AIMC Topic: Prognosis

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An automated cascade framework for glioma prognosis via segmentation, multi-feature fusion and classification techniques.

Biomedical physics & engineering express
Glioma is one of the most lethal types of brain tumors, accounting for approximately 33% of all diagnosed brain tumor cases. Accurate segmentation and classification are crucial for precise glioma characterization, emphasizing early detection of mali...

Fully volumetric body composition analysis for prognostic overall survival stratification in melanoma patients.

Journal of translational medicine
BACKGROUND: Accurate assessment of expected survival in melanoma patients is crucial for treatment decisions. This study explores deep learning-based body composition analysis to predict overall survival (OS) using baseline Computed Tomography (CT) s...

Explainable predictive models of short stature and exploration of related environmental growth factors: a case-control study.

BMC endocrine disorders
BACKGROUND: Short stature is a prevalent pediatric endocrine disorder for which early detection and prediction are pivotal for improving treatment outcomes. However, existing diagnostic criteria often lack the necessary sensitivity and specificity be...

Interpretable artificial intelligence model for predicting heart failure severity after acute myocardial infarction.

BMC cardiovascular disorders
BACKGROUND: Heart failure (HF) after acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Accurate prediction and early identification of HF severity are crucial for initiating preventive measures and optimizing ...

Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning.

Nature communications
Predicting treatment response is an important problem in real-world applications, where the heterogeneity of the treatment response remains a significant challenge in practice. Unsupervised machine learning methods have been proposed to address this ...

A machine learning-based prediction model for colorectal liver metastasis.

Clinical and experimental medicine
Colorectal liver metastasis (CRLM) is a primary factor contributing to poor prognosis and metastasis in colorectal cancer (CRC) patients. This study aims to develop and validate a machine learning (ML)-based risk prediction model using conventional c...

AI-driven predictive biomarker discovery with contrastive learning to improve clinical trial outcomes.

Cancer cell
Modern clinical trials can capture tens of thousands of clinicogenomic measurements per individual. Discovering predictive biomarkers, as opposed to prognostic markers, remains challenging. To address this, we present a neural network framework based...

Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation.

Journal of cancer research and clinical oncology
PURPOSE: This study aims to develop an effective machine learning (ML)-based predictive model for the recurrence of borderline ovarian tumor (BOT), and provide the guidelines of accurate clinical diagnosis and precise treatment for patients.