AIMC Topic: Radiomics
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Prediction of T Stage of Rectal Cancer After Neoadjuvant Therapy by Multi-Parameter Magnetic Resonance Radiomics Based on Machine Learning Algorithms.
Technology in cancer research & treatment
Jan 1, 2024
INTRODUCTION: Since the response of patients with rectal cancer (RC) to neoadjuvant therapy is highly variable, there is an urgent need to develop accurate methods to predict the post-treatment T (pT) stage. The purpose of this study was to evaluate ...
Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics.
Technology in cancer research & treatment
Jan 1, 2024
This study aimed to develop an automated classification framework for distinguishing between cervical cancer tumor and normal uterine tissue, leveraging CT images for radiomics feature extraction. We retrospectively analyzed CT images from 117 cervic...
Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.
Advances in experimental medicine and biology
Jan 1, 2024
This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative car...
Machine Learning and Radiomics in Gliomas.
Advances in experimental medicine and biology
Jan 1, 2024
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...
Predicting Bone Marrow Metastasis in Neuroblastoma: An Explainable Machine Learning Approach Using Contrast-Enhanced Computed Tomography Radiomics Features.
Technology in cancer research & treatment
Jan 1, 2024
PURPOSE: To predict bone marrow metastasis in neuroblastoma using contrast-enhanced computed tomography (CECT) radiomics features and explainable machine learning.
Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI.
Technology in cancer research & treatment
Jan 1, 2024
PURPOSE: Our study aimed to investigate the potential of radiomics with DCE-MRI for predicting Ki-67 expression in invasive ductal breast cancer.
An XGBoost Machine Learning Based Model for Predicting Ki-67 Value ≥ 15% in TNM Stage Primary Breast Cancer Receiving Neoadjuvant Chemotherapy Using Clinical Data and Delta-Radiomic Features on Ultrasound Images and Overall Survival Analysis: A 5-Year Postoperative Follow-Up Study.
Technology in cancer research & treatment
Jan 1, 2024
To establish a model based on clinical and delta-radiomic features within ultrasound images using XGBoost machine learning to predict proliferation-associated nuclear antigen Ki-67 value ≥ 15% in TNM stage primary breast cancer (BC). Data were coll...
[Advances in artificial intelligence-assisted MRI radiomics in the diagnosis and treatment of prostate cancer].
Zhonghua nan ke xue = National journal of andrology
Jan 1, 2024
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
Jan 1, 2024
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
Jan 1, 2024
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...
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