AIMC Topic: Nomograms
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Immunological biomarkers and gene signatures predictive of radiotherapy resistance in non-small cell lung cancer.
Frontiers in immunology
May 29, 2025
INTRODUCTION: A significant challenge in treating non-small cell lung cancer (NSCLC) is its inherent resistance to radiation therapy, leading to poor patient prognosis. This study aimed to identify key genes influencing radiotherapy resistance in NSC...
The Role of PANoptosis-Related Genes in Predicting Breast Cancer Survival and Immune Prospect.
BioMed research international
May 28, 2025
The function of PANoptosis in breast cancer (BC) remains indistinct. We constructed a nomogram model to predict the prognosis of BC to identify high-risk patients and help them receive more accurate treatment. We used Cox regression and least absol...
Development and validation of a LASSO logistic regression based nomogram for predicting live births in women with polycystic ovary syndrome: a retrospective cohort study.
Frontiers in endocrinology
May 27, 2025
OBJECTIVE: There is limited study on predictive models for live births in patients with polycystic ovarian syndrome (PCOS). The study aimed to develop and validate a nomogram for predicting live births in Chinese women with PCOS, as well as to identi...
Development and validation of a predictive model for refracture risk in elderly individuals with osteoporotic vertebral compression fracture: a retrospective study in China.
Aging clinical and experimental research
May 26, 2025
BACKGROUND: The rising incidence of refractures and associated adverse outcomes among individuals with osteoporotic vertebral compression fractures has gained significant attention. Identifying refracture risk is crucial for implementing effective pr...
Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage.
BMC medical imaging
May 23, 2025
BACKGROUNDS: Delayed cerebral ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH), leading to poor prognosis and high mortality. This study developed a non-contrast CT (NCCT)-based radiomics nomogram for e...
Construction of a predictive model for relapse of primary autoimmune hemolytic anemia: a retrospective cohort study.
Annals of medicine
May 22, 2025
OBJECTIVES: To develop a machine learning-based model to predict the relapse risk of Primary Autoimmune Haemolytic Anaemia (AIHA) after the last remission.
Transcriptomic insights into the mechanism of action of telomere-related biomarkers in rheumatoid arthritis.
Frontiers in immunology
May 22, 2025
BACKGROUND: Rheumatoid arthritis (RA) is an autoimmune inflammatory disease. The mechanism by which telomeres are involved in the development of RA remains unclear. This study aimed to investigate the relationship between RA and telomeres.
Development and validation of a nomogram for differentiating immune checkpoint inhibitor-related pneumonitis from pneumonia in patients undergoing immunochemotherapy: a multicenter, real-world, retrospective study.
Frontiers in immunology
May 19, 2025
BACKGROUND: Immune Checkpoint Inhibitor-related Pneumonitis (CIP) exhibits high morbidity and mortality rates in the real world, often coexisting with pneumonia, particularly after immunochemotherapy. We aimed to develop and validate a non-invasive n...
Predicting infections with multidrug-resistant organisms (MDROs) in neurocritical care patients with hospital-acquired pneumonia (HAP): development of a novel multivariate prediction model.
Microbiology spectrum
May 15, 2025
Hospital-acquired pneumonia (HAP) is prevalent in the neuro-intensive care unit (NICU), significantly increasing susceptibility to infections with multidrug-resistant organisms (MDROs), which result in high mortality rates and substantial healthcare ...
Adopting machine learning to predict nomogram for small incision lenticule extraction (SMILE).
International ophthalmology
May 5, 2025
PURPOSE: To predict nomogram for small incision lenticule extraction (SMILE) using machine learning technology and preoperative clinical data.
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