AIMC Topic: Prognosis

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Construction of a prognostic model for endometrial cancer related to programmed cell death using WGCNA and machine learning algorithms.

Frontiers in immunology
BACKGROUND: Programmed cell death (PCD) refers to a regulated and active process of cellular demise, initiated by specific biological signals. PCD plays a crucial role in the development, progression, and drug resistance of uterine corpus endometrial...

Preoperative kidney tumor risk estimation with AI: From logistic regression to transformer.

PloS one
We consider the problem of renal mass risk classification to support doctors in adjuvant treatment decisions following nephrectomy. Recommendation of adjuvant therapy based on the mass appearance poses two major challenges: first, morphologic pattern...

Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis.

Frontiers in immunology
OBJECTIVE: The objective of this study is to compare the clinical features and survival outcomes of class IV ± V lupus nephritis (LN) patients, identify risk factors, and develop an accurate prognostic model.

Tumor-infiltrating immune cell signature score reveals prognostic biomarkers and therapeutic targets for colorectal cancer.

Frontiers in immunology
BACKGROUND: Colorectal cancer (CRC) is one of the leading contributors to cancer-related deaths worldwide, with more than 900,000 new diagnoses and related deaths each year. This study aims to explore the prognostic value of tumor-infiltrating immune...

Integrative analysis of epigenetic subtypes in acute myeloid Leukemia: A multi-center study combining machine learning for prognostic and therapeutic insights.

PloS one
BACKGROUND: Acute Myeloid Leukemia (AML) exhibits significant heterogeneity in clinical outcomes, yet current prognostic stratification systems based on genetic alterations alone cannot fully capture this complexity. This study aimed to develop an in...

Predicting central lymph node metastasis in papillary thyroid microcarcinoma: a breakthrough with interpretable machine learning.

Frontiers in endocrinology
OBJECTIVE: To develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).

Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis.

PloS one
INTRODUCTION: Tuberculous meningitis (TBM) leads to high mortality, especially amongst individuals with HIV. Predicting the incidence of disease-related complications is challenging, for which purpose the value of brain magnetic resonance imaging (MR...

Personalized treatment decision-making using a machine learning-derived lactylation signature for breast cancer prognosis.

Frontiers in immunology
BACKGROUND: Breast cancer is a heterogeneous malignancy with complex molecular characteristics, making accurate prognostication and treatment stratification particularly challenging. Emerging evidence suggests that lactylation, a novel post-translati...

Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma.

PeerJ
BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effective immunotherapy, this study was designed to screen molecular markers related to tumor infiltrating...