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Background Radiomics has shown promising results in the diagnosis, efficacy, and prognostic assessments of multiple myeloma (MM). However, little evidence exists on the utility of radiomics in predicting a high‐risk cytogenetic (HRC) status in MM. Purpose To develop and test a magnetic resonance imaging (MRI)‐based radiomics model for predicting an HRC status in MM patients. Study Type Retrospective. Population Eighty‐nine MM patients (HRC [n: 37] and non‐HRC [n: 52]). Field Strength/Sequence A 3.0 T; fast spin‐echo (FSE): T1‐weighted image (T1WI) and fat‐suppression T2WI (FS‐T2WI). Assessment Overall, 1409 radiomics features were extracted from each volume of interest drawn by radiologists. Three sequential feature selection steps—variance threshold, SelectKBest, and least absolute shrinkage selection operator—were repeated 10 times with 5‐fold cross‐validation. Radiomics models were constructed with the top three frequency features of T1WI/T2WI/two‐sequence MRI (T1WI and FS‐T2WI). Radiomics models, clinical data (age and visually assessed MRI pattern), or radiomics combined with clinical data were used with six classifiers to distinguish between HRC and non‐HRC statuses. Six classifiers used were support vector machine, random forest, logistic regression (LR), decision tree, k‐nearest neighbor, and XGBoost. Model performance was evaluated with area under the curve (AUC) values. Statistical Tests Mann–Whitney U‐test, Chi‐squared test, Z test, and DeLong method. Results The LR classifier performed better than the other classifiers based on different data (AUC: 0.65–0.82; P < 0.05). The two‐sequence MRI models performed better than the other data models using different classifiers (AUC: 0.68–0.82; P < 0.05). Thus, the LR two‐sequence model yielded the best performance (AUC: 0.82 ± 0.02; sensitivity: 84.1%; specificity: 68.1%; accuracy: 74.7%; P < 0.05). Conclusion The LR‐based machine learning method appears superior to other classifier methods for assessing HRC in MM. Radiomics features based on two‐sequence MRI showed good performance in differentiating HRC and non‐HRC statuses in MM. Evidence Level 3 Technical Efficacy Stage 2

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This study aims to develop and validate an artificial intelligence model based on deep learning to predict early hematoma enlargement (HE) in patients with intracerebral hemorrhage. A total of 1,899 noncontrast computed tomography (NCCT) images of cerebral hemorrhage patients were retrospectively analyzed to establish a predicting model and 1,117 to validate the model. And a total of 118 patients with intracerebral hemorrhage were selected based on inclusion and exclusion criteria so as to validate the value of the model for clinical prediction. The baseline noncontrast computed tomography images within 6 h of intracerebral hemorrhage onset and the second noncontrast computed tomography performed at 24 ± 3 h from the onset were used to evaluate the prediction of intracerebral hemorrhage growth. In validation dataset 1, the AUC was 0.778 (95% CI, 0.768–0.786), the sensitivity was 0.818 (95% CI, 0.790–0.843), and the specificity was 0.601 (95% CI, 0.565–0.632). In validation dataset 2, the AUC was 0.780 (95% CI, 0.761–0.798), the sensitivity was 0.732 (95% CI, 0.682–0.788), and the specificity was 0.709 (95% CI, 0.658–0.759). The sensitivity of intracerebral hemorrhage hematoma expansion as predicted by an artificial intelligence imaging system was 89.3%, with a specificity of 77.8%, a positive predictive value of 55.6%, a negative predictive value of 95.9%, and a Yoden index of 0.671, which were much higher than those based on the manually labeled noncontrast computed tomography signs. Compared with the existing prediction methods through computed tomographic angiography (CTA) image features and noncontrast computed tomography image features analysis, the artificial intelligence model has higher specificity and sensitivity in the prediction of early hematoma enlargement in patients with intracerebral hemorrhage.