Search Results (26)

Background: Pancreatic neuroendocrine tumors (PanNETs) are relatively rare neoplasms with heterogeneous behavior, ranging from indolent to aggressive disease. The evolution of nuclear medicine has allowed the development of an efficient and advanced toolkit for the diagnosis and treatment of PanNETs. Case: A 45-year-old woman was diagnosed with a grade 1 PanNET and multiple liver metastases. She underwent distal pancreatectomy with splenectomy, extended liver resection, and radiofrequency ablation (RFA). Surgical planning was guided by [^99mTc]Tc-EDDA/HYNIC-TOC SPECT/CT (single-photon emission computed tomography/computed tomography) and preoperative [^99mTc]Tc-mebrofenin-based functional liver volumetry. Functional liver volumetry based on dynamic [^99mTc]Tc-mebrofenin SPECT/CT facilitated precise surgical planning and reliable assessment of the efficacy of parenchymal modulation, thereby aiding in the prevention of post-hepatectomy liver failure. Liver fibrosis was non-invasively evaluated using two-dimensional shear wave elastography (2D-SWE). Tumor progression was monitored using somatostatin receptor scintigraphy, chromogranin A, and contrast-enhanced CT. Recurrent disease was treated with somatostatin analogues (SSAs) and [¹⁷⁷Lu]Lu-DOTA-TATE peptide receptor radionuclide therapy (PRRT). Despite progression to grade 3 disease (Ki-67 from 1% to 30%), the patient remains alive 53 months post-diagnosis, in complete remission, with an ECOG (Eastern Cooperative Oncology Group) status of 0. Conclusions: Functional imaging played a pivotal role in guiding therapeutic decisions throughout the disease course. This case not only underscores the clinical utility of advanced nuclear imaging but also illustrates the dynamic nature of pancreatic neuroendocrine tumors. The transition from low-grade to high-grade disease highlights the need for further studies on tumor progression mechanisms and the potential role of adjuvant therapies in managing PanNETs. Full article

Background/Objectives: Malnutrition is a key contributor to poor outcomes in older adults with fragility fractures, increasing the risk of complications, functional decline, prolonged hospital stays, mortality, and healthcare costs. Substantial evidence limited to hip fracture supports early, interdisciplinary nutrition care. However, global audits reveal that most hip fracture patients do not receive recommended interventions. This quality improvement (QI) project aimed to co-create and test a pathway and toolkit to help apply evidence-based nutrition care in different fragility fracture settings globally. Methods: The SIMPLER Pathway and toolkit (SIMPLER) were developed through a multiphase, co-creation QI initiative (2018–2025), guided by the Knowledge-to-Action framework. Global experts and clinical teams synthesized evidence, identified the “know-do” gap, and adapted SIMPLER to context through iterative action–reflection cycles. The Model for Improvement guided team building, goal setting, testing changes, and measuring outcomes at pilot sites. Results: Over 100 co-creation activities between 2018 and 2025 engaged staff and patients to shape and refine SIMPLER. A global clinician survey (n = 308, 46 countries), two bi-national audits (n = 965, 63 hospitals), and qualitative interviews (n = 15) confirmed a widespread evidence-practice gap. The pathway and toolkit were pilot-tested in five hospitals across four countries, with action–reflection cycles enabling continuous refinement of prioritized nutrition improvements tailored to the local context. Following endorsement in late 2024, 46 healthcare services in 23 countries have formally committed to implementing SIMPLER. Conclusions: The SIMPLER Nutrition Pathway provides a scalable, adaptable framework to support the delivery of evidence-based nutrition care in fragility fracture settings. A global evaluation is underway. Full article

Background/Objectives: Lutetium-177 is a widely used radioisotope in targeted radionuclide therapy, particularly for treating certain types of cancers relying on beta and low-energy gamma emissions, making it suitable for both therapeutic and post-therapy monitoring purposes. The purpose of this study was to evaluate the technical parameters for developing a prototype portable gamma camera dedicated to ¹⁷⁷Lu imaging applications. Methods: The well-validated GATE Monte Carlo toolkit was used to study the characteristics of the system and evaluate its performance in terms of spatial resolution, sensitivity, and image quality. For this purpose, a series of Monte Carlo simulations were executed, modeling a channel-edge aperture pinhole collimator incorporating a variety of computational phantoms. The final configuration of the prototype was standardized, incorporating the crystal size, collimator design, shielding, and the optimal FOV. After the development of the actual prototype camera, the system was also validated experimentally on the same setups as the simulations. Results: The final configuration of the prototype imaging system was standardized based on simulation results and then experimentally validated using physical phantoms under equivalent conditions. A minification of 1:5, spatial resolution of 1.0 cm, and sensitivity of 5.2 Cps/MBq at 10 cm distance source-to-collimator distance were assessed and confirmed. The experimental results agreed within 5% of simulated values. Conclusions: This study establishes the technical feasibility and foundational performance of a portable pinhole imaging system for potential clinical use in ¹⁷⁷Lu imaging workflows and thereby improving therapeutic effectiveness. Full article

Background: The Standards for the Psychosocial Care of Children with Cancer and their Families provide a framework for the delivery of psychosocial care to families experiencing pediatric cancer. Similarly, the Pediatric Psychosocial Preventative Health Model (PPPHM) guides intervention approaches by identifying three tiers of psychosocial support based on a family’s level of risk. Employing both the Standards and the PPPHM, we developed a comprehensive three-tiered approach to support the psychosocial needs of families in a pediatric oncology setting. Methods: After publication of the Standards, our institution merged existing psychosocial disciplines into one unified Psychosocial Services department. The new department worked to clearly define the role and scope of each discipline’s practice to ensure the psychosocial needs of patients and families were being comprehensively met. Interprofessional workgroups were established to evaluate and enhance the psychosocial services offered to patients, siblings, and caregivers using a three-tiered model of support. Membership included representation from patients and parent/caregiver advisors to ensure their perspectives were included in program development. Results: Over ten new programs have been developed to enhance the support of families facing pediatric cancer. At the Universal tier, new programs available to all families include caregiver and sibling support groups, a caregiver podcast, and a relationship health toolkit and workshop. At the Targeted tier, psychosocial interventions and parent mentor supports were implemented. At the Clinical/Intervention tier, a partnership was developed with an external tele-mental health company to provide mental health services to caregivers with significant needs or preexisting mental health disorders. Conclusions: Given the complex needs of families facing pediatric cancer, use of an interdisciplinary approach is paramount to successful support throughout the treatment trajectory. By leveraging the expertise and strengths of diverse disciplines with the perspectives of patients and families, new psychosocial programs can comprehensively address the unique challenges of patients and families impacted by illness. Full article

While pharmacy education successfully employs various methodologies including case-based learning and simulated patient interactions, providing consistent, individualized guidance at scale remains challenging in team-based learning environments. Artificial intelligence (AI) offers potential solutions through automated facilitation, but its possible utility in pharmacy education remains unexplored. We developed and evaluated an AI-guided patient case discussion simulation to enhance learners’ ability to integrate foundational science knowledge with clinical decision-making in a didactic immunology course in a US PharmD program. We utilized a large language model programmed with specific educational protocols and rubrics. Here, we present the step-by-step prompt engineering protocol as a toolkit. The system was structured around three core components in an immunology team-based learning activity: (1) symptomatology analysis, (2) laboratory test interpretation, and (3) pharmacist role definition and PPCP. Performance evaluation was conducted using a comprehensive rubric assessing multiple clinical reasoning and pharmaceutical knowledge domains. The standardized evaluation rubric showed reliable assessment across key competencies including condition identification (30% weighting), laboratory test interpretation (40% weighting), and pharmacist role understanding (30% weighting). Our AI patient simulator offers a scalable solution for standardizing clinical case discussions while maintaining individualized learning experiences. Full article

Background/Objectives: We assessed the influence of local patients and clinical characteristics on the performance of commercial deep learning (DL) segmentation models for head-and-neck (HN), breast, and prostate cancers. Methods: Clinical computed tomography (CT) scans and clinically approved contours of 210 patients (53 HN, 49 left breast, 55 right breast, and 53 prostate cancer) were used to train and validate segmentation models integrated within a vendor-supplied DL training toolkit and to assess the performance of both vendor-pretrained and custom-trained models. Four custom models (HN, left breast, right breast, and prostate) were trained and validated with 30 (training)/5 (validation) HN, 34/5 left breast, 39/5 right breast, and 30/5 prostate patients to auto-segment a total of 24 organs at risk (OARs). Subsequently, both vendor-pretrained and custom-trained models were tested on the remaining patients from each group. Auto-segmented contours were evaluated by comparing them with clinically approved contours via the Dice similarity coefficient (DSC) and mean surface distance (MSD). The performance of the left and right breast models was assessed jointly according to ipsilateral/contralateral locations. Results: The average DSCs for all structures in vendor-pretrained and custom-trained models were as follows: 0.81 ± 0.12 and 0.86 ± 0.11 in HN; 0.67 ± 0.16 and 0.80 ± 0.11 in the breast; and 0.87 ± 0.09 and 0.92 ± 0.06 in the prostate. The corresponding average MSDs were 0.81 ± 0.76 mm and 0.76 ± 0.56 mm (HN), 4.85 ± 2.44 mm and 2.42 ± 1.49 mm (breast), and 2.17 ± 1.39 mm and 1.21 ± 1.00 mm (prostate). Notably, custom-trained models showed significant improvements over vendor-pretrained models for 14 of 24 OARs, reflecting the influence of data/contouring variations in segmentation performance. Conclusions: These findings underscore the substantial impact of institutional preferences and clinical practices on the implementation of vendor-pretrained models. We also found that a relatively small amount of institutional data was sufficient to train customized segmentation models with sufficient accuracy. Full article

Background: Assessment of upper limb function is critical in the rehabilitation process of people with Parkinson’s Disease (PD), and universally validated outcome measures are needed to allow comparisons across the practice. Moreover, the study of psychometric properties of the same tool on different clinical populations guarantees the possibility of reliably evaluating the same rehabilitation treatment in people with different clinical conditions. Aim of the study: The aim of this research was to evaluate the psychometric characteristics of the Italian adaptation of the Jebsen Taylor Hand Function Test (JTHFT) in individuals with PD. Methods: The reliability and validity of the test were assessed in accordance with international standards. Internal consistency was measured using Cronbach’s alpha, and test–retest reliability was determined via the intraclass correlation coefficient (ICC). The construct validity and cross-cultural validity of the test were evaluated using Pearson’s correlation coefficient with three assessment tools on upper limb function, independence, and quality of life, with hand grip power measured by a dynamometer and an Italian pangram. Finally, responsiveness after a one month of rehabilitation treatment was measured using the Wilcoxon rank test. Results: Fifty-two Italian people with PD were recruited. Cronbach’s alpha values ranged from 0.556 (non-dominant hand) to 0.668 (dominant hand); ICC values ranged from 0.754 to 0.988. Construct validity showed that several statistically significant correlations were detected. Wilcoxon’s test showed that the assessment tool can detect a change in this population after treatment. Conclusions: The JTHFT is a reliable, valid, and respondent tool to evaluate the upper limb and hand functionalities in PD patients. It should be added to the toolkit for measuring upper limb performance in this population, adding value to clinical evaluation and ensuring comparable results for different clinical populations and different countries. Full article

Wastewater-based epidemiology (WBE) evaluates the health status, environmental exposure, and lifestyle habits of community inhabitants through the investigation of chemical or biological markers present in urban wastewater systems. This approach is frequently employed in discerning drug abuse, disease prevalence, and the presence of environmental contaminants. To comprehend the current state and developmental trajectories in WBE research, the current study utilizes the source literature of the Web of Science Core Collection (WOSCC) database. Implementing the Bibliometrix toolkit in R language and employing CiteSpace and VOSviewer for bibliometric analysis, this investigative pursuit effectuates an all-encompassing evaluation of the WBE literature, traversing a substantial time span of 16 years, encompassing 2008 through 2023. The results of this bibliometric analysis illuminate annual propensities and disciplinary distribution related to WBE research, while discerning the most impactful and prolific contributors, including authors, institutions, countries, and scholarly journals. The onset of the COVID-19 pandemic has engendered the expedited progression of WBE, leading to a substantial escalation in research endeavors in the past three years. By meticulously evaluating highly-cited publications, co-occurrence network of keywords, and keyword burst analysis, it is concluded that the research hotspots in this field focus on the monitoring of illicit drugs, psychoactive substances, and viruses in sewage. Subsequent investigations possess the capacity to propel the advancement of emerging methodologies for biomarker identification and analytical techniques. By concurrently integrating big data technologies (including artificial intelligence and cloud computing) with epidemiological and clinical data sets, a more expansive, precise, and efficacious rendition of WBE research can be realized. Full article

Urinary tract infections (UTIs) are highly prevalent and have significant implications for patients. As internet-based health information becomes more relied upon, ChatGPT has emerged as a potential source of healthcare advice. In this study, ChatGPT-3.5 was subjected to 16 patient-like UTI queries, with its responses evaluated by a panel of urologists. ChatGPT can address general UTI questions and exhibits some reasoning capacity in specific contexts. Nevertheless, it lacks source verification, occasionally overlooks vital information, and struggles with contextual clinical advice. ChatGPT holds promise as a supplementary tool in the urologist’s toolkit, demanding further refinement and validation for optimal integration. Full article

The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based machine learning toolkit—to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics. Full article

The Trail Making Test (TMT) is one of the most commonly administered tests in clinical and research neuropsychological settings. The two parts of the test (part A (TMT-A) and part B (TMT-B)) enable the evaluation of visuoperceptual tracking and processing speed (TMT-A), as well as divided attention, set-shifting and cognitive flexibility (TMT-B). The main cognitive processes that are assessed using TMT, i.e., processing speed, divided attention, and cognitive flexibility, are often affected in patients with stroke. Considering the wide use of TMT in research and clinical settings since its introduction in neuropsychological practice, the purpose of our review was to provide a comprehensive overview of the use of TMT in stroke patients. We present the most representative studies assessing processing speed and attentional shift/mental flexibility in stroke settings using TMT and applying scoring methods relying on conventional TMT scores (e.g., time-to-complete part A and part B), as well as derived measures (e.g., TMT-(B-A) difference score, TMT-(B/A) ratio score, errors in part A and part B). We summarize the cognitive processes commonly associated with TMT performance in stroke patients (e.g., executive functions), lesion characteristics and neuroanatomical underpinning of TMT performance post-stroke, the association between TMT performance and patients’ instrumental activities of daily living, motor difficulties, speech difficulties, and mood statue, as well as their driving ability. We also highlight how TMT can serve as an objective marker of post-stroke cognitive recovery following the implementation of interventions. Our comprehensive review underscores that the TMT stands as an invaluable asset in the stroke assessment toolkit, contributing nuanced insights into diverse cognitive, functional, and emotional dimensions. As research progresses, continued exploration of the TMT potential across these domains is encouraged, fostering a deeper comprehension of post-stroke dynamics and enhancing patient-centered care across hospitals, rehabilitation centers, research institutions, and community health settings. Its integration into both research and clinical practice reaffirms TMT status as an indispensable instrument in stroke-related evaluations, enabling holistic insights that extend beyond traditional neurological assessments. Full article

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines (“MRI-QAMPER”, current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER’s functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test–retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials. Full article

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F₁-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature. Full article

Early identification of frailty is crucial to prevent or reverse its progression but faces challenges due to frailty’s insidious onset. Monitoring behavioral changes in real life may offer opportunities for the early identification of frailty before clinical visits. This study presented a sensor-based system that used heterogeneous sensors and cloud technologies to monitor behavioral and physical signs of frailty from home settings. We aimed to validate the concurrent validity of the sensor measurements. The sensor system consisted of multiple types of ambient sensors, a smart speaker, and a smart weight scale. The selection of these sensors was based on behavioral and physical signs associated with frailty. Older adults’ perspectives were also included in the system design. The sensor system prototype was tested in a simulated home lab environment with nine young, healthy participants. Cohen’s Kappa and Bland–Altman Plot were used to evaluate the agreements between the sensor and ground truth measurements. Excellent concurrent validity was achieved for all sensors except for the smart weight scale. The bivariate correlation between the smart and traditional weight scales showed a strong, positive correlation between the two measurements (r = 0.942, n = 24, p < 0.001). Overall, this work showed that the Frailty Toolkit (FT) is reliable for monitoring physical and behavioral signs of frailty in home settings. Full article

Depression and anxiety are the most prevalent mental health difficulties in the EU, causing immense suffering and costing the global economy EUR 1 trillion each year in lost productivity. Employees in construction, health and information and communications technology have an elevated risk of mental health difficulties. Most mental health interventions for the workplace have been targeted at larger companies and small and medium-sized enterprises (SMEs) are often overlooked despite most people being employed in SMEs. The MENTUPP intervention aims to improve mental health and wellbeing and reduce depression, anxiety, and suicidal behaviour. The MENTUPP project involves the development, implementation, and evaluation of a multilevel intervention targeting both clinical and non-clinical mental health issues and combating the stigma of mental (ill-)health, with a specific focus on SMEs. The intervention is underpinned by a framework of how to create a mentally healthy workplace by employing an integrated approach and has been informed by several systematic reviews designed to understand organisational mental health interventions and a consultation survey with key experts in the area. The intervention is facilitated through the MENTUPP Hub, an online platform that presents interactive psychoeducational materials, toolkits, and links to additional resources in an accessible and user-friendly manner. This paper presents the pilot study protocol for delivering the MENTUPP intervention in eight European countries and Australia. Each intervention country will aim to recruit at least 23 participants in 1–3 SMEs in one of the three high-risk sectors. The central aim of the pilot study will be to examine the feasibility, acceptability, and uptake of the MENTUPP intervention across the target SMEs. The findings will contribute to devising the protocol for a cluster randomised controlled trial (cRCT) of the MENTUPP intervention. Findings from this study will also be used to inform the optimisation phase of the MENTUPP intervention which will aim to improve the materials and the implementation of the intervention as well as enhancing the evaluation strategy which will be employed for the cRCT. Full article