AIMC Topic: Radiomics
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CT-based machine learning model integrating intra- and peri-tumoral radiomics features for predicting occult lymph node metastasis in peripheral lung cancer.
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 21, 2025
BACKGROUND: Accurate preoperative assessment of occult lymph node metastasis (OLNM) plays a crucial role in informing therapeutic decision-making for lung cancer patients. Computed tomography (CT) is the most widely used imaging modality for preopera...
Predicting knee osteoarthritis progression using neural network with longitudinal MRI radiomics, and biochemical biomarkers: A modeling study.
PLoS medicine
Aug 21, 2025
BACKGROUND: Knee osteoarthritis (KOA) worsens both structurally and symptomatically, yet no model predicts KOA progression using Magnetic Resonance Image (MRI) radiomics and biomarkers. This study aimed to develop and test the longitudinal Load-Beari...
Current imaging applications, radiomics, and machine learning modalities of CNS demyelinating disorders and its mimickers.
Journal of neurology
Aug 12, 2025
Distinguishing among neuroinflammatory demyelinating diseases of the central nervous system can present a significant diagnostic challenge due to substantial overlap in clinical presentations and imaging features. Collaboration between specialists, n...
Machine learning models for the prediction of preclinical coal workers' pneumoconiosis: integrating CT radiomics and occupational health surveillance records.
Journal of translational medicine
Aug 11, 2025
OBJECTIVES: This study aims to integrate CT imaging with occupational health surveillance data to construct a multimodal model for preclinical CWP identification and individualized risk evaluation.
Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.
BMC medical imaging
Aug 11, 2025
BACKGROUND: Gliomas exhibit a high recurrence rate, particularly in the peritumoural brain zone after surgery. This study aims to develop and validate a radiomics-based model using preoperative fluid-attenuated inversion recovery (FLAIR) and T1-weigh...
18F-FDG PET/CT-based deep radiomic models for enhancing chemotherapy response prediction in breast cancer.
Medical oncology (Northwood, London, England)
Aug 11, 2025
Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer. In this study, we developed deep radiomic models to enhance the prediction of chemotherapy response after...
Deep learning and radiomics fusion for predicting the invasiveness of lung adenocarcinoma within ground glass nodules.
Scientific reports
Aug 11, 2025
Microinvasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) require distinct treatment strategies and are associated with different prognoses, underscoring the importance of accurate differentiation. This study aims to develop a predictive m...
Prediction of cervical cancer lymph node metastasis based on multisequence magnetic resonance imaging radiomics and deep learning features: a dual-center study.
Scientific reports
Aug 10, 2025
Cervical cancer is a leading cause of death from malignant tumors in women, and accurate evaluation of occult lymph node metastasis (OLNM) is crucial for optimal treatment. This study aimed to develop several predictive models-including Clinical mode...
MRI-based radiomics for preoperative T-staging of rectal cancer: a retrospective analysis.
International journal of colorectal disease
Aug 8, 2025
PUROPOSE: Preoperative T-staging in rectal cancer is essential for treatment planning, yet conventional MRI shows limited accuracy (~ 60-78). Our study investigates whether radiomic analysis of high-resolution T2-weighted MRI can non-invasively impro...
Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.
Alzheimer's research & therapy
Aug 7, 2025
BACKGROUND: Alzheimer's disease (AD) is the principal cause of dementia and requires the early diagnosis of people with mild cognitive impairment (MCI) who are at high risk of progressing. Early diagnosis is imperative for optimizing clinical managem...
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