AIMC Topic: Prognosis

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Gadoxetic acid-enhanced MRI for identifying cholangiocyte phenotype hepatocellular carcinoma by interpretable machine learning: individual application of SHAP.

BMC cancer
PURPOSE: Cholangiocyte phenotype hepatocellular carcinoma (HCC) is highly invasive. This study aims to develop and validate an optimal machine learning model to predict cholangiocyte phenotype HCC based on T1 mapping gadoxetic acid-enhanced MRI and t...

Relationship prediction between clinical subtypes and prognosis of critically ill patients with cirrhosis based on unsupervised learning methods: A study from two critical care databases.

International journal of medical informatics
BACKGROUND: Our objective was to identify distinct clinical subtypes among critically ill patients with cirrhosis and analyze the clinical features and prognosis of each subtype.

Peritoneal cytology predicting distant metastasis in uterine carcinosarcoma: machine learning model development and validation.

World journal of surgical oncology
OBJECTIVE: This study develops and validates a machine learning model using peritoneal cytology to predict distant metastasis in uterine carcinosarcoma, aiding clinical decision-making.

Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study.

JCO clinical cancer informatics
PURPOSE: Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic response varies among patients. This study aimed to predict tumor response to nivolumab anti-PD-1 antibody ...

UBTD2 protein molecules emerges as a key prognostic protein marker in glioma: Insights from integrated omics and machine learning analysis of GRM7, NCAPG, CEP55, and other biomarkers.

International journal of biological macromolecules
Glioma is a malignant brain tumor with poor prognosis, and there is an urgent need to find effective biomarkers for early diagnosis and treatment. The aim of this study was to explore the potential of UBTD2 as a key prognostic protein marker for glio...

Construction and validation of prognostic model for ICU mortality in cardiac arrest patients: an interpretable machine learning modeling approach.

European journal of medical research
BACKGROUND: The incidence and mortality of cardiac arrest (CA) is high. We developed interpretable machine learning models for early prediction of ICU mortality risk in patients diagnosed with CA.

Leveraging TME features and multi-omics data with an advanced deep learning framework for improved Cancer survival prediction.

Scientific reports
Glioma, a malignant intracranial tumor with high invasiveness and heterogeneity, significantly impacts patient survival. This study integrates multi-omics data to improve prognostic prediction and identify therapeutic targets. Using single-cell data ...

Personalized therapeutic strategies and prognosis for advanced laryngeal squamous cell carcinoma: Insights from machine learning models.

American journal of otolaryngology
PURPOSE: Despite the development of diverse treatment options, there has been an increase in mortality rates for laryngeal squamous cell carcinoma (LSCC). Our research employed survival analysis and machine learning (ML) techniques to evaluate the im...

Options for postoperative radiation therapy in patients with de novo metastatic breast cancer.

Breast (Edinburgh, Scotland)
BACKGROUND: Although meta-analyses have demonstrated survival benefits associated with primary tumor resection in MBC, guidelines lack consensus on the survival benefit of postoperative radiation therapy (RT).