Computer-aided Detection Of Prostate Cancer Research Articles

Computer-aided detection of prostate cancer in early stages using multi-parameter MRI: A promising approach for early diagnosis.

Transrectal ultrasound-guided prostate biopsy is the gold standard diagnostic test for prostate cancer, but it is an invasive examination of non-targeted puncture and has a high false-negative rate. In this study, we aimed to develop a computer-assisted prostate cancer diagnosis method based on multiparametric MRI (mpMRI) images. We retrospectively collected 106 patients who underwent radical prostatectomy after diagnosis with prostate biopsy. mpMRI images, including T2 weighted imaging (T2WI), diffusion weighted imaging (DWI), and dynamic-contrast enhanced (DCE), and were accordingly analyzed. We extracted the region of interest (ROI) about the tumor and benign area on the three sequential MRI axial images at the same level. The ROI data of 433 mpMRI images were obtained, of which 202 were benign and 231 were malignant. Of those, 50 benign and 50 malignant images were used for training, and the 333 images were used for verification. Five main feature groups, including histogram, GLCM, GLGCM, wavelet-based multi-fractional Brownian motion features and Minkowski function features, were extracted from the mpMRI images. The selected characteristic parameters were analyzed by MATLAB software, and three analysis methods with higher accuracy were selected. Through prostate cancer identification based on mpMRI images, we found that the system uses 58 texture features and 3 classification algorithms, including Support Vector Machine (SVM), K-nearest Neighbor (KNN), and Ensemble Learning (EL), performed well. In the T2WI-based classification results, the SVM achieved the optimal accuracy and AUC values of 64.3% and 0.67. In the DCE-based classification results, the SVM achieved the optimal accuracy and AUC values of 72.2% and 0.77. In the DWI-based classification results, the ensemble learning achieved optimal accuracy as well as AUC values of 75.1% and 0.82. In the classification results based on all data combinations, the SVM achieved the optimal accuracy and AUC values of 66.4% and 0.73. The proposed computer-aided diagnosis system provides a good assessment of the diagnosis of the prostate cancer, which may reduce the burden of radiologists and improve the early diagnosis of prostate cancer.

Prostate Cancer Classification Using Artificial Neural Networks

Prostate cancer is one of the major causes of cancer death in men in the Western world. One in 6 men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs [Computer-aided detection of prostate cancer in MRI. IEEE, May 2014]. The artificial neural network (ANN) is used to classify and extract the feature of the magnetic resonance imaging (MRI) scan. This classification is computer-based and has the advantage that it enriches the patient from the doctor and undergoes the different diagnostic techniques that may appear to be stressful and painful. A computer-aided classification set of MRI scans of the prostate subjects these images to a set of preprocessing and image enhancement giving an accurate classification to improve the properties of the image such as noise filtering to improve the contrast of the image and then using segmentation to separate the tumor region and feature extraction; this step can be done by using a set of rolls (explained below). This feature is used to input data to ANN. It classifies the given data set into benign or malignant (prostate cancer). The system succeeded in classifying malignant tumors with 92.4%, increasing decision-making and providing particular treatment. The computerized method detects the tumor in its early stage; thus, good classification is an essential part. The first stage is to extract features from MRI using principal component analysis (computer aided diagnosis), and the second stage is to train the probabilistic neural network (ANN) for classification. The computer aided diagnosis method gives existing attributes a new set of calculated parameters.

Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone

In this paper we propose a prostate cancer computer-aided diagnosis (CAD) system and suggest a set of discriminant texture descriptors extracted from T2-weighted MRI data which can be used as a good basis for a multimodality system. For this purpose, 215 texture descriptors were extracted and eleven different classifiers were employed to achieve the best possible results. The proposed method was tested based on 418 T2-weighted MR images taken from 45 patients and evaluated using 9-fold cross validation with five patients in each fold. The results demonstrated comparable results to existing CAD systems using multimodality MRI. We achieved an area under the receiver operating curve (Az) values equal to , , and for Bayesian networks, ADTree, random forest and multilayer perceptron classifiers, respectively, while a meta-voting classifier using average probability as a combination rule achieved .

Computer-aided detection of prostate cancer in MRI.

Prostate cancer is one of the major causes of cancer death for men in the western world. Magnetic resonance imaging (MRI) is being increasingly used as a modality to detect prostate cancer. Therefore, computer-aided detection of prostate cancer in MRI images has become an active area of research. In this paper we investigate a fully automated computer-aided detection system which consists of two stages. In the first stage, we detect initial candidates using multi-atlas-based prostate segmentation, voxel feature extraction, classification and local maxima detection. The second stage segments the candidate regions and using classification we obtain cancer likelihoods for each candidate. Features represent pharmacokinetic behavior, symmetry and appearance, among others. The system is evaluated on a large consecutive cohort of 347 patients with MR-guided biopsy as the reference standard. This set contained 165 patients with cancer and 182 patients without prostate cancer. Performance evaluation is based on lesion-based free-response receiver operating characteristic curve and patient-based receiver operating characteristic analysis. The system is also compared to the prospective clinical performance of radiologists. Results show a sensitivity of 0.42, 0.75, and 0.89 at 0.1, 1, and 10 false positives per normal case. In clinical workflow the system could potentially be used to improve the sensitivity of the radiologist. At the high specificity reading setting, which is typical in screening situations, the system does not perform significantly different from the radiologist and could be used as an independent second reader instead of a second radiologist. Furthermore, the system has potential in a first-reader setting.

Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis

In this paper, a fully automatic computer-aided detection (CAD) method is proposed for the detection of prostate cancer. The CAD method consists of multiple sequential steps in order to detect locations that are suspicious for prostate cancer. In the initial stage, a voxel classification is performed using a Hessian-based blob detection algorithm at multiple scales on an apparent diffusion coefficient map. Next, a parametric multi-object segmentation method is applied and the resulting segmentation is used as a mask to restrict the candidate detection to the prostate. The remaining candidates are characterized by performing histogram analysis on multiparametric MR images. The resulting feature set is summarized into a malignancy likelihood by a supervised classifier in a two-stage classification approach. The detection performance for prostate cancer was tested on a screening population of 200 consecutive patients and evaluated using the free response operating characteristic methodology. The results show that the CAD method obtained sensitivities of 0.41, 0.65 and 0.74 at false positive (FP) levels of 1, 3 and 5 per patient, respectively. In conclusion, this study showed that it is feasible to automatically detect prostate cancer at a FP rate lower than systematic biopsy. The CAD method may assist the radiologist to detect prostate cancer locations and could potentially guide biopsy towards the most aggressive part of the tumour.

Predictive Deconvolution and Hybrid Feature Selection for Computer-Aided Detection of Prostate Cancer

Computer-aided detection (CAD) schemes are decision making support tools, useful to overcome limitations of problematic clinical procedures. Trans-rectal ultrasound image based CAD would be extremely important to support prostate cancer diagnosis. An effective approach to realize a CAD scheme for this purpose is described in this work, employing a multi-feature kernel classification model based on generalized discriminant analysis. The mutual information of feature value and tissue pathological state is used to select features essential for tissue characterization. System-dependent effects are reduced through predictive deconvolution of the acquired radio-frequency signals. A clinical study, performed on ground truth images from biopsy findings, provides a comparison of the classification model applied before and after deconvolution, showing in the latter case a significant gain in accuracy and area under the receiver operating characteristic curve.

Computer-aided Detection of Prostate Cancer on Tissue Sections

We report an automated computer technique for detection of prostate cancer in prostate tissue sections processed with immunohistochemistry. Two sets of color optical images were acquired from prostate tissue sections stained with a double-chromogen triple-antibody cocktail combining alpha-methylacyl-CoA racemase, p63, and high-molecular-weight cytokeratin. The first set of images consisted of 20 training images (10 malignant) used for developing the computer technique and 15 test images (7 malignant) used for testing and optimizing the technique. The second set of images consisted of 299 images (114 malignant) used for evaluation of the performance of the computer technique. The computer technique identified image segments of alpha-methylacyl-CoA racemase-labeled malignant epithelial cells (red), p63, and high-molecular-weight cytokeratin-labeled benign basal cells (brown), and secretory and stromal cells (blue) for identifying prostate cancer automatically. The sensitivity and specificity of the computer technique were 94% (16/17) and 94% (17/18), respectively, on the first (training and test) set of images, and 88% (79/90) and 97% (136/140), respectively, on the second (validation) set of images. If high-grade prostatic intraepithelial neoplasia, which is a precursor of cancer, and atypical cases were included, the sensitivity and specificity were 85% (97/114) and 89% (165/185), respectively. These results show that the novel automated computer technique can accurately identify prostatic adenocarcinoma in the triple-antibody cocktail-stained prostate sections.

Computer-aided detection of prostate cancer

Background Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in developed countries. Detection of prostate carcinoma at an early stage is crucial for successful treatment. Material and methods A method for the analysis of transrectal ultrasound images aimed at computer-aided diagnosis of prostate cancer is tested in this paper. First, two classifiers based on k-nearest neighbors and Hidden Markov models are compared. Second, the diagnostic capacity of our system is tested by means of a set of experiments where humans with varying degrees of experience classified a set of ultrasound images with and without the aid of the computer-aided system. The corpus used in this study was specifically acquired for this purpose. It consists of 4944 ultrasound images corresponding to 303 patients, and is publicly available for non-commercial use upon request. Results The best classification results achieve an area under the receiver operating characteristic curve of 61.6%. However, the diagnostic capacity of an expert urologist using the computer-aided system improves only slightly compared with his/her capacity without the aid of the system. Conclusions Despite the difficulty of this task, the obtained results indicate that discrimination between cancerous and non-cancerous tissue is possible to a certain degree. The computer-aided system helps an inexperienced user to make a better diagnosis, however it must be able to perform better in order to be useful in a real-world clinical context.