A study of T<sub>2</sub>-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer

Peng, Yahui; Jiang, Yulei; Antic, Tatjana; Giger, Maryellen L.; Eggener, Scott E.; Oto, Aytekin

doi:10.1117/12.2007979

Cited by 24 publications

(41 citation statements)

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“…Khalvati et al [20] proposed a multi-parametric MRI texture feature model for radiomics driven prostate cancer sensing. The texture feature model comprises 19 low-level texture features extracted from each MRI modality, and is based on the model proposed by Peng et al [21]. More recently, Khalvati et al [8] extended this texture feature model to include additional MRI modalities and low-level features, as well as feature selection.…”

Section: Radiomics Driven Cancer Sensingmentioning

confidence: 99%

“…Chung et al [25] proposed a fully-automated discovery radiomics system for sensing prostate lesion candidates using multi-parametric MRI (MP-MRI). Radiomics features were extracted using a discovered radiomics sequencer consisting of 17 convolutional sequencing layers and 2 fully-connected sequencing layers, and the discovered sequences were evaluated against the hand-engineered radiomic sequences described in [8,21]. More recently, Chung et al [26] introduced a Layered Random Projection (LaRP) sequencer, which was evaluated using the same framework as [25].…”

Section: Radiomics Driven Cancer Sensingmentioning

confidence: 99%

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Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing

Dulhanty

Wang

Cheng

et al. 2020

Sensors

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Prostate cancer is the most commonly diagnosed cancer in North American men; however, prognosis is relatively good given early diagnosis. This motivates the need for fast and reliable prostate cancer sensing. Diffusion weighted imaging (DWI) has gained traction in recent years as a fast non-invasive approach to cancer sensing. The most commonly used DWI sensing modality currently is apparent diffusion coefficient (ADC) imaging, with the recently introduced computed high-b value diffusion weighted imaging (CHB-DWI) showing considerable promise for cancer sensing. In this study, we investigate the efficacy of ADC and CHB-DWI sensing modalities when applied to zone-level prostate cancer sensing by introducing several radiomics driven zone-level prostate cancer sensing strategies geared around hand-engineered radiomic sequences from DWI sensing (which we term as Zone-X sensing strategies). Furthermore, we also propose Zone-DR, a discovery radiomics approach based on zone-level deep radiomic sequencer discovery that discover radiomic sequences directly for radiomics driven sensing. Experimental results using 12,466 pathology-verified zones obtained through the different DWI sensing modalities of 101 patients showed that: (i) the introduced Zone-X and Zone-DR radiomics driven sensing strategies significantly outperformed the traditional clinical heuristics driven strategy in terms of AUC, (ii) the introduced Zone-DR and Zone-SVM strategies achieved the highest sensitivity and specificity, respectively for ADC amongst the tested radiomics driven strategies, (iii) the introduced Zone-DR and Zone-LR strategies achieved the highest sensitivities for CHB-DWI amongst the tested radiomics driven strategies, and (iv) the introduced Zone-DR, Zone-LR, and Zone-SVM strategies achieved the highest specificities for CHB-DWI amongst the tested radiomics driven strategies. Furthermore, the results showed that the trade-off between sensitivity and specificity can be optimized based on the particular clinical scenario we wish to employ radiomic driven DWI prostate cancer sensing strategies for, such as clinical screening versus surgical planning. Finally, we investigate the critical regions within sensing data that led to a given radiomic sequence generated by a Zone-DR sequencer using an explainability method to get a deeper understanding on the biomarkers important for zone-level cancer sensing.

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Section: Radiomics Driven Cancer Sensingmentioning

confidence: 99%

Section: Radiomics Driven Cancer Sensingmentioning

confidence: 99%

Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing

Dulhanty

Wang

Cheng

et al. 2020

Sensors

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“…SVM achieved the highest performance in this study. LDA was also employed by Peng et al in the development of texture features on T2WI and ADC features on DWI [ 51 , 67 ]. Other works utilizing SVM for classifying prostate cancer on multiparametric MRI include Liu et al and Moradi et al [ 39 , 52 ].…”

Section: Computer Aided-diagnosis For Prostate Cancermentioning

confidence: 99%

Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

Wang

Burtt

Türkbey

et al. 2014

BioMed Research International

103

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Prostate cancer (PCa) is the most commonly diagnosed cancer among men in the United States. In this paper, we survey computer aided-diagnosis (CADx) systems that use multiparametric magnetic resonance imaging (MP-MRI) for detection and diagnosis of prostate cancer. We review and list mainstream techniques that are commonly utilized in image segmentation, registration, feature extraction, and classification. The performances of 15 state-of-the-art prostate CADx systems are compared through the area under their receiver operating characteristic curves (AUC). Challenges and potential directions to further the research of prostate CADx are discussed in this paper. Further improvements should be investigated to make prostate CADx systems useful in clinical practice.

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“…Our results were comparable with the reported in the literature AUC values achieved by the experienced human reader: 0.79 and 0.83 for PI-RADS v1 and PI-RADS v2, respectively. 29 As radiomic features are sensitive to variations in matrix size, resolution, signal-to-noise ratio, and pulse sequence parameters including TR/TE, [30][31][32][33][34][35] differences between training and test data may affect the performance of the model. However, the random forest model for the training data resulted in AUC of ∼0.88.…”

Section: Discussionmentioning

confidence: 99%

Classification of suspicious lesions on prostate multiparametric MRI using machine learning

et al. 2018

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We present a radiomics-based approach developed for the SPIE-AAPM-NCI PROSTATEx challenge. The task was to classify clinically significant prostate cancer in multiparametric (mp) MRI. Data consisted of a "training dataset" (330 suspected lesions from 204 patients) and a "test dataset" (208 lesions/140 patients). All studies included T2-weighted (T2-W), proton density-weighted, dynamic contrast enhanced, and diffusionweighted imaging. Analysis of the images was performed using the MIM imaging platform (MIM Software, Cleveland, Ohio). Prostate and peripheral zone contours were manually outlined on the T2-W images. A workflow for rigid fusion of the aforementioned images to T2-W was created in MIM. The suspicious lesion was outlined using the high b-value image. Intensity and texture features were extracted on four imaging modalities and characterized using nine histogram descriptors: 10%, 25%, 50%, 75%, 90%, mean, standard deviation, kurtosis, and skewness (216 features). Three classification methods were used: classification and regression trees (CART), random forests, and adaptive least absolute shrinkage and selection operator (LASSO). In the held out by the organizers test dataset, the areas under the curve (AUCs) were: 0.82 (random forests), 0.76 (CART), and 0.76 (adaptive LASSO). AUC of 0.82 was the fourth-highest score of 71 entries (32 teams) and the highest for feature-based methods.

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A study of T₂-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer

Cited by 24 publications

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Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing

Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing

Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

Classification of suspicious lesions on prostate multiparametric MRI using machine learning

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A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer

Cited by 24 publications

References 0 publications

Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing

Radiomics Driven Diffusion Weighted Imaging Sensing Strategies for Zone-Level Prostate Cancer Sensing

Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research

Classification of suspicious lesions on prostate multiparametric MRI using machine learning

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A study of T₂-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer