MRI imaging texture features in prostate lesions classification

Sobecki, Piotr; Życka-Malesa, Dominika; Mykhalevych, Ihor; Sklinda, Katarzyna; Przelaskowski, Artur

doi:10.1007/978-981-10-5122-7_207

Cited by 7 publications

(10 citation statements)

References 9 publications

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“…Most of them utilized conventional machine-learning techniques, which typically extract low-level radiomics features to characterize images and train a separate classifier. [4][5][6][7][8][9][10][11][12][13][14][15][16] Fehr et al 8 utilized first-order (mean, SD, skewness, and kurtosis) and second-order texture features (Haralick Features from gray level co-occurrence matrix (GLCM)) to assess the Gleason scores. Vignati et al 9 used the contrast and homogeneity of GLCM texture features on T2w images and ADC maps to help differentiate between patients with different Gleason scores.…”

Section: A Existing Prostate Cancer Diagnosis Methodsmentioning

confidence: 99%

“…Vignati et al 9 used the contrast and homogeneity of GLCM texture features on T2w images and ADC maps to help differentiate between patients with different Gleason scores. Sobecki et al 11 applied the contrast, homogeneity, energy, angular second moment, and correlation features from GLCM to present features of prostate cancers. Then a multilayer feed-forward artificial neural network (ANN) with stochastic gradient decent was implemented to classify prostate lesions.…”

Section: A Existing Prostate Cancer Diagnosis Methodsmentioning

confidence: 99%

“…Many methods have been proposed to classify prostate cancer. Most of them utilized conventional machine‐learning techniques, which typically extract low‐level radiomics features to characterize images and train a separate classifier . Fehr et al .…”

Section: Related Workmentioning

confidence: 99%

“…used the contrast and homogeneity of GLCM texture features on T2w images and ADC maps to help differentiate between patients with different Gleason scores. Sobecki et al . applied the contrast, homogeneity, energy, angular second moment, and correlation features from GLCM to present features of prostate cancers.…”

Section: Related Workmentioning

confidence: 99%

“…Many machine learning approaches [4][5][6][7][8][9][10][11][12][13][14][15][16] have been proposed for prostate cancer classification with MRI images. Most of these methods used low-level radiomics features based on the previous clinical experiences, which could not take full advantage of the complete information included in MRI images.…”

Section: Introductionmentioning

confidence: 99%

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Prostate cancer classification with multiparametric MRI transfer learning model

et al. 2019

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Purpose Prostate cancer classification has a significant impact on the prognosis and treatment planning of patients. Currently, this classification is based on the Gleason score analysis of biopsied tissues, which is neither accurate nor risk free. This study aims to learn discriminative features in prostate images and assist physicians in classifying prostate cancer automatically. Methods We develop a novel multiparametric magnetic resonance transfer learning (MPTL) method to automatically stage prostate cancer. We first establish a deep convolutional neural network with three branch architectures, which transfer pretrained model to compute features from multiparametric MRI images (mp‐MRI): T2w transaxial, T2w sagittal, and apparent diffusion coefficient (ADC). The learned features are concatenated to represent information of mp‐MRI sequences. A new image similarity constraint is then proposed to enable the distribution of the features within the same category in a narrow angle region. With the joint constraints of softmax loss and image similarity loss in the fine‐tuning process, the MPTL can provide descriptive features with intraclass compactness and interclass separability. Results Two cohorts: 132 cases from our institutional review board‐approved patient database and 112 cases from the PROSTATEx‐2 Challenge are utilized to evaluate the robustness and effectiveness of the proposed MPTL model. Our model achieved high accuracy of prostate cancer classification (accuracy of 86.92%). Moreover, the comparison results demonstrate that our method outperforms both hand‐crafted feature‐based methods and existing deep learning models in prostate cancer classification with higher accuracy. Conclusion The experiment results showed that the proposed method can learn discriminative features in prostate images and classify the cancer accurately. Our MPTL model could be further applied in the clinical practice to provide valuable information for cancer treatment and precision medicine.

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Section: A Existing Prostate Cancer Diagnosis Methodsmentioning

confidence: 99%

Section: A Existing Prostate Cancer Diagnosis Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prostate cancer classification with multiparametric MRI transfer learning model

et al. 2019

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Assessment of prostate cancer prognostic Gleason grade group using zonal‐specific features extracted from biparametric MRI using a KNN classifier

Jensen

Carl

Boesen

et al. 2019

J Applied Clin Med Phys

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Purpose: To automatically assess the aggressiveness of prostate cancer (PCa) lesions using zonal-specific image features extracted from diffusion weighted imaging (DWI) and T2W MRI. Methods: Region of interest was extracted from DWI (peripheral zone) and T2W MRI (transitional zone and anterior fibromuscular stroma) around the center of 112 PCa lesions from 99 patients. Image histogram and texture features, 38 in total, were used together with a k-nearest neighbor classifier to classify lesions into their respective prognostic Grade Group (GG) (proposed by the International Society of Urological Pathology 2014 consensus conference). A semi-exhaustive feature search was performed (1-6 features in each feature set) and validated using threefold stratified cross validation in a one-versus-rest classification setup. Results: Classifying PCa lesions into GGs resulted in AUC of 0.87, 0.88, 0.96, 0.98, and 0.91 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5 for the peripheral zone, respectively. The results for transitional zone and anterior fibromuscular stroma were AUC of 0.85, 0.89, 0.83, 0.94, and 0.86 for GG1, GG2, GG1 + 2, GG3, and GG4 + 5, respectively. Conclusion: This study showed promising results with reasonable AUC values for classification of all GG indicating that zonal-specific imaging features from DWI and T2W MRI can be used to differentiate between PCa lesions of various aggressiveness.

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