AIMC Topic: Retrospective Studies
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Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.
Physics in medicine and biology
Jun 6, 2025
High-dose-rate (HDR) brachytherapy is integral to the standard-of-care for locally advanced cervical cancer (LACC). Currently, selection of brachytherapy applicators relies on physician's clinical experience, which can lead to variability in treatmen...
Multitask deep learning model based on multimodal data for predicting prognosis of rectal cancer: a multicenter retrospective study.
BMC medical informatics and decision making
Jun 5, 2025
BACKGROUND: Prognostic prediction is crucial to guide individual treatment for patients with rectal cancer. We aimed to develop and validated a multitask deep learning model for predicting prognosis in rectal cancer patients.
A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models.
BMC medical informatics and decision making
Jun 5, 2025
BACKGROUND AND OBJECTIVE: The machine learning (ML) models for acute myocardial infarction (AMI) are considered to have better predictive ability for mortality compared to conventional risk scoring models. However, previous ML prediction models have ...
Development and validation of a predictive nomogram for bilateral posterior condylar displacement using cone-beam computed tomography and machine-learning algorithms: a retrospective observational study.
BMC oral health
Jun 5, 2025
BACKGROUND: Temporomandibular disorders (TMDs) are frequently associated with posterior condylar displacement; however, early prediction of this displacement remains a significant challenge. Therefore, in this study, we aimed to develop and evaluate ...
A radiogenomics study on F-FDG PET/CT in endometrial cancer by a novel deep learning segmentation algorithm.
BMC cancer
Jun 5, 2025
OBJECTIVE: To create an automated PET/CT segmentation method and radiomics model to forecast Mismatch repair (MMR) and TP53 gene expression in endometrial cancer patients, and to examine the effect of gene expression variability on image texture feat...
Maximizing Lung Cancer Screening in High-Risk Population Leveraging ML-Developed Risk-Prediction Algorithms: Danish Retrospective Validation of LungFlag.
Clinical lung cancer
Jun 4, 2025
BACKGROUND: Early detection of lung cancer (LC) is crucial for curative treatment, but current screening methods face challenges due to high costs and poor adherence. Artificial intelligence tools, such as the LungFlag model, uses routine clinical da...
Predicting clinical prognosis in gastric cancer using deep learning-based analysis of tissue pathomics images.
Computer methods and programs in biomedicine
Jun 4, 2025
OBJECTIVE: Evaluate the utility of a machine learning-based pathomics model in predicting overall survival (OS) post-surgery for gastric cancer patients.
Radiomics-Based Differentiation of Primary Central Nervous System Lymphoma and Solitary Brain Metastasis Using Contrast-Enhanced T1-Weighted Imaging: A Retrospective Machine Learning Study.
Academic radiology
Jun 4, 2025
PURPOSE: To develop and evaluate radiomics-based models using contrast-enhanced T1-weighted imaging (CE-T1WI) for the non-invasive differentiation of primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (SBM), aiming to impro...
Predicting clinical outcomes using 18F-FDG PET/CT-based radiomic features and machine learning algorithms in patients with esophageal cancer.
Nuclear medicine communications
Jun 4, 2025
OBJECTIVE: This study evaluated the relationship between 18F-fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) radiomic features and clinical parameters, including tumor localization, histopathological subtype, lymph node metastasis, mortal...
The value of machine learning based on magnetic resonance imaging (MRI) and biopsy whole-slide image to predict pathological complete response to breast cancer after neoadjuvant chemotherapy: a two-centre study.
Clinical radiology
Jun 4, 2025
AIM: To develop and validate a combined model based on magnetic resonance imaging (MRI), and whole-slide imaging (WSI) to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer.
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