Differentiation of pilocytic astrocytoma from glioblastoma using a machine-learning framework based upon quantitative T1 perfusion MRI
Background and purpose - Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T1 perfusion MRI parameters combined under a machine learn...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
22 January 2023
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| In: |
Magnetic resonance imaging
Year: 2023, Jahrgang: 98, Pages: 76-82 |
| ISSN: | 1873-5894 |
| DOI: | 10.1016/j.mri.2022.12.013 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.mri.2022.12.013 Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0730725X22002284 
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| Verfasserangaben: | Neha Vats, Anirban Sengupta, Rakesh K. Gupta, Rana Patir, Sandeep Vaishya, Sunita Ahlawat, Jitender Saini, Sumeet Agarwal, Anup Singh |
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| 245 | 1 | 0 | |a Differentiation of pilocytic astrocytoma from glioblastoma using a machine-learning framework based upon quantitative T1 perfusion MRI |c Neha Vats, Anirban Sengupta, Rakesh K. Gupta, Rana Patir, Sandeep Vaishya, Sunita Ahlawat, Jitender Saini, Sumeet Agarwal, Anup Singh |
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| 520 | |a Background and purpose - Differentiation of pilocytic astrocytoma (PA) from glioblastoma is difficult using conventional MRI parameters. The purpose of this study was to differentiate these two similar in appearance tumors using quantitative T1 perfusion MRI parameters combined under a machine learning framework. - Materials and methods - This retrospective study included age/sex and location matched 26 PA and 33 glioblastoma patients with tumor histopathological characterization performed using WHO 2016 classification. Multi-parametric MRI data were acquired at 3 T scanner and included T1 perfusion and DWI data along with conventional MRI images. Analysis of T1 perfusion data using a leaky-tracer-kinetic-model, first-pass-model and piecewise-linear-model resulted in multiple quantitative parameters. ADC maps were also computed from DWI data. Tumors were segmented into sub-components such as enhancing and non-enhancing regions, edema and necrotic/cystic regions using T1 perfusion parameters. Enhancing and non-enhancing regions were combined and used as an ROI. A support-vector-machine classifier was developed for the classification of PA versus glioblastoma using T1 perfusion MRI parameters/features. The feature set was optimized using a random-forest based algorithm. Classification was also performed between the two tumor types using the ADC parameter. - Results - T1 perfusion parameter values were significantly different between the two groups. The combination of T1 perfusion parameters classified tumors more accurately with a cross validated error of 9.80% against that of ADC's 17.65% error. - Conclusion - The approach of using quantitative T1 perfusion parameters based upon a support-vector-machine classifier reliably differentiated PA from glioblastoma and performed better classification than ADC. | ||
| 650 | 4 | |a Glioblastoma | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Pilocytic astrocytoma | |
| 650 | 4 | |a SVM | |
| 650 | 4 | |a T perfusion MRI | |
| 700 | 1 | |a Sengupta, Anirban |e VerfasserIn |4 aut | |
| 700 | 1 | |a Gupta, Rakesh K. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Patir, Rana |e VerfasserIn |4 aut | |
| 700 | 1 | |a Vaishya, Sandeep |e VerfasserIn |4 aut | |
| 700 | 1 | |a Ahlawat, Sunita |e VerfasserIn |4 aut | |
| 700 | 1 | |a Saini, Jitender |e VerfasserIn |4 aut | |
| 700 | 1 | |a Agarwal, Sumeet |e VerfasserIn |4 aut | |
| 700 | 1 | |a Singh, Anup |e VerfasserIn |4 aut | |
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