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.

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.

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.

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.

Evaluation of Dispersion MRI for Improved Prostate Cancer Diagnosis in a Multicenter Study.

OBJECTIVE: The purpose of this study is to compare dispersion MRI and Tofts model (TM) for analysis of quantitative dynamic contrast-enhanced (DCE) MRI (DCE-MRI) for localization of prostate cancer and to assess the correlation between quantitative DCE-MRI parameters and tumor grade. MATERIALS AND METHODS: This retrospective multicenter study included 80 patients with biopsy-proven prostate cancer who underwent DCE-MRI followed by radical prostatectomy. DCE-MRI parameters were extracted from dispersion MRI analysis (the dispersion parameter [kd], the flux rate [kep], and the intravascular mean transit time) and TM analysis (the forward volume transfer constant [Ktrans], kep, and the extravascular extracellular volume fraction [ve]). ROIs representing benign and malignant tissue were drawn on each DCE-MRI slice according to the histopathologic findings, and the diagnostic performance of the estimated parameters for the diagnosis of prostate cancer was evaluated using fivefold cross-validation and ROC curve analysis. Further analysis was conducted for the two most relevant parameters (i.e., kd [for dispersion MRI] and kep [for TM]), to investigate the correlation between DCE-MRI parameters and tumor grade. RESULTS: DCE-MRI parameters were significantly different between benign and malignant prostate tissue (p < 0.0001). The dispersion MRI parameter kd outperformed all other DCE-MRI parameters for prostate cancer diagnosis, showing the highest area under the ROC curve value (p < 0.0001). Only a weak linear correlation (Pearson r = 0.18; p < 0.05) was found between the dispersion parameter and the Gleason grade group. CONCLUSION: Dispersion MRI outperformed TM analysis, improving the diagnostic performance of quantitative DCE-MRI for prostate cancer localization. Of the DCE-MRI parameters, kd (for dispersion MRI) and kep (for TM) provided only poor characterization of tumor grade.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.