Molecular and metabolic pattern classification for detection of brain glioma progression

Abstract

Objectives: The ability to differentiate between brain tumor progression and radiation therapy induced necrosis is critical for appropriate patient management. In order to improve the differential diagnosis, we combined fluorine-18 2-fluoro-deoxyglucose positron emission tomography (18 F-FDG PET), proton magnetic resonance spectroscopy (1 H MRS) and histological data to develop a multi-parametric machine-learning model. Methods: We enrolled twelve post-therapy patients with grade 2 and 3 gliomas that were suspicious of tumor progression. All patients underwent 18 F-FDG PET and 1 H MRS. Maximal standardized uptake value (SUVmax) of the tumors and reference regions were obtained. Multiple 2D maps of choline (Cho), creatine (Cr), and N-acetylaspartate (NAA) of the tumors were generated. A support vector machine (SVM) learning model was established to take imaging biomarkers and histological data as input vectors. A combination of clinical follow-up and multiple sequential MRI studies served as the basis for assessing the clinical outcome. All vector combinations were evaluated for diagnostic accuracy and cross validation. The optimal cutoff value of individual parameters was calculated using Receiver operating characteristic (ROC) plots. Results: The SVM and ROC analyses both demonstrated that SUVmax of the lesion was the most significant single diagnostic parameter (75% accuracy) followed by Cho concentration (67% accuracy). SVM analysis of all paired parameters showed SUVmax and Cho concentration in combination could achieve 83% accuracy. SUVmax of the lesion paired with SUVmax of the white matter as well as the tumor Cho paired with the tumor Cr both showed 83% accuracy. These were the most significant paired diagnostic parameters of either modality. Combining all four parameters did not improve the results. However, addition of two more parameters, Cho and Cr of brain parenchyma contralateral to the tumor, increased the accuracy to 92%. Conclusion: This study suggests that SVM models may improve detection of glioma progression more accurately than single parametric imaging methods. Research support: National Cancer Institute, Cancer Center Support Grant Supplement Award, Imaging Response Assessment Teams.

Pseudoprogression of brain tumors

“…SVM may be useful for distinction of metastases and radiation necrosis . In SVM testing, Cho was suggested as the most discriminatory MRS parameter in early treated glioblastoma but with relatively low (<70%) accuracy …”

Choline metabolism-based molecular diagnosis of cancer: an update

“…The use of multiparametric MRI in locally advanced breast cancer for the prediction of partial clinical response has been shown to be highly predictive [96]. Multimodal multiparametric studies combining [ 18 F]-fluorodeoxyglucose PET and MRI approaches that incorporate the MRSI-detected tCho signal and rely on multiparametric machine-learning approaches demonstrated a significant improvement in the detection of glioma progression [97]. A novel application for MRSI is in the intraoperative MR suite for tissue characterization and optimization of tumor resection in glioma patients [98].…”

“…The tCho signal as one parameter in multiparametric MRI and multimodal PET/MRI approaches, in conjunction with multiparametric machine-learning models, has so far been used in limited studies [94,95,97] and should be further explored and expanded. The use of tCho MRSI in the intraoperative MR suite for optimizing resected tumor margins has shown promise during brain tumor surgeries [98] and requires further evaluation in larger clinical studies.…”

2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors

“…SVM was employed for the classification and sensitivity analysis in our study due to its high performance in many studies [25, 27, 28]. …”