Wanqun Yang's research while affiliated with Guangdong Academy of Medical Sciences and General Hospital and other places

Objective There are no specific magnetic resonance imaging (MRI) features that distinguish pilocytic astrocytoma (PA) from adamantinomatous craniopharyngioma (ACP). In this study we compared the frequency of a novel enhancement characteristic on MRI (called the cut green pepper sign) in PA and ACP. Methods Consecutive patients with PA (n = 24) and ACP (n = 36) in the suprasellar region were included in the analysis. The cut green pepper sign was evaluated on post-contrast T1WI images independently by 2 neuroradiologists who were unaware of the pathologic diagnosis. The frequency of cut green pepper sign in PA and ACP was compared with Fisher’s exact test. Results The cut green pepper sign was identified in 50% (12/24) of patients with PA, and 5.6% (2/36) with ACP. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the cut green pepper sign for diagnosing PA were 50%, 94.4%, 85.7% and 73.9%, respectively. There was a statistically significant difference in the age of patients with PA with and without the cut green pepper sign (12.3 ± 9.2 years vs. 5.5 ± 4.4 years, p = 0.035). Conclusion The novel cut green pepper sign can help distinguish suprasellar PA from ACP on MRI.

Purpose This study evaluated the performance of multiparametric magnetic resonance imaging (MRI)–based fusion radiomics models (MMFRs) to predict telomerase reverse transcriptase (TERT) promoter mutation status and progression-free survival (PFS) in glioblastoma patients. Methods We retrospectively analysed 208 glioblastoma patients from two hospitals. Quantitative imaging features were extracted from each patient’s T1-weighted, T1-weighted contrast-enhanced, and T2-weighted preoperative images. Using a coarse-to-fine feature selection strategy, four radiomics signature models were constructed based on the three MRI sequences and their combination for TERT promoter mutation status and PFS; model performance was subsequently evaluated. Subgroup analyses were performed by the radiomics signature of TERT promoter mutation status and PFS to distinguish patients who could benefit from prolonged temozolomide chemotherapy cycles. Results TERT promoter mutation status was best predicted by MMFR, with an area under the curve (AUC) of 0.816 and 0.812 for the training and internal validation sets, respectively. The external test set also achieved stable and optimal prediction results (AUC, 0.823). MMFR better predicted patient PFS compared with the single-sequence radiomics signature in the test set (C-index, 0.643 vs 0.561 vs 0.620 vs 0.628). Subgroup analyses showed that more than six cycles of postoperative temozolomide chemotherapy were associated with improved PFS for patients in class 2 (high TERT promoter mutation and high survival rates; HR, 0.222; 95% CI, 0.054 − 0.923; p = 0.025). Conclusion MMFR is an effective method to predict TERT promoter mutations and PFS in patients with glioblastoma. Moreover, subgroup analysis could differentiate patients who may benefit from prolonged TMZ chemotherapy cycles.

Background Studies have shown that deep‐learning radiomics (DLR) could help differentiate glioblastoma (GBM) from solitary brain metastasis (SBM), but whether integrating demographic‐MRI and DLR features can more accurately distinguish GBM from SBM remains uncertain. Purpose To construct and validate a demographic‐MRI deep‐learning radiomics nomogram (DDLRN) integrating demographic‐MRI and DLR signatures to differentiate GBM from SBM preoperatively. Study Type Retrospective. Population Two hundred and thirty‐five patients with GBM (N = 115) or SBM (N = 120), randomly divided into a training cohort (90 GBM and 98 SBM) and a validation cohort (25 GBM and 22 SBM). Field Strength/Sequence Axial T2‐weighted fast spin‐echo sequence (T2WI), T2‐weighted fluid‐attenuated inversion recovery sequence (T2‐FLAIR), and contrast‐enhanced T1‐weighted spin‐echo sequence (CE‐T1WI) using 1.5‐T and 3.0‐T scanners. Assessment The demographic‐MRI signature was constructed with seven imaging features (“pool sign,” “irregular ring sign,” “regular ring sign,” “intratumoral vessel sign,” the ratio of the area of peritumoral edema to the enhanced tumor, the ratio of the lesion area on T2‐FLAIR to CE‐T1WI, and the tumor location) and demographic factors (age and sex). Based on multiparametric MRI, radiomics and deep‐learning (DL) models, DLR signature, and DDLRN were developed and validated. Statistical Tests The Mann–Whitney U test, Pearson test, least absolute shrinkage and selection operator, and support vector machine algorithm were applied for feature selection and construction of radiomics and DL models. Results DDLRN showed the best performance in differentiating GBM from SBM with area under the curves (AUCs) of 0.999 and 0.947 in the training and validation cohorts, respectively. Additionally, the DLR signature (AUC = 0.938) outperformed the radiomics and DL models, and the demographic‐MRI signature (AUC = 0.775) was comparable to the T2‐FLAIR radiomics and DL models in the validation cohort (AUC = 0.762 and 0.749, respectively). Data Conclusion DDLRN integrating demographic‐MRI and DLR signatures showed excellent performance in differentiating GBM from SBM. Level of Evidence 3 Technical Efficacy Stage 2