A Multiparametric MRI-Based Radiomics Nomogram for Preoperative Prediction of Survival Stratification in Glioblastoma Patients With Standard Treatment.

OBJECTIVE: To construct and validate a radiomics nomogram for preoperative prediction of survival stratification in glioblastoma (GBM) patients with standard treatment according to radiomics features extracted from multiparameter magnetic resonance imaging (MRI), which could facilitate clinical decision-making. METHODS: A total of 125 eligible GBM patients (53 in the short and 72 in the long survival group, separated by an overall survival of 12 months) were randomly divided into a training cohort (n = 87) and a validation cohort (n = 38). Radiomics features were extracted from the MRI of each patient. The T-test and the least absolute shrinkage and selection operator algorithm (LASSO) were used for feature selection. Next, three feature classifier models were established based on the selected features and evaluated by the area under curve (AUC). A radiomics score (Radscore) was then constructed by these features for each patient. Combined with clinical features, a radiomics nomogram was constructed with independent risk factors selected by the logistic regression model. The performance of the nomogram was assessed by AUC, calibration, discrimination, and clinical usefulness. RESULTS: There were 5,216 radiomics features extracted from each patient, and 5,060 of them were stable features judged by the intraclass correlation coefficients (ICCs). 21 features were included in the construction of the radiomics score. Of three feature classifier models, support vector machines (SVM) had the best classification effect. The radiomics nomogram was constructed in the training cohort and exhibited promising calibration and discrimination with AUCs of 0.877 and 0.919 in the training and validation cohorts, respectively. The favorable decision curve analysis (DCA) indicated the clinical usefulness of the radiomics nomogram. CONCLUSIONS: The presented radiomics nomogram, as a non-invasive tool, achieved satisfactory preoperative prediction of the individualized survival stratification of GBM patients.

Mid-term follow-up surgical results in 284 cases of clival chordomas: the risk factors for outcome and tumor recurrence.

OBJECTIVE: Skull base chordoma (SBC) is rare and one of the most challenging diseases to treat. We aimed to assess the optimal timing of adjuvant radiation therapy (RT) and to evaluate the factors that influence resection and long-term outcomes. METHODS: In total, 284 patients with 382 surgeries were enrolled in this retrospective study. Postsurgically, 64 patients underwent RT before recurrence (pre-recurrence RT), and 47 patients underwent RT after recurrence. During the first attempt to achieve gross-total resection (GTR), when the entire tumor was resected, 268 patients were treated with an endoscopic midline approach, and 16 patients were treated with microscopic lateral approaches. Factors associated with the success of GTR were identified using χ2 and logistic regression analyses. Risk factors associated with chordoma-specific survival (CSS) and progression-free survival (PFS) were evaluated with the Cox proportional hazards model. RESULTS: In total, 74.6% of tumors were marginally resected [GTR (40.1%), near-total resection (34.5%)]. History of surgery, large tumor volumes, and tumor locations in the lower clivus were associated with a lower GTR rate. The mean follow-up period was 43.9 months. At the last follow-up, 181 (63.7%) patients were alive. RT history, histologic subtype (dedifferentiated and sarcomatoid), non-GTR, no postsurgical RT, and the presence of metastasis were associated with poorer CSS. Patients with pre-recurrence RT had the longest PFS and CSS, while patients without postsurgical RT had the worst outcome. CONCLUSION: GTR is the goal of initial surgical treatment. Pre-recurrence RT would improve outcome regardless of GTR.

Development and validation of a preoperative MRI-based radiomics nomogram to predict progression-free survival in patients with clival chordomas.

Objectives: The aim of this study was to establish and validate a MRI-based radiomics nomogram to predict progression-free survival (PFS) of clival chordoma. Methods: A total of 174 patients were enrolled in the study (train cohort: 121 cases, test cohort: 53 cases). Radiomic features were extracted from multiparametric MRIs. Intraclass correlation coefficient analysis and a Lasso and Elastic-Net regularized generalized linear model were used for feature selection. Then, a nomogram was established via univariate and multivariate Cox regression analysis in the train cohort. The performance of this nomogram was assessed by area under curve (AUC) and calibration curve. Results: A total of 3318 radiomic features were extracted from each patient, of which 2563 radiomic features were stable features. After feature selection, seven radiomic features were selected. Cox regression analysis revealed that 2 clinical factors (degree of resection, and presence or absence of primary chordoma) and 4 radiomic features were independent prognostic factors. The AUC of the established nomogram was 0.747, 0.807, and 0.904 for PFS prediction at 1, 3, and 5 years in the train cohort, respectively, compared with 0.582, 0.852, and 0.914 in the test cohort. Calibration and risk score stratified survival curves were satisfactory in the train and test cohort. Conclusions: The presented nomogram demonstrated a favorable predictive accuracy of PFS, which provided a novel tool to predict prognosis and risk stratification. Our results suggest that radiomic analysis can effectively help neurosurgeons perform individualized evaluations of patients with clival chordomas.

Prediction of blood supply in vestibular schwannomas using radiomics machine learning classifiers.

This study attempts to explore the radiomics-based features of multi-parametric magnetic resonance imaging (MRI) and construct a machine-learning model to predict the blood supply in vestibular schwannoma preoperatively. By retrospectively collecting the preoperative MRI data of patients with vestibular schwannoma, patients were divided into poor and rich blood supply groups according to the intraoperative recording. Patients were divided into training and test cohorts (2:1), randomly. Stable features were retained by intra-group correlation coefficients (ICCs). Four feature selection methods and four classification methods were evaluated to construct favorable radiomics classifiers. The mean area under the curve (AUC) obtained in the test set for different combinations of feature selecting methods and classifiers was calculated separately to compare the performance of the models. Obtain and compare the best combination results with the performance of differentiation through visual observation in clinical diagnosis. 191 patients were included in this study. 3918 stable features were extracted from each patient. Least absolute shrinkage and selection operator (LASSO) and logistic regression model was selected as the optimal combinations after comparing the AUC calculated by models, which predicted the blood supply of vestibular schwannoma by K-Fold cross-validation method with a mean AUC = 0.88 and F1-score = 0.83. Radiomics machine-learning classifiers can accurately predict the blood supply of vestibular schwannoma by preoperative MRI data.

Preoperative Prediction of Meningioma Consistency via Machine Learning-Based Radiomics.

OBJECTIVES: The aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making. METHODS: A total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada's consistency grading system. Radiomics features were extracted from multiparametric MRI. Variance selection and LASSO regression were used for feature selection. Then, radiomics models were constructed by five classifiers, and the area under curve (AUC) was used to evaluate the performance of each classifiers. A radiomics nomogram was developed using the best classifier. The performance of this nomogram was assessed by AUC, calibration and discrimination. RESULTS: A total of 3840 radiomics features were extracted from each patient, of which 3719 radiomics features were stable features. 28 features were selected to construct the radiomics nomogram. Logistic regression classifier had the highest prediction efficacy. Radiomics nomogram was constructed using logistic regression in the train cohort. The nomogram showed a good sensitivity and specificity with AUCs of 0.861 and 0.960 in train and test cohorts, respectively. Moreover, the calibration graph of the nomogram showed a favorable calibration in both train and test cohorts. CONCLUSIONS: The presented radiomics nomogram, as a non-invasive prediction tool, could predict meningiomas consistency preoperatively with favorable accuracy, and facilitated the determination of individualized operation schemes.

Clinical Implication of Systemic Immune-Inflammation Index and Prognostic Nutritional Index in Skull Base Chordoma Patients.

Inflammation associated markers and nutritional indexes are associated with survival, and act as novel prognostic grading systems in patients with cancer, though the role of these markers in chordoma remains unclear. The current study aimed to characterize systemic immune-inflammation index (SII) and prognostic nutritional index (PNI), and their relationship with clinicopathological data and survival in skull base chordoma. Our retrospective study enrolled 183 patients with primary skull base chordoma who received surgical treatment. Clinicopathological data and preoperative blood tests including neutrophil, lymphocyte, platelet counts and albumin level were collected from medical records. Neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), SII, PNI were calculated and the optimal cut-off values of these markers were used for further survival analysis via Kaplan-Meier survival analysis and Cox proportional hazards regression analysis. The value of NLR, PLR, SII, and PNI in skull base chordoma ranged from 0.44-6.48, 45.36-273.94, 113.37-1761.45, and 43.40-70.65, respectively. PNI was significantly correlated with patients' sex (p = 0.005) and age (p = 0.037). SII was positively correlated with NLR and PLR, but negatively correlated with PNI. The median overall survival (OS) time was 74.0 months and Kaplan-Meier survival analysis indicated that all four indexes were associated with OS. Multivariable Cox proportional hazards regression analysis identified that high SII was an independent prognostic factor for poor OS. More importantly, patients with high SII and PNI had the worst outcomes and combined use of SII and PNI increased the predictive ability for patients' survival in skull base chordoma. Our results suggest SII and PNI may be effective prognostic indicators of OS for patients with primary skull base chordoma after surgical resection.

Effects of Preoperative Carbamazepine Treatment on Microvascular Decompression for Classical Trigeminal Neuralgia.

BACKGROUND: Carbamazepine (CBZ) is the first-line therapy for trigeminal neuralgia (TN), and microvascular decompression (MVD) is considered to be an effective surgical treatment for TN. However, the effect of preoperative CBZ treatment on MVD outcome is not clear. METHODS: From 2013 to 2019, 63 patients with classical TN underwent MVD at the First Affiliated Hospital of Zhengzhou University, China. Data were collected through telephone follow-up and electronic medical records in April 2020. Short-term surgical outcome and long-term follow-up data were estimated by univariate and multivariate analysis. RESULTS: Multivariate analysis indicated that preoperative CBZ treatment was not a significant predictor for short-term outcomes of MVD (P > 0.05). Multivariate analysis for the long-term outcome of MVD indicated that preoperative CBZ treatment could predict postoperative recurrence of TN (P < 0.05). CONCLUSIONS: For patients with classical TN, a longer preoperative medication history of CBZ treatment had no significant effect on short-term outcome of MVD, but CBZ treatment was associated with a poor long-term outcome following MVD.

Preoperative Predictors of Early Mortality Risk in Isocitrate Dehydrogenase-Wild-Type Glioblastoma Patients Treated with Standard Therapy.

PURPOSE: Early identification of early mortality for glioblastoma (GBM) patients based on laboratory findings at the time of diagnosis could improve the overall survival. The study aimed to explore preoperative factors associated with higher risk of early death (within 1 year after surgery) for isocitrate dehydrogenase (IDH) -wild-type (wt) GBM patients. PATIENTS AND METHODS: We conducted a retrospective analysis of 194 IDH-wt GBM patients who underwent standard treatment. The probability of dying within 1 year after gross total resection (GTR) was defined as the end point "early mortality". Retrospective collection of predictive factors including clinical characteristics and laboratory data at diagnosis. RESULTS: Median follow-up time after GTR was 16 months (3-41 months). Forty-two patients died within 1 year after surgery (1-year mortality rate: 21.6%). All potential predictive factors were assessed on univariate analyses, which revealed the following factors as associated with higher risk of early death: older age (P = 0.013), occurrence of non-seizures symptoms (P = 0.042), special tumor positions (P = 0.046), higher neutrophil-to-lymphocyte ratio (NLR) (P = 0.015), higher red blood cell distribution width (RDW) (P = 0.019), higher lactate dehydrogenase (LDH) (P = 0.005), and higher fibrinogen (FIB) (P = 0.044). In a multivariate analysis, tumor location (P = 0.012), NLR (P = 0.032) and LDH (P = 0.002) were independent predictors of early mortality. The C-index of the nomogram was 0.795. The calibration curve showed good agreement between prediction by nomogram and actual observation. CONCLUSION: Tumor location, preoperative elevated NLR and serum LDH level were independent predictors for 1-year mortality after GTR. We indicate that increased preoperative NLR or LDH may guide patients to review head magnetic resonance imaging (MRI) more frequently and regularly to monitor tumor progression.

Whole genome sequencing of skull-base chordoma reveals genomic alterations associated with recurrence and chordoma-specific survival.

Chordoma is a rare bone tumor with an unknown etiology and high recurrence rate. Here we conduct whole genome sequencing of 80 skull-base chordomas and identify PBRM1, a SWI/SNF (SWItch/Sucrose Non-Fermentable) complex subunit gene, as a significantly mutated driver gene. Genomic alterations in PBRM1 (12.5%) and homozygous deletions of the CDKN2A/2B locus are the most prevalent events. The combination of PBRM1 alterations and the chromosome 22q deletion, which involves another SWI/SNF gene (SMARCB1), shows strong associations with poor chordoma-specific survival (Hazard ratio [HR] = 10.55, 95% confidence interval [CI] = 2.81-39.64, p = 0.001) and recurrence-free survival (HR = 4.30, 95% CI = 2.34-7.91, p = 2.77 × 10-6). Despite the low mutation rate, extensive somatic copy number alterations frequently occur, most of which are clonal and showed highly concordant profiles between paired primary and recurrence/metastasis samples, indicating their importance in chordoma initiation. In this work, our findings provide important biological and clinical insights into skull-base chordoma.

Porous Functionalized Covalent-Triazine Frameworks for Enhanced Adsorption Toward Polysulfides in Li-S Batteries and Organic Dyes.

The incorporation of functional building blocks to construct functionalized and highly porous covalent triazine frameworks (CTFs) is essential to the emerging adsorptive-involved field. Herein, a series of amide functionalized CTFs (CTF-PO71) have been synthesized using a bottom-up strategy in which pigment PO71 with an amide group is employed as a monomer under ionothermal conditions with ZnCl2 as the solvent and catalyst. The pore structure can be controlled by the amount of ZnCl2 to monomer ratio. Benefitting from the highly porous structure and amide functionalities, CTF-PO71, as a sulfur cathode host, simultaneously demonstrates physical confinement and chemical anchoring of sulfur species, thus leading to superior capacity, cycling stability, and rate capability in comparison to unfunctionalized CTF. Meanwhile, as an adsorbent of organic dye molecules, CTF-PO71 was demonstrated to exhibit strong chemical interactions with dye molecules, facilitating adsorption kinetics and thereby promoting the adsorption rate and capacity. Furthermore, the dynamic adsorption experiments of organic dyes from solutions showed selectivity/priority of CTF-PO71s for specific dye molecules.