Prediction of brain invasion pre-operatively rather than postoperatively would contribute to the selection of surgical techniques, predicting meningioma grading and prognosis. Here, we aimed to predict the risk of brain invasion in meningioma pre-operatively using a nomogram by incorporating radiomic and clinical features. In this case-control study, 1728 patients from Beijing Tiantan Hospital (training cohort: n = 1070) and Lanzhou University Second Hospital (external validation cohort: n = 658) were diagnosed with meningiomas by histopathology. Radiomic features were extracted from the T1-weighted post-contrast and T2-weighted magnetic resonance imaging. The least absolute shrinkage and selection operator was used to select the most informative features of different modalities. The support vector machine algorithm was used to predict the risk of brain invasion. Furthermore, a nomogram was constructed by incorporating radiomics signature and clinical risk factors, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Sixteen features were significantly correlated with brain invasion. The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0•857 (95% CI, 0•831–0•887) and 0•819 (95% CI, 0•775–0•863) and sensitivities of 72•8% and 90•1% in the training and validation cohorts, respectively. Our clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas. This work was supported by the 10.13039/501100001809National Natural Science Foundation of China (81772006, 81922040); the 10.13039/501100004739Youth Innovation Promotion Association CAS (grant numbers 2019136); special fund project for doctoral training program of 10.13039/100012899Lanzhou University Second Hospital (grant numbers YJS-BD-33).

The purpose of this study was to investigate the utility of combining clinical and spectral computed tomography (CT) parameters for the preoperative evaluation of lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancers (GCs). Patients with gastric adenocarcinoma who underwent spectral-CT examination were retrospectively examined. All diagnoses were confirmed by pathology, and the patients were divided into positive and negative groups based on LVI/PNI occurrence. Clinical characteristics, including demographic information, serum tumor markers, and gastroscopic pathological information, were collected. The effective atomic number (Z_eff), iodine concentration (IC), and water concentration were measured in the arterial (AP) and venous phase (VP). Differences between the two groups were searched for using independent sample t-test, Mann-Whitney U test, or chi-square (χ²) test and diagnostic performances of the different variables were evaluated using receiver operating characteristic (ROC) curve. A total of 121 patients (96 men, 25 women; mean age: 59 ± 8.7 [SD] years, range: 36-82 years) with gastric adenocarcinoma were included in the study. The serum level of the tumor marker CA125, as well as Z_eff and IC in the LVI/PNI-positive group, were significantly higher than in the negative group, and the histological grade and Borrmann type differed between the two groups (all P < 0.05). The discriminating capability analysis demonstrated that CA125 exhibited a favorable performance, and the VP parameters' diagnostic efficacy was superior to that of the AP parameters. The efficacy of the combination of clinical and spectral-CT parameters was superior to that of individual parameters (all AUC > 0.85). The clinical parameters combined with Z_eff and IC in the AP and VP exhibited a high evaluation efficacy (AUC = 0.890 [95% CI: 0.826-0.955]; F₁ score = 0.888; accuracy = 84.3% [102/121; 95% CI: 76.7-89.8]; sensitivity = 86.2% [75/87; 95% CI: 76.8-92.4]; specificity = 79.4% [27/34; 95% CI: 61.6-90.1]). Clinical and spectral-CT parameters exhibit considerable capabilities in the preoperative evaluation of LVI and PNI in GCs. The combination of clinical and spectral-CT parameters effectively predicts LVI and PNI in GCs.