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Introduction: Amino-acid positron emission tomography (PET) is a validated metabolic imaging approach for the diagnostic work-up of gliomas. This study aimed to evaluate sex-specific radiomic characteristics of L-[S-methyl- 11 Cmethionine (MET)-PET images of glioma patients in consideration of the prognostically relevant biomarker isocitrate dehydrogenase (IDH) mutation status., Methods: MET-PET of 35 astrocytic gliomas (13 females, mean age 41 ± 13 yrs. and 22 males, mean age 46 ± 17 yrs.) and known IDH mutation status were included. All patients underwent radiomic analysis following imaging biomarker standardization initiative (IBSI)-conform guidelines both from standardized uptake value (SUV) and tumor-to-background ratio (TBR) PET values. Aligned Monte Carlo (MC) 100-fold split was utilized for SUV and TBR dataset pairs for both sex and IDH-specific analysis. Borderline and outlier scores were calculated for both sex and IDH-specific MC folds. Feature ranking was performed by R-squared ranking and Mann-Whitney U-test together with Bonferroni correction. Correlation of SUV and TBR radiomics in relation to IDH mutational status in male and female patients were also investigated., Results: There were no significant features in either SUV or TBR radiomics to distinguish female and male patients. In contrast, intensity histogram coefficient of variation (ih.cov) and intensity skewness (stat.skew) were identified as significant to predict IDH +/-. In addition, IDH+ females had significant ih.cov deviation (0.031) and mean stat.skew (-0.327) differences compared to IDH+ male patients (0.068 and -0.123, respectively) with two-times higher standard deviations of the normal brain background MET uptake as well., Discussion: We demonstrated that female and male glioma patients have significantly different radiomic profiles in MET PET imaging data. Future IDH prediction models shall not be built on mixed female-male cohorts, but shall rely on sex-specific cohorts and radiomic imaging biomarkers., Competing Interests: The 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 © 2023 Papp, Rasul, Spielvogel, Krajnc, Poetsch, Woehrer, Patronas, Ecsedi, Furtner, Mitterhauser, Rausch, Widhalm, Beyer, Hacker and Traub-Weidinger.)

Background: This study proposes machine learning-driven data preparation (MLDP) for optimal data preparation (DP) prior to building prediction models for cancer cohorts., Methods: A collection of well-established DP methods were incorporated for building the DP pipelines for various clinical cohorts prior to machine learning. Evolutionary algorithm principles combined with hyperparameter optimization were employed to iteratively select the best fitting subset of data preparation algorithms for the given dataset. The proposed method was validated for glioma and prostate single center cohorts by 100-fold Monte Carlo (MC) cross-validation scheme with 80-20% training-validation split ratio. In addition, a dual-center diffuse large B-cell lymphoma (DLBCL) cohort was utilized with Center 1 as training and Center 2 as independent validation datasets to predict cohort-specific clinical endpoints. Five machine learning (ML) classifiers were employed for building prediction models across all analyzed cohorts. Predictive performance was estimated by confusion matrix analytics over the validation sets of each cohort. The performance of each model with and without MLDP, as well as with manually-defined DP were compared in each of the four cohorts., Results: Sixteen of twenty established predictive models demonstrated area under the receiver operator characteristics curve (AUC) performance increase utilizing the MLDP. The MLDP resulted in the highest performance increase for random forest (RF) (+0.16 AUC) and support vector machine (SVM) (+0.13 AUC) model schemes for predicting 36-months survival in the glioma cohort. Single center cohorts resulted in complex (6-7 DP steps) DP pipelines, with a high occurrence of outlier detection, feature selection and synthetic majority oversampling technique (SMOTE). In contrast, the optimal DP pipeline for the dual-center DLBCL cohort only included outlier detection and SMOTE DP steps., Conclusions: This study demonstrates that data preparation prior to ML prediction model building in cancer cohorts shall be ML-driven itself, yielding optimal prediction models in both single and multi-centric settings., Competing Interests: MH, LP, and TB are co-founders of Dedicaid GmbH, Austria. 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 © 2022 Krajnc, Spielvogel, Grahovac, Ecsedi, Rasul, Poetsch, Traub-Weidinger, Haug, Ritter, Alizadeh, Hacker, Beyer and Papp.)

Despite increasing clinical interest in adapting checkpoint inhibitor (CPI) therapies for patients with gynecologic malignancies, no accurate clinical biomarkers to predict therapy response and prognosis are currently available. Therefore, we aimed to assess the predictive and prognostic value of pretherapeutic body mass index (BMI) for recurrent gynecologic cancer patients as previously validated for other solid tumors. We evaluated patients with programmed cell death ligand 1 (PD-L1) positive and, in endometrial cancer, also mismatch repair deficient (MMR) gynecologic malignancies, who received the PD-1 inhibitor pembrolizumab as monotherapy (200 mg fixed-dose q3 w) from 2017 to 2020 (n = 48). Thirty-six patients receiving at least four courses were included in the final analysis. Associations between a BMI increase per 5 kg/m 2 and overall response rate (ORR; complete + partial response), disease control rate (DCR; ORR + stable disease), progression-free (PFS), and overall survival (OS) were assessed. An elevated BMI was univariately associated with ORR (OR 10.93 [CI 2.39-49.82], p = 0.002), DCR (OR 2.19 [CI 0.99-4.83], p = 0.048), prolonged PFS (HR 1.54 [CI 1.03-2.34], p = 0.038), and OS (HR 1.87 [CI 1.07-3.29], p = 0.028). All results could be confirmed in the multivariate analyses. Pretherapeutic BMI therefore appears to be a promising readily available biomarker to identify patients with PD-L1-positive and/or MMR-deficient gynecologic malignancies who could particularly benefit from CPI treatment.

Apart from its expression in benign and malignant prostate tissue, prostate specific membrane antigen (PSMA) was shown to be expressed specifically in the neovasculature of solid tumors. For gliomas only little information exists. Therefore, we aimed to correlate PSMA expression in gliomas to tumor metabolism by L-[S-methyl- 11 C]methionine (MET) PET and survival. Therefore, immunohistochemical staining (IHC) for isocitrate dehydrogenase 1-R132H (IDH1-R132H) mutation and PSMA expression was performed on the paraffin embedded tissue samples of 122 treatment-naive glioma patients. The IHC results were then related to the pre-therapeutic semiquantitative MET PET data and patients' survival. Vascular PSMA expression was observed in 26 of 122 samples and was rather specific for high-grade gliomas ([HGG] 81% of glioblastoma multiforme, 10% of WHO grade III and just 2% of grade II gliomas). Significantly higher amounts of gliomas without verifiable IDH1-R132H mutation showed vascular PSMA expression. Significantly shorter median survival times were seen for patients with vascular PSMA staining in all tumors as well as HGG only. Additionally, significantly higher numbers of PSMA staining vessels were found in tumors with high amino acid metabolic rates. Vascular PSMA expression in gliomas was seen as a high-grade specific feature associated with elevated amino acid metabolism and short survival.

Objectives: To assess the visibility of clinically significant prostate cancer (PCA) lesions on the sequences multiparametric MRI of the prostate (mpMRI) and to evaluate whether the addition of dynamic contrast-enhanced imaging (DCE) improves the overall visibility., Methods: We retrospectively evaluated multiparametric MRI images of 119 lesions in 111 patients with biopsy-proven clinically significant PCA. Three readers assigned visual grading scores for visibility on each sequence, and a visual grading characteristic analysis was performed. Linear regression was used to explore which factors contributed to visibility in individual sequences., Results: The visibility of lesions was significantly better with mpMRI when compared to biparametric MRI in visual grading characteristic (VGC) analysis, with an AUC VGC of 0.62 (95% CI 0.55-0.69; p < 0.001). This benefit was seen across all readers. Multivariable linear regression revealed that a location in the peripheral zone was associated with better visibility on T2-weighted imaging (T2w). A higher Prostate Imaging-Reporting and Data System (PI-RADS) score was associated with better visibility on both diffusion-weighted imaging (DWI) and DCE. Increased lesion size was associated with better visibility on all sequences., Conclusions: Visibility of clinically significant PCA is improved by using mpMRI. DCE and DWI images independently improve lesion visibility compared to T2w images alone. Further research into the potential of DCE to impact on clinical decision-making is suggested., Key Points: • DCE and DWI images independently improve clinically significant prostate cancer lesion visibility compared to T2w images alone. • Multiparametric MRI (DCE, DWI, T2w) achieved significantly higher visibility scores than biparametric MRI (DWI, T2w). • Location in the transition zone is associated with poor visibility on T2w, while it did not affect visibility on DWI or DCE.

Purpose: It is still not clear whether to screen women with primary premature ovarian insufficiency for autoimmunity. Moreover, a possible association of autoimmunity with decreased bone mass density in premature ovarian insufficiency patients has not been evaluated. Thus, the objectives of this study were to review our experience with the use of an autoimmune screening panel in premature ovarian insufficiency women and to focus on bone mass density., Methods: In a retrospective cohort study, 76 chromosomally normal women with primary premature ovarian insufficiency were included. The main outcome parameters were the results of an autoimmune screening panel and of dual-energy X-ray absorptiometry., Results: Median age was 33 years. Sixty percent of premature ovarian insufficiency patients revealed abnormal dual-energy X-ray absorptiometry results (minimal T-score < -1.0). Any signs of autoimmunity were found in 21 women (36.2%). The most frequent abnormal results were increased thyroperoxidase antibodies (24.1%) and thyroglobulin antibodies (20.7%). A longer duration of amenorrhea (β = -0.015; p = 0.007), any abnormality during autoimmune screening (β = -0.940; p = 0.010), and a lower body mass index (β = -0.057; p = 0.036) were associated with a lower minimal T-score., Conclusion: In chromosomally normal women with primary premature ovarian insufficiency, the prevalence of autoimmunity and decreased bone mass density seem high. Our data highlight the association between autoimmune abnormalities and decreased dual-energy X-ray absorptiometry results.

Background: Deficiency in butyrylcholinesterase (BChE), a condition commonly noticed in liver damage, inflammation, and malnutrition, has previously been associated with impaired prognosis in different malignancies. The aim of the present study was to investigate the value of pretreatment serum BChE levels as a prognostic biomarker in patients with cervical cancer treated with primary (chemotherapy-[chemo-])radiation therapy., Methods: We retrospectively evaluated data of a consecutive series of patients with cervical cancer treated with primary (chemo-)radiation therapy between 1998 and 2015. Pretreatment serum BChE levels were correlated with clinico-pathological parameters and response to treatment. Uni- and multivariate survival analyses were performed to assess the association between decreased serum BChE levels and progression-free (PFS), cancer-specific (CSS), and overall survival (OS)., Results: A total of 356 patients were eligible for inclusion into the present study. The median (IQR) pretreatment serum BChE level was 6180 (4990-7710) IU/l. Lower serum BChE levels were associated with lower BMI (p < 0.001), advanced tumor stage (p = 0.04), poor treatment response (p = 0.002), the occurrence of disease recurrence (p = 0.003), and the risk of death (p < 0.001). In uni- and multivariate analyses, low pretreatment serum BChE levels were independently associated with shorter PFS (HR 1.8 [1.2-2.6]; p = 0.002), CSS (HR 2.2 [1.4-3.5], p < 0.001), and OS (HR 2.0 [1.4-2.9]; p < 0.001)., Conclusions: Low pretreatment serum BChE levels are associated with advanced tumor stage and poor response to treatment, and serve as an independent prognostic biomarker for shorter PFS, CSS, and OS in patients with cervical cancer treated with primary (chemo-)radiation therapy.

Background: Few data exist regarding the prognostic value of L-[S-methyl-11C]methionine (MET) PET for treatment-naïve gliomas., Methods: A total of 160 glioma patients (89 men, 71 women; mean age: 45, range 18-84 y) underwent a MET PET prior to any therapy. The PET scans were evaluated visually and semiquantitatively by tumor-to-background (T/N) ratio thresholds chosen by analysis of receiver operating characteristics. Additionally, isocitrate dehydrogenase 1-R132H (IDH1-R132H) immunohistochemistry was performed. Survival analysis was done using Kaplan-Meier estimates and the Cox proportional hazards model., Results: Significantly shorter mean survival times (7.2 vs 8.6 y; P = 0.024) were seen in patients with amino acid avid gliomas (n = 137) compared with visually negative tumors (n = 33) in MET PET. T/N ratio thresholds of 2.1 and 3.5 were significantly associated with survival (10.3 vs 7 vs 4.3 y; P < 0.001). Mean survival differed significantly using the median T/N ratio of 2.4 as cutoff, independent of histopathology (P < 0.01; mean survival: 10.2 ± 0.8 y vs 5.5 ± 0.6 y). In the subgroup of 142 glioma patients characterized by IDH1-R132H status, METT/N ratio demonstrated a significant prognostic impact in IDH1-R132H wildtype astrocytomas and glioblastoma (P = 0.001). Additionally, multivariate testing revealed semiquantitative MET PET as an independent prognostic parameter for treatment-naïve glioma patients without (P = 0.031) and with IDH1-R132H characterization of gliomas (P = 0.024; odds ratio 1.57)., Conclusion: This retrospective analysis demonstrates the value of MET PET as a prognostic parameter on survival in treatment-naïve glioma patients., (© The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com)