Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status

2019 ◽  
Vol 62 (3) ◽  
pp. 319-326 ◽  
Author(s):  
Chae Jung Park ◽  
Yoon Seong Choi ◽  
Yae Won Park ◽  
Sung Soo Ahn ◽  
Seok-Gu Kang ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Isocitrate Dehydrogenase ◽  
Diffusion Tensor ◽  
Lower Grade ◽  
Mutation Status ◽  
Lower Grade Glioma ◽  
Isocitrate Dehydrogenase Mutation

scholarly journals QOLP-37. QUALITATIVE RESEARCH IN LOWER GRADE GLIOMA: UNDERSTANDING SIGNS, SYMPTOMS, AND DISEASE IMPACTS DURING EXPECTANT MANAGEMENT

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi206-vi206
Author(s):  
Audra Boscoe ◽  
Ted Wells ◽  
Christina Graham ◽  
Caitlin Pohl ◽  
Brooke Witherspoon ◽  
...  
Keyword(s):  
Treatment Options ◽  
Expectant Management ◽  
Signs And Symptoms ◽  
Molecular Therapy ◽  
Patient Specific ◽  
Lower Grade ◽  
Concept Elicitation ◽  
Post Surgery ◽  
Lower Grade Glioma ◽  
Isocitrate Dehydrogenase Mutation

Abstract BACKGROUND Patients with lower grade glioma (LGG) (i.e., grade II or III) have limited treatment options. After surgical resection of their tumor, patients will undergo either a period of expectant management (watch and wait) or treatment with adjuvant chemotherapy and/or radiotherapy. Approximately 80% of patients with LGG have an isocitrate dehydrogenase mutation, which is a viable target for molecular therapy. This offers a therapeutic intervention that could potentially delay the need for chemotherapy and/or radiotherapy in select patients. Several prognostic and patient-specific factors contribute to the decision to recommend expectant management, including concerns about the side effects of chemotherapy and radiotherapy. The aim of this project was to understand patients’ signs and symptoms during the expectant management period and how LGG impacts their lives. METHODS Concept elicitation interviews were conducted in the US with patients with LGG as well as key opinion leaders (KOLs) with experience treating patients with LGG. Interview data were analyzed using Atlas.ti, and patient data were reviewed against KOL data. RESULTS Seven patients with ≥ 3 months of expectant management experience and three KOLs were interviewed. During their expectant management periods, patients reported 12 signs/symptoms, mostly related to deficits in cognition. Patients reported 16 impacts across four categories, with a substantial proportion of the impacts identified as negatively affecting emotional function. The signs/symptoms and impacts reported by patients were generally also reported by KOLs. During expectant management, patients typically resume their original quality of life post-surgery, but may also experience anxiety. Patients and KOLs indicated a preference for expectant management and delaying chemotherapy or radiotherapy. CONCLUSIONS Patient and KOL interviews characterized the LGG experience and indicated a preference for expectant management, which may be supported by therapies that delay the initiation of chemotherapy and/or radiotherapy.

scholarly journals Radiomics Features Predict CIC Mutation Status in Lower Grade Glioma

2020 ◽  
Vol 10 ◽  
Author(s):  
Luyuan Zhang ◽  
Felipe Giuste ◽  
Juan C. Vizcarra ◽  
Xuejun Li ◽  
David Gutman
Keyword(s):  
Lower Grade ◽  
Mutation Status ◽  
Lower Grade Glioma

NIMG-36. VISUALIZATION OF TUMOR HETEROGENEITY AND PREDICTION OF ISOCITRATE DEHYDROGENASE MUTATION STATUS FOR HUMAN GLIOMAS BY USING MULTIPARAMETRIC PHYSIOLOGIC AND METABOLIC MRI

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi136-vi137
Author(s):  
Akifumi Hagiwara ◽  
Hiroyuki Tatekawa ◽  
Yao Jingwen ◽  
Catalina Raymond ◽  
Richard Everson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sensitivity And Specificity ◽  
Isocitrate Dehydrogenase ◽  
Glucose Transporter ◽  
Support Vector ◽  
Diffuse Glioma ◽  
Idh Mutation ◽  
Self Organizing Map ◽  
Mutation Status ◽  
Isocitrate Dehydrogenase Mutation

Abstract Preoperative prediction of isocitrate dehydrogenase mutation status is clinically meaningful, but remains challenging. This study aimed to predict the isocitrate dehydrogenase (IDH) status of gliomas by using the machine learning voxel-wise clustering method of multiparametric physiologic and metabolic magnetic resonance imaging (MRI) and to show the association of the created cluster labels with the glucose metabolism status of the tumors. Sixty-nine patients with diffuse glioma were scanned by pH-sensitive MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach, including the generation of a self-organizing map followed by the K-means clustering, was used for voxel-wise feature extraction from the acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH mutation status. Bootstrapping and leave-one-out cross-validation were used to calculate the area under the curve (AUC) of receiver operating characteristic curves, accuracy, sensitivity, and specificity for evaluating performance. Targeted biopsies were performed for 14 patients to explore the relationship between clustered labels and the expression of key glycolytic proteins determined using immunohistochemistry. The highest prediction performance to differentiate IDH status was found for 10-class clustering, with a mean AUC, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. The tissues with labels 7 + 8 + 9 + 10 showed high expression levels of hypoxia-inducible factor 1-alpha, glucose transporter 3, and hexokinase 2, which are typical of IDH wild-type glioma, whereas those with labels 1 showed low expression of these proteins. Our machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.

scholarly journals Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhenxing Huang ◽  
Changyu Lu ◽  
Gen Li ◽  
Zhenye Li ◽  
Shengjun Sun ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Molecular Pathology ◽  
Diffusion Tensor ◽  
Lower Grade ◽  
Idh Mutation ◽  
Tert Promoter Mutation ◽  
Mgmt Methylation ◽  
Promoter Mutation ◽  
Tert Promoter ◽  
The Mean

ObjectivesTo explore whether a simplified lesion delineation method and a set of diffusion tensor imaging (DTI) metric-based histogram parameters (mean, 25th percentile, 75th percentile, skewness, and kurtosis) are efficient at predicting the molecular pathology status (MGMT methylation, IDH mutation, TERT promoter mutation, and 1p19q codeletion) of lower grade insular gliomas (grades II and III).Methods40 lower grade insular glioma patients in two medical centers underwent preoperative DTI scanning. For each patient, the entire abnormal area in their b-non (b0) image was defined as region of interest (ROI), and a set of histogram parameters were calculated for two DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD). Then, we compared how these DTI metrics varied according to molecular pathology and glioma grade, with their predictive performance individually and jointly assessed using receiver operating characteristic curves. The reliability of the combined prediction was evaluated by the calibration curve and Hosmer and Lemeshow test.ResultsThe mean, 25th percentile, and 75th percentile of FA were associated with glioma grade, while the mean, 25th percentile, 75th percentile, and skewness of both FA and MD predicted IDH mutation. The mean, 25th percentile, and 75th percentile of FA, and all MD histogram parameters significantly distinguished TERT promoter status. Similarly, all MD histogram parameters were associated with 1p19q status. However, none of the parameters analyzed for either metric successfully predicted MGMT methylation. The 25th percentile of FA yielded the highest prediction efficiency for glioma grade, IDH mutation, and TERT promoter mutation, while the 75th percentile of MD gave the best prediction of 1p19q codeletion. The combined prediction could enhance the discrimination of grading, IDH and TERT mutation, and also with a good fitness.ConclusionsOverall, more invasive gliomas showed higher FA and lower MD values. The simplified ROI delineation method presented here based on the combination of appropriate histogram parameters yielded a more practical and efficient approach to predicting molecular pathology in lower grade insular gliomas. This approach could help clinicians to determine the extent of tumor resection required and reduce complications, enabling more precise treatment of insular gliomas in combination with radiotherapy and chemotherapy.

scholarly journals Fractal analysis of 11C-methionine PET in patients with newly diagnosed glioma

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yukito Maeda ◽  
Yuka Yamamoto ◽  
Takashi Norikane ◽  
Katsuya Mitamura ◽  
Tetsuhiro Hatakeyama ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Fractal Analysis ◽  
Anaplastic Astrocytoma ◽  
Idh1 Mutation ◽  
Lower Grade ◽  
Diffuse Astrocytoma ◽  
Newly Diagnosed ◽  
Mutation Status ◽  
Lower Grade Glioma ◽  
Met Pet

Abstract Background The present study tested the possible utility of fractal analysis from l-[methyl-11C]-methionine (MET) uptake in patients with newly diagnosed gliomas for differentiating glioma, especially in relation to isocitrate dehydrogenase 1 (IDH1) mutation status, and as compared with the conventional standardized uptake value (SUV) parameters. Methods Investigations of MET PET/CT were performed retrospectively in 47 patients with newly diagnosed glioma. Tumors were divided into three groups: lower grade glioma (IDH1-mutant diffuse astrocytoma and IDH1-mutant anaplastic astrocytoma), higher grade glioma (IDH1-wildtype diffuse astrocytoma and IDH1-wildtype anaplastic astrocytoma), and glioblastoma. The fractal dimension for tumor, maximum SUV (SUVmax) for tumor (T) and mean SUV for normal contralateral hemisphere (N) were calculated, and the tumor-to-normal (T/N) ratio was determined. Metabolic tumor volume (MTV) and total lesion MET uptake (TLMU) were also measured. Results There were significant differences in SUVmax (p = 0.006) and T/N ratio (p = 0.02) between lower grade glioma and glioblastoma. There were no significant differences among any of the three groups in MTV or TLMU. Significant differences were obtained in the fractal dimension between lower grade glioma and higher grade glioma (p = 0.006) and glioblastoma (p < 0.001). Conclusions The results of this preliminary study in a small patient population suggest that the fractal dimension using MET PET in patients with newly diagnosed gliomas is useful for differentiating glioma, especially in relation to IDH1 mutation status, which has not been possible with SUV parameters.

Sign in / Sign up

Export Citation Format

Share Document