AIMC Topic: Prognosis

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Role of MRI radiomics in deep learning-based prediction of intestinal diseases.

International journal of colorectal disease
BACKGROUND: Magnetic resonance imaging (MRI) is widely used for the diagnosis, evaluation, and follow-up of intestinal diseases. With advances in artificial intelligence, MRI radiomics and deep learning have emerged as promising tools for prognostic ...

Predicting the progression of difficult-to-treat rheumatoid arthritis by a machine learning scoring system, from the FIRST registry.

RMD open
OBJECTIVES: This study aimed to develop and validate a prediction model for the future progression of difficult-to-treat rheumatoid arthritis (D2T RA) and support the precise use of biologic and targeted synthetic disease-modifying antirheumatic drug...

Machine learning and multi-omics integration identifies immunological predictors and mechanistic insights in autoimmune encephalitis.

Inflammation research : official journal of the European Histamine Research Society ... [et al.]
OBJECTIVE: To develop an interpretable prognostic prediction model for autoimmune encephalitis (AE) using immunological indicators and to investigate the potential role of nucleophosmin (NPM1) in disease pathogenesis through multi-omics approaches.

Explainable AI for Predicting Mortality Risk in Metastatic Cancer: Retrospective Cohort Study Using the Memorial Sloan Kettering-Metastatic Dataset.

JMIR cancer
BACKGROUND: Metastatic cancer remains one of the leading causes of cancer-related mortality worldwide. Yet, the prediction of survivability in this population remains limited by heterogeneous clinical presentations and high-dimensional molecular feat...

Neutrophil Percentage-to-Albumin Ratio as a Novel Prognostic Biomarker in Adult Diffuse Gliomas: Retrospective Study Integrating 3 Machine Learning Models and Cox Regression.

JMIR medical informatics
BACKGROUND: Adult-type diffuse glioma (ADG) is the most common primary malignant tumor of the central nervous system. Its highly invasive nature, marked heterogeneity, and resistance to therapy contribute to a high risk of recurrence and poor prognos...

Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.

International journal of colorectal disease
BACKGROUND: Colon adenocarcinoma (COAD) remains a leading cause of cancer-related mortality worldwide. Although tumor deposits (TDs) are established prognostic indicators, their molecular characteristics and potential for improving risk stratificatio...

An interpretable machine learning model based on MRI radiomics and GAME score for predicting early recurrence after thermal ablation in colorectal liver metastases.

International journal of colorectal disease
OBJECTIVE: To develop and validate machine learning models based on preoperative magnetic resonance imaging(MRI) and baseline clinical characteristics for predicting early recurrence(ER) in patients with colorectal liver metastases(CRLM) treated with...

LIMPACAT: Multi-omics attention transformer for immune prediction in liver cancer using whole-slide imaging.

PloS one
Characterizing the tumor immune microenvironment from histopathological images offers opportunities for ex vivo immune profiling and prognostic assessment. However, the TCGA-LIHC dataset lacks direct immune cell composition data. Therefore, this stud...

Establishment of an amino acid metabolism related signature for prognostic and therapeutic sensitivity prediction in breast cancer by machine learning.

PloS one
Amino acid metabolism plays a critical role in tumor growth and immune regulation, yet its comprehensive function in breast cancer remains underexplored. We developed an amino acid metabolism-related gene signature (AAMRGS) to predict prognosis and t...

Long-term benefit from high-dose ifosfamide in sarcoma depends on sustained prior control and timely intervention: a machine learning analysis.

Journal of cancer research and clinical oncology
PURPOSE: High-dose ifosfamide (HD-IFO) remains an effective regimen for advanced bone and soft tissue sarcomas, but predictors of long-term benefit are poorly defined. This study evaluated clinical outcomes and prognostic factors using machine learni...