Abdominal Radiology, Journal Year: 2024, Volume and Issue: unknown
Published: Dec. 18, 2024
Language: Английский
Seminars in Nuclear Medicine, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 1, 2025
Language: Английский
Citations
3
Metabolites, Journal Year: 2025, Volume and Issue: 15(3), P. 201 - 201
Published: March 13, 2025
Background: Tumor cells engage in continuous self-replication by utilizing a large number of resources and capabilities, typically within an aberrant metabolic regulatory network to meet their own demands. This dysregulation leads the formation tumor microenvironment (TME) most solid tumors. Nanomedicines, due unique physicochemical properties, can achieve passive targeting certain tumors through enhanced permeability retention (EPR) effect, or active deliberate design optimization, resulting accumulation TME. The use nanomedicines target critical pathways holds significant promise. However, requires careful selection relevant drugs materials, taking into account multiple factors. traditional trial-and-error process is relatively inefficient. Artificial intelligence (AI) integrate big data evaluate delivery efficiency nanomedicines, thereby assisting nanodrugs. Methods: We have conducted detailed review key papers from databases, such as ScienceDirect, Scopus, Wiley, Web Science, PubMed, focusing on reprogramming, mechanisms action development metabolism, application AI empowering nanomedicines. integrated content present current status research metabolism potential future directions this field. Results: Nanomedicines possess excellent TME which be utilized disrupt cells, including glycolysis, lipid amino acid nucleotide metabolism. disruption selective killing disturbance Extensive has demonstrated that AI-driven methodologies revolutionized nanomedicine development, while concurrently enabling precise identification molecular regulators involved oncogenic reprogramming pathways, catalyzing transformative innovations targeted cancer therapeutics. Conclusions: great Additionally, will accelerate discovery metabolism-related targets, empower optimization help minimize toxicity, providing new paradigm for development.
Language: Английский
Citations
2
Frontiers in Cell and Developmental Biology, Journal Year: 2025, Volume and Issue: 13
Published: March 26, 2025
Background The tumor boundary of breast cancer represents a highly heterogeneous region. In this area, the interactions between malignant and non-malignant cells influence progression, immune evasion, drug resistance. However, spatial transcriptional profile its role in prognosis treatment response remain unclear. Method Utilizing Cottrazm algorithm, we reconstructed intricate boundaries identified differentially expressed genes (DEGs) associated with these regions. Cell-cell co-positioning analysis was conducted using SpaCET, which revealed key tumor-associated macrophage (TAMs) cancer-associated fibroblasts (CAFs). Additionally, Lasso regression employed to develop body signature (MBS), subsequently validated TCGA dataset for prediction assessment. Results Our research indicates that is characterized by rich reconstruction extracellular matrix (ECM), immunomodulatory regulation, epithelial-to-mesenchymal transition (EMT), underscoring significance progression. Spatial colocalization reveals significant interaction CAFs M2-like (TAM), contributes exclusion MBS score effectively stratifies patients into high-risk groups, survival outcomes exhibiting high scores being significantly poorer. Furthermore, sensitivity demonstrates high-MB tumors had poor chemotherapy strategies, highlighting modulating therapeutic efficacy. Conclusion Collectively, investigate transcription group bulk data elucidate characteristics molecules cancer. CAF-M2 phenotype emerges as critical determinant immunosuppression resistance, suggesting targeting may improve responses. serves novel prognostic tool offers potential strategies guiding personalized approaches
Language: Английский
Citations
1
Diagnostics, Journal Year: 2025, Volume and Issue: 15(8), P. 995 - 995
Published: April 14, 2025
Background: Breast cancer is a heterogeneous disease with distinct molecular subtypes, each requiring tailored therapeutic strategies. Accurate classification of these subtypes crucial for optimizing treatment and improving patient outcomes. While immunohistochemistry remains the gold standard subtyping, it invasive may not fully capture tumor heterogeneity. Artificial Intelligence (AI), particularly Deep Learning (DL), offers promising non-invasive alternative by analyzing medical imaging data. Methods: In this study, we propose multimodal DL model that integrates mammography images clinical metadata to classify breast lesions into five categories: benign, luminal A, B, HER2-enriched, triple-negative. Using publicly available Chinese Mammography Database (CMMD), our was trained evaluated on dataset 4056 from 1775 patients. Results: The proposed approach significantly outperformed unimodal based solely images, achieving an AUC 88.87% multiclass categories, compared 61.3% model. Conclusions: These findings highlight potential AI-driven approaches subtype classification, paving way improved diagnostic precision personalized
Language: Английский
Citations
1
Journal of Translational Medicine, Journal Year: 2025, Volume and Issue: 23(1)
Published: March 20, 2025
Neoadjuvant chemoimmunotherapy (NACI) has emerged as the standard treatment for early-stage triple-negative breast cancer (TNBC). However, reliable biomarkers identifying patients who are likely to benefit from NACI lacking. This study aims develop an intratumoral microbiota-aided radiomics model predicting pathological complete response (pCR) in with TNBC. Intratumoral microbiota characterized by 16S rDNA sequencing and quantified through experimental assays. Single-cell RNA is performed analyze tumor microenvironment of tumors various responses NACI. Radiomics features extracted regions on longitudinal magnetic resonance images (MRIs) scanned before after training set. On basis (pCR or non-pCR) scoring, we select key construct a fusion integrating multi-timepoint (pre-NACI post-NACI) MRI predict efficacy immunotherapy, followed independent external validation. A total 124 enrolled, 88 set 36 validation Tumors achieves pCR present significantly greater load than achieve non-pCR (p < 0.05). Additionally, group exhibit infiltration tumor-associated SPP1+ macrophages, which negatively correlated load. 17 use them model. The highest AUC 0.945 set, outperforming pre-NACI (AUC = 0.875) post-NACI 0.917) models. In this maintains superior 0.873, surpassing those 0.769) 0.802) Clinically, distinguishes do not accuracy 77.8%. Decision curve analysis demonstrates net clinical across varying risk thresholds. Our could serve powerful noninvasive tool TNBC
Language: Английский
Citations
0
Cell Death Discovery, Journal Year: 2025, Volume and Issue: 11(1)
Published: April 24, 2025
Abstract Breast cancer (BC) poses significant challenges globally, necessitating a deeper understanding of its complexities. Exosomes are cell-specific secreted extracellular vesicles interest, characterized by lipid bilayer structure. can carry variety bioactive components, including nucleic acids, lipids, amino and small molecules, to mediate intercellular signaling. CircRNAs novel class single-stranded RNA closed-loop mainly exert ceRNA functions intricately modulate gene expression signaling pathways in breast cancer, influencing tumor progression therapeutic responses. The unique packaging circRNAs within exosomes serves as genetic information transmitters, facilitating communication between BC cells microenvironmental cells, thereby regulating critical aspects progression, immune evasion, drug resistance. Besides, exosomal possess the capabilities serving diagnostic biomarkers BC, due their stability, specificity, regulatory roles tumorigenesis metastasis. Therefore, this review aims elucidate mechanisms well potential for diagnosis therapeutics. ongoing investigations will potentially revolutionize treatment paradigms improve patient outcomes BC.
Language: Английский
Citations
0
Frontiers in Oncology, Journal Year: 2025, Volume and Issue: 15
Published: Feb. 7, 2025
Iron-based nanomaterials (INMs), due to their particular magnetic property, excellent biocompatibility, and functionality, have been developed into powerful tools in both tumor diagnosis therapy. We give an overview here on how INMs such as iron oxide nanoparticles, element-doped nanocomposites, iron-based organic frameworks (MOFs) display versatility for imaging therapy improvement. In terms of imaging, improve the sensitivity accuracy techniques resonance (MRI) photoacoustic (PAI) support development multimodal platforms. Regarding treatment, play a key role advanced strategies immunotherapy, hyperthermia, synergistic combination therapy, which effectively overcome tumor-induced drug resistance reduce systemic toxicity. The integration with artificial intelligence (AI) radiomics further expands its capabilities precise identification, treatment optimization, amplifies monitoring. now link materials science computing clinical innovations enable next-generation cancer diagnostics therapeutics.
Language: Английский
Citations
0
BMC Cancer, Journal Year: 2025, Volume and Issue: 25(1)
Published: Feb. 7, 2025
Gastric cancer patients are prone to lower extremity deep vein thrombosis (DVT) after surgery, which is an important cause of death in postoperative patients. Therefore, it particularly find a suitable way predict the risk occurrence DVT GC This study aims explore effectiveness using machine learning (ML) assisted radiomics build imaging models for prediction surgery. Included this retrospective were eligible who underwent surgery GC. CT data from these collected and divided into training set validation set. The least absolute shrinkage selection operator (LASSO) algorithm was applied reduce dimensionality variables Four algorithms, known as random forest (RF), extreme gradient boosting (XGBoost), support vector (SVM) naive Bayes (NB), used develop predicting These subsequently validated internal external cohort. LASSO analysis identified 10 variables, based on four ML established, then incorporated with clinical characteristics Among models, RF NB demonstrated highest predictive performance, achieving AUC 0.928, while SVM XGBoost achieved slightly 0.915 0.869, respectively. algorithms information may prove be novel non-invasive
Language: Английский
Citations
0
Tomography, Journal Year: 2025, Volume and Issue: 11(2), P. 16 - 16
Published: Feb. 7, 2025
Background/Objectives: Diabetes-related pancreatic changes on MRI remain unclear. Thus, we evaluated the in patients with both type 1 diabetes (T1D) and 2 (T2D) using multiparametric MRI. Methods: This prospective study involved T1D or T2D who underwent upper abdominal 3-T Additionally, without impaired glucose metabolism were retrospectively included as a control. The imaging data anteroposterior (AP) diameter, pancreas-to-muscle signal intensity ratio (SIR) fat-suppressed T1-weighted image (FS-T1WI), apparent diffusion coefficient (ADC) value, T1 value map, proton density fat fraction (PDFF), mean secretion grade of juice flow cine-dynamic magnetic resonance cholangiopancreatography (MRCP). MR measurements compared one-way analysis variance Kruskal–Wallis test. Results: Sixty-one (n = 7) 54) 21 control evaluated. AP diameters significantly smaller than (p < 0.05). average SIR FS-T1WI was lower controls 0.001). ADC values pancreas higher 0.01) 0.019). PDFF 0.029). Conclusions: Patients had reduced size, increased values, decreased MRCP, whereas content.
Language: Английский
Citations
0
Breast Cancer Targets and Therapy, Journal Year: 2025, Volume and Issue: Volume 17, P. 187 - 200
Published: Feb. 1, 2025
Accurate identification of the molecular subtypes breast cancer is essential for effective treatment selection and prognosis prediction. This study aimed to evaluate diagnostic performance a radiomics model, which integrates mammography dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting cancer. We retrospectively included 462 female patients with pathologically confirmed cancer, including 53 cases triple-negative, 94 HER2 overexpression, 95 luminal A, 215 B Radiomics analysis was performed using FAE software, wherein radiomic features were examined about hormone receptor status. The model evaluated area under receiver operating characteristic curve (AUC) accuracy. In multivariate analysis, only independent predictive factors subtypes. that incorporates multimodal fusion from DCE-MRI images exhibited superior overall compared either modality independently. AUC values (or accuracies) six pairings as follows: 0.648 (0.627) A vs B, 0.819 (0.793) 0.725 (0.696) triple-negative subtype, 0.644 (0.560) 0.625 (0.636) 0.598 (0.500) subtype overexpression. radionics utilizing combined showed high distinguishing It significance accurately predict determine strategy by classification.
Language: Английский
Citations
0