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Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment. Radiogenomics is an emerging area in radiomics that investigates the association between imaging characteristics and gene expression profiles. There are an increasing number of research publications using different radiomics approaches without a clear consensus on which method works best. We aim to describe the workflow of radiomics along with a guide of what to expect when starting a radiomics-based research project.
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http://dx.doi.org/10.1016/j.acra.2024.01.024 | DOI Listing |
BMC Cancer
February 2025
Department of Radiology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Objectives: This study was designed to develop and validate models based on delta intratumoral and peritumoral radiomics features from breast masses on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the prediction of axillary lymph node (ALN) pathological complete response (pCR) after neoadjuvant therapy (NAT) in patients with breast cancer (BC).
Methods: We retrospectively collected data from 187 BC patients with ALN metastases. Radiomics features were extracted from the intratumoral and 3 mm-peritumoral regions on DCE-MRI at baseline and after the 2nd course of NAT to calculate delta intratumoral and peritumoral radiomics features, respectively.
Radiol Med
February 2025
School of Intelligent Medicine, China Medical University, No. 77 Puhe Road, Shenyang, Liaoning, 110122, People's Republic of China.
Background: Early and accurate identification of the metastatic tumor types of brain metastasis (BM) is essential for appropriate treatment and management.
Methods: A total of 450 patients were enrolled from two centers as a primary cohort who carry 764 BMs originated from non-small cell lung cancer (NSCLC, patient = 173, lesion = 187), small cell lung cancer (SCLC, patient = 84, lesion = 196), breast cancer (BC, patient = 119, lesion = 200), and gastrointestinal cancer (GIC, patient = 74, lesion = 181). A third center enrolled 28 patients who carry 67 BMs (NSCLC = 24, SCLC = 22, BC = 10, and GIC = 11) to form an external test cohort.
Coron Artery Dis
January 2025
Department of Cardiology.
Background: Previous reports have suggested that coronary computed tomography angiography (CCTA)-based radiomics analysis is a potentially helpful tool for assessing vulnerable plaques. We aimed to investigate whether coronary radiomic analysis of CCTA images could identify vulnerable plaques in patients with stable angina pectoris.
Methods: This retrospective study included patients initially diagnosed with stable angina pectoris.
Acad Radiol
June 2024
Radiology, Northwestern University-Feinberg School of Medicine, 800, Arkes Pavilion, 676 N St. Clair St, Chicago, IL 60611. Electronic address:
Radiomics uses advanced mathematical analysis of pixel-level information from radiologic images to extract existing information in traditional imaging algorithms. It is intended to find imaging biomarkers related to the genomics of tumors or disease patterns that improve medical care by advanced detection of tumor response patterns in tumors and to assess prognosis. Radiomics expands the paradigm of medical imaging to help with diagnosis, management of diseases and prognostication, leveraging image features by extracting information that can be used as imaging biomarkers to predict prognosis and response to treatment.
View Article and Find Full Text PDFPurpose: The aim of this study is to determine if radiomics features extracted from staging magnetic resonance (MR) images could predict 2-year long-term clinical outcome in patients with locally advanced cervical cancer (LACC) after neoadjuvant chemoradiotherapy (NACRT).
Materials And Methods: We retrospectively enrolled patients with LACC diagnosis who underwent NACRT followed by radical surgery in two different institutions. Radiomics features were extracted from pre-treatment 1.