AIMC Topic: Prognosis

Clear Filters Showing 271 to 280 of 3837 articles

Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning-Based Prediction Models in a Retrospective Study.

JMIR cancer
BACKGROUND: Breast cancer is the most prevalent form of cancer worldwide, with 2.3 million new diagnoses in 2022. Recent advancements in treatment have led to a shift in the use of chemotherapy-targeted immunotherapy from a postoperative adjuvant to ...

Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis.

Science advances
Current approaches to estimating cell trajectories, tumor progression dynamics, and cell population diversity of tumor microenvironment often depend on single-cell RNA sequencing, which is costly and resource intensive. To address this limitation, we...

AI-powered skin spectral imaging enables instant sepsis diagnosis and outcome prediction in critically ill patients.

Science advances
With sepsis remaining a leading cause of mortality, early identification of patients with sepsis and those at high risk of death is a challenge of high socioeconomic importance. Given the potential of hyperspectral imaging (HSI) to monitor microcircu...

Integrating bulk and single cell sequencing data to identify prognostic biomarkers and drug candidates in HBV associated hepatocellular carcinoma.

Scientific reports
Hepatitis B virus (HBV) infection is a major driver of hepatocellular carcinoma (HCC), yet the mechanisms by which HBV triggers HCC and how it interacts with the immune system remain largely undefined. In this study, 53 immune-related key genes invol...

Unraveling prognostic biomarkers in oral squamous cell carcinoma: An approach based on explainable artificial intelligence.

Cancer genetics
Oral cancer is among the top malignancies and the leading cause of death worldwide. Poor outcomes are attributed to local recurrence and distant metastasis of disease. There is an urgent need to identify the potential biomarkers that may help in prog...

Reflections on dynamic prediction of Alzheimer's disease: advancements in modeling longitudinal outcomes and time-to-event data.

BMC medical research methodology
BACKGROUND: Individualized prediction of health outcomes supports clinical medicine and decision making. Our primary objective was to offer a comprehensive survey of methods for the dynamic prediction of Alzheimer's disease (AD), encompassing both co...

Explainable machine learning-driven models for predicting Parkinson's disease and its prognosis: obesity patterns associations and models development using NHANES 1999-2018 data.

Lipids in health and disease
BACKGROUND: Parkinson's disease (PD) is a prevalent neurodegenerative condition, the effect of obesity on PD remains controversial. We aimed to investigate the associations of obesity patterns on PD and all-cause mortality, while developing machine l...

Effectiveness of predictive scoring systems in predicting mortality in relation to baseline kidney function in adult intensive care unit patients: a systematic review protocol.

BMJ open
INTRODUCTION: Predictive scoring systems support clinicians in decision-making by estimating the prognosis of patients in intensive care units (ICUs). However, there is limited evidence on the accuracy of these systems in predicting mortality and org...

Lectin-affinity glycosylation pattern analysis of plasma extracellular vesicles: An all-in-one clinical assessment for gastric cancer diagnosis and treatment.

Cancer letters
Extracellular vesicles (EVs) exhibit extensive glycosylation modifications, which are promising biomarkers for gastric cancer (GC). However, EV glycomics and the potential application of EV glycosylation patterns in liquid biopsy remain largely unexp...

Association Between Comorbidity Clusters and Mortality in Patients With Cancer: Predictive Modeling Using Machine Learning Approaches of Data From the United States and Hong Kong.

JMIR cancer
BACKGROUND: Patients with cancer and cancer survivors often experience multiple chronic health conditions, which can impact symptom burden and treatment outcomes. Despite the high prevalence of multimorbidity, research on cancer prognosis has predomi...