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OBJECTIVES: Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks. METHODS: Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. RESULTS: The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously. CONCLUSIONS: A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

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Objective:Glioblastoma(GBM)and brain metastases(BMs)are the two most common malignant brain tumors in adults.Magnetic resonance imaging(MRI)is a commonly used method for screening and evaluating the prognosis of brain tumors,but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited.In recent years,deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system.This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases(SBMs),and to further explore the impact of multimodality data fusion on classification tasks. Methods:Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed.First,structural T1-weight,T1C-weight,and T2-weight were selected as 3 inputs to the entire model,regions of interest(ROIs)were manually delineated on the registered three modal MR images,and multimodality radiomics features were obtained,dimensions were reduced using a random forest(RF)-based feature selection method,and the importance of each feature was further analyzed.Secondly,we used the method of contrast disentangled to find the shared features and complementary features between different modal features.Finally,the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. Results:The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs.Furthermore,compared with single-modal data,the multimodal fusion models using machine learning algorithms such as support vector machine(SVM),Logistic regression,RF,adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT)achieved significant improvements,with area under the curve(AUC)values of 0.974,0.978,0.943,0.938,and 0.947,respectively;our comparative disentangled multi-modal MR fusion method performs well,and the results of AUC,accuracy(ACC),sensitivity(SEN)and specificity(SPE)in the test set were 0.985,0.984,0.900,and 0.990,respectively.Compared with other multi-modal fusion methods,AUC,ACC,and SEN in this study all achieved the best performance.In the ablation experiment to verify the effects of each module component in this study,AUC,ACC,and SEN increased by 1.6%,10.9%and 15.0%,respectively after 3 loss functions were used simultaneously. Conclusion:A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.

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Purpose: To investigate the diagnostic performance of feature selection via a multi-task learning model in distinguishing primary glioblastoma from solitary brain metastases. Method: The study involved 187 patients diagnosed at Xiangya Hospital, Yunnan Provincial Cancer Hospital, and Southern Cancer Hospital between January 2010 and December 2018. Radiomic features were extracted from conventional magnetic resonance imaging including T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences. We proposed a new multi-task learning model using these three sequences as three tasks. Multi-series fusion was performed to complement the information from different dimensions in order to enhance model robustness. Logical loss was used in the model as the data-fitting item, and the feature weights were expressed in the logical loss space as the sum of shared weights and private weights to select the common features of each task and the characteristics having an essential impact on a single task. A diagnostic model was constructed as a feature selection method as well as a classification method. We calculated accuracy, recall, precision, and area under the curve (AUC) and compared the performance of our new multi-task model with traditional diagnostic model performance. Results: A diagnostic model combining the support vector machine algorithm as a classification algorithm and our model as a feature selection method had an average AUC of 0.993 in the training set, with AUC, accuracy, precision, and recall rates respectively of 0.992, 0.920, 0.969, and 0.871 in the test set. The diagnostic model built on our multi-task model alone, in the training set, had an average AUC of 0.987, and in the test set, the AUC, accuracy, precision, and recall rates were 0.984, 0.895, 0.954, and 0.838. Conclusion: It is feasible to implement the multi-task learning model developed in our study using logistic regression to differentiate between glioblastoma and solitary brain metastases.

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BACKGROUND: The effectiveness of conventional MRI (cMRI)-based radiomics in differentiating glioblastoma (GBM) from solitary brain metastases (SBM) is not satisfactory enough. Therefore, we aimed to develop an integrated radiomics model to improve the performance of differentiating GBM from SBM. METHODS: One hundred patients with solitary brain tumors (50 with GBM, 50 with SBM) were retrospectively enrolled and randomly assigned to the training set (n = 80) or validation set (n = 20). A total of 4,424 radiomic features were obtained from contrast-enhanced T1-weighted imaging (CE-T1WI) with the contrast-enhancing and peri-enhancing edema region, T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images. The partial least squares (PLS) regression with fivefold cross-validation is used to analyze the correlation between different radiomic features and different modalities. The cross-validity analysis was performed to judge whether a new principal component or a new feature dimension can significantly improve the final prediction effect. The principal components with effective interpretation in all radiomic features were projected to a low-dimensional space (2D in this study). The effective features of the new projection mapping were then sent to the random forest classifier to predict the results. The performance of differentiating GBM from SBM was compared between the integrated radiomics model and other radiomics models or nonradiomics methods using the area under the receiver operating characteristics curve (AUC). RESULTS: Through the cross-validity analysis of partial least squares, hundreds of radiomic features were projected into a new two-dimensional space to complete the construction of radiomics model. Compared with the combined radiomics model using DWI + 18F-FDG PET (AUC = 0.93, p = 0.014), cMRI + DWI (AUC = 0.89, p = 0.011), cMRI + 8F-FDG PET (AUC = 0.91, p = 0.015), and single radiomics model using cMRI (AUC = 0.85, p = 0.018), DWI (AUC = 0.84, p = 0.017), and 18F-FDG PET (AUC = 0.85, p = 0.421), the integrated radiomics model (AUC = 0.98) showed more efficient diagnostic performance. The integrated radiomics model (AUC = 0.98) also showed significantly better performance than any single ADC, SUV, or TBR parameter (AUC = 0.57-0.71, p < 0.05). The integrated radiomics model showed better performance in the training (AUC = 0.98) and validation (AUC = 0.93) sets than any other models and methods, demonstrating robustness. CONCLUSIONS: We developed an integrated radiomics model incorporating DWI and 18F-FDG PET, which improved the performance of differentiating GBM from SBM greatly.

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Previous studies showed a high diagnostic value of diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) in differentiation among glioblastomas, primary cerebral lymphomas (PCLs), and solitary brain metastases, whereas other studies reported a low or no diagnostic value of DWI and DTI in differentiation among the three types of brain malignant tumors. In order to enhance the strength of evidence, meta-analysis was conducted to summarize results of studies evaluating the diagnostic values of DWI or DTI in differentiation among the three types of brain malignant tumors. Articles evaluating the diagnostic values of DWI or DTI in differentiation among the three types of tumors and published before December 2019 were searched in databases (PubMed, Medline, Web of Science, EMBASE, and Google Scholar). A summary of sensitivity, specificity, positive likelihood ratios (PLR), negative likelihood ratios (NLR), and diagnostic odds ratio (DOR) were calculated from the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) of each study using STATA 12.0 software and Meta-Disc Version 1.4. In addition, the summary receive-operating characteristic (SROC) curve was constructed. Ultimately, we included 19 diagnostic studies (including 735 glioblastomas patients, 31 PCLs patients, and 792 patients with solitary brain metastases). Regarding differentiation between glioblastomas and solitary brain metastases using DWI or DTI, the calculated pooled parameters were as follows: sensitivity, 0.84 [95% confidence interval (CI): 0.78-0.89]; specificity, 0.88 (95% CI: 0.83-0.92); PLR, 7.2 (95% CI: 4.6-11.3); NLR, 0.18 (95% CI: 0.12-0.27); and DOR, 41 (95% CI: 18-93). The analysis showed a significant heterogeneity (sensitivity, I2 = 91.31%, p < 0.01; specificity, I2 = 89.24%, p < 0.01). In conclusion, DWI and DTI showed a moderate diagnostic value for differentiating glioblastomas from solitary brain metastasis. Additionally, large-scale prospective studies are essential to explore differentiation between PCLs and solitary brain metastases using DWI or DTI.

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OBJECTIVES: To determine the utility of amide proton transfer-weighted (APTw) MR imaging in distinguishing solitary brain metastases (SBMs) from glioblastomas (GBMs). METHODS: Forty-five patients with SBMs and 43 patients with GBMs underwent conventional and APT-weighted sequences before clinical intervention. The APTw parameters and relative APTw (rAPTw) parameters in the tumour core and the peritumoral brain zone (PBZ) were obtained and compared between SBMs and GBMs. The receiver-operating characteristic (ROC) curve was used to assess the best parameter for distinguishing between the two groups. RESULTS: The APTwmax, APTwmin, APTwmean, rAPTwmax, rAPTwmin or rAPTwmean values in the tumour core were not significantly different between the SBM and GBM groups (P = 0.141, 0.361, 0.221, 0.305, 0.578 and 0.448, respectively). However, the APTwmax, APTwmin, APTwmean, rAPTwmax, rAPTwmin or rAPTwmean values in the PBZ were significantly lower in the SBM group than in the GBM group (P < 0.001). The APTwmin values had the highest area under the ROC curve 0.905 and accuracy 85.2% in discriminating between the two neoplasms. CONCLUSION: As a noninvasive imaging method, APT-weighted MR imaging can be used to distinguish SBMs from GBMs. KEY POINTS: • APTw values in the tumour core were not different between SBMs and GBMs. • APTw values in peritumoral brain zone were lower in SBMs than in GBMs. • The APTw min was the best parameter to distinguish SBMs from GBMs.

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OBJECTIVE: To compare the value of MRI diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in differentiating high-grade-astrocytomas from solitary-brain-metastases. METHODS: Thirty-one high-grade-astrocytomas and twenty solitary-brain-metastases were retrospectively identified. DKI parameters [mean kurtosis (MK), radial kurtosis (Kr), and axial kurtosis (Ka)] and DTI parameters [fractional anisotropy (FA) and mean diffusivity (MD)] values with and without correction by contralateral normal-appearing white matter (NAWM) in the tumoral solid part and peritumoral edema, were compared using the t-test. Receiver operating characteristic (ROC) curves were used to test for the best parameters. RESULTS: The DKI values (MK, Kr, and Ka) and DTI values (FA and MD) in tumoral solid parts did not show significant differences between the two groups. Corrected and uncorrected MK, Kr, and Ka values in peritumoral edema were significantly higher in high-grade-astrocytomas than solitary-brain-metastases, and MD values without correction were lower in high-grade astrocytomas than solitary-brain-metastases. The areas under curve (AUC) of corrected Ka (1.000), MK (0.889), and Kr (0.880) values were significantly higher than those of MD (0.793) and FA (0.472) values. The optimal thresholds for corrected MK, Kr, Ka, and MD were 0.369, 0.405, 0.483, and 2.067, respectively. CONCLUSION: DKI and directional analysis could lead to improved differentiation with better sensitivity and directional specificity than DTI.

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Solitary brain metastasis in non-small cell lung cancer (NSCLC) patients was previously considered as Stage IV. Gen-erally, only chemotherapy or radiotherapy rather than surgery was considered the treatment for these patients;hence, achieving the de-sired effect was difficult. In recent years, the treatment benefit for these patients significantly increased. Several patients were even pro-vided the chance of being cured through standardized and individualized treatment by a multiple disciplinary team (MDT). This article introduces two solitary brain metastasis patients with NSCLC who were treated by MDT in Tianjin Medical University Cancer Institute and Hospital. This article aims to explore a complete set of diagnostic and treatment practices for the benefit of more patients.