AIMC Topic: Radiomics
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Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer.
Annals of surgical oncology
Mar 23, 2024
BACKGROUND: Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (...
Can we predict pathology without surgery? Weighing the added value of multiparametric MRI and whole prostate radiomics in integrative machine learning models.
European radiology
Mar 20, 2024
OBJECTIVE: To test the ability of high-performance machine learning (ML) models employing clinical, radiological, and radiomic variables to improve non-invasive prediction of the pathological status of prostate cancer (PCa) in a large, single-institu...
Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study.
Academic radiology
Mar 20, 2024
RATIONALE AND OBJECTIVES: Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to expl...
Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.
BMC women's health
Mar 19, 2024
BACKGROUND: Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of i...
Machine learning approach using F-FDG-PET-radiomic features and the visibility of right ventricle F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis.
Japanese journal of radiology
Mar 16, 2024
OBJECTIVES: To investigate the usefulness of machine learning (ML) models using pretreatment F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS).
Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.
Academic radiology
Mar 15, 2024
RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cance...
Non-invasive assessment of response to transcatheter arterial chemoembolization for hepatocellular carcinoma with the deep neural networks-based radiomics nomogram.
Acta radiologica (Stockholm, Sweden : 1987)
Mar 15, 2024
BACKGROUND: Transcatheter arterial chemoembolization (TACE) is a mainstay treatment for intermediate and advanced hepatocellular carcinoma (HCC), with the potential to enhance patient survival. Preoperative prediction of postoperative response to TAC...
Artificial intelligence-based MRI radiomics and radiogenomics in glioma.
Cancer imaging : the official publication of the International Cancer Imaging Society
Mar 14, 2024
The specific genetic subtypes that gliomas exhibit result in variable clinical courses and the need to involve multidisciplinary teams of neurologists, epileptologists, neurooncologists and neurosurgeons. Currently, the diagnosis of gliomas pivots ma...
Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features.
Journal of imaging informatics in medicine
Mar 13, 2024
Breast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection an...
Radioport: a radiomics-reporting network for interpretable deep learning in BI-RADS classification of mammographic calcification.
Physics in medicine and biology
Mar 12, 2024
Generally, due to a lack of explainability, radiomics based on deep learning has been perceived as a black-box solution for radiologists. Automatic generation of diagnostic reports is a semantic approach to enhance the explanation of deep learning ra...
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