European radiology
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To evaluate accuracy and inter-observer variability using Vesical Imaging-Reporting and Data System (VI-RADS) for discrimination between non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). ⋯ • Traditionally, the local staging of bladder cancer relies on transurethral resection of bladder tumor. • However, transurethral resection of bladder tumor carries a significant risk of understaging a cancer; therefore, more accurate, faster, and non-invasive staging techniques are needed to improve outcomes. • Multiparametric MRI has proved to be the best imaging modality for local staging; therefore, its use in suitable patients has the potential to expedite radical treatment when necessary and non-invasive diagnosis in patients with poor fitness.
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The last few decades have witnessed tremendous technological developments in image-based biomarkers for tumor quantification and characterization. Initially limited to manual one- and two-dimensional size measurements, image biomarkers have evolved to harness developments not only in image acquisition technology but also in image processing and analysis algorithms. At the same time, clinical validation remains a major challenge for the vast majority of these novel techniques, and there is still a major gap between the latest technological developments and image biomarkers used in everyday clinical practice. ⋯ Thus, the goal of the present article is to review the current state of the art for image biomarkers and their use for characterization and predictive quantification of solid tumors. Beginning with an overview of validated imaging biomarkers in current clinical practice, we proceed to a review of AI-based methods for tumor characterization, such as radiomics-based approaches and deep learning. Key Points• Recent years have seen tremendous technological developments in image-based biomarkers for tumor quantification and characterization.• Image-based biomarkers can be used on an ongoing basis, in a non-invasive (or mildly invasive) way, to monitor the development and progression of the disease or its response to therapy.• We review the current state of the art for image biomarkers, as well as the recent developments in artificial intelligence (AI) algorithms for image processing and analysis.
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To compare whole-body MRI (WB-MRI) at 1.5/3T and bone scintigraphy in the skeletal staging of Ewing sarcoma (ES) of bone. ⋯ • Whole-body MRI is more sensitive than bone scintigraphy in detecting skeletal metastases in Ewing sarcoma of bone. • Whole-body MRI can safely replace bone scintigraphy for staging of the skeleton, with the acknowledgement of the possibility of missing a clinically occult skull vault metastasis.
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To construct a radiomics nomogram for the individualized estimation of the survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI, which could facilitate the clinical decision-making for GBM patients. ⋯ • Non-invasive survival stratification of GBM patients can be obtained with a radiomics nomogram. • The proposed nomogram constructed by radiomics signature selected from 4000 radiomics features, combined with independent clinical risk factors such as age, Karnofsky performance status, and treatment strategy. • The proposed radiomics nomogram exhibited good calibration and discrimination for survival stratification of GBM patients in both training (C-index, 0.971) and validation (C-index, 0.974) sets.
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To explore and evaluate the feasibility of radiomics in stratifying nasopharyngeal carcinoma (NPC) into distinct survival subgroups through multi-modalities MRI. ⋯ • Radiomics phenotype of MRI revealed the subtype of nasopharyngeal carcinoma (NPC) patients with distinct survival patterns. • The slice-wise analysis method on MRI helps to stratify patients and provides superior prognostic performance over the TNM staging method. • Risk estimation using the highest risk among slices performed better than using the majority risk in prognosis.