Farzin Imani1, Fernando E Boada2, Frank S Lieberman3, Denise K Davis2, James M Mountz2. 1. Department of Radiology, University of Pittsburgh Medical Center, PA, United States. Electronic address: imanif@upmc.edu. 2. Department of Radiology, University of Pittsburgh Medical Center, PA, United States. 3. Department of Neurology, University of Pittsburgh Medical Center, PA, United States.
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.
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 tumorCho paired with the tumorCr 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.
Authors: F Earnest; P J Kelly; B W Scheithauer; B A Kall; T L Cascino; R L Ehman; G S Forbes; P L Axley Journal: Radiology Date: 1988-03 Impact factor: 11.105
Authors: Dita Wagnerova; Vit Herynek; Alberto Malucelli; Monika Dezortova; Josef Vymazal; Dusan Urgosik; Martin Syrucek; Filip Jiru; Antonin Skoch; Robert Bartos; Martin Sames; Milan Hajek Journal: Eur Radiol Date: 2012-06-12 Impact factor: 5.315
Authors: Sebastian Herminghaus; Ulrich Pilatus; Walter Möller-Hartmann; Peter Raab; Heinrich Lanfermann; Wolfgang Schlote; Friedhelm E Zanella Journal: NMR Biomed Date: 2002-10 Impact factor: 4.044
Authors: Ian Law; Nathalie L Albert; Javier Arbizu; Ronald Boellaard; Alexander Drzezga; Norbert Galldiks; Christian la Fougère; Karl-Josef Langen; Egesta Lopci; Val Lowe; Jonathan McConathy; Harald H Quick; Bernhard Sattler; David M Schuster; Jörg-Christian Tonn; Michael Weller Journal: Eur J Nucl Med Mol Imaging Date: 2018-12-05 Impact factor: 9.236
Authors: Xuguang Chen; Vishwa S Parekh; Luke Peng; Michael D Chan; Kristin J Redmond; Michael Soike; Emory McTyre; Doris Lin; Michael A Jacobs; Lawrence R Kleinberg Journal: Neurooncol Adv Date: 2021-10-25