Combined CT-Based Radiomics and Clinic-Radiological Characteristics for Preoperative Differentiation of Solitary-Type Invasive Mucinous and Non-Mucinous Lung Adenocarcinoma
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DOI: 10.2147/ijgm.s479978
Abstract: Purpose The clinical, pathological, gene expression, and prognosis of invasive mucinous adenocarcinoma (IMA) differ from those of invasive non-mucinous adenocarcinoma (INMA), but it is not easy to distinguish these two. This study aims to explore… read more here.
Keywords: adenocarcinoma; clinic radiological; ima; radiological characteristics ... See more keywords
MRI radiomics-based machine learning model integrated with clinic-radiological features for preoperative differentiation of sinonasal inverted papilloma and malignant sinonasal tumors
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DOI: 10.3389/fonc.2022.1003639
Abstract: Objective To explore the best MRI radiomics-based machine learning model for differentiation of sinonasal inverted papilloma (SNIP) and malignant sinonasal tumor (MST), and investigate whether the combination of radiomics features and clinic–radiological features can produce… read more here.
Keywords: radiological features; sinonasal; machine learning; clinic radiological ... See more keywords
Radiomics Nomogram Improves the Prediction of Epilepsy in Patients With Gliomas
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DOI: 10.3389/fonc.2022.856359
Abstract: Purpose To investigate the association between clinic-radiological features and glioma-associated epilepsy (GAE), we developed and validated a radiomics nomogram for predicting GAE in WHO grade II~IV gliomas. Methods This retrospective study consecutively enrolled 380 adult… read more here.
Keywords: combined model; oncology; radiomics nomogram; cohort ... See more keywords