Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to detect visually. Underlying tumor biology can change physical properties of tissues, which affect patterns of image pixels and radiomics features. The main advantage of radiomics is that it can characterize the whole tumor non-invasively, even after a single sampling from an image. Therefore, it can be linked to a "digital biopsy". Physicians need to know about radiomics features to determine how their values correlate with the appearance of lesions and diseases. Indeed, physicians need practical references to conceive of basics and concepts of each radiomics feature without knowing their sophisticated mathematical formulas. In this review, commonly used radiomics features are illustrated with practical examples to help physicians in their routine diagnostic procedures.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.cmpb.2021.106609 | DOI Listing |
Front Oncol
January 2025
Departments of Ultrasound, Jiading District Central Hospital Affiliated Shanghai University of Medicine &Health Sciences, Shanghai, China.
Background: Skip lymph node metastasis (SLNM) in papillary thyroid cancer (PTC) involves cancer cells bypassing central nodes to directly metastasize to lateral nodes, often undetected by standard preoperative ultrasonography. Although multiple models exist to identify SLNM, they are inadequate for clinically node-negative (cN0) patients, resulting in underestimated metastatic risks and compromised treatment effectiveness. Our study aims to develop and validate a machine learning (ML) model that combines elastography radiomics with clinicopathological data to predict pre-surgical SLNM risk in cN0 PTC patients with increased risk of lymph node metastasis (LNM), improving their treatment strategies.
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Purpose: To create a system to enable the identification of histological variants of bladder cancer in a simple, efficient, and noninvasive manner.
Material And Methods: In this multicenter diagnostic study, we retrospectively collected basic information and CT images about the patients concerned from three hospitals. An interactive deep learning-based bladder cancer image segmentation framework was constructed using the Swin UNETR algorithm for further features extraction.
J Pain Res
January 2025
Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People's Republic of China.
Purpose: To investigate whether functional radiomic features in bilateral hippocampi can identify the cognitively impaired patients from low-back-related leg pain (LBLP).
Patients And Methods: For this retrospective study, a total of 95 clinically definite LBLP patients (40 cognitively impaired patients and 45 cognitively preserved patients) were included, and all patients underwent functional MRI and clinical assessments. After calculating the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) imaging, the radiomic features (n = 819) of bilateral hippocampi were extracted from these images, respectively.
J Med Imaging (Bellingham)
January 2025
Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.
Purpose: We investigated the feasibility and advantages of using non-contrast CT calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and its association with major adverse cardiovascular events (MACE). PCAT features from coronary computed tomography angiography (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. If PCAT in CTCS images can be similarly analyzed, it would avoid this issue and enable its inclusion in formal risk assessment from readily available, low-cost CTCS images.
View Article and Find Full Text PDFHeliyon
January 2025
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Previous studies mostly use single-type features to establish a prediction model. We aim to develop a comprehensive prediction model that effectively identify patients with poor prognosis for single hepatocellular carcinoma (HCC) based on artificial intelligence (AI). : 236 single HCC patients were studied to establish a comprehensive prediction model.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!