Non-invasive Tool Research Articles

Ultrasound Imaging of the Superficial and Deep Fasciae Thickness of Upper Limbs in Lymphedema Patients Versus Healthy Subjects

Background/Objectives: Lymphedema, a common source of disability among oncology patients, necessitates continuous targeted rehabilitation. Recent studies have revealed the role of connective tissue in this pathology; however, despite existing research on ultrasound (US) use in lymphedema, no studies have specifically addressed the use of ultrasound to assess fasciae in patients with lymphedema. This study aims to provide a more objective characterization of typical US alterations in these patients by quantifying the thickness of superficial and deep fasciae and comparing them with those of healthy volunteers. Methods: A cross-sectional study was performed using US imaging to measure the thickness of superficial and deep fascia in different regions and levels of the arm and forearm in a sample of 50 subjects: 25 chronic lymphedema patients and 25 healthy participants. Results: No significant difference in fascial thickness was observed between affected and unaffected upper limbs, but patients had notably thinner superficial fascia and deep fascia compared with healthy volunteers. The findings for superficial and deep fascia revealed statistically significant differences (p < 0.0001) in all regions and levels. Conclusions: This study demonstrates the effectiveness of US imaging as a non-invasive tool for detecting subtle fascial changes in chronic lymphedema patients, revealing thinner fasciae compared with those in healthy volunteers. These findings suggest a potential anatomical predisposition to lymphedema, highlighting the importance of incorporating detailed US assessments in diagnosis and management to improve early intervention and patient outcomes. Future studies could, therefore, investigate whether preventive fascia assessment might improve the early identification of individuals at risk.

A short review of application of near-infrared spectroscopy (NIRS) for the assessment of microvascular post-occlusive reactive hyperaemia (PORH) in skeletal muscle

Near-infrared spectroscopy (NIRS) is an optical technique that can be used to non-invasively interrogate haemodynamic changes within skeletal muscle. It can be combined with a short (3–5 min) arterial cuff-occlusion to quantify post-occlusive reactive hyperaemia (PORH). This technique has utility in tracking changes in vascular health in relation to exercise, disease progression or treatment efficacy. However, methods for assessing PORH vary widely and there is little consensus on methodological approaches such as sampling frequency, correction for adipose tissue or the analysis endpoints. The purpose of this review was to: (1) summarise recent advances; (2) compare different methodological approaches and (3) identify current knowledge gaps and future objectives for use of NIRS for vascular assessment. We propose key areas for future work, including optimising occlusion duration and comparing methods of correction for the ischemic stimulus, standardising methods for adjustment of adipose tissue thickness, cross-device comparisons and establishing a standard for minimum sampling rate. Comparisons with alternative methods of capturing PORH or upstream vasodilatory responses would be valuable. Addressing these methodological considerations will aid our understanding of this useful, non-invasive tool for characterising PORH within skeletal muscle and facilitate interpretation of results across studies.

Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial Chemoembolization Combined with Targeted and Immunotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Study

Rationale and ObjectivesTo develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multiparametric magnetic resonance imaging (MRI) radiomic features for predicting treatment response to transarterial chemoembolization combined with molecular targeted therapy plus immunotherapy in unresectable hepatocellular carcinoma (HCC). Materials and methodsThis retrospective study involved 276 patients with unresectable HCC who received combination therapy from 4 medical centers. Patients were divided into one training cohort and two independent external validation cohorts. 16 radiomic features from six multiparametric MRI sequences and 2 clinical features were used to build six machine learning models. The models were evaluated using the area under the curve (AUC), decision curve analysis, and incremental predictive value. ResultsAlpha-fetoprotein and neutrophil-to-lymphocyte ratio are clinical independent predictors of treatment response. In the training cohort and two external validation cohorts, the AUCs and 95% confidence intervals for predicting treatment response were respectively 0.782 (0.673–0.793), 0.695 (0.514–0.708), and 0.679 (0.526–0.719) for the clinical model; 0.942 (0.846–0.932), 0.869 (0.700–0.881), and 0.868 (0.730–0.885) for the radiomics model; and 0.956 (0.859–0.942), 0.895 (0.738–0.903), and 0.892 (0.667–0.828) for the combined clinical-radiomics model. In the three cohorts, the incremental predictive value of the radiomics model over the clinical model was 49.2% (P < 0.001), 28.8% (P < 0.001), and 31.5% (P < 0.001). ConclusionThe combined clinical-radiomics model may provide a reliable and non-invasive tool to predict individual treatment responses and guide and improve clinical decision-making in combination therapy of HCC patients.

Dermoscopic Features of Diaper Dermatoses in Children: A Descriptive Pilot Study

Abstract Background: Diaper dermatoses broadly refer to skin disorders that occur in the diaper area. Dermoscopy is a non-invasive diagnostic tool that magnifies subsurface structures of the skin that are invisible to the unaided eye. Aim: To identify and describe the dermoscopic features of dermatoses in the diaper area. Materials and Methods: A cross-sectional clinical study was conducted, which included 100 children below five years of age with dermatoses in the diaper area. The lesions were initially observed clinically and then under a dermoscope (Dermlite 4, 3GEN Inc., San Juan Capistrano, CA, USA). Findings were recorded and photographed. Results: Of the 100 enrolled children, infectious dermatoses were observed in 46 cases, predominantly comprising scabies and tinea cruris, followed by intertrigo, molluscum contagiosum, and varicella. The most common dermoscopic feature of scabies was a jet wing with a contrail, whereas scaling, red-brown, and grey-brown dots and globules were observed in tinea cruris. Of the non-infectious dermatoses, contact dermatitis and vitiligo were the most common. Dermoscopy revealed irregular-dotted vessels and white structureless areas in cases of contact dermatitis. An absent pigment network and white structureless areas with leukotrichia and white glow were seen in vitiligo, which helped differentiate it from nevus depigmentosus. Conclusion: This study highlights the dermoscopic features of diaper dermatoses and demonstrates the potential of dermoscopy to enhance diagnostic accuracy and optimize patient care without invasive investigations, underscoring its unique role in pediatric dermatology.

Machine learning for early detection of plant viruses: Analyzing post-infection electrical signal patterns

Plant viral diseases significantly threaten global agricultural productivity, necessitating rapid and non-invasive early detection methods to mitigate losses and prevent disease spread. This study presents a novel approach for early detection of viral infections in plants by analyzing electrical signal patterns, focusing on Alfalfa Mosaic Virus (AMV) infection in tobacco plants. A custom-designed, portable signal recording system was developed, incorporating multiple filtering stages at different steps to minimize noise in field conditions without the need for a Faraday cage, ensuring high-quality signal acquisition. Electrical signals were recorded from both healthy and AMV-infected plants before visible symptoms appeared. By extracting a comprehensive range of features in the low-frequency range of 0–4 Hz and carefully examining specific changes in the electrical signals post-infection, significant alterations were observed, characterized by weakened signal strength and increased irregularity. Through precise feature selection techniques, and using only three key features—Median, Autoregressive coefficients, and Autocorrelation—machine learning models, including Support Vector Machine, K-Nearest Neighbors, and Random Forest, achieved approximately 97% accuracy in detecting virus-infected plants even before the appearance of visual symptoms. This represents superior performance with minimal input data compared to existing methods. These results demonstrate the potential of electrical signal analysis combined with machine learning as a practical, rapid, non-invasive, and affordable tool for early virus detection in plants that is easy to use by non-specialists. Implementing this approach could significantly improve disease management strategies and contribute to sustainable agricultural practices by enabling timely interventions in real-world agricultural environments.

Role of echocardiography-derived pulmonary to systemic flow ratio in diagnosing aortopulmonary window: A case report

&lt;b&gt;Background: &lt;/b&gt;Aortopulmonary window (APW) is a rare congenital cardiac anomaly characterized by abnormal communication between the ascending aorta and the main pulmonary artery. Without early surgical correction, this condition has a poor prognosis. However, few case reports have described adult survival of patients with untreated APW.&lt;br /&gt; &lt;b&gt;Case presentation: &lt;/b&gt;We report the case of a 15-year-old girl who developed irreversible pulmonary hypertension due to untreated APW. Initially, we suspected the presence of an extracardiac shunt using a simple calculation of the echocardiography-derived pulmonary-to-systemic flow (Qp/Qs) ratio during a routine echocardiography study, which provided us with a clue to proceed with further multimodal diagnostic evaluation. In this report, we describe a comprehensive diagnostic workup, including right heart catheterization and computed tomography imaging, which confirmed the diagnosis of severe irreversible pulmonary hypertension secondary to a large untreated APW.&lt;br /&gt; &lt;b&gt;Conclusion: &lt;/b&gt;This case report highlights the clinical utility of the echocardiography-derived Qp/Qs ratio as a valuable, noninvasive tool for diagnosing APW, which can lead to severe irreversible pulmonary hypertension. The multidisciplinary approach demonstrated in this case serves as a valuable example for clinicians evaluating similar cases. Therefore, APW should be considered in the differential diagnosis of severe pulmonary hypertension, even in adult patients.

Unveiling human eye temperature with deep learning-powered segmentation

Thermal imaging technology presents a promising, non-invasive tool for medical diagnostics, particularly in eye health assessment. However, accurately delineating the eye region in thermal images of the human face remains a significant challenge for temperature analysis. Extracting eye temperature information from ocular thermal images has traditionally been a manual or semi-automated process, making it time-consuming and tedious. Our research aims to address these issues by introducing an automated systematic approach to eye segmentation and temperature analysis. We employ the semantic segmentation deep learning model SegFormer to segment the eye region and a novel method to extract the thermal-ocular temperature within this region of interest (ROI) for analysis of temperature patterns and diagnosis of any abnormalities. The process starts by extracting temperature data from each pixel in the thermal images. Using the mask and these temperature values, only the temperatures within the masked area are isolated for analysis. Validation is done by comparing and calculating the mean square error against the manual method. A self-created dataset of thermal images of human faces is used due to the lack of available datasets for segmentation. SegFormer (B5) achieves a mean intersection over union (MIoU) of 0.9178 and a dice coefficient of 0.9299. The extracted temperature values from the segmented eye regions provide crucial data for assessing eye health and identifying potential abnormalities. This methodical approach contributes to the progression of thermal image analysis for diagnosing eye-related conditions, particularly in ophthalmology, and in medical domains such as fever screening and reliable Computer-Aided Diagnosis Systems (CADS).

The effects of transcutaneous auricular vagus nerve stimulation (taVNS) on cholinergic neural networks in humans: A neurophysiological study

ObjectiveThe mechanisms of actions of transcutaneous auricular vagus nerve stimulation (taVNS) are still unclear, however the activity of the cholinergic system seems to be critical for the induction of VNS-mediated plasticity. Transcranial Magnetic Stimulation (TMS) is a well-suited, non-invasive tool to investigate cortical microcircuits involving different neurotransmitters. Herein, we evaluated the effect of taVNS on short-latency afferent inhibition (SAI), a TMS paradigm specifically measuring cholinergic neurotransmission. MethodsFifteen healthy subjects participated in this randomized placebo-controlled double-blind study. Each subject underwent two different sessions of 1-hour exposure to taVNS (real and sham) separated by a minimum of 48 h. Real taVNS was administered at left external acoustic meatus, while sham stimulation was performed at left ear lobe. We evaluated SAI bilaterally over the motor cortex before and after exposure to taVNS. ResultsNo side effects were reported by any of the participants.Statistical analysis did not show any significant effect of taVNS on SAI. ConclusionsOur study demonstrated that cholinergic circuits explored by SAI are different from circuits engaged by taVNS. SignificanceSince the influence of VNS on cholinergic neurotransmission has been exhaustively demonstrated in animal models, further studies are mandatory to understand the actual impact of VNS on cholinergic circuits in humans.

Evaluation of a Novel Smart Capsule Bacterial Detection System Device for Diagnosis of Small Intestinal Bacterial Overgrowth.

There is a large unmet need for alternative, non-invasive, and accurate diagnosis of small intestinal bacterial overgrowth (SIBO). The smart capsule bacterial detection system (SCBDS) device contains a targeted sampling technology and an onboard SCBDS assay to detect metabolically active bacteria in the small intestine. Here, we evaluated the agreement of SCBDS assay with duodenal aspiration/culture exvivo in a multicenter clinical study. Duodenal aspiration was performed in subjects with gastrointestinal symptoms suggestive of SIBO. Aspirated fluid was sent to local and central microbiology labs to evaluate the agreement for detecting bacteria with SCBDS assay compared to the total bacterial count (TBC) reference standard. The performance of SCBDS assay was evaluated using a receiver operator curve, sensitivities, and specificities. Aspirates from 66 patients were collected and analyzed for TBC and SCDBS assay. The overall agreement between the two assays was 82%-92% across 3 clinical sites. The SCBDS assay had a sensitivity of 67%-100% and a specificity of 90%-97% using either ≥ 103 or 105 CFU mL-1 cutoff. Additionally, there was a good correlation (r = 0.82) for the TBC culture between the local and central labs. The SCBDS assay showed a high level of agreement with TBC and improved performance compared to other non-invasive tests. These results demonstrate the potential utility of SCBDS device to aid SIBO diagnosis as a simple and non-invasive tool that merits further clinical validation.

External validation of a non-invasive vaginal tool to assess the risk of intra-amniotic inflammation in pregnant women with preterm labor and intact membranes.

To prospectively validate the diagnostic performance of a non-invasive point-of-care tool (Rapid IAI System), including vaginal alpha-fetoprotein and interleukin-6, to predict the occurrence of intra-amniotic inflammation in a Spanish cohort of patients admitted with a diagnosis of preterm labor and intact membranes. From 2017 to 2022, we prospectively evaluated a cohort of pregnant women diagnosed with preterm labor and intact membranes admitted below 34+0weeks who underwent amniocentesis to rule-in/out intra-amniotic infection and/or inflammation. Vaginal sampling was performed at the time of amniocentesis or within 24-48 h. Amniotic fluid IL-6, vaginal alpha-fetoprotein and vaginal IL-6 concentrations were measured using a point-of-care tool provided by Hologic Inc., "Rapid IAI System". We defined intra-amniotic inflammation when amniotic fluid IL-6 values were greater than 11.3 ng/mL. During recruitment, clinicians were blinded to the results of the point-of-care tool. The original prediction model proposed by Hologic Inc. to predict intra-amniotic inflammation was validated in this cohort of patients. We included 151 patients diagnosed with preterm labor and intact membranes. Among these, 29 (19.2 %) had intra-amniotic inflammation. The algorithm including vaginal IL-6 and alpha-fetoprotein showed an area under curve to predict intra-amniotic inflammation of 80.3 % (±5.3 %) with a sensitivity of 72.4 %, specificity of 84.6 %, positive predictive valuve (PPV) of 52.5 %, negative predictive value (NPV) of 92.9 %, and a positive likelihood ratio (LR+) of 4.6 and negative likelihood ratio (LR-) of 0.33. External validation of a non-invasive rapid point-of-care tool, including vaginal alpha-fetoprotein and IL-6, showed very good diagnostic performance for predicting the absence of intra-amniotic inflammation in women with preterm labor and intact membranes.

Spleen stiffness measurements during recompensation in patients with acutely decompensated liver cirrhosis: preliminary findings of a pilot study

BackgroundAcute decompensation (AD) in patients with liver cirrhosis is associated with a dramatic deterioration in prognosis. Immediate initiation of appropriate recompensation measures is essential to improve patient’s outcome, although objective parameters for evaluating the success of recompensation are still lacking. Spleen stiffness measurements (SSM) have emerged as promising non-invasive tool to assess clinically significant portal hypertension (CSPH), which is the main driver of acute decompensation. However, while SSM accurately predicts CSPH and its complication, currently no data are available on its diagnostic performance during recompensation. This pilot-study aimed at evaluating changes in spleen stiffness following the initiation of recompensation measures in cirrhotic patients hospitalized due to AD.MethodsIn this prospective pilot-study, 60 patients with cirrhosis showing AD were included. Liver stiffness measurements (LSM) and SSM were performed on admission and repetitive SSM on day 3 and 5, respectivele, during recompensation measures. A cohort of patients (n = 10) with compensated cirrhosis served as control.ResultsA total of 36 data sets from the originally enrolled 60 patients were eligible for final analysis. On admission, patients with AD revealed a significantly increased spleen stiffness compared to the control group (70.51 vs. 29.06 kPa, p &amp;lt; 0.0001). Following the initiation of recompensation measures SSM revealed a significant reduction in spleen stiffness compared to the baseline assessment on day 3 (−18.5 kPa, −21.53%; p = 0.0002) with no further decrease on day 5 (−17.63 kPa, −21.23%; p = 0.0326).ConclusionRepetitive SSM seems to be a useful non-invasive clinical marker to assess the effectiveness of recompensation measures in cirrhotic patients with AD.

Prevalence of left ventricular diastolic dysfunction by tissue doppler imaging in patients with end stage renal disease undergoing regular dialysis with preserved systolic function

Background: Patients with end-stage renal disease (ESRD) undergoing regular dialysis face a heightened risk of cardiovascular complications, with left ventricular (LV) diastolic dysfunction being a significant concern. Despite preserved systolic function, diastolic dysfunction can lead to notable morbidity. Tissue Doppler imaging (TDI) is a reliable noninvasive tool for assessing diastolic function. This study aims to investigate the prevalence and severity of LV diastolic dysfunction in ESRD patients undergoing regular dialysis with preserved systolic function. Methods: A cohort of 100 ESRD patients, each on dialysis for more than six months, was evaluated. Standard echocardiographic and TDI metrics were measured, including left ventricular ejection fraction (EF), E/A ratio, E/e’ ratio, and left atrial volume index (LAVI). Patients were classified into different grades of diastolic function according to the guidelines of the American Society of Echocardiography. Results: The study found that 78% of patients exhibited LV diastolic dysfunction, with 46% classified as grade I, 26% as grade II, and 6% as grade III. Significant correlations were observed between hemoglobin levels and both the E/e’ ratio (r = -0.29, p &lt; 0.003) and LAVI (r = -0.23, p = 0.002). The E/e’ ratio demonstrated greater sensitivity (74.3%) for detecting diastolic dysfunction compared to the E/A ratio (28.2%) (p &lt; 0.0001). Conclusion: LV diastolic dysfunction is highly prevalent in ESRD patients on regular dialysis, even when systolic function remains intact. TDI, and particularly the E/e’ ratio, is an invaluable tool for the early detection and management of diastolic dysfunction, allowing for better cardiovascular risk stratification in this population

Unlocking Chemotherapy Success: The Role of Diffusion Tensor Imaging in Breast Cancer Treatment

Background: This study investigates the role of Diffusion Tensor Imaging (DTI) in predicting the response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer. Methods: A Diffusion Tensor Imaging magnetic resonance imaging (DTI MRI) sequence, evaluating water diffusion along tissue structures, was performed before and after two chemotherapy cycles. This study included 23 patients with 27 malignant masses, comparing changes in DTI parameters with Residual Cancer Burden (RCB) scores. Results: We found a significant correlation between changes in specific DTI parameters (e.g., λ2, FA, RA) and pathological response, suggesting that DTI could serve as a sensitive marker for early chemotherapy response. However, differences in sensitivity were observed between DTI sequences with 6 and 12 directions, indicating that 12-direction DTI may provide better diagnostic accuracy. The percentage change in DTI parameters, particularly FA, demonstrated a strong ability to predict pathological complete response (pCR) with high sensitivity. Conclusions: As a non-invasive tool, DTI has the potential to assess chemotherapy efficacy, although larger studies with standardized protocols are necessary to validate its clinical utility.

SCAN: a nanopore-based, cost effective decision-supporting tool for mass screening of aneuploidies

In developed countries, Newborn Screening (NBS) programs aim to detect treatable yet clinically silent disorders. The selection of disorders to be included in NBS considers severity, treatment availability, prevalence, and analysis cost. However, numerous genetic disorders remain excluded from routine testing due to high expenses and specialized equipment requirements. Here we present SCAN, a novel, non-invasive, and cost-effective decision-support tool utilizing nanopore sequencing for estimating proportions of chromosomes responsible for the most common aneuploidies. SCAN combines DNA enrichment (amplification), barcoding, nanopore sequencing, and machine learning predictive modeling. In a proof-of-concept study for Klinefelter Syndrome, SCAN achieved 100% sensitivity, specificity, and accuracy, becoming the world’s first IVD-certified genetic test utilising nanopore sequencing. Further model training shows promise in expanding this assay to detect other chromosomal aneuploidies included in the protocol.

Right ventricular outflow tract fractional shortening: a novel diagnostic parameter for pulmonary hypertension.

Pulmonary hypertension (PH) is a progressive vascular disease characterized by elevated pulmonary arterial pressure and resistance in the pulmonary vascular bed. It is associated with high morbidity and mortality. Although right heart catheterization (RHC) is the gold standard for diagnosis, noninvasive methods, such as echocardiography, are essential for early detection and management. This study aimed to elucidate the potential of right ventricular outflow tract fractional shortening (RVOT-FS) as a noninvasive diagnostic tool for PH. This single-center observational cohort study was conducted between November 2023 and May 2024. The study included 141 patients referred to the PH clinic (75 with confirmed PH and 66 controls). Echocardiographic measurements were performed using standard protocols, and RVOT-FS was calculated. Hemodynamic parameters were obtained via RHC. RVOT-FS was significantly lower in the PH group (35.71%, IQR: 27.15-43.33) than in the control group (54.50%, IQR: 45.21-69.17) (p < 0.001). RVOT-FS showed negative correlations with mean pulmonary artery pressure (mPAP) (r = -0.664, p < 0.001), pulmonary vascular resistance (PVR) (r = -0.526, p < 0.001), and other RHC and RV echocardiographic parameters. ROC analysis demonstrated that RVOT-FS is a reliable parameter for predicting PH, with an area under the curve (AUC) of 0.866. An RVOT-FS value of 44.05% had a sensitivity of 82.7% and specificity of 83.3% for diagnosing PH. This study revealed that RVOT-FS was significantly lower in the PH group than in the control group. The correlations also observed between RVOT-FS and mPAP, PVR, and other RHC and echocardiographic parameters imply its potential clinical utility.

Manganese-based type I collagen-targeting MRI probe for in vivo imaging of liver fibrosis.

Liver fibrosis is a common pathway shared by all forms of progressive chronic liver disease. There is an unmet clinical need for noninvasive imaging tools to diagnose and stage fibrosis, which presently relies heavily on percutaneous liver biopsy. Here we explored the feasibility of using a novel type I collagen-targeted manganese (Mn)-based MRI probe, Mn-CBP20, for liver fibrosis imaging. In vitro characterization of Mn-CBP20 demonstrated its high binding affinity for human collagen (Kd = 9.6 µM), high T1-relaxivity (48.9 mM-1s-1 at 1.4T and 27°C), and kinetic inertness to Mn release under forcing conditions. We demonstrated MRI using Mn-CBP20 performs comparably to previously reported gadolinium-based type I collagen-targeted probe EP-3533 in a mouse model of carbon tetrachloride-induced liver fibrosis, and further demonstrate efficacy to detect fibrosis in a diet-induced mouse model of metabolically-associated steatohepatitis. Biodistribution studies using the Mn-CBP20 radio-labeled with the positron-emitting 52Mn isotope demonstrate efficient clearance of Mn-CBP20 primarily via renal excretion. Mn-CBP20 represents a promising candidate that merits further evaluation and development for molecular imaging of liver fibrosis.

Recent Progress and Opportunities of Wearable Non-Invasive Epidermal Sensors for Skin Disease Diagnosis.

With deteriorating environment and increased stress in modern life, skin diseases have become the fourth leading cause of nonfatal and chronic diseases. An early diagnosis might improve the chances of a successful treatment. Wearable epidermal sensors have been emerged as new non-invasive tools for clinical practice and research in dermatology, which can act as a complement to the otherwise mostly visual and tactile judgments. This review discusses the recent progress and opportunities of wearable epidermal sensors for skin disease diagnosis. The configuration, material choice, and fundamental platforms of wearable epidermal sensors are first summarized. Then, their emerging application in monitoring skin diseases is demonstrated by detecting skin hardness, skin hydration, and biomakers. With the advances highlighted here and the ongoing research efforts, the continuous breakthrough in wearable epidermal sensors and their attractive application in skin disease management is foreseeable in the future.

Novel Biomarkers and Imaging Tests for AKI Diagnosis in Patients with Cancer.

The lack of non-invasive urine and blood-based biomarkers for the diagnosis of acute kidney injury (AKI) in patients with cancer is an area of significant unmet clinical need. Traditional non-invasive diagnostic tools that are currently utilized in the clinic, such as creatinine and cystatin C-based eGFR measurements, urinalysis, urine sediment exam, urine protein quantification, and urine electrolyte measurement, lack the sensitivity and specificity to distinguish between the various underlying etiologies of AKI in patients with cancer. Imaging-based diagnostics can be helpful to rule out urinary obstruction, but also lack sensitivity and specificity to diagnose the etiology of AKI. Kidney biopsy is often required for definitive diagnosis. As our scientific understanding of the biological pathways that are dysregulated in AKI has advanced, there has been considerable interest in developing new biomarkers for AKI. For example, the diagnosis of acute interstitial nephritis (AIN), which can occur in patients treated with immune checkpoint inhibitors (ICIs), promises to be revolutionized by the incorporation of urinary testing for inflammatory biomarkers such as C-X-C motif ligand 9 (CXCL9), tumor necrosis factor alpha (TNF-α), and interleukin 9 (IL-9). In the case of cisplatin administration, biomarkers such as neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule 1 (KIM-1) may improve prognostication, differentiating between persistent AKI resulting from acute tubular injury versus pre-renal azotemia. The development and validation of blood, urine and imaging biomarkers into widely utilized diagnostic tests will require a concerted effort, but could improve diagnosis, management and prognostication for a growing group of patients who are at high risk of developing AKI during the course of their illness.

Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.

Recent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize isocitrate dehydrogenase (IDH)-wildtype glioblastoma, a disease characterized by notable tumoral heterogeneity, which is crucial for effective treatment. Neuroimaging techniques, such as diffusion tensor imaging and magnetic resonance radiomics, provide noninvasive insights into tumor infiltration patterns and metabolic profiles, aiding in accurate diagnosis and prognostication. Machine learning algorithms further enhance glioblastoma characterization by identifying distinct imaging patterns and features, facilitating precise diagnoses and treatment planning. Integration of these technologies allows for the development of image-based biomarkers, potentially reducing the need for invasive biopsy procedures and enabling personalized therapy targeting specific pro-tumoral signaling pathways and resistance mechanisms. Although significant progress has been made, ongoing innovation is essential to address remaining challenges and further improve these methodologies. Future directions should focus on refining machine learning models, integrating emerging imaging techniques, and elucidating the complex interplay between imaging features and underlying molecular processes. This review highlights the pivotal role of neuroimaging and machine learning in glioblastoma research, offering invaluable noninvasive tools for diagnosis, prognosis prediction, and treatment planning, ultimately improving patient outcomes. These advances in the field promise to usher in a new era in the understanding and classification of IDH-wildtype glioblastoma.

Endomyocardial biopsy in patients with myocarditis-still justified in the CMR era? A single-centre experience.

In the past decades, cardiovascular magnetic resonance (CMR) was established as a non-invasive tool supporting the diagnosis of myocarditis and there is often reluctance in performing EMB due to potentially severe complications. We sought to identify patient subgroups that could still benefit from EMB in the CMR era. Data of patients presenting with myocarditis between 01/2016 and 06/2023 were analysed according to patient risks. Prespecified risk factors were (i) left ventricular ejection fraction (LVEF) ≤ 30%; (ii) severe arrhythmias; or (iii) pre-existing autoimmune disease. Furthermore, the subgroup of recurrent myocarditis cases was analysed separately. A total of 137 patients (35.5 ± 14.8years, 80.3% male) were included. 26/137 patients had a documented LVEF ≤ 30%, 13/137 a LVEF > 30% with at least one other risk factor and 98/137 a LVEF > 30% without risk factors. EMB was performed in 21/26 patients with LVEF ≤ 30% (80.8%), in 7/13 patients with LVEF > 30% and risk factors (53.8%) and in 16/98 (16%) patients without risk factors. EMB led to the initiation of immunosuppressive therapy in 11/28 patients with risk factors (39.3%) and in none of the patients without risk factors (0/16, 0%, p = 0.003). With respect to the subgroup of patients presenting with recurrent myocarditis (n = 10), no specific therapy was initiated. Due to a high therapeutic yield for initiation of immunosuppressive therapy in non-infectious myocarditis, performing EMB should be considered in all high-risk patients. In patients without clinical risk factors including cases of recurrent or relapsing myocarditis no specific therapy was initiated.