BackgroundPreoperative differentiation between malignant and benign soft‐tissue masses is important for treatment decisions.Purpose/HypothesisTo construct/validate a radiomics‐based machine method for differentiation between malignant and benign soft‐tissue masses.Study TypeRetrospective.PopulationIn all, 206 cases.Field Strength/SequenceThe T1 sequence was acquired with the following range of parameters: relaxation time / echo time (TR/TE), 352–550/2.75–19 msec. The T2 sequence was acquired with the following parameters: TR/TE, 700–6370/40–120 msec. The data were divided into a 3.0T training cohort, a 1.5T MR validation cohort, and a 3.0T external validationcohort.AssessmentTwelve machine‐learning methods were trained to establish classification models to predict the likelihood of malignancy of each lesion. The data of 206 cases were separated into a training set (n = 69) and two validation sets (n = 64, 73, respectively).Statistical Tests1) Demographic characteristics: a one‐way analysis of variance (ANOVA) test was performed for continuous variables as appropriate. The χ2 test or Fisher's exact test was performed for comparing categorical variables as appropriate. 2) The performance of four feature selection methods (least absolute shrinkage and selection operator [LASSO], Boruta, Recursive feature elimination [RFE, and minimum redundancy maximum relevance [mRMR]) and three classifiers (support vector machine [SVM], generalized linear models [GLM], and random forest [RF]) were compared for selecting the likelihood of malignancy of each lesion. The performance of the radiomics model was assessed using area under the receiver‐operating characteristic curve (AUC) and accuracy (ACC) values.ResultsThe LASSO feature method + RF classifier achieved the highest AUC of 0.86 and 0.82 in the two validation cohorts. The nomogram achieved AUCs of 0.96 and 0.88, respectively, in the two validation sets, which was higher than that of the radiomic algorithm in the two validation sets and clinical model of the validation 1 set (0.92, 0.88 respectively). The accuracy, sensitivity, and specificity of the radiomics nomogram were 90.5%, 100%, and 80.6%, respectively, for validation set 1; and 80.8%, 75.8%, and 85.0% for validation set 2.Data ConclusionA machine‐learning nomogram based on radiomics was accurate for distinguishing between malignant and benign soft‐tissue masses.Evidence Level3Technical EfficacyStage 2 J. Magn. Reson. Imaging 2020;52:873–882.

Background Preoperative prediction of soft tissue sarcoma (STS) grade is important for treatment decisions. Therefore, formulation an STS grade model is strongly needed. Purpose To develop and test an magnetic resonance imaging (MRI)‐based radiomics nomogram for predicting the grade of STS (low‐grade vs. high grade). Study Type Retrospective Population One hundred and eighty patients with STS confirmed by pathologic results at two independent institutions were enrolled (training set, N = 109; external validation set, N = 71). Field Strength/Sequence Unenhanced T1‐weighted (T1WI) and fat‐suppressed T2‐weighted images (FS‐T2WI) were acquired at 1.5 T and 3.0 T. Assessment Clinical‐MRI characteristics included age, gender, tumor‐node‐metastasis (TNM) stage, American Joint Committee on Cancer (AJCC) stage, progression‐free survival (PFS), and MRI morphological features (ie, margin). Radiomics feature extraction were performed on T1WI and FS‐T2WI images by minimum redundancy maximum relevance (MRMR) method and least absolute shrinkage and selection operator (LASSO) algorithm. The selected features constructed three radiomics signatures models (RS‐T1， RS‐FST2, and RS‐Combined). Univariate and multivariate logistic regression analysis were applied for screening significant risk factors. Radiomics nomogram was constructed by incorporating the radiomics signature and risk factors. Statistical Tests Clinical‐MRI characteristics were performed by a univariate analysis. Model performances (discrimination, calibration, and clinical usefulness) were validated in the external validation set. The RS‐T1 model, RS‐FST2 model, and RS‐Combined model had an area under curves (AUCs) of 0.645, 0.641, and 0.829, respectively, in the external validation set. The radiomics nomogram, incorporating significant risk factors and the RS‐Combined model had AUCs of 0.916 (95%CI, 0.866–0.966, training set) and 0.879 (95%CI, 0.791–0.967, external validation set), and demonstrated good calibration and good clinical utility. Data Conclusion The proposed noninvasive MRI‐based radiomics models showed good performance in differentiating low‐grade from high‐grade STSs. Level of Evidence 3 Technical Efficacy Stage 2

Background: Preoperative discrimination between malignant and benign sinonasal tumors is important for treatment plan selection. Purpose: To build and validate a radiomic nomogram for preoperative discrimination between malignant and benign sinonasal tumors. Study Type: Retrospective. Population: In all, 197 patients with histopathologically confirmed 84 benign and 113 malignant sinonasal tumors. Field Strength/Sequences: Fast-spin-echo (FSE) T 1-weighted and fat-suppressed FSE T 2-weighted imaging on a 1.5T and 3.0T MRI. Assessment: T 1 and fat-suppressed T 2-weighted images were selected for feature extraction. The least absolute shrinkage selection operator (LASSO) algorithm was applied to establish a radiomic score. Multivariate logistic regression analysis was applied to determine independent risk factors, and the radiomic score was combined to build a radiomic nomogram. The nomogram was assessed in a training dataset (n = 138/3.0T MRI) and tested in a validation dataset (n = 59/1.5T MRI). Statistical Tests: Independent t-test or Wilcoxon's test, chi-square-test, or Fisher's-test, univariate analysis, LASSO, multivariate logistic regression analysis, area under the curve (AUC), Hosmer-Lemeshow test, decision curve, and the Delong test. Results: In the validation dataset, the radiomic nomogram could differentiate benign from malignant sinonasal tumors with an AUC of 0.91. There was no significant difference in AUC between the combined radiomic score and radiomic nomogram (P > 0.05), and the radiomic nomogram showed a relatively higher AUC than the combined radiomic score. There was a significant difference in AUC between each two of the following models (the radiomic nomogram vs. the clinical model, all P < 0.001; the combined radiomic score vs. the clinical model, P = 0.0252 and 0.0035, respectively, in the training and validation datasets). The radiomic nomogram outperformed the radiomic scores and clinical model. Data Conclusion: The radiomic nomogram combining the clinical model and radiomic score is a simple, effective, and reliable method for patient risk stratification. Level of Evidence: 4 Technical Efficacy Stage: 2

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Purpose. This study was performed to determine whether diffusion-weighted imaging (DWI) plus unenhanced computed tomography (CT) of the brain increases the diagnostic value of routine magnetic resonance (MR) imaging findings of early-stage glioblastoma. Methods. Postcontrast MR images of eight unenhanced lesions that had been pathologically diagnosed as glioblastoma were retrospectively examined. The location, margin, signal intensity, and attenuation on MR imaging and CT were assessed. Results. On MR imaging, all lesions were ill-defined, small, and isointense to hypointense on T1-weighted images and hyperintense on T2-weighted images. Four patients had perilesional edema. In seven patients, DWI showed an inhomogeneous hyperintense lesion (n = 1) or isointense lesion with a hyperintense region (n = 6). On unenhanced CT, all masses presented as a hypoattenuated lesion with a hyperattenuated region (n = 7) or isoattenuated region (n = 1). The hyperattenuated region (n = 6) or isoattenuated region (n = 1) on CT appeared on DWI as an inhomogeneous hyperintense lesion (n = 1), isointense lesion with a hyperintense region (n = 3), or ring-like peritumoral hyperintensity (n = 3). Conclusions. MR imaging was the most sensitive imaging method for depicting early-stage glioblastoma. The CT finding of a hyperattenuated or isoattenuated region combined with the DWI finding of the same region containing an inhomogeneous hyperintense lesion or isointense lesion with a hyperintense region may be a specific diagnostic sign for early-stage glioblastoma. DWI plus unenhanced CT added diagnostic value to the routine MR imaging findings of early-stage glioblastoma.

BackgroundSinonasal malignant tumors (SNMTs) have a high recurrence risk, which is responsible for the poor prognosis of patients. Assessing recurrence risk in SNMT patients is a current problem.PurposeTo establish an MRI‐based radiomics nomogram for assessing relapse risk in patients with SNMT.Study TypeRetrospective.PopulationA total of 143 patients with 68.5% females (development/validation set, 98/45 patients).Field Strength/SequenceA 1.5‐T and 3‐T, fat‐suppressed fast spin echo (FSE) T2‐weighted imaging (FS‐T2WI), FSE T1‐weighted imaging (T1WI), and FSE contrast‐enhanced T1WI (T1WI + C).AssessmentThree MRI sequences were used to manually delineate the region of interest. Three radiomics signatures (T1WI and FS‐T2WI sequences, T1WI + C sequence, and three sequences combined) were built through dimensional reduction of high‐dimensional features. The clinical model was built based on clinical and MRI features. The Ki‐67‐based and tumor‐node‐metastasis (TNM) model were established for comparison. The radiomics nomogram was built by combining the clinical model and best radiomics signature. The relapse‐free survival analysis was used among 143 patients.Statistical TestsThe intraclass/interclass correlation coefficients, univariate/multivariate Cox regression analysis, least absolute shrinkage and selection operator Cox regression algorithm, concordance index (C index), area under the curve (AUC), integrated Brier score (IBS), DeLong test, Kaplan–Meier curve, log‐rank test, optimal cutoff values. A P value < 0.05 was considered statistically significant.ResultsThe T1 + C‐based radiomics signature had best prognostic ability than the other two signatures (T1WI and FS‐T2WI sequences, and three sequences combined). The radiomics nomogram had better prognostic ability and less error than the clinical model, Ki‐67‐based model, and TNM model (C index, 0.732; AUC, 0.765; IBS, 0.185 in the validation set). The cutoff values were 0.2 and 0.7 and then the cumulative risk rates were calculated.Data ConclusionA radiomics nomogram for assessing relapse risk in patients with SNMT may provide better prognostic ability than the clinical model, Ki‐67‐based model, and TNM model.Evidence Level3.Technical EfficacyStage 5.

Pyridinium 1,4-zwitterionic thiolates were regarded as powerful and versatile building blocks to prepare nitrogen-and sulfur-containing heterocycles. Herein, we reported a coppercatalyzed formal [4 + 1] annulation of pyridinium 1,4-zwitterionic thiolates and diazo compounds without any additives to access a library of trifunctionalized indolizines in good yields. Besides, isoquinolinium 1,4-zwitterionic thiolates and imidazolium 1,4zwitterionic thiolates were also applied to this formal [4 + 1] annulation reaction. Of particular note is that various functional groups such as −CO 2 R, −CO 2 NR 2 , −CF 3 , −CN, and −(O)P-(OR) 2 could be easily introduced into cycloaddition products indolizines by this strategy.

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