-value < 0.01), earlier than possible by tumor volume quantification. USMI parameters could significantly distinguish between clinical responders and non-responders (p-value << 0.01) and correlated well with the ex-vivo quantification of VEGFR2 expression and the percentage blood vessels area (p-value << 0.01).

CONCLUSION

USMI (semi)quantitative parameters provide earlier assessment of the response to therapy compared to tumor volume, permit early prediction of non-responders, and correlate well with ex-vivo angiogenesis biomarkers.

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.

Machine learning for classification of uterine activity outside pregnancy

The objective of this study was to investigate the use of classification methods by a machine-learning approach for discriminating the uterine activity during the four phases of the menstrual cycle. Four different classifiers, including support vector machine (SVM), K-nearest neighbors (KNN), Gaussian mixture model (GMM) and naïve Bayes are here proposed. A set of amplitude- and frequency-features were extracted from signals measured by two different quantitative and noninvasive methods, such as electrohysterography and ultrasound speckle tracking. The proposed classifiers were trained using all possible feature combinations. The method was applied on a database (24 measurements) collected in different phases of the menstrual cycle, comprising uterine active and quiescent phases. The SVM classifier showed the best performance for discrimination between the different menstrual phases. The classification accuracy, sensitivity, and specificity were 90%, 79%, 93%, respectively. Similar methods can in the future contribute to the diagnosis of infertility or other common uterine diseases such as endometriosis.

The Veterans Affairs Low-Vision Visual Functioning Questionnaire-48 (VA LV VFQ-48): Performance of the Italian version

PURPOSE

The Veterans Affairs Low-Vision Visual Functioning Questionnaire-48 is among the most validated tools to collect patient-reported outcomes in a low-vision population. We have aimed to conduct a pilot validation of the Italian version of the Veterans Affairs Low-Vision Visual Functioning Questionnaire-48.

METHODS

The Veterans Affairs Low-Vision Visual Functioning Questionnaire-48 was translated using a standardized procedure and then administered to consecutive low-vision patients attending rehabilitation services in three centers. Patients were interviewed by a trained psychologist regarding the individual items of the tool.

RESULTS

We included 131 patients with a mean visual acuity of 0.91 logMAR (standard deviation: 0.42 logMAR), mostly affected by age-related macular degeneration. The Veterans Affairs Low-Vision Visual Functioning Questionnaire-48 showed high internal consistency (Cronbach's alpha: 0.98) and good item-test and item-rest correlation (median: 0.73 and 0.71, respectively). Both the overall score and the subscale (reading, visual motor, mobility and visual information) scores significantly correlated with visual acuity, reading acuity and speed. Reading speed achieved the best absolute correlation with the Veterans Affairs Low-Vision Visual Functioning Questionnaire-48 scores (Spearman r: 0.39-0.49).

CONCLUSION

The Italian version of the Veterans Affairs Low-Vision Visual Functioning Questionnaire-48 is a valid tool to assess patients attending low-vision services. Revising a few items may further improve the tool.

Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review

Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.

Bernhard Sabel and 'Residual Vision Activation Theory': a History Spanning Three Decades

This review has the purpose of retracing the work of Professor Bernard Sabel and his group over the last 2-3 decades, in order to understand how they achieved formulation of the 'Residual Vision Activation Theory'. The methodology proposed is described, from the first studies in 1995 with High Resolution Perimetry requiring a six-months training period, to the new technologies, such as repetitive transorbital Alternating Current Stimulation, that require ten days of training. Vision restoration therapy has shown improvement in visual responses irrespective of age at the training, lesion aetiology and site of lesion. The hypothesis that visual training may induce network plasticity, improving neuronal networks in cortical and subcortical areas of both hemispheres, appears to be confirmed by recent studies including observation of the cerebral activity by fMRI and EEG. However, the results are quite variable and the mechanisms that influence cerebral activity are still unclear. The residual vision activation theory has been much criticized, both for its methodology and analysis of the results, but it gave a new impulse to the research in this area, stimulating more studies on induced cerebral plasticity.

Closed-form solution of the convolution integral in the magnetic resonance dispersion model for quantitative assessment of angiogenesis

Prostate cancer (PCa) diagnosis and treatment is still limited due to the lack of reliable imaging methods for cancer localization. Based on the fundamental role played by angiogenesis in cancer growth and development, several dynamic contrast enhanced (DCE) imaging methods have been developed to probe tumor angiogenic vasculature. In DCE magnetic resonance imaging (MRI), pharmacokinetic modeling allows estimating quantitative parameters related to the physiology underlying tumor angiogenesis. In particular, novel magnetic resonance dispersion imaging (MRDI) enables quantitative assessment of the microvascular architecture and leakage, by describing the intravascular dispersion kinetics of an extravascular contrast agent with a dispersion model. According to this model, the tissue contrast concentration at each voxel is given by the convolution between the intravascular concentration, described as a Brownian motion process according to the convective-dispersion equation, with the interstitium impulse response, represented by a mono-exponential decay, and describing the contrast leakage in the extravascular space. In this work, an improved formulation of the MRDI method is obtained by providing an analytical solution for the convolution integral present in the dispersion model. The performance of the proposed method was evaluated by means of dedicated simulations in terms of estimation accuracy, precision, and computation time. Moreover, a preliminary clinical validation was carried out in five patients with proven PCa. The proposed method allows for a reduction by about 40% of computation time without any significant change in estimation accuracy and precision, and in the clinical performance.

Vision Rehabilitation in Patients with Age-related Macular Degeneration

Background

The aim of this study was to investigate the rehabilitative process and visual rehabilitation outcomes in patients with central vision loss due to age-related macular degeneration (AMD).

Methods

Ninety-five subjects with AMD selected from the attendees of the National Centre of Services and Research for the Prevention of Blindness and Rehabilitation of Low Vision Patients—International Agency for Prevention of Blindness—IAPB Italia Onlus, were evaluated for this retrospective study. Low vision examination included psychological counseling, best corrected visual acuity (BCVA), near visual acuity, Pelli-Robson contrast sensitivity, and fixation stability analysis. Once the clinical assessment was completed, patients attended a low-vision rehabilitative pathway based on visual stimulation, devices training and, if needed, psychological support. Required magnification and reading speed were also evaluated.

Results

For the whole sample, the mean BCVA of the better eye was 0.7 (±0.2) LogMAR and of the worse eye was 1 (±0.2) LogMAR. Restoring reading ability was the most important focus for the patients examined as it was requested by 85% of the whole sample. Mean power of optical magnifying aids for near activities was 10.6 (±9.1) positive spherical diopters. Mean reading speed for the whole sample was 33.1 (±18.2) words per minute (wpm) before visual rehabilitation sessions and increased to 55.2 (±33.1) wpm after visual rehabilitation path. To cope with distance difficulties, 78 distance refractive correction, 10 Galilean telescopes, and 7 Keplerian telescopes were prescribed. For intermediate distance activities, 22 compensation lenses and 10 Galilean telescopes were suggested. Moreover, PC magnifier softwares were prescribed to nine patients. Sixty-five polarized medical filters were prescribed to reduce glare of sunlight. Because of unstable fixation in their better eye (32.3% (±19.7) within 2° circle and 54.8% (±22.9) within 4° circle) and visual acuity < 1.2 LogMAR in the fellow eye, 38 subjects, before starting the devices training sessions, attended a bio-feedback rehabilitation session with flickering pattern stimulus. In these subjects, fixation stability increased significantly to 75.6 (±14.9) within 2° and 89.4 (±19.5) within 4° ( P < 0.05), respectively.

Conclusions

Attending a customized low-vision intervention based on a multidisciplinary approach seems to be effective for improving visual functions in AMD. Both optical/electronic magnifiers and specific visual stimulation program can enhance visual performances.

Effects of polarization and apodization on laser induced optical breakdown threshold

We investigated the influence of polarization and apodization on laser induced optical breakdown threshold in transparent and diffuse media using linearly and radially polarized light. We demonstrate a lower irradiance threshold for optical breakdown using radially polarized light. The dominance of radial polarization in higher-order multiphoton ionization has important medical applications where a lower irradiance threshold may allow reaching deeper layers inside the skin with less risk of collateral damage and thereby improving safety and efficacy of treatment.

Cell fate following ER stress: just a matter of "quo ante" recovery or death?

The endoplasmic reticulum (ER) is a complex and multifunctional organelle. It is the intracellular compartment of protein folding, a complex task, both facilitated and monitored by ER folding enzymes and molecular chaperones. The ER is also a stress-sensing organelle. It senses stress caused by disequilibrium between ER load and folding capacity and responds by activating signal transduction pathways, known as unfolded protein response (UPR). Three major classes of transducer are known, inositol-requiring protein-1 (IRE1), activating transcription factor-6 (ATF6), and protein kinase RNA (PKR)-like endoplasmic reticulum kinase (PERK), which sense with their endoluminal domain the state of protein folding, although the exact mechanism(s) involved is not entirely clear. Depending on whether the homeostatic response of the UPR is successful in restoring an equilibrium between ER load and protein folding or not, the two possible outcomes of the UPR so far considered have been life or death. Indeed, recent efforts have been devoted to understand the life/death switch mechanisms. However, recent data suggest that what appears to be a pure binary decision may in fact be more complex, and survival may be achieved at the expenses of luxury cell functions, such as expression of differentiation genes.

Ophthalmological aspects of IBD

Ocular manifestations occur in 4-12% of patients with IBD. Uveitis and iritis are more frequently associated with ulcerative colitis while episcleritis is more common in Crohn's disease. Some ocular manifestations in IBD can be secondary to treatment and/or effects of the intestinal disease itself. The specific management of ocular manifestations in IBD requires the use of topical steroids and FANS, cycloplegics, systemic steroids or immunosuppressive drugs. When conventional therapies fail to control the ocular manifestations in IBD, the new biologic drugs can be considered as good alternative treatments. Early diagnosis and effective treatment may avoid the onset of severe and sometimes persisting complications. In some cases, a surgical approach is required to treat eye complications, i.e. cataract, and to improve the patient's quality of life.