Endometrial Adenofibroma in a Patient Receiving Toremifene: A Case Report

INTRODUCTION: Adenofibroma is a rare benign Müllerian mixed tumor composed of epithelial and mesenchymal cells. This tumor may occasionally be associated with toremifene therapy which is used as an adjuvant drug for breast cancer. CASE PRESENTATION: We describe a case of a 55-year-old woman with adenofibroma of the endometrium. This patient was receiving toremifene after surgery and neoadjuvant chemotherapy for breast cancer. She underwent a total abdominal hysterectomy and bilateral salpingectomy. There was no evidence of tumor residual or recurrence at 32 months of MRI follow-up. CONCLUSION: In conclusion, we report a rare case of endometrial adenofibroma in a patient receiving toremifene. It must be borne in mind that long-term toremifene therapy may increase the frequency of endometrial neoplasms.

Machine learning-based nomogram for distinguishing between supratentorial extraventricular ependymoma and supratentorial glioblastoma.

Objective: To develop a machine learning-based nomogram for distinguishing between supratentorial extraventricular ependymoma (STEE) and supratentorial glioblastoma (GBM). Methods: We conducted a retrospective analysis on MRI datasets obtained from 140 patients who were diagnosed with STEE (n=48) and GBM (n=92) from two institutions. Initially, we compared seven different machine learning algorithms to determine the most suitable signature (rad-score). Subsequently, univariate and multivariate logistic regression analyses were performed to identify significant clinical predictors that can differentiate between STEE and GBM. Finally, we developed a nomogram by visualizing the rad-score and clinical features for clinical evaluation. Results: The TreeBagger (TB) outperformed the other six algorithms, yielding the best diagnostic efficacy in differentiating STEE from GBM, with area under the curve (AUC) values of 0.735 (95% CI: 0.625-0.845) and 0.796 (95% CI: 0.644-0.949) in the training set and test set. Furthermore, the nomogram incorporating both the rad-score and clinical variables demonstrated a robust predictive performance with an accuracy of 0.787 in the training set and 0.832 in the test set. Conclusion: The nomogram could serve as a valuable tool for non-invasively discriminating between STEE and GBM.

Amide proton transfer-weighted and arterial spin labeling imaging may improve differentiation between high-grade glioma recurrence and radiation-induced brain injury.

Rationale and objectives: The management of tumor recurrence (TR) and radiation-induced brain injury (RIBI) poses significant challenges, necessitating the development of effective differentiation strategies. In this study, we investigated the potential of amide proton transfer-weighted (APTw) and arterial spin labeling (ASL) imaging for discriminating between TR and RIBI in patients with high-grade glioma (HGG). Methods: A total of 64 HGG patients receiving standard treatment were enrolled in this study. The patients were categorized based on secondary pathology or MRI follow-up results, and the demographic characteristics of each group were presented. The APTw, rAPTw, cerebral blood flow (CBF) and rCBF values were quantified. The differences in various parameters between TR and RIBI were assessed using the independent-samples t-test. The discriminative performance of these MRI parameters in distinguishing between the two conditions was assessed using receiver operating characteristic (ROC) curve analysis. Additionally, the Delong test was employed to further evaluate their discriminatory ability. Results: The APTw and CBF values of TR were significantly higher compared to RIBI (P < 0.05). APTw MRI demonstrated superior diagnostic efficiency in distinguishing TR from RIBI (area under the curve [AUC]: 0.864; sensitivity: 75.0 %; specificity: 81.8 %) when compared to ASL imaging. The combined utilization of APTw and CBF value further enhanced the AUC to 0.922. The Delong test demonstrated that the combination of APTw and ASL exhibited superior performance in the identification of TR and RIBI, compared to ASL alone (P = 0.048). Conclusion: APTw exhibited superior diagnostic efficacy compared to ASL in the evaluation of TR and RIBI. Furthermore, the combination of APTw and ASL exhibits greater discriminatory capability and diagnostic performance.

A radiomics-based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma.

BACKGROUND AND PURPOSE: As a crucial diagnostic and prognostic biomarker, telomerase reverse transcriptase (TERT) promoter mutation holds immense significance for personalized treatment of patients with glioblastoma (GBM). In this study, we developed a radiomics nomogram to determine the TERT promoter mutation status and assessed its prognostic efficacy in GBM patients. METHODS: The study retrospectively included 145 GBM patients. A comprehensive set of 3736 radiomics features was extracted from preoperative magnetic resonance imaging, including T2-weighted imaging, T1-weighted imaging (T1WI), contrast-enhanced T1WI, and fluid-attenuated inversion recovery. The construction of the radiomics model was based on integrating the radiomics signature (rad-score)with clinical features. Receiver-operating characteristic curve analysis was employed to evaluate the discriminative ability of the prediction model, and the risk score was used to stratify patient outcomes. RESULTS: The least absolute shrinkage and selection operator classifier identified 10 robust features for constructing the prediction model, and the radiomics nomogram exhibited excellent performance in predicting TERT promoter mutation status, with area under the curve values of.906 (95% confidence interval [CI]:.850-.963) and.899 (95% CI:.708-.966) in the training and validation sets, respectively. The clinical utility of the radiomics nomogram is further supported by calibration curve and decision curve analyses. Additionally, the radiomics nomogram effectively stratified GBM patients with significantly different prognoses (HR = 1.767, p = .019). CONCLUSION: The radiomics nomogram holds promise as a modality for evaluating TERT promoter mutations and prognostic outcomes in patients with GBM.

MRI radiomics model for predicting TERT promoter mutation status in glioblastoma.

BACKGROUND AND PURPOSE: The presence of TERT promoter mutations has been associated with worse prognosis and resistance to therapy for patients with glioblastoma (GBM). This study aimed to determine whether the combination model of different feature selections and classification algorithms based on multiparameter MRI can be used to predict TERT subtype in GBM patients. METHODS: A total of 143 patients were included in our retrospective study, and 2553 features were obtained. The datasets were randomly divided into training and test sets in a ratio of 7:3. The synthetic minority oversampling technique was used to achieve data balance. The Pearson correlation coefficients were used for dimension reduction. Three feature selections and five classification algorithms were used to model the selected features. Finally, 10-fold cross validation was applied to the training dataset. RESULTS: A model with eight features generated by recursive feature elimination (RFE) and linear discriminant analysis (LDA) showed the greatest diagnostic performance (area under the curve values for the training, validation, and testing sets: 0.983, 0.964, and 0.926, respectively), followed by relief and random forest (RF), analysis of variance and RF. Furthermore, the relief was the optimal feature selection for separately evaluating those five classification algorithms, and RF was the most preferable algorithm for separately assessing the three feature selectors. ADC entropy was the parameter that made the greatest contribution to the discrimination of TERT mutations. CONCLUSIONS: Radiomics model generated by RFE and LDA mainly based on ADC entropy showed good performance in predicting TERT promoter mutations in GBM.