BACKGROUND AND PURPOSE: Solitary MET and GBM are difficult to distinguish by using MR imaging. Differentiation is useful before any metastatic work-up or biopsy. Our hypothesis was that MET and GBM tumors differ in morphology. Shape analysis was proposed as an indicator for discriminating these 2 types of brain pathologies. The purpose of this study was to evaluate the accuracy of this approach in the discrimination of GBMs and brain METs. MATERIALS AND METHODS: The dataset consisted of 33 brain MR imaging sets of untreated patients, of which 18 patients were diagnosed as having a GBM and 15 patients, as having solitary metastatic brain tumor. The MR imaging was segmented by using the K-means algorithm. The resulting set of classes (also called "clusters") represented the variety of tissues observed. A morphology-based approach allowed discrimination of the 2 types of tumors. This approach was validated by a leave-1-patient-out procedure. RESULTS: A method was developed for the discrimination of GBMs and solitary METs. Two masses out of 33 were wrongly classified; the overall results were accurate in 93.9% of the observed cases. CONCLUSIONS: A semiautomated method based on a morphologic analysis was developed. Its application was found to be useful in the discrimination of GBM from solitary MET.
BACKGROUND AND PURPOSE: Solitary MET and GBM are difficult to distinguish by using MR imaging. Differentiation is useful before any metastatic work-up or biopsy. Our hypothesis was that MET and GBM tumors differ in morphology. Shape analysis was proposed as an indicator for discriminating these 2 types of brain pathologies. The purpose of this study was to evaluate the accuracy of this approach in the discrimination of GBMs and brain METs. MATERIALS AND METHODS: The dataset consisted of 33 brain MR imaging sets of untreated patients, of which 18 patients were diagnosed as having a GBM and 15 patients, as having solitary metastatic brain tumor. The MR imaging was segmented by using the K-means algorithm. The resulting set of classes (also called "clusters") represented the variety of tissues observed. A morphology-based approach allowed discrimination of the 2 types of tumors. This approach was validated by a leave-1-patient-out procedure. RESULTS: A method was developed for the discrimination of GBMs and solitary METs. Two masses out of 33 were wrongly classified; the overall results were accurate in 93.9% of the observed cases. CONCLUSIONS: A semiautomated method based on a morphologic analysis was developed. Its application was found to be useful in the discrimination of GBM from solitary MET.
Authors: Hermann B Frieboes; Xiaoming Zheng; Chung-Ho Sun; Bruce Tromberg; Robert Gatenby; Vittorio Cristini Journal: Cancer Res Date: 2006-02-01 Impact factor: 12.701
Authors: Hermann B Frieboes; John S Lowengrub; S Wise; X Zheng; Paul Macklin; Elaine L Bearer; Vittorio Cristini Journal: Neuroimage Date: 2007-03-23 Impact factor: 6.556
Authors: A Devos; L Lukas; J A K Suykens; L Vanhamme; A R Tate; F A Howe; C Majós; A Moreno-Torres; M van der Graaf; C Arús; S Van Huffel Journal: J Magn Reson Date: 2004-09 Impact factor: 2.229
Authors: Juan M García-Gómez; Jan Luts; Margarida Julià-Sapé; Patrick Krooshof; Salvador Tortajada; Javier Vicente Robledo; Willem Melssen; Elies Fuster-García; Iván Olier; Geert Postma; Daniel Monleón; Angel Moreno-Torres; Jesús Pujol; Ana-Paula Candiota; M Carmen Martínez-Bisbal; Johan Suykens; Lutgarde Buydens; Bernardo Celda; Sabine Van Huffel; Carles Arús; Montserrat Robles Journal: MAGMA Date: 2008-11-07 Impact factor: 2.310