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Xiaopan Xu and Xi Zhang: co-first authors.
This study aims to determine the three-dimensional (3D) texture features extracted from intensity and high-order derivative maps that could reflect textural differences between bladder tumors and wall tissues, and propose a noninvasive, image-based strategy for bladder tumor differentiation preoperatively.
A total of 62 cancerous and 62 wall volumes of interest (VOI) were extracted from T2-weighted MRI datasets of 62 patients with pathologically confirmed bladder cancer. To better reflect heterogeneous distribution of tumor tissues, 3D high-order derivative maps (the gradient and curvature maps) were calculated from each VOI. Then 3D Haralick features based on intensity and high-order derivative maps and Tamura features based on intensity maps were extracted from each VOI. Statistical analysis and recursive feature elimination-based support vector machine classifier (RFE-SVM) was proposed to first select the features with significant differences and then obtain a more predictive and compact feature subset to verify its differentiation performance.
From each VOI, a total of 58 texture features were derived. Among them, 37 features showed significant inter-class differences (\(P\le 0.01\)). With 29 optimal features selected by RFE-SVM, the classification results namely the sensitivity, specificity, accuracy and area under the curve (AUC) of the receiver operating characteristics were 0.9032, 0.8548, 0.8790 and 0.9045, respectively. By using synthetic minority oversampling technique to augment the sample number of each group to 200, the sensitivity, specificity, accuracy an AUC value of the feature selection-based classification were improved to 0.8967, 0.8780, 0.8874 and 0.9416, respectively.
Our results suggest that 3D texture features derived from intensity and high-order derivative maps can better reflect heterogeneous distribution of cancerous tissues. Texture features optimally selected together with sample augmentation could improve the performance on differentiating bladder carcinomas from wall tissues, suggesting a potential way for tumor noninvasive staging of bladder cancer preoperatively.
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American Cancer Society (2015) Cancer facts and figures 2015. American Cancer Society, Atlanta, pp 8–16
National Comprehensive Cancer Network (2015) NCCN clinical practice guidelines in oncology, pp 30–33
Makram M, Michaël P, Marc Z, Djillali S, Bernard D (2003) The value of a second transurethral resection in evaluating patients with bladder tumours. Eur Urol 43(3):241–245 CrossRef
Rais-Bahrami S, Pietryga J, Nix J (2015) Contemporary role of advanced imaging for bladder cancer staging. Urol Oncol 18(2):168–177
Sheshadri H, Kandaswamy A (2007) Experimental investigation on breast tissue classification based on statistical feature extraction of mammograms. Comput Med Imag Graph 31:46–58 CrossRef
Fu J, Yu Y, Lin H, Chai J, Chen CC (2014) Feature extraction and pattern classification of colorectal polyps in colonoscopic imaging. Comput Med Imag Graph 38(4):267–275 CrossRef
Hu Y, Liang Z, Song B, Han H, Pickhardt P, Zhu W, Duan C, Zhang H, Barish M, Lascarides C (2016) Texture feature extraction and analysis for polyp differentiation via computed tomography colonography. IEEE Trans Med Imag 35(6):1522–1531 CrossRef
Xu X, Zhang X, Tian Q, Q Tian Q, Zhang G, Lu H (2016) Differentiating bladder carcinoma from bladder wall using 3D textural features: an initial study. SPIE Med Image Process 2016:1–11
Simoes R, Walsum A, Slump C (2014) Classification and localization of early-stage Alzheimer’s disease in magnetic resonance images using a patch-based classifier ensemble. Neuroradiology 56(9):1–12
Zhang G, Song B, Zhu H, Liang Z (2012) Computer-aided diagnosis in CT colonography based on bi-labeled classifier. Int J CARS 7(Suppl):S274
Han F, Wang H, Zhang G, Han H, Song B, Li L, Moore W, Lu H, Zhao H, Liang Z (2015) Texture feature analysis for computer-aided diagnosis on pulmonary nodules. J Digit Imag 28(1):99–115 CrossRef
Haralick R, Shanmugan K, Dinstein I (1973) Texture features for image classification. Trans Syst Man Cybern SMC–3(6):610–621 CrossRef
Majtner T, Svoboda D (2012) Extension of tamura texture features for 3D fluorescence microscopy. Second international conference on 3D Imaging, pp 301–307
Tamura H, Mori S, Yamawaki T (1978) Texture features corresponding to visual perception. IEEE Trans Syst Man Cybern SMC–8(6):460–473 CrossRef
Zyout I, Czajkowska J, Grzegorzek M (2015) Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography. Comput Med Imag Graph 46:95–107 CrossRef
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422 CrossRef
Rakotomamonjy A (2003) Variable selection using SVM-based criteria. J Mach Learn Res 3:1357–1370
Chang C, Lin C (2001) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(27):1–27
Chawla N, Bowyer K, Hall L, Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(2002):321–357
Lerski R, Straughan K, Schad L, Boyce D, Blüml S, Zuna I (1993) MR image texture analysis—an approach to tissue characterization. Magn Reson Imag 11(6):873–887 CrossRef
Isabelle G, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182
Zarogianni E, Storkey A, Johnstone E, Owen D, Lawrie S (2016) Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features. Schizophr Res S0920–9964(16):30377–303772. doi: 10.1016/j.schres.2016.08.027
- Three-dimensional texture features from intensity and high-order derivative maps for the discrimination between bladder tumors and wall tissues via MRI
- Springer International Publishing
International Journal of Computer Assisted Radiology and Surgery
A journal for interdisciplinary research, development and applications of image guided diagnosis and therapy
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
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