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Radiomics

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Machine Learning and Radiomics in Gliomas.

Advances in experimental medicine and biology
The integration of machine learning (ML) and radiomics is emerging as a pivotal advancement in glioma research, offering novel insights into the diagnosis, prognosis, and treatment of these complex tumors. Radiomics involves the extraction of a multi...

[Advances in artificial intelligence-assisted MRI radiomics in the diagnosis and treatment of prostate cancer].

Zhonghua nan ke xue = National journal of andrology
Prostate cancer (PCa) is the second most common cancer worldwide and the fifth leading cause of cancer deaths in men. Magnetic resonance imaging (MRI), with its high sensitivity and specificity in detecting PCa, is currently the most widely used imag...

Radiomics and Clinical Characters Based Gaussian Naive Bayes (GNB) Model for Preoperative Differentiation of Pulmonary Pure Invasive Mucinous Adenocarcinoma From Mixed Mucinous Adenocarcinoma.

Technology in cancer research & treatment
To develop and validate predictive models based on clinical parameters, and radiomic features to distinguish pulmonary pure invasive mucinous adenocarcinoma (pIMA) from mixed mucinous adenocarcinoma (mIMA) before surgery. From January 2017 to Decem...

Radiomics and Artificial Intelligence in Renal Lesion Assessment.

Critical reviews in oncogenesis
Radiomics, the extraction and analysis of quantitative features from medical images, has emerged as a promising field in radiology with the potential to revolutionize the diagnosis and management of renal lesions. This comprehensive review explores t...

Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy.

Critical reviews in oncogenesis
Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehens...

Gd-EOB-DTPA-enhanced MRI Image Characteristics and Radiomics Characteristics Combined with Machine Learning for Assessment of Functional Liver Reserve.

Current medical imaging
OBJECTIVE: To investigate the feasibility of image characteristics and radiomics combined with machine learning based on Gd-EOB-DTPA-enhanced MRI for functional liver reserve assessment in cirrhotic patients.

The application value of deep learning in the background of precision medicine in glioblastoma.

Science progress
Glioblastoma is a highly malignant central nervous system tumor, World Health Organization Ⅳ, glioblastoma is the most common primary malignancy, due to its own specificity and complexity, different patients often benefit from the current convention...