Radiomics - AI Medical Compendium

Large language models in methodological quality evaluation of radiomics research based on METRICS: ChatGPT vs NotebookLM vs radiologist.

European journal of radiology Jan 29, 2025

OBJECTIVES: This study aimed to evaluate the effectiveness of large language models (LLM) in assessing the methodological quality of radiomics research, using METhodological RadiomICs Score (METRICS) tool.

Large Language Models Biomedical Research Humans Generative Artificial Intelligence Language Natural Language Processing Reproducibility of Results Radiology Radiomics Radiologists

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The impact of the novel CovBat harmonization method on enhancing radiomics feature stability and machine learning model performance: A multi-center, multi-device study.

European journal of radiology Jan 29, 2025

PURPOSE: This study aims to assess whether the novel CovBat harmonization method can further reduce radiomics feature variability from different imaging devices in multi-center studies and improve machine learning model performance compared to the Co...

Radiomics Male Middle Aged Liver Radiographic Image Interpretation, Computer-Assisted Spleen Radiography, Abdominal Tomography, X-Ray Computed Female Reproducibility of Results Adult Sensitivity and Specificity Humans Machine Learning Retrospective Studies

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Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.

Frontiers in immunology Jan 29, 2025

PURPOSE: Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with different backgrounds and expertise. This study aimed to construct and evaluate several Radiomics-based mac...

ROC Curve Magnetic Resonance Imaging Humans Diagnosis, Differential Adult Femur Head Machine Learning Radiomics Retrospective Studies Osteoarthritis Male Female Middle Aged Aged

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F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

La Radiologia medica Jan 28, 2025

PURPOSE: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.

Lymphatic Metastasis Predictive Value of Tests Positron Emission Tomography Computed Tomography Radiopharmaceuticals Fluorodeoxyglucose F18 Chemotherapy, Adjuvant Breast Neoplasms Radiomics Middle Aged Treatment Outcome Neoadjuvant Therapy Humans Retrospective Studies Artificial Intelligence Female Machine Learning Aged Adult

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Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach.

Magnetic resonance imaging Jan 27, 2025

BACKGROUND: Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectivene...

Humans Retrospective Studies Breast Neoplasms Radiomics Middle Aged Female Deep Learning Image Processing, Computer-Assisted Aged Adult Sensitivity and Specificity Contrast Media Magnetic Resonance Imaging Neoplasm Invasiveness Breast Image Interpretation, Computer-Assisted Preoperative Care Diffusion Magnetic Resonance Imaging

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A radiomics model combining machine learning and neural networks for high-accuracy prediction of cervical lymph node metastasis on ultrasound of head and neck squamous cell carcinoma.

Oral surgery, oral medicine, oral pathology and oral radiology Jan 24, 2025

OBJECTIVE: This study aimed to develop an ultrasound image-based radiomics model for diagnosing cervical lymph node (LN) metastasis in patients with head and neck squamous cell carcinoma (HNSCC) that shows higher accuracy than previous models.

Radiomics Humans Neural Networks, Computer Male Machine Learning Middle Aged Female Ultrasonography Lymph Nodes Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Neck Lymphatic Metastasis

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Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.

Neurosurgical review Jan 24, 2025

Currently, the World Health Organization (WHO) grade of meningiomas is determined based on the biopsy results. Therefore, accurate non-invasive preoperative grading could significantly improve treatment planning and patient outcomes. Considering rece...

Machine Learning Radiomics Deep Learning Neoplasm Grading Meningioma Humans Meningeal Neoplasms

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Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.

JCO clinical cancer informatics Jan 24, 2025

PURPOSE: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during daily scans could be analyzed using delta-radiomics to predict disease progression events. We evalu...

Male Middle Aged Disease Progression ROC Curve Lung Neoplasms Female Aged Adult Radiomics Magnetic Resonance Imaging Adrenal Gland Neoplasms Pancreatic Neoplasms Radiotherapy, Image-Guided Humans Machine Learning Prognosis

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Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.

Cancer imaging : the official publication of the International Cancer Imaging Society Jan 21, 2025

BACKGROUND: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significa...

Brain Neoplasms Male Middle Aged Retrospective Studies Deep Learning Female Immunotherapy Aged Glioma Humans Adult Magnetic Resonance Imaging Neural Networks, Computer Radiomics Neoplasm Grading Prognosis Neoplasm Recurrence, Local

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Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.

BMC gastroenterology Jan 21, 2025

OBJECTIVE: To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarteri...

Female Aged Adult Radiomics Male Humans Antineoplastic Combined Chemotherapy Protocols Prognosis Middle Aged Combined Modality Therapy Retrospective Studies Deep Learning Chemoembolization, Therapeutic Tomography, X-Ray Computed Liver Neoplasms Protein Kinase Inhibitors Carcinoma, Hepatocellular Tyrosine Kinase Inhibitors Immune Checkpoint Inhibitors

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AIMC Topic: Radiomics

Large language models in methodological quality evaluation of radiomics research based on METRICS: ChatGPT vs NotebookLM vs radiologist.

The impact of the novel CovBat harmonization method on enhancing radiomics feature stability and machine learning model performance: A multi-center, multi-device study.

Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts.

F-FDG PET/CT radiomic analysis and artificial intelligence to predict pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach.

A radiomics model combining machine learning and neural networks for high-accuracy prediction of cervical lymph node metastasis on ultrasound of head and neck squamous cell carcinoma.

Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.

Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.

Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.

Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors.

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AIMC Topic: Radiomics

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