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

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

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

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

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

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

Quantitative assessment of carotid ultrasound diameter measurements in the operating room: a comparable analysis of long-axis versus rotated and tilted orientation.

Objective. Carotid ultrasound (US) has been studied as a non-invasive alternative for hemodynamic monitoring. A long-axis (LA) view is traditionally employed but is difficult to maintain and operator experience may impact the diameter estimates, making it unsuitable for monitoring. Preliminary results show that a new, i.e. rotated and tilted (RT) view is more robust to motion and less operator-dependent. This study aimed to quantitatively assess common carotid diameter estimates obtained in a clinical setting from an RT view and compare those to corresponding estimates obtained using other views.Approach. Carotid US measurements were performed in 30 adult cardiac-surgery patients (26 males, 4 females) with short-axis (SA), LA, and RT probe orientations, the first being used as a reference for measuring the true vessel diameter. Per 30 s acquisition, the median and spread in diameter values were computed, the latter representing a measure of robustness, and were statistically compared between views.Main results. The median (IQR) over all the patients of the median diameter per 30 s acquisition was 7.15 (1.15) mm for the SA view, 7.03 (1.51) mm for the LA view, and 6.99 (1.72) mm for the RT view. The median spread in diameter values was 0.18 mm for the SA view, 0.16 mm for the LA view, and 0.18 mm for the RT view. There were no statistically significant differences between views in the median diameter values (p= 0.088) or spread (p= 0.122).Significance. The RT view results in comparable and equally robust median carotid diameter values compared to the reference. These findings open the path for future studies investigating the use of the RT view in new applications, such as in wearable ultrasound devices.

Quantitative ultrasound measurement of uterine contractility in adenomyotic vs. normal uteri: a multicenter prospective study.

OBJECTIVE: To evaluate uterine contractility in patients with adenomyosis compared with healthy controls using a quantitative two-dimensional transvaginal ultrasound (TVUS) speckle tracking method. DESIGN: A multicenter prospective observational study took place in three European centers between 2014 and 2023. SETTING: One university teaching hospital, 1 teaching hospital and 1 specialised clinic. PATIENTS: A total of 46 women with a sonographic or magnetic resonance imaging diagnosis of adenomyosis were included. 106 healthy controls without uterine pathologies were included. INTERVENTION: Four-minute TVUS recordings were performed and four uterine contractility features were extracted using a speckle tracking algorithm. MAIN OUTCOMES MEASURES: The extracted features were contraction frequency (contractions/min), amplitude, velocity (mm/s), and coordination. Women with adenomyosis were compared with healthy controls according to the phase of the menstrual cycle. RESULTS: Throughout the different phases of the menstrual cycle, trends of increased amplitude, decreased frequency and velocity, and reduced contraction coordination were seen in patients with adenomyosis compared with healthy controls. These were statistically significant in the late follicular phase, with a higher amplitude (0.087 ± 0.042 vs. 0.050 ± 0.018), lower frequency and velocity (1.49 ± 0.22 vs. 1.68 ± 0.25 contractions/min, and 0.65 ± 0.18 vs. 0.88 ± 0.29 mm/s, respectively), and reduced contraction coordination (0.34 ± 0.08 vs. 0.26 ± 0.17), in the late luteal phase, with higher amplitude (0.050 ± 0.022 vs. 0.035 ± 0.013), lower velocity (0.51 ± 0.11 vs. 0.65 ± 0.13 mm/s), and reduced contraction coordination (0.027 ± 0.06 vs. 0.18 ± 0.07), and in the midfollicular phase, with decreased frequency (1.48 ± 0.21 vs. 1.69 ± 0.16 contractions/min) in patients with adenomyosis compared with healthy controls. During menses, a higher pain score was significantly associated with lower frequency and velocity and higher contraction amplitude. Results remained significant after correcting for age, parity, and body mass index. CONCLUSION: Uterine contractility differs in patients with adenomyosis compared with healthy controls throughout the phases of the menstrual cycle. This suggests an etiologic mechanism for the infertility and dysmenorrhea seen in patients with adenomyosis. Moreover, it presents new potential therapeutic targets and diagnostic markers.

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).

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.

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.

Corrigendum to "Clinical Trial Protocol: Developing an Image Classification Algorithm for Prostate Cancer Diagnosis on Three-dimensional Multiparametric Transrectal Ultrasound" [Eur. Urol. Open Sci. 49 (2023) 32-43].

[This corrects the article DOI: 10.1016/j.euros.2022.12.018.].

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.

Clinical Trial Protocol: Developing an Image Classification Algorithm for Prostate Cancer Diagnosis on Three-dimensional Multiparametric Transrectal Ultrasound.

Introduction and hypothesis: The tendency toward population-based screening programs for prostate cancer (PCa) is expected to increase demand for prebiopsy imaging. This study hypothesizes that a machine learning image classification algorithm for three-dimensional multiparametric transrectal prostate ultrasound (3D mpUS) can detect PCa accurately. Design: This is a phase 2 prospective multicenter diagnostic accuracy study. A total of 715 patients will be included in a period of approximately 2 yr. Patients are eligible in case of suspected PCa for which prostate biopsy is indicated or in case of biopsy-proven PCa for which radical prostatectomy (RP) will be performed. Exclusion criteria are prior treatment for PCa or contraindications for ultrasound contrast agents (UCAs). Protocol overview: Study participants will undergo 3D mpUS, consisting of 3D grayscale, 4D contrast-enhanced ultrasound, and 3D shear wave elastography (SWE). Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. Patients included prior to prostate biopsy will be used for subsequent preliminary validation. There is a small, anticipated risk for participants associated with the administration of a UCA. Informed consent has to be given prior to study participation, and (serious) adverse events will be reported. Statistical analysis: The primary outcome will be the diagnostic performance of the algorithm for detecting clinically significant PCa (csPCa) on a per-voxel and a per-microregion level. Diagnostic performance will be reported as the area under the receiver operating characteristic curve. Clinically significant PCa is defined as the International Society of Urological grade group ≥2. Full-mount RP histopathology will be used as the reference standard. Secondary outcomes will be sensitivity, specificity, negative predictive value, and positive predictive value for csPCa on a per-patient level, evaluated in patients included prior to prostate biopsy, using biopsy results as the reference standard. A further analysis will be performed on the ability of the algorithm to differentiate between low-, intermediate-, and high-risk tumors. Discussion and summary: This study aims to develop an ultrasound-based imaging modality for PCa detection. Subsequent head-to-head validation trials with magnetic resonance imaging have to be performed in order to determine its role in clinical practice for risk stratification in patients suspected for PCa.

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.

The ADENO study: ADenomyosis and its Effect on Neonatal and Obstetric outcomes: a retrospective population-based study.

BACKGROUND: Adenomyosis is a benign gynecologic condition arising from the uterine junctional zone. Recent studies suggest a relationship between adenomyosis and adverse obstetrical outcomes, but evidence remains conflicting. There is no large-scale study investigating obstetrical outcomes in women with adenomyosis using the gold standard of histopathologic diagnosis. OBJECTIVE: This study aimed to investigate the prevalence of adverse obstetrical and neonatal outcomes in women with histopathologic adenomyosis and that of the general (Dutch) population. STUDY DESIGN: This retrospective population-based study used 2 Dutch national databases (Perined, the perinatal registry, and the nationwide pathology databank [Pathologisch Anatomisch Landelijk Geautomiseerd Archief], from 1995 to 2018) to compare obstetrical outcomes in women before histopathologic adenomyosis diagnosis to the general Dutch population without registered histopathologic adenomyosis. The adjusted odds ratios (95% confidence interval) were calculated for adverse obstetrical outcomes. The outcomes were adjusted for maternal age, parity, ethnicity, year of registered birth, induction of labor, hypertensive disorders in previous pregnancies, multiple gestation, and low socioeconomic status. RESULTS: The pregnancy outcomes of 7925 women with histopathologic adenomyosis were compared with that of 4,615,803 women without registered adenomyosis. When adjusted for confounders, women with adenomyosis had adjusted odds ratios of 1.37 (95% confidence interval, 1.25-1.50) for hypertensive disorders, 1.37 (95% confidence interval, 1.25-1.51) for preeclampsia, 1.15 (95% confidence interval, 1.07-1.25) for small-for-gestational-age infants, 1.54 (95% confidence interval, 1.41-1.68) for emergency cesarean delivery, 1.24 (95% confidence interval, 1.12-1.37) for failure to progress, 1.29 (95% confidence interval, 1.10-1.48) for placental retention, and 1.23 (95% confidence interval, 1.10-1.38) for postpartum hemorrhage. No increased risk of HELLP (hemolysis, elevated liver enzymes, and low platelet count) syndrome, placental abruption, or operative vaginal delivery or need for oxytocin stimulation was found. CONCLUSION: Women with a histopathologic diagnosis of adenomyosis showed an increased prevalence of hypertensive disorders of pregnancy and small-for-gestational-age infants, failure to progress in labor, and placental retention compared with the general population in previous pregnancies. This suggests that uterine (contractile) function in labor and during pregnancy is impaired in women with adenomyosis.

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.

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.

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.

B/A Measurement of Clear Cell Renal Cell Carcinoma versus Healthy Kidney Tissue.

The acoustic parameter of non-linearity B/A has been found capable of discriminating some types of pathological tissue from healthy tissue. The literature on the utility of B/A for cancer diagnostics is very limited, with measurements on the human breast and liver. This work expands the current research on cancer diagnostics by B/A assessment of eight slices of human clear cell renal cell carcinoma (ccRCC) from two patients and four slices of healthy kidney tissue from two healthy kidney samples. The Wilcoxon test identified the B/A distribution of malignant tissue as not significantly different from that of healthy tissue. An alternative way of defining outliers resulted in median B/A values of 8.1 for ccRCC and 6.8 for healthy tissue (p < 0.05). Acoustic attenuation at 2.1 MHz was significantly greater (p < 0.05) for ccRCC (1.7 dB/cm) than for healthy tissue (1.0 dB/cm). The observed differences in the measured values suggest that B/A and acoustic attenuation may represent potential diagnostic markers of ccRCC. More data and an improved experimental design are required to provide a definitive conclusion on the utility of B/A for cancer diagnostics.

Women with combined adenomyosis and endometriosis on MRI have worse IVF/ICSI outcomes compared to adenomyosis and endometriosis alone: A matched retrospective cohort study.

STUDY OBJECTIVES: To assess the effect of adenomyosis, endometriosis and combined adenomyosis and endometriosis, diagnosed on MRI, on IVF/ICSI outcomes versus male subfertility controls. STUDY DESIGN: This single-centre matched retrospective cohort study was carried out at Catharina Hospital in Eindhoven, The Netherlands. The study group consisted of infertile women undergoing their first, fresh embryo transfer during IVF/ICSI, with adenomyosis only (N = 36), endometriosis only (N = 61), and combined adenomyosis and endometriosis (N = 93) based on MRI. The control group consisted of IVF/ICSI patients undergoing treatment due to male subfertility (N = 889). 1:2 case-control matching based on age during IVF/ICSI, parity and number of embryos transferred was performed. Odds ratios were calculated for biochemical pregnancy, ongoing pregnancy and live birth rate versus matched male subfertility controls, and were corrected for embryo quality. RESULTS: Only the combined adenomyosis and endometriosis group showed a significantly reduced OR for biochemical pregnancy (p = 0.004, OR 0.453 (95% CI :(0.284-0.791)), ongoing pregnancy (p = 0.001, OR 0.302 (95% CI: (0.167-0.608)) and live birth (p = 0.001, OR 0.309 (95% CI: (0.168-0.644)) compared to matched male subfertility controls. CONCLUSIONS: The lower (ongoing) pregnancy and live birth rates in the combined adenomyosis and endometriosis women can be attributed to more severe disease in these women, ultimately resulting in increased chances for failed implantation and miscarriage. This highlights the importance of screening for adenomyosis in endometriosis patients, and identifies these women target for additional (hormonal) treatment prior to IVF/ICSI.