[en] X-ray luminescence tomography (XLT) is an imaging technology based on X-ray-excitable materials. The main purpose of this paper is to obtain quantitative luminescence concentration using the structural information of the X-ray computed tomography (XCT) in the hybrid cone beam XLT/XCT system. A multi-wavelength luminescence cone beam XLT method with the structural a priori information is presented to relieve the severe ill-posedness problem in the cone beam XLT. The nanophosphors and phantom experiments were undertaken to access the linear relationship of the system response. Then, an in vivo mouse experiment was conducted. The in vivo experimental results show that the recovered concentration error as low as 6.67% with the location error of 0.85 mm can be achieved. The results demonstrate that the proposed method can accurately recover the nanophosphor inclusion and realize the quantitative imaging

[en] The aim of this study was to develop a radiomics nomogram by combining the optimized radiomics signatures extracted from 2D and/or 3D CT images and clinical predictors to assess the overall survival of patients with non-small cell lung cancer (NSCLC). One training cohort of 239 and two validation datasets of 80 and 52 NSCLC patients were enrolled in this study. Nine hundred seventy-five radiomics features were extracted from each patient’s 2D and 3D CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate a radiomics signature. Cox hazard survival analysis and Kaplan-Meier were performed in both cohorts. The radiomics nomogram was developed by integrating the optimized radiomics signature and clinical predictors, its calibration and discrimination were evaluated. The radiomics signatures were significantly associated with NSCLC patients’ survival time. The signature derived from the combined 2D and 3D features showed a better prognostic performance than those from 2D or 3D alone. Our radiomics nomogram integrated the optimal radiomics signature with clinical predictors showed a significant improvement in the prediction of patients’ survival compared with clinical predictors alone in the validation cohort. The calibration curve showed predicted survival time was very close to the actual one. The radiomics signature from the combined 2D and 3D features further improved the predicted accuracy of survival prognosis for the patients with NSCLC. Combination of the optimal radiomics signature and clinical predictors performed better for individualied survival prognosis estimation in patients with NSCLC. These findings might affect trearment strategies and enable a step forward for precise medicine.

[en] A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA. In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression. Four anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area (p < 0.001), the narrowest aortic diameter (CoA diameter) indexed to height (p < 0.001), the diameter of the descending aorta at the diaphragmatic level (p < 0.001) and weight (p = 0.005). With these features, accuracy of 88.6% and 90.2%, sensitivity of 65.0% and 72.1%, and specificity of 92.9% and 100% were obtained for classifying the CoA severity in the non-PDA and PDA groups, respectively. Moreover, CoA diameter indexed to weight was associated with the risk of re-coarctation. CoA severity can be evaluated by using LDA with anatomical features. When quantifying the severity of CoA and risk of re-coarctation, both anatomical alternations at the CoA site and the growth of the patients need to be considered.

[en] To develop and identify a MRI-based radiomics nomogram for the preoperative prediction of parametrial invasion (PMI) in patients with early-stage cervical cancer (ECC). All 137 patients with ECC (FIGO stages IB–IIA) underwent T2WI and DWI scans before radical hysterectomy surgery. The radiomics signatures were calculated with the radiomics features which were extracted from T2WI and DWI and selected by the least absolute shrinkage and selection operation regression. The support vector machine (SVM) models were built using radiomics signatures derived from T2WI and joint T2WI and DWI respectively to evaluate the performance of radiomics signatures for distinguishing patients with PMI. A radiomics nomogram was drawn based on the radiomics signatures with a better performance, patient’s age, and pathological grade; its discrimination and calibration performances were estimated. For T2WI and joint T2WI and DWI, the radiomics signatures yielded an AUC of 0.797 (95% CI, 0.682–0.911) vs 0.946 (95% CI, 0.899–0.994), and 0.780 (95% CI, 0.641–0.920) vs 0.921 (95% CI, 0.832–1) respectively in the primary and validation cohorts. The radiomics nomogram, integrating the radiomics signatures from joint T2WI and DWI, patient’s age, and pathological grade, showed excellent discrimination, with C-index values of 0.969 (95% CI, 0.933–1) and 0.941 (95% CI, 0.868–1) in the primary and validation cohorts, respectively. The calibration curve showed a good agreement. The radiomics nomogram performed well for the preoperative prediction of PMI in patients with ECC and may be used as a supplementary tool to provide individualized treatment plans for patients with ECC.

[en] ObjectiveTo explore an MRI-based radiomics nomogram for preoperatively predicting of pelvic lymph node (PLN) metastasis in patients with early-stage cervical cancer (ECC).