AIMC Topic: Prognosis

Clear Filters Showing 851 to 860 of 3837 articles

Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.

BMC cancer
Glioblastoma Multiforme (GBM), classified as a grade IV glioma by the World Health Organization (WHO), is a prevalent and notably aggressive form of brain tumor derived from glial cells. It stands as one of the most severe forms of primary brain canc...

Prognostic Impact of Tumor Cell Nuclear Size Assessed by Artificial Intelligence in Esophageal Squamous Cell Carcinoma.

Laboratory investigation; a journal of technical methods and pathology
Tumor cell nuclear size (NS) indicates malignant potential in breast cancer; however, its clinical significance in esophageal squamous cell carcinoma (ESCC) is unknown. Artificial intelligence (AI) can quantitatively evaluate histopathological findin...

Association between serum endocan levels and organ failure in hospitalized patients with cirrhosis.

PloS one
BACKGROUND & AIMS: Acute-on-chronic liver failure is a syndrome characterized by organ failure and high short-term mortality. The lack of reliable biomarkers for the early detection of acute-on-chronic liver failure is a significant challenge. Endoth...

Mucoepidermoid carcinoma: Enhancing diagnostic accuracy and treatment strategy through machine learning models and web-based prognostic tool.

Journal of stomatology, oral and maxillofacial surgery
BACKGROUND: Oral cancer, particularly mucoepidermoid carcinoma (MEC), presents diagnostic challenges due to its histological diversity and rarity. This study aimed to develop machine learning (ML) models to predict survival outcomes for MEC patients ...

Radiomics and deep learning models for glioblastoma treatment outcome prediction based on tumor invasion modeling.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: We investigate the feasibility of using a biophysically guided approach for delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by evaluating its impact on the treatment outcomes, specifically Overall Survival (OS) ...

Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma.

BMC cancer
BACKGROUND AND AIMS: Hepatocellular carcinoma (HCC) exhibits a propensity for early recurrence following liver resection, resulting in a bleak prognosis. At present, majority of the predictive models for the early postoperative recurrence of HCC rely...

Value of the combination of intraepithelial tumor-infiltrating lymphocyte density and the heterogeneity of density as a prognostic marker in stage III colorectal cancers.

Histology and histopathology
Tumor-infiltrating lymphocyte (TIL) density is both a prognostic and a predictive factor in colorectal cancer (CRC). Whether the heterogeneity of TIL density across the tumor plays an important role in the clinical outcome of CRC is not well known. A...

Prognostic value of a composite physiologic index developed by adding bronchial and hyperlucent volumes quantified via artificial intelligence technology.

Respiratory research
BACKGROUND: The composite physiologic index (CPI) was developed to estimate the extent of interstitial lung disease (ILD) in idiopathic pulmonary fibrosis (IPF) patients based on pulmonary function tests (PFTs). The CALIPER-revised version of the CPI...

Machine learning for predicting acute myocardial infarction in patients with sepsis.

Scientific reports
Acute myocardial infarction (AMI) and sepsis are the leading causes of high mortality rates in intensive care units. While sepsis frequently affects the cardiovascular system, distinguishing between sepsis-induced cardiomyopathy and AMI remains chall...