MRI‐based prostate cancer detection with high‐level representation and hierarchical classification

Abstract

Purpose Extracting the high-level feature representation by using deep neural networks for detection of prostate cancer, and then based on high-level feature representation constructing hierarchical classification to refine the detection results. Methods High-level feature representation is first learned by a deep learning network, where multi-parametric MR images are used as the input data. Then, based on the learned high-level features, a hierarchical classification method is developed, where multiple random forest classifiers are iteratively constructed to refine the detection results of prostate cancer. Results The experiments were carried on 21 real patient subjects, and the proposed method achieves an averaged section-based evaluation (SBE) of 89.90%, an averaged sensitivity of 91.51%, and an averaged specificity of 88.47%. Conclusions The high-level features learned from our proposed method can achieve better performance than the conventional handcrafted features (e.g., LBP and Haar-like features) in detecting prostate cancer regions, also the context features obtained from the proposed hierarchical classification approach are effective in refining cancer detection result.

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

“…In clinical settings the interpretation of prostate MRI is based on the clinical guideline prostate imaging reporting and data system version 2 (PI‐RADS v2) . PI‐RADS v2 uses a dominant MRI sequence based on zonal location for lesion scoring (DWI for peripheral zone (PZ) lesions and T2W for transitional zone (TZ) lesions) since the zones differ significantly in both biological and imaging features . DCE imaging is used for equivocal findings in PZ but is not used for TZ lesions…”

Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine

“…Several investigators [15][16][17][18] have developed ML-based models for detection of cancer, e.g., lung nodules [15] in thoracic computed tomography (CT) using massive training artificial neural network (ANN), micro-calcification breast masses [16] in mammography using a convolutional neural network (CNN), prostate cancer [17] and brain lesion [18] on magnetic resonance imaging (MRI) data using deep learning. Chan et al [16] achieved a very good accuracy, an area under a receiver operating characteristic curve (AUC) of 0.90, in the automatic detection of clustered of breast microcalcifications on mammograms.…”

“…Suzuki et al [15] reported an improved accuracy in the detection of lung nodules in low-dose CT images. Zhu et al [17] reported an averaged detection rate of 89.90% of prostate cancer on MR images, with clear indication that the high-level features learned from the deep learning method can achieve better performance than the handcrafted features in detecting prostate cancer regions. Rezaei et al [18] results demonstrated the superior ability of the deep learning approach in brain lesions detection.…”

“…ML tools for computer-aided detection/diagnosis [15][16][17][18][19][20][21][22] as "second opinion" systems for clinical decision-making support would undoubtedly enhance the radiologists' performance and hence improved diagnostic performance. The emerging paradigms in radiomics for therapeutic outcome predictions (i.e., patient's survival, decrease recurrence, late complication, etc.)…”

3D Spatial Pyramid Dilated Network for Pulmonary Nodule Classification

“…The model achieved a classification accuracy of 95.52% task, which outperformed other state-of-the-art methods. Zhu et al [22] extracted the high-level feature representation by using the convolution neural network for prostate cancer detection; the experimental results showed that the proposed method achieved an averaged sensitivity of 91.51% and specificity of 88.47%. For the pulmonary nodule classification, the convolution neural network has also shown its effectiveness in malignant suspicious classification task.…”