Journal of Personalized Medicine最新文献

{"title":"Interstitial Lung Diseases and Lung Cancer: A Review on Similarities, Common Pathogenesis and Therapeutic Approach.","authors":"Gioele Castelli, Elisabetta Cocconcelli, Giuliana Grimaudo, Irene Di Leo, Serena Bellani, Giordano Fiorentù, Giacomo Giulianelli, Nicol Bernardinello, Elisabetta Balestro, Paolo Spagnolo","doi":"10.3390/jpm15050213","DOIUrl":"https://doi.org/10.3390/jpm15050213","url":null,"abstract":"<p><p>Interstitial lung disease (ILD) prevalence and survival are increasing due to improvement in scientific research together with clinical complications typical of advanced disease. Lung cancer (LC) is described as a possible event occurring in lung parenchyma in the context of fibrotic abnormalities that worsen patients' prognosis. This growth of malignant cells on a fibrotic background has also been called scar-cinoma. For this reason, not only an early diagnosis but also personalized decisions on the best treatment approach should be considered for each patient in a multidisciplinary discussion, since in some cases chemotherapy or surgery could be detrimental for patients with pulmonary fibrosis. LC and lung fibrosis may share common pathogenetic mechanisms like an altered healing process in response to repeated tissue damage from environmental exposure in genetically susceptible individuals. Smoking history and air pollution together with mutations in telomere and surfactant protein genes lead to the production of cytokines and nitro derivatives in the microenvironment that facilitate the carcinomatous transformation during fibrogenesis. The evolution of LC therapy and the implementation of immunotherapy acting on targetable immune checkpoints have raised interest in evaluating ILD-LC actionable mutations. The main pathogenetic mechanisms, clinical presentations and treatment implications are presented in this review.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Dataset for Activity of Daily Living (ADL) Research Compiled by Unifying and Processing Multiple Data Sources.","authors":"Jaime Pabón, Daniel Gómez, Jesús D Cerón, Ricardo Salazar-Cabrera, Diego M López, Bernd Blobel","doi":"10.3390/jpm15050210","DOIUrl":"https://doi.org/10.3390/jpm15050210","url":null,"abstract":"<p><p><b>Background</b>: Activities of Daily Living (ADLs) are essential tasks performed at home and used in healthcare to monitor sedentary behavior, track rehabilitation therapy, and monitor chronic obstructive pulmonary disease. The Barthel Index, used by healthcare professionals, has limitations due to its subjectivity. Human activity recognition (HAR) is a more accurate method using Information and Communication Technologies (ICTs) to assess ADLs more accurately. This work aims to create a singular, adaptable, and heterogeneous ADL dataset that integrates information from various sources, ensuring a rich representation of different individuals and environments. <b>Methods</b>: A literature review was conducted in Scopus, the University of California Irvine (UCI) Machine Learning Repository, Google Dataset Search, and the University of Cauca Repository to obtain datasets related to ADLs. Inclusion criteria were defined, and a list of dataset characteristics was made to integrate multiple datasets. Twenty-nine datasets were identified, including data from various accelerometers, gyroscopes, inclinometers, and heart rate monitors. These datasets were classified and analyzed from the review. Tasks such as dataset selection, categorization, analysis, cleaning, normalization, and data integration were performed. <b>Results:</b> The resulting unified dataset contained 238,990 samples, 56 activities, and 52 columns. The integrated dataset features a wealth of information from diverse individuals and environments, improving its adaptability for various applications. <b>Conclusions:</b> In particular, it can be used in various data science projects related to ADL and HAR, and due to the integration of diverse data sources, it is potentially useful in addressing bias in and improving the generalizability of machine learning models.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lactate as a Preoperative Predictor of Mortality in Patients Undergoing Emergency Type A Aortic Dissection Repair.","authors":"Sombuddha Bhadra, Rachel H Drgastin, Howard K Song, Frederick A Tibayan, Gurion Lantz, Julie W Doberne, Castigliano M Bhamidipati","doi":"10.3390/jpm15050211","DOIUrl":"https://doi.org/10.3390/jpm15050211","url":null,"abstract":"<p><p><b>Background:</b> Aortic dissection is a life-threatening condition where emergent surgical repair is the standard of care. However, despite operative intervention, mortality is 10-15% in all patients. Objective markers to distinguish when surgical repair is more beneficial versus being futile are warranted. Currently, no such known measures are widely agreed upon. Since most complications from aortic dissection stem from malperfusion, serum lactate is thought to be a surrogate marker for malperfusion. This scoping review aims to examine the preoperative predictive value of lactate or lactate dehydrogenase (LDH) in assessing postoperative mortality in patients undergoing surgical repair for acute Stanford Type A aortic dissection (ATAAD). <b>Methods:</b> PubMed was searched for the following search terms: \"Dissection, Ascending Aorta\", \"Dissection, Thoracic Aorta\", or \"Aortic Dissection\". Prospective and retrospective randomized controlled trials, case reports, and cohort studies were included in the initial search. Studies were first screened for inclusion of preoperative lactate or LDH level with a search of \"lac\" or \"LDH\". Included studies consisted of patients aged 18 or older diagnosed with Stanford Type A/Debakey Type I and II aortic dissection with reported preoperative lactate or LDH levels and postoperative mortality treated within 14 days of symptom onset. Preoperative laboratory values were measured from samples collected prior to patient transfer to the operating room or before utilization of ECMO intraoperatively. <b>Results:</b> A comprehensive database search identified a total of 4722 articles. After a rigid screening process, 46 studies fit the inclusion criteria. These papers reported a combined 4696 participants with either preoperative lactate or LDH levels and postoperative mortality. The mean preoperative lactate level was 2.4 mmol/L, whereas the LDH level was 424.9 U/L. Postoperative mortality was 16.51%. Average creatinine, BUN, platelets, INR, PT, PTT, and hemoglobin were all within normal lab analysis limits. <b>Conclusions:</b> Neither lactate nor LDH should be used as a solo predictor of postoperative mortality after ATAAD due to lack of consensus on the cut-off values. Accompanying clinical signs, lab abnormalities, and radiographic findings taken together may be better predictors of prognosis.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations of <i>PPARG</i> and <i>PPARGC1A</i> Polymorphisms with Ritodrine-Induced Adverse Events in Patients with Preterm Labor.","authors":"Eun Jeong Jang, Da Hoon Lee, Yubin Song, Jung Sun Kim, Young Ju Kim, Jeong Yee, Hye Sun Gwak","doi":"10.3390/jpm15050212","DOIUrl":"https://doi.org/10.3390/jpm15050212","url":null,"abstract":"<p><p><b>Objectives:</b> Ritodrine, a tocolytic agent used to delay preterm labor, can cause several cardiovascular-associated adverse events (AEs). This study aimed to examine the relationship between gene polymorphisms in peroxisome proliferator-activated receptor gamma (<i>PPARG</i>) and PPARG coactivator-1α (<i>PPARGC1A</i>) and the occurrence of ritodrine-induced AEs. Additionally, a risk-scoring system was developed to identify patients at high risk of AEs. <b>Methods:</b> Patients aged 18 years or older who were administered ritodrine to manage preterm labor with intact membranes and uterine contractions occurring at 20-36 weeks of gestation were enrolled in this study. A total of 70 common <i>PPARG</i> and <i>PPARGC1A</i> variants (minor allele frequency ≥ 0.2) with low linkage disequilibrium (r² < 0.8) were selected from an Axiom™ Precision Medicine Research Array (AMPRA). <b>Results:</b> A total of 149 patients were included in the analysis. After adjusting for confounders (age, gestational age, and the maximum infusion rate), weight and rs2946385, rs35523565, and rs2240748 of <i>PPARGC1A</i> were identified as significant predictors associated with ritodrine-induced AEs. Based on the risk-scoring system, the predicted probabilities of AEs for patients with scores of 0, 1, 2, 3, 4, and 5 points were 4%, 9%, 18%, 35%, 55%, and 74%, respectively. The AUROC for the risk score predicting ritodrine-induced AEs was 0.729 (95% CI: 0.672-0.831, <i>p</i> < 0.001). <b>Conclusions:</b> This study indicates that ritodrine-induced AEs are related to <i>PPARGC1A</i> polymorphisms. A risk-scoring system based on genetic variants showed moderate predictive ability for ritodrine-induced AEs, suggesting potential utility in females with preterm labor.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corneal Endothelial Changes After Phacoemulsification Using the Eight-Chop Technique in Diabetic Eyes.","authors":"Tsuyoshi Sato","doi":"10.3390/jpm15050209","DOIUrl":"https://doi.org/10.3390/jpm15050209","url":null,"abstract":"<p><p><b>Background/Objectives</b>: To analyze corneal endothelial changes and intraocular pressure (IOP) after phacoemulsification combined with the eight-chop technique and intraoperative parameters in patients with diabetes mellitus. <b>Methods</b>: The eyes of patients with cataracts who underwent phacoemulsification were analyzed in this study. Based on their hemoglobin A1c levels, patients were divided into two groups. The eight-chop technique was used for cataract surgery. The operative time, the phaco time, the aspiration time, the cumulative energy dissipated, and the volume of fluid used were determined. Best corrected visual acuity, IOP, corneal endothelial cell density (CECD), central corneal thickness (CCT), coefficient of variation (CV), and percentage of hexagonal cells (PHC) were recorded before and after surgery. <b>Results</b>: Overall, 181 eyes of 138 patients with cataracts were evaluated. In the diabetes group, the CECD loss rates were 5.1%, 3.9%, and 2.1% at 7 weeks, 19 weeks, and 1 year postoperatively, respectively. In the control group, the CECD loss rates were 2.8%, 2.6%, and 1.2% at 7 weeks, 19 weeks, and 1 year postoperatively, respectively. Significant differences in the percentage decrease in CECD were observed between the two groups at 7 and 19 weeks postoperatively. Significant differences in the CV and PHC were observed preoperatively and postoperatively between the diabetes and control groups (<i>p</i> < 0.01 or <i>p</i> = 0.01, 0.02). Significant differences were also observed between CV and PHC preoperatively, at 19 weeks, and 1 year postoperatively in the diabetes and control groups (<i>p</i> < 0.01). At 1 year postoperatively, IOP reduction rates were 8.0% and 11.2% in the diabetes and control groups, respectively. <b>Conclusions</b>: CECD loss was minimal with the eight-chop technique; however, the diabetes group showed a higher percentage decrease than the control group up to 19 weeks postoperatively. In addition, although IOP decreased in both groups after surgery, the percentage decrease was significantly different at 1 year postoperatively. This study suggests that the corneal endothelial cells of diabetic eyes may be more fragile than those of normal eyes and that the long-term postoperative IOP-lowering effect may be attenuated. These findings will contribute to advances in personalized treatment strategies for patients with diabetes.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Point of View: A Holistic Four-Interface Conceptual Model for Personalizing Shock Resuscitation.","authors":"Philippe Rola, Eduardo Kattan, Matthew T Siuba, Korbin Haycock, Sara Crager, Rory Spiegel, Max Hockstein, Vimal Bhardwaj, Ashley Miller, Jon-Emile Kenny, Gustavo A Ospina-Tascón, Glenn Hernandez","doi":"10.3390/jpm15050207","DOIUrl":"https://doi.org/10.3390/jpm15050207","url":null,"abstract":"<p><p>The resuscitation of a patient in shock is a highly complex endeavor that should go beyond normalizing mean arterial pressure and protocolized fluid loading. We propose a holistic, four-interface conceptual model of shock that we believe can benefit both clinicians at the bedside and researchers. The four circulatory interfaces whose uncoupling results in shock are as follows: the left ventricle to arterial, the arterial to capillary, the capillary to venular, and finally the right ventricle to pulmonary artery. We review the pathophysiology and clinical consequences behind the uncoupling of these interfaces, as well as how to assess them, and propose a strategy for approaching a patient in shock. Bedside assessment of shock may include these critical interfaces in order to avoid hemodynamic incoherence and to focus on microcirculatory restoration rather than simply mean arterial pressure. The purpose of this model is to serve as a mental model for learners as well as a framework for further resuscitation research that incorporates these concepts.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative Evaluation of Automatic Detection and Classification of Daily Living Activities Using Batch Learning and Stream Learning Algorithms.","authors":"Paula Sofía Muñoz, Ana Sofía Orozco, Jaime Pabón, Daniel Gómez, Ricardo Salazar-Cabrera, Jesús D Cerón, Diego M López, Bernd Blobel","doi":"10.3390/jpm15050208","DOIUrl":"https://doi.org/10.3390/jpm15050208","url":null,"abstract":"<p><p><b>Background/Objectives:</b> Activities of Daily Living (ADLs) are crucial for assessing an individual's autonomy, encompassing tasks such as eating, dressing, and moving around, among others. Predicting these activities is part of health monitoring, elderly care, and intelligent systems, improving quality of life, and facilitating early dependency detection, all of which are relevant components of personalized health and social care. However, the automatic classification of ADLs from sensor data remains challenging due to high variability in human behavior, sensor noise, and discrepancies in data acquisition protocols. These challenges limit the accuracy and applicability of existing solutions. This study details the modeling and evaluation of real-time ADL classification models based on batch learning (BL) and stream learning (SL) algorithms. <b>Methods:</b> The methodology followed is the Cross-Industry Standard Process for Data Mining (CRISP-DM). The models were trained with a comprehensive dataset integrating 23 ADL-centric datasets using accelerometers and gyroscopes data. The data were preprocessed by applying normalization and sampling rate unification techniques, and finally, relevant sensor locations on the body were selected. <b>Results:</b> After cleaning and debugging, a final dataset was generated, containing 238,990 samples, 56 activities, and 52 columns. The study compared models trained with BL and SL algorithms, evaluating their performance under various classification scenarios using accuracy, area under the curve (AUC), and F1-score metrics. Finally, a mobile application was developed to classify ADLs in real time (feeding data from a dataset). <b>Conclusions:</b> The outcome of this study can be used in various data science projects related to ADL and Human activity recognition (HAR), and due to the integration of diverse data sources, it is potentially useful to address bias and improve generalizability in Machine Learning models. The principal advantage of online learning algorithms is dynamically adapting to data changes, representing a significant advance in personal autonomy and health care monitoring.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Features of Heart Failure with Preserved Ejection Fraction in Patients with Chronic Obstructive Pulmonary Disease and Systemic Sclerosis-Associated Interstitial Lung Diseases.","authors":"Lyazat Ibrayeva, Meruyert Aubakirova, Irina Bacheva, Assel Alina, Nazira Bazarova, Aizhan Zhanabayeva, Olga Avdiyenko, Seda Borchashvili, Saltanat Tazhikhanova, Askhat Murzabaeyev","doi":"10.3390/jpm15050206","DOIUrl":"https://doi.org/10.3390/jpm15050206","url":null,"abstract":"<p><p><b>Background/Objectives</b>: This study aims to investigate the potential etiopathogenesis of HFpEF development and identify possible different phenotypes of HFpEF in patients with chronic obstructive pulmonary disease (COPD) and systemic sclerosis-associated interstitial lung diseases (SS-ILDs). It could help clinicians improve early HFpEF personalized detection and management. <b>Methods</b>: This study included 150 patients with chronic lung diseases (CLDs), such as COPD and SS-ILD, who were outside of exacerbation, had no history of chronic heart failure (CHF), and had a left ventricular ejection fraction (LV EF) of ≥50%. The functional status of the lungs, heart, endothelial dysfunction, and acid-base balance was assessed. The results obtained were compared in groups of patients with CLD depending on the presence or absence of HF with preserved ejection fraction (HFpEF). The diagnosis of HFpEF was established based on the HFA-PEFF Score classification. Nonparametric statistical methods were used. <b>Results</b>: In patients with CLD, indicators such as age, longitudinal size of the right atrium, mid-regional pro-atrial natriuretic peptide (MR-proANP), and highly sensitive cardiac troponin T (hsTnT) were higher than in the group of patients without HFpEF. In patients with COPD and HFpEF, statistically significant changes were found in the volume of the left atrium. In patients with SS-ILD and HFpEF, statistically significant differenceswere found in SBP before and after the 6 min walk test (6MWT), the Borg scale before 6MWT, MR-proANP, and the longitudinal dimension of the right atrium. <b>Conclusions</b>: The results of our study allow us to identify two different mechanisms of HFpEF development: In patients with COPD, the predominant factor in the development of HFpEF was hypoxia, while in patients with SS-ILD, myocardial dysfunction with remodeling developed against the background of secondary pulmonary hypertension, highlighting the importance of phenotype-specific evaluation. These findings suggest potential approaches for personalized risk stratification and the development of targeted management strategies for patients with HFpEF.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Talk with ChatGPT: The Role of Artificial Intelligence in Shaping the Future of Cardiology and Electrophysiology.","authors":"Angelica Cersosimo, Elio Zito, Nicola Pierucci, Andrea Matteucci, Vincenzo Mirco La Fazia","doi":"10.3390/jpm15050205","DOIUrl":"https://doi.org/10.3390/jpm15050205","url":null,"abstract":"<p><p><b>Background</b>: Artificial intelligence (AI) is poised to significantly impact the future of cardiology and electrophysiology, offering new tools to interpret complex datasets, improve diagnosis, optimize clinical workflows, and personalize therapy. ChatGPT-4o, a leading AI-based language model, exemplifies the transformative potential of AI in clinical research, medical education, and patient care. <b>Aim and Methods</b>: In this paper, we present an exploratory dialogue with ChatGPT to assess the role of AI in shaping the future of cardiology, with a particular focus on arrhythmia management and cardiac electrophysiology. Topics discussed include AI applications in ECG interpretation, arrhythmia detection, procedural guidance during ablation, and risk stratification for sudden cardiac death. We also examine the risks associated with AI use, including overreliance, interpretability challenges, data bias, and generalizability. <b>Conclusions</b>: The integration of AI into cardiovascular care offers the potential to enhance diagnostic accuracy, tailor interventions, and support decision-making. However, the adoption of AI must be carefully balanced with clinical expertise and ethical considerations. By fostering collaboration between clinicians and AI developers, it is possible to guide the development of reliable, transparent, and effective tools that will shape the future of personalized cardiology and electrophysiology.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-Driven Defragmentation: Achieving Value-Based Sarcoma and Rare Cancer Care Through Integrated Care Pathway Mapping.","authors":"Bruno Fuchs, Philip Heesen","doi":"10.3390/jpm15050203","DOIUrl":"https://doi.org/10.3390/jpm15050203","url":null,"abstract":"<p><p>Sarcomas, a rare and complex group of cancers, require multidisciplinary care across multiple healthcare settings, often leading to delays, redundant testing, and fragmented data. This fragmented care landscape obstructs the implementation of Value-Based Healthcare (VBHC), where care efficiency is tied to measurable patient outcomes.ShapeHub, an interoperable digital platform, aims to streamline sarcoma care by centralizing patient data across providers, akin to a logistics system tracking an item through each stage of delivery. ShapeHub integrates diagnostics, treatment records, and specialist consultations into a unified dataset accessible to all care providers, enabling timely decision-making and reducing diagnostic delays. In a case study within the Swiss Sarcoma Network, ShapeHub has shown substantial impact, improving diagnostic pathways, reducing unplanned surgeries, and optimizing radiotherapy protocols. Through AI-driven natural language processing, Fast Healthcare Interoperability Resources, and Health Information Exchanges, HIEs, the platform transforms unstructured records into real-time, actionable insights, enhancing multidisciplinary collaboration and clinical outcomes. By identifying redundancies, ShapeHub also contributes to cost efficiency, benchmarking treatment costs across institutions and optimizing care pathways. This data-driven approach creates a foundation for precision medicine applications, including digital twin technology, to predict treatment responses and personalize care plans. ShapeHub offers a scalable model for managing rare cancers and complex diseases, harmonizing care pathways, improving precision oncology, and transforming VBHC into a reality. This article outlines the potential of ShapeHub to overcome fragmented data barriers and improve patient-centered care.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":"15 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144150764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}