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Objective: To investigate whether T2-weighted imaging (T2WI)-based intratumoral and peritumoral radiomics can predict extranodal extension (ENE) and prognosis in patients with resectable rectal cancer., Methods: One hundred sixty-seven patients with resectable rectal cancer including T3T4N + cases were prospectively included. Radiomics features were extracted from intratumoral, peritumoral 3 mm, and peritumoral-mesorectal fat on T2WI images. Least absolute shrinkage and selection operator regression were used for feature selection. A radiomics signature score (Radscore) was built with logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of each Radscore. A clinical-radiomics nomogram was constructed by the most predictive radiomics signature and clinical risk factors. A prognostic model was constructed by Cox regression analysis to identify 3-year recurrence-free survival (RFS)., Results: Age, cT stage, and lymph node-irregular border and/or adjacent fat invasion were identified as independent clinical risk factors to construct a clinical model. The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and independent clinical risk factors achieved a better AUC than the clinical model in the training (0.799 vs. 0.736) and validation cohorts (0.723 vs. 0.667). Nomogram-based ENE (hazard ratio [HR] = 2.625, 95% CI = 1.233-5.586, p = 0.012) and extramural vascular invasion (EMVI) (HR = 2.523, 95% CI = 1.247-5.106, p = 0.010) were independent risk factors for predicting 3-year RFS. The prognostic model constructed by these two indicators showed good performance for predicting 3-year RFS in the training (AUC = 0.761) and validation cohorts (AUC = 0.710)., Conclusion: The nomogram incorporating intratumoral and peritumoral 3 mm Radscore and clinical risk factors could predict preoperative ENE. Combining nomogram-based ENE and MRI-reported EMVI may be useful in predicting 3-year RFS., Critical Relevance Statement: A clinical-radiomics nomogram could help preoperative predict ENE, and a prognostic model constructed by the nomogram-based ENE and MRI-reported EMVI could predict 3-year RFS in patients with resectable rectal cancer., Key Points: • Intratumoral and peritumoral 3 mm Radscore showed the most capability for predicting ENE. • Clinical-radiomics nomogram achieved the best predictive performance for predicting ENE. • Combining clinical-radiomics based-ENE and EMVI showed good performance for 3-year RFS., (© 2024. The Author(s).)

Purpose: To build computed tomography enterography (CTE)-based multiregional radiomics model for distinguishing Crohn's disease (CD) from intestinal tuberculosis (ITB)., Materials and Methods: A total of 105 patients with CD and ITB who underwent CTE were retrospectively enrolled. Volume of interest segmentation were performed on CTE and radiomic features were obtained separately from the intestinal wall of lesion, the largest lymph node (LN), and region surrounding the lesion in the ileocecal region. The most valuable radiomic features was selected by the selection operator and least absolute shrinkage. We established nomogram combining clinical factors, endoscopy results, CTE features, and radiomic score through multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to compare the performance of the models., Results: The clinical-radiomic combined model comprised of four variables including one radiomic signature from intestinal wall, one radiomic signature from LN, involved bowel segments on CTE, and longitudinal ulcer on endoscopy. The combined model showed good diagnostic performance with an area under the ROC curve (AUC) of 0.975 (95% CI 0.953-0.998) in the training cohort and 0.958 (95% CI 0.925-0.991) in the validation cohort. The combined model showed higher AUC than that of the clinical model in cross-validation set (0.958 vs. 0.878, P = 0.004). The DCA showed the highest benefit for the combined model., Conclusion: Clinical-radiomic combined model constructed by combining CTE-based radiomics from the intestinal wall of lesion and LN, endoscopy results, and CTE features can accurately distinguish CD from ITB., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

To compare the ability of CT angiography (CTA) obstruction score and perfusion defect score on dual energy CT perfusion imaging (DEPI) for clinical risk stratification of patients with acute pulmonary embolism (PE). 55 patients diagnosed as acute PE either by CTA or DEPI were retrospectively enrolled. Patients were grouped into high-, intermediate-, and low-risk groups in accordance to the renewed guidelines of 2014. Consistency between DEPI and CTA in diagnosis of PE were assessed. Correlations between CT parameters and right-to-left ventricular (RV/LV) diameter ratio were evaluated. Difference of CTA obstruction score and perfusion defect score across three groups with different risks were analyzed. The consistent rate of DEPI with CTA was 75.4%, and the Kappa value was 0.412 (p = 0.000). 44.3% of partially obstructive PE showed on CTA did not lead to perfusion defect on DEPI. Perfusion defect score was significantly correlated with CTA obstruction score and with RV/LV (r = 0.622 and 0.599, respectively, p < 0.001), and CTA obstruction score had lower correlation with RV/LV (r = 0.403, p = 0.003). Perfusion defect score could distinguish low- from intermediate-risk groups (p = 0.011). However, CTA obstruction score could not distinguish the two groups (p = 0.149). DEPI had fine consistency with CTA to diagnose acute PE and offered additional information of physiologic changes. Comparing with CTA obstruction score, perfusion defect score could better correlate with right ventricular dysfunction, and could be a more promising biomarker for clinical risk stratification.