AIMC Topic: ROC Curve
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Radiomic analysis based on machine learning of multi-sequences MR to assess early treatment response in locally advanced nasopharyngeal carcinoma.
Science progress
May 8, 2025
ObjectiveThe prediction of early response in locally advanced nasopharyngeal carcinoma (LA-NPC) after concurrent chemoradiotherapy (CCRT) is important for determining the need for timely consolidation therapy. We developed a radiomic analysis of mult...
Construction of exosome non-coding RNA feature for non-invasive, early detection of gastric cancer patients by machine learning: a multi-cohort study.
Gut
May 7, 2025
BACKGROUND AND OBJECTIVE: Gastric cancer (GC) remains a prevalent and preventable disease, yet accurate early diagnostic methods are lacking. Exosome non-coding RNAs (ncRNAs), a type of liquid biopsy, have emerged as promising diagnostic biomarkers f...
Stacking classifiers based on integrated machine learning model: fusion of CT radiomics and clinical biomarkers to predict lymph node metastasis in locally advanced gastric cancer patients after neoadjuvant chemotherapy.
BMC cancer
May 6, 2025
BACKGROUND: The early prediction of lymph node positivity (LN+) after neoadjuvant chemotherapy (NAC) is crucial for optimizing individualized treatment strategies. This study aimed to integrate radiomic features and clinical biomarkers through machin...
Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births.
BMC pregnancy and childbirth
May 3, 2025
OBJECTIVE: This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk as...
Interpretable machine learning model for predicting post-hepatectomy liver failure in hepatocellular carcinoma.
Scientific reports
May 3, 2025
Post-hepatectomy liver failure (PHLF) is a severe complication following liver surgery. We aimed to develop a novel, interpretable machine learning (ML) model to predict PHLF. We enrolled 312 hepatocellular carcinoma (HCC) patients who underwent hepa...
Label-free rapid diagnosis of jaw osteonecrosis via the intersection of Raman spectroscopy and deep learning.
Bone
May 2, 2025
OBJECTIVES: To establish a precise and efficient diagnostic framework for distinguishing medication-related osteonecrosis of the jaw, radiation-induced osteonecrosis of the jaw, and normal bone tissue, thus enhancing clinical decision-making and enab...
Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases.
Journal of cancer research and therapeutics
May 2, 2025
BACKGROUND: To develop a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CECT) to assess the rat sarcoma (RAS) oncogene status and predict targeted therapy response in colorectal cancer liver metastases (CRLM).
Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy.
Scientific reports
May 1, 2025
Anoikis and immune cell infiltration are pivotal factors in the pathophysiological mechanism of diabetic nephropathy (DN), yet a comprehensive understanding of the mechanism is lacking. This work aimed to pinpoint distinctive anoikis-related genes (A...
Development and validation a radiomics combined clinical model predicts treatment response for esophageal squamous cell carcinoma patients.
BMC gastroenterology
Apr 29, 2025
PURPOSE: This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC)...
Risk for ocular hypertension progression to early glaucoma: A predictive model and key predictors.
Photodiagnosis and photodynamic therapy
Apr 28, 2025
BACKGROUND: Ocular hypertension (OHT) is the most significant risk factor for glaucoma. We aimed to develop a model for predicting OHT progression to early glaucoma and to identify key predictors.
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