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Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI: 0.616–0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P <.001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment. [ABSTRACT FROM AUTHOR]

Background: Glioblastoma (GBM) is the most malignant primary tumor in the brain, with poor prognosis and limited effective therapies. Although Bevacizumab (BEV) has shown promise in extending progression-free survival (PFS) treating GBM, there is no evidence for its ability to prolong overall survival (OS). Given the uncertainty surrounding BEV treatment strategies, we aimed to provide an evidence map associated with BEV therapy for recurrent GBM (rGBM). Methods: PubMed, Embase, and the Cochrane Library were searched for the period from January 1, 1970, to March 1, 2022, for studies reporting the prognoses of patients with rGBM receiving BEV. The primary endpoints were overall survival (OS) and quality of life (QoL). The secondary endpoints were PFS, steroid use reduction, and risk of adverse effects. A scoping review and an evidence map were conducted to explore the optimal BEV treatment (including combination regimen, dosage, and window of opportunity). Results: Patients with rGBM could gain benefits in PFS, palliative, and cognitive advantages from BEV treatment, although the OS benefits could not be verified with high-quality evidence. Furthermore, BEV combined therapy (especially with lomustine and radiotherapy) showed higher efficacy than BEV monotherapy in the survival of patients with rGBM. Specific molecular alterations (IDH mutation status) and clinical features (large tumor burden and double-positive sign) could predict better responses to BEV administration. A low dosage of BEV showed equal efficacy to the recommended dose, but the optimal opportunity window for BEV administration remains unclear. Conclusions: Although OS benefits from BEV-containing regimens could not be verified in this scoping review, the PFS benefits and side effects control supported BEV application in rGBM. Combining BEV with novel treatments like tumor-treating field (TTF) and administration at first recurrence may optimize the therapeutic efficacy. rGBM with a low apparent diffusion coefficient (ADCL), large tumor burden, or IDH mutation is more likely to benefit from BEV treatment. High-quality studies are warranted to explore the combination modality and identify BEV-response subpopulations to maximize benefits. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to investigate the predictive models based on O-[2-(18F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography (18F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas. Methods: Fifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment 18F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUVSD), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models. Results: The AUC of the simple predictive model consists of semi-quantitative parameter SUVSD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three 18F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined 18F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model (p = 0.048) and simple predictive model (p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973). Conclusion: The predictive model combining 18F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment. [ABSTRACT FROM AUTHOR]

Objectives: The objective was to utilize a smartwatch sphygmomanometer to predict new-onset hypertension within a short-term follow-up among individuals with high-normal blood pressure (HNBP)., Methods: This study consisted of 3180 participants in the training set and 1000 participants in the validation set. Participants underwent both ambulatory blood pressure monitoring (ABPM) and home blood pressure monitoring (HBPM) using a smartwatch sphygmomanometer. Multivariable Cox regressions were used to analyze cumulative events. A nomogram was constructed to predict new-onset hypertension. Discrimination and calibration were assessed using the C-index and calibration curve, respectively., Results: Among the 3180 individuals with HNBP in the training set, 693 (21.8%) developed new-onset hypertension within a 6-month period. The nomogram for predicting new-onset hypertension had a C-index of 0.854 (95% CI, 0.843-0.867). The calibration curve demonstrated good agreement between the nomogram's predicted probabilities and actual observations for short-term new-onset hypertension. In the validate dataset, during the 6-month follow-up, the nomogram had a good C-index of 0.917 (95% CI, 0.904-0.930) and a good calibration curve. As the score increased, the risk of new-onset hypertension significantly increased, with an HR of 8.415 (95% CI: 5.153-13.744, p = .000) for the middle-score vs. low-score groups and 86.824 (95% CI: 55.071-136.885, p  = .000) for the high-score vs. low-score group., Conclusions: This study provides evidence for the use of smartwatch sphygmomanometer to monitor blood pressure in individuals at high risk of developing new-onset hypertension in the near future., Trial Registration: ChiCTR2200057354.

Purpose: We aimed to investigate the predictive models based on O-[2-( 18 F)fluoroethyl]-l-tyrosine positron emission tomography/computed tomography ( 18 F-FET PET/CT) radiomics features for the isocitrate dehydrogenase (IDH) genotype identification in adult gliomas., Methods: Fifty-eight consecutive pathologically confirmed adult glioma patients with pretreatment 18 F-FET PET/CT were retrospectively enrolled. One hundred and five radiomics features were extracted for analysis in each modality. Three independent radiomics models (PET-Rad Model, CT-Rad Model and PET/CT-Rad Model) predicting IDH mutation status were generated using the least absolute shrinkage and selection operator (LASSO) regression analysis based on machine learning algorithms. All-subsets regression and cross validation were applied for the filter and calibration of the predictive radiomics models. Besides, semi-quantitative parameters including maximum, peak and mean tumor to background ratio (TBRmax, TBRpeak, TBRmean), standard deviation of glioma lesion standardized uptake value (SUV SD ), metabolic tumor volume (MTV) and total lesion tracer uptake (TLU) were obtained and filtered for the simple model construction with clinical feature of brain midline involvement status. The area under the receiver operating characteristic curve (AUC) was applied for the evaluation of the predictive models., Results: The AUC of the simple predictive model consists of semi-quantitative parameter SUV SD and dichotomized brain midline involvement status was 0.786 (95% CI 0.659-0.883). The AUC of PET-Rad Model building with three 18 F-FET PET radiomics parameters was 0.812 (95% CI 0.688-0.902). The AUC of CT-Rad Model building with three co-registered CT radiomics parameters was 0.883 (95% CI 0.771-0.952). While the AUC of the combined 18 F-FET PET/CT-Rad Model building with three CT and one PET radiomics features was 0.912 (95% CI 0.808-0.970). DeLong test results indicated the PET/CT-Rad Model outperformed the PET-Rad Model ( p = 0.048) and simple predictive model ( p = 0.034). Further combination of the PET/CT-Rad Model with the clinical feature of dichotomized tumor location status could slightly enhance the AUC to 0.917 (95% CI 0.814-0.973)., Conclusion: The predictive model combining 18 F-FET PET and integrated CT radiomics features could significantly enhance and well balance the non-invasive IDH genotype prediction in untreated gliomas, which is important in clinical decision making for personalized treatment., Competing Interests: Authors YL and YD were employed by Jinan Guoke Medical Engineering Technology Development Co., LTD. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zhou, Huang, Wen, Li, Zhu, Liu, Dai, Guan, Zhou and Hua.)

Background: The present study aimed to explore the efficacy of easily obtained intratumoral heterogeneous parameters, other than regular semi-quantitative parameters, based on static O-(2-[18F]fluoroethyl)-l-tyrosine ( 18 F-FET) positron emission tomography (PET) imaging in glioma grade and isocitrate dehydrogenase ( IDH ) gene 1 mutation prediction., Methods: Fifty-eight adult patients with untreated glioma (grades II-IV) who underwent preoperative 18 F-FET PET/computed tomography (CT) imaging were enrolled in the present study. Eight semi-automatically obtained static PET imaging parameters after lesion delineation were chosen for analysis. These were: maximal tumor-to-background ratio (TBRmax), peak tumor-to-background ratio (TBRpeak), mean tumor-to-background ratio (TBRmean), coefficient of variation (COV), heterogeneity index (HI), the standard deviation of lesion standardized uptake value (SUVsd), metabolic tumor volume (MTV), and total lesion tracer standardized uptake (TLU). Pathological and immunohistochemical results were used as a reference. The receiver-operating characteristic analysis was used to investigate the predictive efficacy of these parameters in glioma grade and IDH1 mutation status., Results: TLU [area under the curve (AUC): 0.841, P<0.0001], TBRpeak (AUC: 0.832, P<0.0001), and HI (AUC: 0.826, P<0.0001) had the top 3 single-parameter predictive performance between grade II or III and grade IV glioma patients. Combinations of TBRmax, SUVsd, and TBRmean (AUC: 0.850, P<0.0001); HI, SUVsd, and MTV (AUC: 0.848, P<0.0001); and HI, SUVsd, and TLU (AUC: 0.848, P<0.0001) had the top 3 multiple-parameter predictive performance. SUVsd (AUC: 0.710, P=0.0028), TLU (AUC: 0.698, P=0.0074), and HI (AUC: 0.676, P=0.0159) had the top 3 single-parameter predictive performance in the IDH1 genotype. Combinations of TBRmax, SUVsd, and TBRmean (AUC: 0.821, P<0.0001); SUVsd and TBRmean (AUC: 0.804, P<0.0001); and SUVsd, HI, and TBRmean (AUC: 0.799, P<0.0001) had the top 3 multiple-parameter predictive performance., Conclusions: These easily obtained and highly repetitive heterogeneous parameters based on static 18 F-FET PET/CT imaging can non-invasively predict glioma grade and IDH1 mutation, crucial in treatment planning, and prognostic evaluation., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-723). The authors have no conflicts of interest to declare., (2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Purpose: We developed a 11 C-Methionine positron emission tomography/computed tomography ( 11 C-MET PET/CT)-based nomogram model that uses easy-accessible imaging and clinical features to achieve reliable non-invasive isocitrate dehydrogenase (IDH)-mutant prediction with strong clinical translational capability. Methods: One hundred and ten patients with pathologically proven glioma who underwent pretreatment 11 C-MET PET/CT were retrospectively reviewed. IDH genotype was determined by IDH1 R132H immunohistochemistry staining. Maximum, mean and peak tumor-to-normal brain tissue (TNRmax, TNRmean, TNRpeak), metabolic tumor volume (MTV), total lesion methionine uptake (TLMU), and standard deviation of SUV (SUV SD ) of the lesions on MET PET images were obtained via a dedicated workstation (Siemens. syngo.via). Univariate and multivariate logistic regression models were used to identify the predictive factors for IDH mutation. Nomogram and calibration plots were further performed. Results: In the entire population, TNRmean, TNRmax, TNRpeak, and SUV SD of IDH-mutant glioma patients were significantly lower than these values of IDH wildtype. Receiver operating characteristic (ROC) analysis suggested SUV SD had the best performance for IDH-mutant discrimination (AUC = 0.731, cut-off ≤ 0.29, p < 0.001). All pairs of the 11 C-MET PET metrics showed linear associations by Pearson correlation coefficients between 0.228 and 0.986. Multivariate analyses demonstrated that SUV SD (>0.29 vs. ≤ 0.29 OR: 0.053, p = 0.010), dichotomized brain midline structure involvement (no vs. yes OR: 26.52, p = 0.000) and age (≤ 45 vs. >45 years OR: 3.23, p = 0.023), were associated with a higher incidence of IDH mutation. The nomogram modeling showed good discrimination, with a C-statistics of 0.866 (95% CI: 0.796-0.937) and was well-calibrated. Conclusions: 11 C-Methionine PET/CT imaging features (SUV SD and the involvement of brain midline structure) can be conveniently used to facilitate the pre-operative prediction of IDH genotype. The nomogram model based on 11 C-Methionine PET/CT and clinical age features might be clinically useful in non-invasive IDH mutation status prediction for untreated glioma patients., (Copyright © 2020 Zhou, Zhou, Wen, Xie, Zhu, Zhang, Xiao, Chen, Li, Guan and Hua.)

Non-small cell lung cancer (NSCLC) is the most common cancer and the leading cause of cancer-related deaths worldwide. Age at diagnosis of advanced NSCLC is much older, but studies describing the practice patterns for octogenarians with distant metastasis NSCLC are limited. A retrospective, population-based study using national representative data from the Surveillance, Epidemiology, and End Results (SEER) program was conducted to evaluate 34 882 NSCLC patients with extrathoracic metastases from 2010 to 2013. Patients were classified into three groups (older group: ≥80 yrs, middle-aged group: 60-79 yrs, and younger group: ≤59 yrs). The role of different age at diagnosis of NSCLC in metastasis patterns was investigated, and survival of different age groups of metastatic NSCLC was assessed. The analysis revealed that older patients were more likely to only have bone or liver metastasis (p< 0.001), but less likely to have brain only metastasis (p<0.001) and multiple metastatic sites (p< 0.001) than other two groups. Age at diagnosis was an independent risk factor for different metastasis types. Older group had the worst overall survival (p<0.001) and cancer-specific survival (p<0.001). Furthermore, older age patients with only bone metastasis had the best cancer specific survival (p<0.05) while younger patients with only brain metastasis had the best prognosis (p<0.001). Over 60% octogenarians with metastatic NSCLC did not receive anti-cancer therapy and had the highest rate of cancer deaths among all patients. Our results may help clinicians make positive decisions regarding personalized treatment of metastatic NSCLC in the elderly., Competing Interests: Conflict of interest The authors declare that they have no conflict of interest., (Copyright: © 2019 Gu et al.)