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PubMed2026

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2×2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I²=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗

PubMed1998

Learning by reflection: the effect on educational outcomes.

Learning by experience involves being able to reflect on a personal happening and through a process of analysis, come to understand it. Such an activity should result in 'deep learning' when carried out in a structured way. Higher education establishments are keen to develop such learning methods in students, as a secondary effect of this form of learning is to create student independence from the teacher. This is a much sought after ability as recent government changes have meant higher student numbers without a corresponding rise in teacher numbers. This threatens the quality of student knowledge unless it is compensated for in some way. This study sets out to examine the learning of two student groups. The experimental design was that of group comparison using matched pairs of students. One group, the experimental group, were exposed to reflective teaching methods, whilst the other group (the control group) were exposed to conventional teaching methods only. At the end of a set period of time, the learning achieved in both groups was estimated using an especially designed test paper. The results obtained from both groups were compared and it was found that there was no significant difference obtained in the learning between the groups (P>5%) Therefore, we concluded that students learnt just as well using reflective methods when compared to the conventional methods of learning. However, the potential for enhancement of learning was evident and invites further investigation. All the students in this study were on the Diploma in Higher Education (Nursing) course. The subject area used throughout the study was in the biological sciences.

Adolescent↗

PubMed2004

The approaches to learning of support workers employed in the care home sector: an evaluation study.

This study examined the approaches to learning of a cohort (n=76) of National Vocational Qualification (NVQ) Care Award candidates using the Approaches and Study Skills Inventory for Students. The NVQ candidates were support workers (SWs) (sometimes called care assistants) employed in United Kingdom (UK) care homes for older people. The aim was to identify SWs' approaches to learning and to determine whether or not a preparatory six-week College-based course had any impact on these approaches. The findings were encouraging. The course had a positive impact with a statistically significant increase in orientation towards a deep learning approach, which is associated with desirable learning outcomes as well as self-directed and lifelong learning skills. The UK government recognises that lifelong learning enables people to continually develop their talents, thereby enhancing local communities and contributing to a civilised, cohesive society. In a health care environment, adopting a deep approach is likely to be beneficial. Those who use evidence to inform practice, who are able to relate elements of what they are taught to their working experiences and who are able to adapt to meet new challenges, are more likely to enhance their practice and become more effective carers.

Adolescent↗

PubMed2026

Machine Learning in Hyperlipidaemia Research: Screening and Experimental Insights into Lipid Metabolism Modulators.

Hyperlipidemia, characterized by elevated blood lipid levels, represents a major global health concern due to its strong association with cardiovascular disease, diabetes, and metabolic syndrome. While current therapies - such as statins, fibrates, bile acid sequestrants, and PCSK9 inhibitors - are effective in controlling hyperlipidemia, they are often associated with adverse effects, potential drug resistance, and suboptimal efficacy in certain patient populations. All of the above underscore the urgent need for safer and more effective therapeutic alternatives. Among the major molecular targets involved in the regulation of lipid metabolism are HMG-CoA reductase, PCSK9, peroxisome proliferator-activated receptors (PPARs), cholesteryl ester transfer protein (CETP), and nuclear receptors, including the liver X receptor (LXR) and farnesoid X receptor (FXR), which are also targets for future antihyperlipidemic drug development. Recent advancements in artificial intelligence (AI) and machine learning (ML) have significantly transformed and accelerated drug discovery by enabling the processing of vast amounts of genomic, proteomic, and chemical data. Furthermore, ML tools such as quantitative structure-activity relationship (QSAR) modelling, deep learning, random forest, and support vector machines (SVM) have proven predictive and effective in identifying novel lipid metabolism modulators, thereby enhancing the efficacy and accuracy of virtual screening. Meanwhile, molecular docking has become an integral part of structure-based drug design (SBDD), and software such as AutoDock, Glide, and GOLD have proven effective in generating accurate ligand-target docking models. Molecular docking, together with ML-based approaches, enables the identification of potent and selective drug candidates. Overall, the combination of ML and molecular docking offers an efficient and accurate platform for antihyperlipidemic drug discovery, helping to overcome the limitations of currently available therapeutic strategies.

HMG-CoA reductase↗

PubMed2026

High-Dimensional Sensitivity Analysis for Genomic Studies: An Adversarial Framework for Learning Worst-Case Latent Confounders.

High-dimensional genomics studies are frequently confounded by unmeasured biological processes that obscure disease-specific signals. While existing workflows can estimate these latent confounders, they fail to quantify how robust a discovery is to varying levels of hypothetical confounding. We introduce sensGAN, a deep-learning adversarial framework that systematically explores the confounding spectrum by learning "worst-case" latent variables that nullify the most gene associations under novel predictive-gain constraints. By identifying the minimum confounding strength required to explain away an observed effect, our method shifts the paradigm toward a formal, quantitative sensitivity analysis. In diverse simulations, sensGAN accurately recovers latent structures and outperforms existing methods in identifying confounder-sensitive genes. Applied to human Alzheimer's disease microglia, our framework prioritizes robust disease pathways while successfully isolating signals driven by unmeasured co-occurring neurodegenerative pathologies. Our method is publicly available, deposited at the GitHub repository yifanlinz/ADsensitivityICML.

Journal Article↗

PubMed2025

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.

Epigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-associated genes, enabling GSCs to adapt to environmental cues and evade targeted therapies. Importantly, epigenetic reprogramming allows GSCs to transition between cellular states, including therapy-resistant mesenchymal-like phenotypes, underscoring the need for epigenetic-targeting strategies to disrupt these adaptive processes. Understanding these epigenetic drivers of gene expression provides a foundation for novel therapeutic interventions aimed at eradicating GSCs and improving glioblastoma outcomes. Using machine learning (ML), we employ cross-patient prediction of transcript expression in GSCs by combining epigenetic features from various sources, including ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq. We investigate different ML and deep learning (DL) models for this task and ultimately build our final pipeline using XGBoost. The model trained on one patient generalizes to other 11 patients with high performance. Notably, H3K27Ac alone from a single patient is sufficient to predict gene expression in all 11 patients. Furthermore, the distribution of H3K27Ac peaks across the genomes of all patients is remarkably similar. These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. This pattern can predict gene expression across patient samples, providing valuable insights into the biology of GSCs.

Glioblastoma↗

PubMed2000

Benefiting from clinical experience: the influence of learning style and clinical experience on performance in an undergraduate objective structured clinical examination.

OBJECTIVE: To assess the relationship between clinical experience, learning style and performance in an objective structured clinical examination (OSCE) in medical students at the end of their first clinical year. DESIGN: Prospective study of undergraduate students taking an OSCE examination at the end of their first clinical year. SUBJECTS: 194 undergraduate medical students (95 male). MAIN OUTCOME MEASURES: Performance in the OSCE examination, the Entwhistle Learning Style Inventory1 and a composite self-reported score of clinical activity during the students first clinical year. RESULTS: Performance in the OSCE examination was related to well-organized study methods but not to clinical experience. A significant relationship between clinical experience and organized deep-learning styles suggests that knowledge gained from clinical experience is related to learning style. CONCLUSIONS: The relationship between clinical experience and student performance is complex. Well-organized and strategic learning styles appear to influence the benefits of increased clinical exposure. Further work is required to elucidate the most beneficial aspects of clinical teaching.

Adult↗

PubMed2002

Assessment of attitudes about new learners' roles: factor analysis of the beliefs about Working in Groups Questionnaire.

This article presents an analysis of the factor structure of the Beliefs about Working in Groups Questionnaire, which is based on a model of teaching focused on the complementary roles of teachers as models and coaches and students who have to regulate their own learning and learn together with and from peers. This self-report questionnaire presents statements describing salient aspects of group work to elicit beliefs students hold about two main aspects of the quality of working in groups, firstly, the belief that working in small groups has important advantages over working individually for developing deep learning; secondly, beliefs that working with peers in close interaction does or does not facilitate learning-focused dialogue. The questionnaire was administered to university sophomores. The hypothesized two-factor structure emerged. It was tested whether the two factors were related to the students' familiarity with working in small groups in high school, to the frequency with which they worked in groups, and to their perception of the value high school teachers attached to working in small groups.

Adult↗

PubMed2000

Learning gross anatomy in a clinical skills course.

Recent developments in undergraduate medical education in the United Kingdom have produced changes in the content and delivery of component courses, including human anatomy. Anatomy can retain its place in the medical course in the new world of problem-based learning and clinical skills teaching by gaining recognition as an integral part of the curriculum which underpins much of the practice of clinical medicine. In these new courses, anatomical information is clinically relevant and discussed in the context of medical problems and the acquisition of clinical skills. Students are encouraged to study in a manner in which information is retained (deep learning) and where understanding replaces rote learning of facts. Students take responsibility for their own learning, with appropriate support and resources. In clinical skills courses, anatomy underpins the development and retention of clinical knowledge and skills.

Anatomy↗

PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗

PubMed2006

Teaching and assessment in otolaryngology and neurology: Does the timing of clinical courses matter?

Little is known about the effectiveness of clinical courses as a learning environment. To accurately assess performance in these courses, equal conditions for all candidates are required. We investigated the influence of the proximity of the course to the students test taking, the students' learning styles, and their self-motivation for learning in relation to performance success. One hundred and eleven students were randomized into eight groups, each attending a 2 week course in otolaryngology with a high proportion of patient-related teaching, and a 2 week long course in neurology with a low level of patient-related teaching. All students took multiple-choice end-of-term exams to assess their knowledge in both subjects. There was a different time interval between the course participation and the test taking for each of the groups. Performance success was correlated with the different groups, as well as with the type of learning style (LIST questionnaire) and with motivation for learning (study interest questionnaire). Explorative rank variance analysis showed a significant correlation between students' performance on the written exam and the time interval between completion of the neurology course and test-taking, with the shortest interval corresponding to highest scores (P = 0.002). There was no such effect on the success rate in otolaryngology (P = 0.28). Study motivation was not the major component for performance success, but a strong correlation between the use of strategic and deep learning styles and success in the exam was observed (R = 0.62; P < 0.001). The duration of time between a clinical course with little practical teaching and the students' taking of the exam plays a significant role on performance success; this effect does not occur in a course with a high proportion of practical patient-related teaching. More studies on clinical courses are needed to establish how students can be given adequate opportunities to develop necessary skills for patient care and for objective success on assessment. With such further information, the effectiveness of clinical courses as a learning experience might be enhanced.

Clinical Competence↗

PubMed2003

Evaluation of a formative interprofessional team objective structured clinical examination (ITOSCE): a method of shared learning in maternity education.

Shared learning at undergraduate level provides a potential means of promoting a more multi-professional approach to maternity care. Interprofessional education uses shared interactive sessions to promote different professional groups' understanding of each other and working together. This paper describes the use of a formative objective structured clinical examination as a method of interprofessional education. Mixed groups of student doctors and student midwives rotate through a series of clinical stations based on common labour ward scenarios. After completing each station they are given feedback by a facilitator on their problem-solving skills, knowledge and attitude to team working. The interactive nature of the sessions encourages deep learning, is student centred and promotes a positive attitude to multidisciplinary working. Both student groups felt they benefited from shared learning in this way and that the formative OSCE was an effective method of developing their clinical skills.

Education, Medical, Undergraduate↗

PubMed2026

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

Humans↗

PubMed2003

[The interactive lecture. A simple form of student-activating learning].

INTRODUCTION: Activation of students in lectures to enhance learning by means of questions to be answered in buzz groups has been described in pedagogic handbooks and articles. We tried the concept in three lectures in biochemistry in order to evaluate use of time, training requirements of the lecturer, and method acceptance by students. MATERIAL AND METHODS: The experiment was carried out with a group of 87 medical students from a 4th semester course in biochemistry. Evaluation was made by direct observation and analysis of quantitative and qualitative data from a student questionnaire. RESULTS: Buzz groups and questions took less time than anticipated and not more than ten minutes of the lecture time. The lecturer needed supervision from a colleague to function well. Acceptance of the procedure was high among the students. Qualitative data indicate that students used more time for self-studies and moved towards deep learning. DISCUSSION: We conclude that interactive lecture could be implemented without major problems in lecture based educational programmes and that it is useful for the learning of the students.

Biochemistry↗

PubMed2025

Leveraging functional annotations to map rare variants associated with Alzheimer disease with gruyere.

Increased availability of whole-genome sequencing (WGS) has facilitated the study of rare variants (RVs) in complex diseases. Multiple RV association tests are available to study the relationship between genotype and phenotype, but most do not fully leverage the availability of variant-level functional annotations. We propose genome-wide rare variant enrichment evaluation (gruyere), an empirical Bayesian framework that complements existing methods by learning global, trait-specific weights for functional annotations to improve variant prioritization. We apply gruyere to WGS data from the Alzheimer's Disease Sequencing Project to identify Alzheimer disease (AD)-associated genes and annotations. Growing evidence suggests that the disruption of microglial regulation is a key contributor to AD risk, yet existing methods have not examined rare non-coding effects that incorporate such cell-type-specific information. To address this gap, we (1) define per-gene non-coding RV test sets using predicted enhancer and promoter regions in microglia and other brain cell types (oligodendrocytes, astrocytes, and neurons) and (2) include cell-type-specific variant effect predictions (VEPs) as functional annotations. gruyere identifies 13 significant genetic associations not detected by other RV methods, four of which remain significant in omnibus tests. We find that deep-learning-based VEPs for splicing, transcription factor binding, and chromatin state are highly predictive of functional non-coding RVs. Our study establishes a robust framework incorporating functional annotations, coding RVs, and cell-type-associated non-coding RVs to perform genome-wide association tests, uncovering AD-relevant genes and annotations.

Alzheimer Disease↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2026

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29 709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85) and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.

Humans↗

PubMed2026

The molecular landscape of chordoma: Current frontiers from multi-omics to artificial intelligence.

Chordoma is a rare and aggressive malignant bone tumor of the axial skeleton that has historically challenged clinicians due to its complex anatomical locations and a high recurrence rate of up to 85%. This review synthesizes the most recent advances in chordoma research and offers an overview of how multi-omics, advanced immunology, and artificial intelligence are reshaping the treatment paradigm. Central to its pathogenesis is the T-box transcription factor Brachyury, which this review highlights as both the pathognomonic diagnostic marker and the primary therapeutic vulnerability. Cutting-edge innovations targeting this driver include covalent small-molecule binders, targeted protein degradation, and peptide-centric CAR-T cells designed to attack the intracellular oncoprotein. The tumor immune microenvironment is functionally dynamic, and new dimensions in cellular therapy, such as dual-specific CAR constructs and NK-cell platforms, are being engineered to neutralize immunosuppressive factors. Beyond biological insights, the review emphasizes the role of computational biology, specifically how deep-learning and machine-learning models achieve expert-level precision in tumor segmentation and personalized survival forecasting. By integrating genomic, transcriptomic, epigenomic, and proteomic data, multiomics approaches can fully elucidate chordoma subtypes and underlying resistance mechanisms, ultimately paving the way for more precise and personalized therapeutic strategies.

Humans↗