Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation.
OBJECTIVE
Pseudoprogression (PsP) can mimic true tumor progression (TTP) on magnetic resonance imaging in patients with glioblastoma multiform (GBM). The phenotypical similarity between PsP and TTP makes it a challenging task for physicians to distinguish these entities. So far, no approved biomarkers or computer-aided diagnosis systems have been used clinically for this purpose.


METHODS
To address this challenge, the authors developed an objective classification system for PsP and TTP based on longitudinal diffusion tensor imaging. A novel spatio-temporal discriminative dictionary learning scheme was proposed to differentiate PsP and TTP, thereby avoiding segmentation of the region of interest. The authors constructed a novel discriminative sparse matrix with the classification-oriented dictionary learning approach by excluding the shared features of two categories, so that the pooled features captured the subtle difference between PsP and TTP. The most discriminating features were then identified from the pooled features by their feature scoring system. Finally, the authors stratified patients with GBM into PsP and TTP by a support vector machine approach. Tenfold cross-validation (CV) and the area under the receiver operating characteristic (AUC) were used to assess the robustness of the developed system.


RESULTS
The average accuracy and AUC values after ten rounds of tenfold CV were 0.867 and 0.92, respectively. The authors also assessed the effects of different methods and factors (such as data types, pooling techniques, and dimensionality reduction approaches) on the performance of their classification system which obtained the best performance.


CONCLUSIONS
The proposed objective classification system without segmentation achieved a desirable and reliable performance in differentiating PsP from TTP. Thus, the developed approach is expected to advance the clinical research and diagnosis of PsP and TTP.
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Xiaohua
X
Qian
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Hua
H
Tan
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Jian
J
Zhang
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Weilin
W
Zhao
Department of Radiation Oncology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Michael D
MD
Chan
Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina 27157.
Xiaobo
X
Zhou
D001932:Brain Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology*; D003937:Diagnosis, Differential; D056324:Diffusion Tensor Imaging; D018450:Disease Progression; D005909:Glioblastoma / Q000000981:diagnostic imaging* / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008137:Longitudinal Studies; D062211:Spatio-Temporal Analysis; D060388:Support Vector Machine