Background: About 20%-40% of patients diagnosed with ductal carcinoma in situ (DCIS) by core needle biopsy (CNB) will develop invasive cancer at the time of excision. Improving the preoperative diagnosis of DCIS is important for surgical planning. Purpose: To establish an MRI-based radiomics nomogram for preoperatively evaluating the upstaging of DCIS patients and help with risk stratification. Study Type: Retrospective. Population: A total of 227 patients (50.5 AE 9.7 years; 67 upstaged DCIS) were divided into training (n = 109), internal (n = 47), and external (n = 71) validation cohort. Field Strength/Sequence: 1.5-T or 3-T, dynamic contrast-enhanced (DCE) imaging, and diffusion-weighted imaging (DWI). Assessment: DCIS lesions were manually segmented using ITK-SNAP software and 1304 radiomic features were extracted from DCE, DWI, and apparent diffusion coef-ficient (ADC) maps, respectively. A radscore was calculated by a random forest algo-rithm based on DCIS upstaging-related radiomic features, which selected by a coarse-to-fine method including interclass correlation coefficient, single-factor anal-ysis, and the least absolute shrinkage and selection operator (LASSO) method. Uni-variate and multivariate logistic regression was used to analyze the independent risk factors, including age, location, lesion size, estrogen receptor (ER) status, and other clinico-pathologic factors. Finally, Mann-Whitney U tests were performed to com-pare the differences in radscore between low/intermediate and high nuclear grade groups for pure DCIS patients. Statistical Tests: Student's t-tests or Mann-Whitney U tests, chi-square-tests, or Fisher's-tests, univariate and multivariate logistic regression analysis, calibration curve, Youden index, the area under the curve (AUC), Delong test, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) analyses. Results: Eight important radiomic features (two from ADC, three from DWI, and three from DCE) were selected for calculating radscore. Clinical model including age and ER was established with AUCs of 0.747 and 0.738 in the internal and external validation cohorts, respectively. A combined model integrating age, estrogen receptor (ER), and radscore were also constructed with AUCs of 0.887 and 0.881. Further subgroup analysis showed that pure DCIS patients with different nuclear grade have significant differences in radscore. Data Conclusion: Multisequence MRI radiomics may preoperatively evaluate the upstaging of DCIS and might provide personalized image-based clinical decision support. Evidence Level: 4. Technical Efficacy: Stage 2.

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Purpose To develop and validate a preoperative enhanced CT-based radiomics nomogram for prediction of recurrence or metastasis in patients with high-risk gastrointestinal stromal tumor (GIST). Method 100 high-risk GIST patients (training cohort: 60; validation cohort: 40) with preoperative enhanced CT images were enrolled. The radiomics features were extracted and a risk score was built using least absolute shrinkage and selection operator (LASSO)-Cox model. The clinicopathological factors were analyzed and nomogram was established with and without radiomics risk score. The concordance index (C-index), calibration plot, and decision curve analysis (DCA) were used to evaluate the performance of the radiomics nomograms. Result 11 radiomics features associated with recurrence or metastasis were selected. The risk score was calculated and significantly associated with disease-free survival (DFS) in both training and validation group. Cox regression analysis showed that Ki67 was an independent risk factor for DFS (p = 0.004, HR 4.615, 95%CI 1.624–13.114). The combined radiomics nomogram, which integrated the radiomics risk score and significant clinicopathological factors, showed good performance in predicting DFS, with C-index of 0.832 (95% CI:0.761–0.903), which was better than the clinical nomogram (C-index 0.769, 95% CI: 0.679–0.859) in training cohort. The calibration curves and the decision curve analysis (DCA) plot suggested satisfying accuracy and clinical utility of the model. Conclusion The CT-based radiomics nomogram, combined with the clinicopathological factors and risk score, have good potential to assess the recurrence or metastasis of patients with high-risk GIST.