In a groundbreaking review article published in the prestigious journal Molecular Biomedicine, researchers from Shanghai Jiao Tong University School of Medicine present an exhaustive synthesis of emerging single-cell multi-omics technologies that are revolutionizing BTC research. These state-of-the-art techniques integrate genomic, transcriptomic, epigenomic, and proteomic data at the resolution of individual cells, thereby illuminating the heterogeneity of tumor tissues with unprecedented clarity. This integrative approach enables scientists to dissect the cellular constituents, molecular features, and dynamic interactions within tumors, fostering a comprehensive atlas that can inform and transform clinical management strategies.

Single-cell multi-omics methodologies delve deeply into the distinct molecular subtypes that coexist within BTC tumors, revealing the clonal architecture and evolutionary pathways that define tumor progression. By mapping these heterogeneous populations, the studies elucidate how specific genetic mutations, gene expression patterns, and epigenetic modifications contribute to tumor biology. Such detailed cellular profiling holds the promise of identifying novel biomarkers predictive of disease course and therapeutic response, ultimately paving the way for highly personalized oncological interventions.

One of the pivotal insights emerging from this review highlights the intricate composition of the tumor microenvironment (TME), a complex ecosystem that encompasses a diverse array of cancer-associated fibroblasts (CAFs), immune cell populations, endothelial cells, and extracellular matrix components. Among CAFs, functional heterogeneity is particularly notable, with myofibroblastic CAFs (myoCAFs) implicated in driving angiogenesis through hepatocyte growth factor (HGF) and transforming growth factor-beta (TGF-β) signaling cascades. In contrast, inflammatory CAFs (iCAFs) secrete cytokines such as interleukin-6 (IL-6) and vascular endothelial growth factor A (VEGFA), promoting an inflammatory milieu that fosters tumor progression and immune modulation.

Moreover, single-cell analyses have shed light on the diverse immune cell subsets within BTCs, including tumor-infiltrating lymphocytes and macrophages, which engage in complex cross-talk with both tumor cells and stromal elements. The immune microenvironment’s spatial and functional heterogeneity affects tumor immunogenicity and resistance to immune checkpoint blockade therapies. Understanding the mechanistic underpinnings of immune evasion, facilitated by metabolic reprogramming and epigenetic alterations within tumor and stromal cells, is critical for devising effective immunotherapeutic strategies.

The application of single-cell multi-omics data has also revealed the dynamic metabolic states of tumor cells, illustrating how metabolic plasticity supports survival, proliferation, and immune escape. Specific metabolic pathways and epigenetic modifications have been identified as contributors to the immunosuppressive TME, representing potential targets for combination therapies designed to disrupt tumor metabolism and restore antitumor immunity. These findings underscore the necessity of multi-layered molecular analyses to capture the full spectrum of tumor biology and therapeutic vulnerabilities.

Mengyao Li, a corresponding author of the review, emphasizes the transformative potential of integrating data across multiple molecular layers. He remarks that such integrative efforts convert the simplistic, averaged view of tumors into a high-resolution, multidimensional atlas that captures cellular diversity and functional states. This refinement is not merely academic; it is foundational for the next frontier in individualized cancer therapy, enabling clinicians to tailor interventions based on the specific cellular and molecular context of each patient’s tumor.

The translation of single-cell multi-omics insights into clinical practice is already underway, with patient-derived organoids (PDOs) emerging as powerful platforms for drug screening and precision medicine. PDOs faithfully recapitulate the molecular heterogeneity and microenvironmental features of primary tumors, allowing for functional assays that predict drug sensitivities and resistances. This application represents a tangible leap toward personalized oncology, bridging bench discoveries with bedside decisions.

Despite remarkable advancements, the review acknowledges that significant hurdles remain. Technical challenges in sample dissociation, particularly from solid tumor tissues, pose limitations on preserving cell viability and capturing rare cell populations. Additionally, the computational complexity inherent in integrating multi-omics datasets demands sophisticated bioinformatic tools and standardized analytical workflows. Addressing these obstacles requires collaborative, large-scale, multi-institutional initiatives that leverage artificial intelligence and machine learning to extract actionable insights from voluminous single-cell data.

The authors advocate for an expanded global effort to generate comprehensive single-cell atlases of BTCs, encompassing diverse patient populations and clinical contexts. Such endeavors will enrich our understanding of disease mechanisms, refine diagnostic criteria, and identify novel therapeutic targets. Collaborative networks combining high-throughput molecular profiling, functional modeling, and clinical trials promise to accelerate the translation of multi-omics knowledge into improved patient outcomes.

Intriguingly, the review also points toward the integration of spatial transcriptomics and imaging mass cytometry with single-cell multi-omics, technologies that add topographical context to molecular data. By preserving spatial relationships among cells within the tumor milieu, researchers can better understand cellular interactions and niche-specific signaling dynamics, key factors in tumor progression and therapy resistance. This comprehensive spatial-molecular mapping will constitute the next milestone in BTC research.

In sum, the synthesis presented by the Shanghai Jiao Tong University team marks a paradigm shift in our approach to biliary tract cancers. Single-cell multi-omics has unveiled the staggering complexity and plasticity of tumor ecosystems, charting new paths from molecular discovery to clinical innovation. As this technology matures and integrates with computational advances, it holds the promise of transforming BTCs from a grim prognosis to a landscape of tailored, effective therapies, reshaping patient care in the gastrointestinal oncology realm.

Subject of Research: Biliary Tract Cancers and Single-cell Multi-omics Technologies

Article Title: Single-cell multi-omics in biliary tract cancers: decoding heterogeneity, microenvironment, and treatment strategies

News Publication Date: 15-Oct-2025

Web References:
10.1186/s43556-025-00330-2

Image Credits: Nannan Tang (Renji Hospital, Shanghai Jiao Tong University School of Medicine)

Keywords: Biliary Tract Cancer, Single-cell Multi-omics, Tumor Heterogeneity, Tumor Microenvironment, Cancer-associated Fibroblasts, Immune Evasion, Metabolic Reprogramming, Epigenetics, Precision Oncology, Patient-derived Organoids, Molecular Subtypes, Immunotherapy

For decades, microbiome research has sought to decode the complex interplay between gut bacteria and human health. While traditional 16S ribosomal RNA sequencing has served as a cornerstone in assessing microbial communities, it is hampered by limitations such as low taxonomic resolution and an inability to detect elusive, uncultivated microbial species. Recognizing these constraints, researchers have now turned to more sophisticated whole-metagenome sequencing techniques that capture the full spectrum of genetic material present in microbiome samples. This holistic approach enables unprecedented insights into the diversity and function of gut microorganisms, many of which have remained hidden until now.

The novel study employs a metagenomic co-assembly and binning strategy to analyze two diverse colorectal cancer cohorts drawn from Asian and Caucasian populations. By integrating these data sets, the researchers identified a remarkable overlap in microbial species across both groups, an observation that hints at fundamental microbial signatures linked to CRC regardless of ethnic background. However, the investigation also uncovers subtle yet significant differences, as the species strongly associated with cancer status diverged between the populations. This nuanced understanding challenges the one-size-fits-all model of microbial diagnostics and underscores the necessity of population-specific microbiome research.

Central to this research is the discovery that low abundance genomes — those microbial species present in minimal quantities — wield outsized influence in predicting colorectal cancer. Unlike previous studies focused primarily on dominant bacteria, this work highlights the critical role of rare, uncultivated species, which were recovered through the metagenomic co-assembly and binning process. These microbes, largely overlooked in standard analyses, appear instrumental in distinguishing cancerous from healthy states. The study’s machine learning algorithms, particularly random forest models, identified dozens of these “important” low abundance genomes that achieved impressive predictive accuracy, reaching area under the receiver operating characteristic curves (AUROC) of 0.90 for the Asian cohort and an astounding 0.98 for the Caucasian cohort.

Such high accuracy metrics signify a potential paradigm shift in CRC diagnostics, illustrating how deep sequencing and computational analysis of previously inaccessible microbial genomes could dramatically enhance early detection. The identification of these uncultivated species brings forth a promising avenue where microbial biomarkers can be leveraged to develop non-invasive screening tools and personalized therapies. Furthermore, it sheds light on the biological roles these microorganisms might play in cancer progression or suppression, opening new research frontiers in tumor-microbiome interactions.

The findings take on added significance given the use of a metagenomic co-assembly approach. Rather than analyzing samples individually, co-assembly pools sequencing data from multiple samples, increasing the ability to assemble complete genomes, including rare and uncultivated microbes. Genome binning further refines this process, clustering genomic fragments into coherent units representing single microbial species. This state-of-the-art pipeline enables researchers to reconstruct high-quality genomes from complex metagenomic data, circumventing the need for traditional culturing methods that exclude a vast majority of microorganisms.

Intriguingly, the study emphasizes that the sets of “important” species linked to CRC status do not overlap between Asian and Caucasian cohorts. This reveals a striking example of microbial biogeography influencing disease associations, whereby distinct microbial communities emerge as hallmarks of colorectal cancer in different populations. Such insights advocate for tailored microbiome analyses and caution against universal diagnostic models that may overlook demographic-specific microbial signatures. Future studies aiming to develop globally robust CRC biomarkers will need to incorporate this population variability to ensure accuracy and relevance.

Beyond its diagnostic achievements, this research holds profound implications for understanding the pathophysiology of colorectal cancer. The uncultivated species detected may contribute to disease mechanisms either through metabolic activities, interactions with the host immune system, or modulation of the larger microbial ecosystem. By identifying these microbes, scientists can now investigate their functional roles, potentially unveiling new targets for intervention or prevention. This multidimensional perspective enhances our grasp of how microbial ecosystems influence human health and disease.

From a technological standpoint, the reliance on whole-metagenome sequencing coupled with advanced bioinformatics represents a leap forward for microbiome studies. The ability to detect and quantify low abundance genomes with high fidelity paves the way for more comprehensive microbial profiling across biomedical research. Moreover, the integration of machine learning not only improves predictive performance but also enables the prioritization of microbes most relevant to disease states, facilitating focused experimental validation.

The promise of this research extends into clinical practice, where early and accurate detection of colorectal cancer dramatically improves patient outcomes. Conventional screening techniques such as colonoscopy, while effective, are invasive and resource-intensive, limiting accessibility. Microbiome-based non-invasive diagnostics, inspired by the findings of this study, could revolutionize screening paradigms by offering rapid, cost-effective, and patient-friendly alternatives. This could lead to increased screening rates and earlier intervention, ultimately reducing mortality from one of the world’s deadliest cancers.

Additionally, the research underscores the importance of maintaining microbial diversity as a component of health. The role of low abundance and uncultivated species may reflect broader ecosystem stability within the gut; disruptions to these rare populations could signal or even precipitate disease. This ecological perspective invites a more holistic approach to cancer prevention, incorporating lifestyle, diet, and therapeutic strategies aimed at preserving or restoring beneficial microbiome diversity.

Importantly, the identification of population-specific microbial signatures opens exciting prospects for personalized medicine. Tailoring diagnostics and treatments based on an individual’s unique microbiome profile, alongside genetic and environmental factors, aligns with the future vision of precision oncology. Such customized approaches promise to enhance efficacy and minimize adverse effects, marking a milestone in patient-centered care.

The methodology itself, involving metagenomic co-assembly and binning, sets a new standard for microbiome research. By overcoming the limitations of conventional sequencing and cultivation techniques, it allows scientists to reach a deeper understanding of microbial communities, even in low-biomass or complex samples. This methodological innovation will likely inspire similar applications across various diseases where microbiota play a crucial role.

Looking ahead, these findings urge the scientific community to expand metagenomic studies to diverse populations and conditions, broadening our knowledge of the microbiome’s influence on health. Collaborative efforts integrating microbiology, oncology, computational biology, and clinical sciences will be critical to harnessing the full potential of these discoveries. Such interdisciplinary research is poised to unlock new diagnostic tools, therapies, and preventive measures against colorectal cancer and beyond.

In summary, this pioneering study exemplifies the power of modern metagenomics combined with computational prowess to unearth critical, previously hidden microbial contributions to colorectal cancer. It invites a rethinking of microbiome research strategies to include rare and uncultivated organisms, emphasizing their vital roles in disease dynamics. With the potential to deliver highly accurate, non-invasive CRC diagnostics tailored to diverse populations, the work marks a significant stride toward better cancer outcomes worldwide.

As our understanding deepens, the intricate relationship between humans and their microbial inhabitants continues to reveal itself as a cornerstone of health and disease. This study not only advances colorectal cancer research but also enriches the broader narrative of microbiome science, heralding transformative possibilities for medicine in the 21st century.

Subject of Research: Colorectal cancer prediction using gut microbiome metagenomics

Article Title: Highly-accurate prediction of colorectal cancer through low abundance uncultivated genomes recovered using metagenomic co-assembly and binning approach

Article References:
Lin, PT., Wu, YW. Highly-accurate prediction of colorectal cancer through low abundance uncultivated genomes recovered using metagenomic co-assembly and binning approach. BMC Cancer 25 (Suppl 2), 1418 (2025). https://doi.org/10.1186/s12885-025-14787-5

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14787-5

Intestinal-type gastric cancer, a predominant histological variant of stomach malignancies, has long challenged oncologists due to its heterogeneous molecular profile and relatively poor prognosis. Among the known markers, the cell surface glycoprotein CD44 has garnered attention not only as a cancer stem cell marker but also due to its association with tumor aggressiveness, metastasis, and resistance to conventional therapies. Nonetheless, the metabolic underpinnings correlating with CD44 expression in this cancer subtype remained poorly defined until this landmark study offered compelling evidence implicating the one-carbon metabolic pathway as a cornerstone molecular feature.

The one-carbon metabolism cascade encompasses a series of enzymatic reactions crucial for nucleotide biosynthesis, methylation reactions, and redox homeostasis—metabolic processes fundamentally necessary for rapid cell proliferation and genomic fidelity. By integrating transcriptomic and metabolomic analyses, the researchers revealed that CD44-positive intestinal-type gastric cancers exhibit a robust upregulation of key enzymes involved in this pathway, including serine hydroxymethyltransferase (SHMT), methylenetetrahydrofolate dehydrogenase (MTHFD), and thymidylate synthase (TYMS). This enhanced metabolic flux suggests a tailored biochemical reprogramming facilitating the proliferative and survival advantage observed in these cancer cells.

Notably, the study utilized clinical tumor specimens alongside in vitro gastric cancer cell models to validate the observed molecular signatures. High-throughput gene expression profiling demonstrated a consistent correlation between CD44 positivity and elevated one-carbon metabolism gene expression networks. Metabolic flux assays further corroborated these findings, showing increased folate-mediated one-carbon unit transfer rates—a biochemical hallmark indicating an amplified anabolic state that supports nucleotide synthesis and epigenetic modifications critical for malignant transformation and progression.

The implications of this metabolic signature are profound. By harnessing advanced CRISPR-Cas9 gene editing and pharmacologic inhibition of select one-carbon enzymes, the authors experimentally diminished CD44-positive gastric cancer cell viability and tumorigenicity in xenograft mouse models. These manipulations led to cell cycle arrest, increased apoptosis, and compromised DNA repair mechanisms, underscoring one-carbon metabolism’s pivotal role in maintaining malignant phenotypes within this cancer subset. Such findings propel the one-carbon pathway as an attractive therapeutic target, championing a shift toward metabolism-centric precision oncology.

Further dissection of molecular interactions unveiled epigenetic modifications driven by methyl group donors generated through one-carbon flux as a potential mechanism reinforcing CD44 expression itself, suggesting a possible feedback loop sustaining stemness and oncogenicity. This bidirectional relationship between metabolism and gene regulation adds an additional layer of complexity to cancer biology, wherein metabolic circuits intertwine with transcriptional programs and epigenetic landscapes to dictate tumor behavior and heterogeneity.

Clinically, these discoveries bear significant promise for the development of diagnostic biomarkers. Liquid biopsy approaches detecting metabolic enzyme transcripts or circulating metabolites linked to the one-carbon pathway could serve as minimally invasive indicators predicting CD44 status and disease aggressiveness. Such advances would facilitate early identification of high-risk patients and real-time monitoring of therapeutic responses, advancing personalized medicine paradigms.

One-carbon metabolism inhibitors have previously been explored in other cancer contexts, yet this research provides the first compelling rationale to prioritize these agents specifically for CD44-positive intestinal-type gastric cancer. Drugs like methotrexate and pemetrexed, classical antifolates targeting this metabolic axis, might be repurposed or optimized to exploit the metabolic dependencies uncovered by Joo et al., potentially enhancing clinical outcomes in a patient population that often exhibits resistance to conventional chemotherapy.

The study’s comprehensive methodological approach—combining omics analyses, functional genomics, and preclinical models—offers an exemplary framework illustrating how dissecting cancer metabolism at the molecular circuitry level unravels novel vulnerabilities. This strategy not only deepens fundamental understanding but also charts a translational course for bringing laboratory insights to bedside application, accelerating the pipeline of innovative therapeutics.

Moreover, this research highlights the broader relevance of metabolic pathways in defining cancer subtypes beyond mere genetic mutations, advocating increased incorporation of metabolic phenotyping in future oncologic classification systems. Such integrative taxonomy would refine prognostic stratification and foster development of metabolism-informed therapeutic regimens tailored to specific tumor metabolic profiles.

While promising, the authors acknowledge limitations including the need for larger cohort validations and exploration of potential metabolic crosstalk with other tumor microenvironment components such as immune cells and stromal elements. Future investigations may also examine resistance mechanisms arising from metabolic plasticity and compensatory pathways, as well as combinatorial strategies integrating metabolic inhibitors with immunotherapy or targeted agents.

This discovery of the one-carbon metabolic pathway as a molecular hallmark of CD44-positive intestinal-type gastric cancer opens an exciting frontier. By illuminating how altered metabolism intertwines with cellular phenotypes fundamental to cancer aggressiveness, this work sets the stage for innovative therapeutic designs centered on disrupting cancer cell metabolic networks. It represents a crucial step towards metabolic precision oncology tailored to the molecular identities of gastric tumor subtypes.

With gastric cancer representing a significant global health burden and survival rates stagnating, breakthroughs such as these offer hope of translating molecular understanding into meaningful clinical benefit. As research continues to elucidate metabolism’s multifaceted roles in tumor biology, integrating such insights promises to transform gastric cancer management through targeted interventions exploiting tumor-specific metabolic dependencies.

In summary, the identification of the one-carbon metabolic pathway as a novel molecular signature for CD44-expressing intestinal-type gastric cancer reframes our understanding of tumor biology and revitalizes metabolic targeting as a cornerstone of future therapeutic strategies. The study by Joo and colleagues is not merely a significant academic advance but a clarion call to the cancer research community to harness metabolism in the ongoing quest to ameliorate lethal malignancies through science-driven precision medicine.

Subject of Research: Molecular and metabolic characterization of CD44-positive intestinal-type gastric cancer with emphasis on the one-carbon metabolic pathway.

Article Title: One-carbon metabolic pathway is a novel molecular signature for CD44-positive intestinal-type gastric cancer.

Article References:
Joo, S., Bae, Y., Yoon, B.K. et al. One-carbon metabolic pathway is a novel molecular signature for CD44-positive intestinal-type gastric cancer. Cell Death Discov. 11, 399 (2025). https://doi.org/10.1038/s41420-025-02704-5

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41420-025-02704-5

Chondroid tumors originate from cartilaginous cells within the bone, with enchondromas being benign lesions and chondrosarcomas representing malignant transformations with varying degrees of aggressiveness. Distinguishing these tumor grades has traditionally relied on invasive biopsy procedures and histopathological evaluation, both of which carry inherent limitations including sampling errors and procedural risks. Consequently, non-invasive imaging biomarkers capable of discerning tumor grade hold immense clinical appeal.

The research team embarked on a retrospective study involving 120 patients who underwent contrast-enhanced MRI examinations between 2009 and 2019. Their cohort included 92 cases of enchondromas, 16 low-grade chondrosarcomas, and 12 intermediate to high-grade chondrosarcomas, creating a robust dataset for analysis. Each tumor underwent meticulous manual segmentation by an expert musculoskeletal radiologist, with validation by a senior radiology consultant to ensure accuracy and consistency.

Central to this study was the application of radiomics—a cutting-edge analytic framework that extracts high-dimensional quantitative features from medical images beyond what the human eye can discern. These features capture subtle textural, shape, and intensity variations within tumor tissue, correlating with underlying pathophysiological processes. The researchers leveraged this data-rich environment to build predictive models capable of classifying tumors with high fidelity.

To optimize feature selection and classification, the study employed a two-pronged machine learning approach combining least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms. LASSO served to reduce the dimensionality of extracted features by penalizing less informative variables, while random forest facilitated robust ensemble classification through decision tree aggregation. This synergy was designed to maximize predictive accuracy while mitigating overfitting risks inherent in high-dimensional data.

Recognizing the imbalance in tumor grade representation—particularly the comparatively fewer cases of higher-grade chondrosarcomas—the researchers incorporated the synthetic minority oversampling technique (SMOTE). SMOTE generates synthetic examples of minority class samples to balance the training dataset, preventing bias towards the more prevalent classes. Models were thus trained and tested both with and without SMOTE enhancement to assess its impact on classification performance.

Evaluation metrics focused on average precision, overall accuracy, area under the receiver operating characteristic curve (AUC), and weighted kappa statistics, providing a comprehensive assessment of model reliability. Notably, the combined LASSO plus random forest model trained on all MRI sequences outperformed others, achieving a striking accuracy of approximately 82.6% and an AUC nearing 0.97. These figures underscore the model’s exceptional ability to discriminate among tumor subtypes.

Interestingly, the model utilizing T2-weighted imaging sequences paired with SMOTE enhancement achieved the highest mean average precision (mAP) of 0.75, signaling the critical role that managing class imbalance plays in refining predictions. This finding aligns with broader machine learning literature emphasizing balanced datasets for optimal classifier training, especially in medical imaging contexts where pathological heterogeneity is common.

Quadratic weighted kappa values ranged from 0.65 to 0.73 across the evaluated models, which translates to substantial agreement when cross-referenced with pathological diagnoses. This statistic measures the concordance between predicted classification and ground truth, implying that the radiomics-driven approach closely approximates gold-standard histopathology without invasive procedures.

The implications of this work extend beyond mere diagnostic refinement. By providing clinicians with a non-invasive, highly accurate tool for tumor grading, patient management could be revolutionized through tailored treatment regimens. Accurate differentiation between benign and malignant chondroid lesions is paramount for determining the necessity of surgical intervention or conservative monitoring, directly impacting morbidity and healthcare resources.

Further, this radiomics framework suggests a path toward integrating artificial intelligence into routine musculoskeletal imaging workflows. As MRI is widely accessible and routinely employed in clinical practice, embedding these analytic techniques could enable real-time decision support, augmenting radiologist expertise and standardizing assessments across institutions.

While the study’s retrospective design and relatively modest sample size emphasize the need for prospective validation in larger, multi-center cohorts, its findings establish a compelling proof-of-concept. Expanding such research will be crucial to ascertain generalizability across different MRI platforms, scanning protocols, and patient demographics.

Moreover, future investigations may explore the fusion of radiomics features with other omics data—such as genomics or proteomics—to further enhance tumor characterization. The integration of multi-modal data promises a holistic understanding of tumor biology, ultimately driving personalized medicine approaches in orthopedic oncology.

This advancement indicates a paradigm shift in the evaluation of cartilaginous bone tumors, reducing dependence on invasive tissue sampling, and mitigating associated risks. Patients stand to benefit from quicker, less burdensome diagnoses and optimized therapeutic strategies tailored to the biological aggressiveness of their lesions.

In conclusion, the integration of MRI-based radiomics and advanced machine learning algorithms offers a potent solution to the longstanding challenge of grading chondroid tumors with high precision. This method’s ability to distinguish benign enchondromas from low- and high-grade chondrosarcomas non-invasively heralds a future where artificial intelligence augments clinical acumen in musculoskeletal oncology.

As research in this domain accelerates, we may soon witness widespread adoption of radiomics pipelines in radiology departments worldwide, transforming how bone tumors are diagnosed, graded, and managed. The cross-disciplinary collaboration between radiologists, oncologists, data scientists, and bioinformaticians reflects the innovative frontier pushing personalized healthcare.

Ultimately, this study exemplifies the promise of combining medical imaging with computational prowess to solve intricate clinical problems. With continued refinement and expansive validation, MRI radiomics stands to become an indispensable ally in the fight against bone cancers, improving diagnostic accuracy and patient care worldwide.

Subject of Research:
Development of a multiclass radiomics model utilizing preoperative MRI to differentiate between enchondroma, low-grade chondrosarcoma, and higher-grade chondrosarcoma.

Article Title:
Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma.

Article References:
Park, H., Lee, J., Lee, S. et al. Grading chondroid tumors through MRI radiomics: enchondroma, low-grade chondrosarcoma and higher-grade chondrosarcoma. BMC Cancer 25, 918 (2025). https://doi.org/10.1186/s12885-025-14330-6

Image Credits: Scienmag.com

DOI: https://doi.org/10.1186/s12885-025-14330-6