Whole-body MRI in oncology: A comprehensive review.

Whole-Body Magnetic Resonance Imaging (WB-MRI) has cemented its position as a pivotal tool in oncological diagnostics. It offers unparalleled soft tissue contrast resolution and the advantage of sidestepping ionizing radiation. This review explores the diverse applications of WB-MRI in oncology. We discuss its transformative role in detecting and diagnosing a spectrum of cancers, emphasizing conditions like multiple myeloma and cancers with a proclivity for bone metastases. WB-MRI's capability to encompass the entire body in a singular scan has ushered in novel paradigms in cancer screening, especially for individuals harboring hereditary cancer syndromes or at heightened risk for metastatic disease. Additionally, its contribution to the clinical landscape, aiding in the holistic management of multifocal and systemic malignancies, is explored. The article accentuates the technical strides achieved in WB-MRI, its myriad clinical utilities, and the challenges in integration into standard oncological care. In essence, this review underscores the transformative potential of WB-MRI, emphasizing its promise as a cornerstone modality in shaping the future trajectory of cancer diagnostics and treatment.

MRI-based nomograms and radiomics in presurgical prediction of extraprostatic extension in prostate cancer: a systematic review.

PURPOSE: Prediction of extraprostatic extension (EPE) is essential for accurate surgical planning in prostate cancer (PCa). Radiomics based on magnetic resonance imaging (MRI) has shown potential to predict EPE. We aimed to evaluate studies proposing MRI-based nomograms and radiomics for EPE prediction and assess the quality of current radiomics literature. METHODS: We used PubMed, EMBASE, and SCOPUS databases to find related articles using synonyms for MRI radiomics and nomograms to predict EPE. Two co-authors scored the quality of radiomics literature using the Radiomics Quality Score (RQS). Inter-rater agreement was measured using the intraclass correlation coefficient (ICC) from total RQS scores. We analyzed the characteristic s of the studies and used ANOVAs to associate the area under the curve (AUC) to sample size, clinical and imaging variables, and RQS scores. RESULTS: We identified 33 studies-22 nomograms and 11 radiomics analyses. The mean AUC for nomogram articles was 0.783, and no significant associations were found between AUC and sample size, clinical variables, or number of imaging variables. For radiomics articles, there were significant associations between number of lesions and AUC (p < 0.013). The average RQS total score was 15.91/36 (44%). Through the radiomics operation, segmentation of region-of-interest, selection of features, and model building resulted in a broader range of results. The qualities the studies lacked most were phantom tests for scanner variabilities, temporal variability, external validation datasets, prospective designs, cost-effectiveness analysis, and open science. CONCLUSION: Utilizing MRI-based radiomics to predict EPE in PCa patients demonstrates promising outcomes. However, quality improvement and standardization of radiomics workflow are needed.