Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation

• Published in: Medical physics (Lancaster) 2016-11, Vol.43 (11), p.5889-5902
• Main Authors: Qian, Xiaohua, Tan, Hua, Zhang, Jian, Zhao, Weilin, Chan, Michael D., Zhou, Xiaobo
• Format: Article
• Language: English
• Subjects: biodiffusion; Biological material, e.g. blood, urine; Haemocytometers; biomedical MRI; brain; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - pathology; cancer; Clinical applications; Cluster analysis; Diagnosis, Differential; Dictionaries; Diffusion; Diffusion Tensor Imaging; Digital computing or data processing equipment or methods, specially adapted for specific applications; discriminative sparse matrix; Disease Progression; Glioblastoma - diagnostic imaging; Glioblastoma - pathology; glioblastoma multiform; Humans; image classification; Image data processing or generation, in general; Image Processing, Computer-Assisted; In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines; Inference methods or devices; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging; Learning; longitudinal DTI; Longitudinal Studies; Magnetic resonance imaging; medical image processing; Medical image segmentation; MRI: anatomic, functional, spectral, diffusion; pseudoprogression; QUANTITATIVE IMAGING AND IMAGE PROCESSING; Radiation therapy; Spatio-Temporal Analysis; spatio‐temporal dictionary learning; Support Vector Machine; support vector machines
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4963812

Purpose: 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.