Gliomas are the most common primary brain tumors. The diffuse infiltration of white matter (WM) tracts by cerebral gliomas is a major cause of their appalling prognosis: tumor cells invade, displace, and possibly destroy WM. An early diagnosis and a comprehensive evaluation of tumor extent and relationships with surrounding ana- tomical structures are crucial in determining prognosis and treatment planning. Conventional MRI sequences (e.g. T1- or T2-weighted images) have limited sensitivity and specificity in diagnosing brain tumors [1], because they do not always allow precise delineation of tumor mar- gins, or tumor differentiation from edema and/or treatment effects. In particular, contrast-enhanced MR images may underestimate lesion margins, which is critical for image-guided tumor resection, radio- therapy planning, and for assessing the response to chemotherapy. On the contrary, Diffusion Tensor Imaging (DTI) can identify peritumoral white-matter abnormalities [2], by detecting the presence of small areas with tumor-cell infiltration in WM around the edge of the gross tumor, as confirmed by image guided biopsies. In particular the tumor core is characterized by reduced anisotropy and increased isotropy, while, around this area, tumor infiltration shows increased isotropy, but normal anisotropy [3]. The aims of this study were: (a) to characterize pathological and healthy tissue in DTI datasets by 3D statistical texture analysis; (b) to develop a (semi-automated) Computer Assisted Detection (CAD) system for cerebral tumors, remotely accessed over the Internet.
A CAD system for cerebral gliomas based on 3D texture features in Diffusion Tensor MR images
DE NUNZIO, Giorgio;DONATIVI, MARINA;
2012-01-01
Abstract
Gliomas are the most common primary brain tumors. The diffuse infiltration of white matter (WM) tracts by cerebral gliomas is a major cause of their appalling prognosis: tumor cells invade, displace, and possibly destroy WM. An early diagnosis and a comprehensive evaluation of tumor extent and relationships with surrounding ana- tomical structures are crucial in determining prognosis and treatment planning. Conventional MRI sequences (e.g. T1- or T2-weighted images) have limited sensitivity and specificity in diagnosing brain tumors [1], because they do not always allow precise delineation of tumor mar- gins, or tumor differentiation from edema and/or treatment effects. In particular, contrast-enhanced MR images may underestimate lesion margins, which is critical for image-guided tumor resection, radio- therapy planning, and for assessing the response to chemotherapy. On the contrary, Diffusion Tensor Imaging (DTI) can identify peritumoral white-matter abnormalities [2], by detecting the presence of small areas with tumor-cell infiltration in WM around the edge of the gross tumor, as confirmed by image guided biopsies. In particular the tumor core is characterized by reduced anisotropy and increased isotropy, while, around this area, tumor infiltration shows increased isotropy, but normal anisotropy [3]. The aims of this study were: (a) to characterize pathological and healthy tissue in DTI datasets by 3D statistical texture analysis; (b) to develop a (semi-automated) Computer Assisted Detection (CAD) system for cerebral tumors, remotely accessed over the Internet.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
