AIMC Topic: Biomarkers, Tumor
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Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.
European journal of radiology
Mar 19, 2025
OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers assoc...
Gallium Radioisotopes
Gallium Isotopes
Heterocyclic Compounds, 1-Ring
Humans
Prostate-Specific Antigen
Machine Learning
Radiopharmaceuticals
Retrospective Studies
Dipeptides
Aged
Middle Aged
Risk Assessment
Positron Emission Tomography Computed Tomography
Aged, 80 and over
Prostatic Neoplasms
Sensitivity and Specificity
Biomarkers, Tumor
Male
Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer.
Scientific reports
Mar 19, 2025
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers known to humans. However, not all patients fare equally poor survival, and a minority of patients even survives advanced disease for months or years. Thus, there is a clinical ...
Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.
Frontiers in immunology
Mar 18, 2025
BACKGROUND: Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. Neoadjuvant therapy (NAT), administered prior to surgery, is integral to breast cancer treatment strategies....
Identification of prognostic subtypes and the role of FXYD6 in ovarian cancer through multi-omics clustering.
Frontiers in immunology
Mar 18, 2025
BACKGROUND: Ovarian cancer (OC), as a malignant tumor that seriously endangers the lives and health of women, is renowned for its complex tumor heterogeneity. Multi-omics analysis, as an effective method for distinguishing tumor heterogeneity, can mo...
Utility of comprehensive genomic profiling combined with machine learning for prognostic stratification in stage II/III colorectal cancer after adjuvant chemotherapy.
International journal of clinical oncology
Mar 17, 2025
BACKGROUND AND PURPOSE: Accurate recurrence risk evaluation in patients with stage II and III colorectal cancer (CRC) remains difficult. Traditional histopathological methods frequently fall short in predicting outcomes after adjuvant chemotherapy. T...
Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.
Nature communications
Mar 17, 2025
Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predictive biomarkers are lacking. CD31, a marker of vasculature, is insufficient, and the Angioscore, an RN...
Interpretable machine learning for thyroid cancer recurrence predicton: Leveraging XGBoost and SHAP analysis.
European journal of radiology
Mar 14, 2025
PURPOSE: For patients suffering from differentiated thyroid cancer (DTC), several clinical, laboratory, and pathological features (including patient age, tumor size, extrathyroidal extension, or serum thyroglobulin levels) are currently used to ident...
PLSKB: An Interactive Knowledge Base to Support Diagnosis, Treatment, and Screening of Lynch Syndrome on the Basis of Precision Oncology.
JCO clinical cancer informatics
Mar 14, 2025
PURPOSE: Understanding the genetic heterogeneity of Lynch syndrome (LS) cancers has led to significant scientific advancements. However, these findings are widely dispersed across various resources, making it difficult for clinicians and researchers ...
Machine learning reveals glycolytic key gene in gastric cancer prognosis.
Scientific reports
Mar 13, 2025
Glycolysis is recognized as a central metabolic pathway in the neoplastic evolution of gastric cancer, exerting profound effects on the tumor microenvironment and the neoplastic growth trajectory. However, the identification of key glycolytic genes t...
An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 12, 2025
BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine l...
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