Combination of IVIM-DWI and 3D-ASL for differentiating true progression from pseudoprogression of Glioblastoma multiforme after concurrent chemoradiotherapy: study protocol of a prospective diagnostic trial

Liu, Zhicheng; Yan, Lin‐Feng; Hu, Yu-Chuan; Sun, Ying-Zhi; Tian, Qiang; Nan, Hai-Yan; Yu, Ying; Sun, Qian; Wang, Wen; Cui, Guangbin

doi:10.1186/s12880-017-0183-y

Cited by 42 publications

(42 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The study data of the current project were derived from a diagnostic trial that has been registered to ClinicalTrials.gov with the trial protocol published and the study protocol was approved. The study population is the same as the one described in a previous work and written informed consent was obtained from all individuals .…”

Section: Methodsmentioning

confidence: 99%

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

Yang

Yan

Zhang

et al. 2019

Magnetic Resonance Imaging

Self Cite

View full text Add to dashboard Cite

Background Accurate glioma grading plays an important role in patient treatment. Purpose To investigate the influence of varied texture retrieving models on the efficacy of grading glioma with support vector machine (SVM). Study Type Retrospective. Population In all, 117 glioma patients including 25, 29, and 63 grade II, III, and IV gliomas, respectively, based on WHO 2007. Field Strength/Sequence 3.0T MRI/ T1WI, T2 fluid‐attenuated inversion recovery, contrast enhanced T1, arterial spinal labeling, diffusion‐weighted imaging (0, 30, 50, 100, 200, 300, 500, 800, 1000, 1500, 2000, 3000, and 3500 sec/mm2), and dynamic contrast‐enhanced. Assessment Texture attributes from 30 parametric maps were retrieved using four models, including Global, gray‐level co‐occurrence matrix (GLCM), gray‐level run‐length matrix (GLRLM), and gray‐level size‐zone matrix (GLSZM). Attributes derived from varied models were input into radial basis function SVM (RBF‐SVM) combined with attribute selection using SVM‐recursive feature elimination (SVM‐RFE). The SVM model was trained and established with 80% randomly selected data of each category using 10‐fold crossvalidation. The model performance was further tested using the remaining 20% data. Statistical Tests Ten‐fold crossvalidation was used to validate the model performance. Results Based on 30 parametric maps, 90, 240, 390, or 390 texture attributes were retrieved using the Global, GLCM, GLRLM, or GLSZM model, respectively. SVM‐RFE was able to reduce attribute redundancy as well as improve RBF‐SVM performance. Training data were oversampled by applying the Synthetic Minority Oversampling Technique (SMOTE) method to overcome the data imbalance problem; test results were able to further demonstrate the classifying performance of the final models. GLSZM using gray‐level 64 was the optimal model to retrieve powerful image texture attributes to produce enough classifying power with an accuracy / area under the curve of 0.760/0.867 for the training and 0.875/0.971 for the independent test. Fifteen attributes were selected with SVM‐RFE to provide comparable classifying efficacy. Data Conclusion When using image textures‐based SVM classification of gliomas, the GLSZM model in combination with gray‐level 64 and attribute selection may be an optimized solution. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1263–1274.

show abstract

Section: Methodsmentioning

confidence: 99%

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

Yang

Yan

Zhang

et al. 2019

Magnetic Resonance Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…The perfusion fraction was significantly higher in tumor progression compared with pseudoprogression. Currently, a study is under investigation to compare IVIM and ASL for the differentiation of tumor and pseudoprogression …”

Section: New Developments and Perspectivesmentioning

confidence: 99%

“…Currently, a study is under investigation to compare IVIM and ASL for the differentiation of tumor and pseudoprogression. 62 Analyses Analysis of perfusion MRI can be done using many different methods, of which most are semiquantitative or are determined by arterial input function. A new postprocessing technique using wavelet-based reconstruction might further improve visual assessment, as background structures and vessels are better suppressed.…”

Section: New Mri Perfusion Techniquesmentioning

confidence: 99%

Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

Dijken

Laar

Smits

et al. 2018

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Treatment evaluation of patients with glioblastomas is important to aid in clinical decisions. Conventional MRI with contrast is currently the standard method, but unable to differentiate tumor progression from treatment‐related effects. Pseudoprogression appears as new enhancement, and thus mimics tumor progression on conventional MRI. Contrarily, a decrease in enhancement or edema on conventional MRI during antiangiogenic treatment can be due to pseudoresponse and is not necessarily reflective of a favorable outcome. Neovascularization is a hallmark of tumor progression but not for posttherapeutic effects. Perfusion‐weighted MRI provides a plethora of additional parameters that can help to identify this neovascularization. This review shows that perfusion MRI aids to identify tumor progression, pseudoprogression, and pseudoresponse. The review provides an overview of the most applicable perfusion MRI methods and their limitations. Finally, future developments and remaining challenges of perfusion MRI in treatment evaluation in neuro‐oncology are discussed. Level of Evidence: 3 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:11–22.

show abstract

“…We did not give extra gadolinium to patients during radiotherapy in this preliminary study given recent concerns about gadolinium toxicity (35). However, given these ndings we do plan to dose gadolinium for 1-2 additional timepoints during RT in future patients for response to therapy and radiotherapy treatment adaptation as well as investigate non-invasive perfusion methods during RT (36,37). Nevertheless, the association of edema during radiotherapy with post-treatment contrast volume increase suggests that the two are related in this study.…”

Section: Discussionmentioning

confidence: 99%

MRI-guided Radiotherapy Identifies Early Pseudoprogression of Glioblastoma

Jones

Dooley

Maziero

et al. 2020

Preprint

View full text Add to dashboard Cite

Background The standard glioblastoma treatment paradigm consists of surgery, pre-radiotherapy MRI, six weeks of chemoradiotherapy, followed by post-radiotherapy MRI and continued adjuvant temozolomide. In a significant proportion of patients, post-radiotherapy MRI demonstrates tumor enlargement due to either treatment failure (true progression) or treatment response (pseudoprogression). Recently introduced MRI-guided radiotherapy systems obtain daily MRI of glioblastoma patients, and we hypothesized that progression can be identified early during radiotherapy.Methods Fourteen glioblastoma patients underwent tri-Cobalt-60 MRI-guided radiotherapy in 30 fractions over 6 weeks delivered with concurrent temozolomide. The tumor target volume was delineated on MRI before each of the 30 fractions. The images obtained by the 0.35 T MRI-guided system is shown to be similar to T2-weighted images obtained by a clinical diagnostic MRI-scanner. Hyperintense volumes were measured over time through radiotherapy.Results Four of fourteen patients demonstrated increases of at least 25% and 1.5 cc in T2 hyperintense volume through radiation therapy. This volume expansion correlated with both T2/FLAIR and contrast-enhanced volume expansion on post-radiotherapy diagnostic MRIs. In three of four cases, significant volume growth only started at week three of treatment, with the most prominent changes occurring during weeks four and five. While patient numbers are limited, patients with growth during therapy exhibited excellent survival, consistent with the known improved survival of patients with pseudoprogression.Conclusions Daily MRI acquisition during radiotherapy identifies early pseudoprogression typically starting during week 3 or 4 of treatment. This and other daily MRI techniques during radiotherapy could enable early adaptation of therapy in glioblastoma patients.

show abstract

Combination of IVIM-DWI and 3D-ASL for differentiating true progression from pseudoprogression of Glioblastoma multiforme after concurrent chemoradiotherapy: study protocol of a prospective diagnostic trial

Cited by 42 publications

References 27 publications

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

Optimizing Texture Retrieving Model for Multimodal MR Image‐Based Support Vector Machine for Classifying Glioma

Perfusion MRI in treatment evaluation of glioblastomas: Clinical relevance of current and future techniques

MRI-guided Radiotherapy Identifies Early Pseudoprogression of Glioblastoma

Contact Info

Product

Resources

About