Non-Hodgkin lymphoma response evaluation with MRI texture classification
Pertovaara, Hannu; Heinonen, Tomi T; Järvenpää, Ritva; Harrison, Lara CV (Tay); Luukkaala, Tiina (Tay); Saarinen, Tuomas O (Tay); Soimakallio, Seppo (Tay); Kellokumpu-Lehtinen, Pirkko-Liisa (Tay); Dastidar, Prasun (Tay) (2009)
2009
Journal of Experimental & Clinical Cancer Research 28
87
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Julkaisun pysyvä osoite on
http://urn.fi/urn:nbn:uta-3-551
http://urn.fi/urn:nbn:uta-3-551
Kuvaus
BioMed Central Open access
Tiivistelmä
Background
To show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis.
Methods
A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests.
Results
NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images.
Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue.
Conclusion
Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.
To show magnetic resonance imaging (MRI) texture appearance change in non-Hodgkin lymphoma (NHL) during treatment with response controlled by quantitative volume analysis.
Methods
A total of 19 patients having NHL with an evaluable lymphoma lesion were scanned at three imaging timepoints with 1.5T device during clinical treatment evaluation. Texture characteristics of images were analyzed and classified with MaZda application and statistical tests.
Results
NHL tissue MRI texture imaged before treatment and under chemotherapy was classified within several subgroups, showing best discrimination with 96% correct classification in non-linear discriminant analysis of T2-weighted images.
Texture parameters of MRI data were successfully tested with statistical tests to assess the impact of the separability of the parameters in evaluating chemotherapy response in lymphoma tissue.
Conclusion
Texture characteristics of MRI data were classified successfully; this proved texture analysis to be potential quantitative means of representing lymphoma tissue changes during chemotherapy response monitoring.
Kokoelmat
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