Prostate focal peripheral zone lesions: characterization at multiparametric MR imaging--influence of a computer-aided diagnosis system.

PURPOSE: To assess the impact of a computer-aided diagnosis (CAD) system in the characterization of focal prostate lesions at multiparametric magnetic resonance (MR) imaging. MATERIALS AND METHODS: Formal institutional review board approval was not required. Thirty consecutive 1.5-T multiparametric MR imaging studies (with T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging) obtained before radical prostatectomy in patients between September 2008 and February 2010 were reviewed. Twelve readers assessed the likelihood of malignancy of 88 predefined peripheral zone lesions by using a five-level (level, 0-4) subjective score (SS) in reading session 1. This was repeated 5 weeks later in reading session 2. The CAD results were then disclosed, and in reading session 3, the readers could amend the scores assigned during reading session 2. Diagnostic accuracy was assessed by using a receiver operating characteristic (ROC) regression model and was quantified with the area under the ROC curve (AUC). RESULTS: Mean AUCs were significantly lower for less experienced (<1 year) readers (P < .02 for all sessions). Seven readers improved their performance between reading sessions 1 and 2, and 12 readers improved their performance between sessions 2 and 3. The mean AUCs for reading session 1 (83.0%; 95% confidence interval [CI]: 77.9%, 88.0%) and reading session 2 (84.1%; 95% CI: 78.1%, 88.7%) were not significantly different (P = .76). Although the mean AUC for reading session 3 (87.2%; 95% CI: 81.0%, 92.0%) was higher than that for session 2, the difference was not significant (P = .08). For an SS positivity threshold of 3, the specificity of reading session 2 (79.0%; 95% CI: 71.1%, 86.4%) was not significantly different from that of session 1 (78.7%; 95% CI: 70.5%, 86.8%) but was significantly lower than that of session 3 (86.2%; 95% CI: 77.1%, 93.1%; P < .03). The sensitivity of reading session 2 (68.4%; 95% CI: 57.5%, 77.7%) was significantly higher than that of session 1 (64.0%; 95% CI: 52.9%, 73.9%; P = .003) but was not significantly different from that of session 3 (71.4%; 95% CI: 58.3%, 82.7%). CONCLUSION: A CAD system may improve the characterization of prostate lesions at multiparametric MR imaging by increasing reading specificity.

Influence of imaging and histological factors on prostate cancer detection and localisation on multiparametric MRI: a prospective study.

OBJECTIVES: To assess factors influencing prostate cancer detection on multiparametric (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced) MRI. METHODS: One hundred and seventy-five patients who underwent radical prostatectomy were included. Pre-operative MRI performed at 1.5 T (n = 71) or 3 T (n = 104), with (n = 58) or without (n = 117) an endorectal coil were independently interpreted by two radiologists. A five-point subjective suspicion score (SSS) was assigned to all focal abnormalities (FAs). MR findings were then compared with whole-mount sections. RESULTS: Readers identified 192-214/362 cancers, with 130-155 false positives. Detection rates for tumours of <0.5 cc (cm(3)), 0.5-2 cc and >2 cc were 33-45/155 (21-29 %), 15-19/35 (43-54 %) and 8-9/12 (67-75 %) for Gleason ≤6, 17/27 (63 %), 42-45/51 (82-88 %) and 34/35 (97 %) for Gleason 7 and 4/5 (80 %), 13/14 (93 %) and 28/28 (100 %) for Gleason ≥8 cancers respectively. At multivariate analysis, detection rates were influenced by tumour Gleason score, histological volume, histological architecture and location (P < 0.0001), but neither by field strength nor coils used for imaging. The SSS was a significant predictor of both malignancy of FAs (P < 0.005) and aggressiveness of tumours (P < 0.00001). CONCLUSIONS: Detection rates were significantly influenced by tumour characteristics, but neither by field strength nor coils used for imaging. The SSS significantly stratified the risk of malignancy of FAs and aggressiveness of detected tumours. KEY POINTS: • Prostate cancer volume, Gleason score, architecture and location are MRI predictors of detection. • Field strength and coils used do not influence the tumour detection rate. • Multiparametric MRI is accurate for detecting aggressive tumours. • A subjective suspicion score can stratify the risk of malignancy and tumour aggressiveness.

Kernel-based learning from both qualitative and quantitative labels: application to prostate cancer diagnosis based on multiparametric MR imaging.

Building an accurate training database is challenging in supervised classification. For instance, in medical imaging, radiologists often delineate malignant and benign tissues without access to the histological ground truth, leading to uncertain data sets. This paper addresses the pattern classification problem arising when available target data include some uncertainty information. Target data considered here are both qualitative (a class label) or quantitative (an estimation of the posterior probability). In this context, usual discriminative methods, such as the support vector machine (SVM), fail either to learn a robust classifier or to predict accurate probability estimates. We generalize the regular SVM by introducing a new formulation of the learning problem to take into account class labels as well as class probability estimates. This original reformulation into a probabilistic SVM (P-SVM) can be efficiently solved by adapting existing flexible SVM solvers. Furthermore, this framework allows deriving a unique learned prediction function for both decision and posterior probability estimation providing qualitative and quantitative predictions. The method is first tested on synthetic data sets to evaluate its properties as compared with the classical SVM and fuzzy-SVM. It is then evaluated on a clinical data set of multiparametric prostate magnetic resonance images to assess its performances in discriminating benign from malignant tissues. P-SVM is shown to outperform classical SVM as well as the fuzzy-SVM in terms of probability predictions and classification performances, and demonstrates its potential for the design of an efficient computer-aided decision system for prostate cancer diagnosis based on multiparametric magnetic resonance (MR) imaging.

Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI.

This study evaluated a computer-assisted diagnosis (CADx) system for determining a likelihood measure of prostate cancer presence in the peripheral zone (PZ) based on multiparametric magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI at 1.5 T. Based on a feature set derived from grey-level images, including first-order statistics, Haralick features, gradient features, semi-quantitative and quantitative (pharmacokinetic modelling) dynamic parameters, four kinds of classifiers were trained and compared: nonlinear support vector machine (SVM), linear discriminant analysis, k-nearest neighbours and naïve Bayes classifiers. A set of feature selection methods based on t-test, mutual information and minimum-redundancy-maximum-relevancy criteria were also compared. The aim was to discriminate between the relevant features as well as to create an efficient classifier using these features. The diagnostic performances of these different CADx schemes were evaluated based on a receiver operating characteristic (ROC) curve analysis. The evaluation database consisted of 30 sets of multiparametric MR images acquired from radical prostatectomy patients. Using histologic sections as the gold standard, both cancer and nonmalignant (but suspicious) tissues were annotated in consensus on all MR images by two radiologists, a histopathologist and a researcher. Benign tissue regions of interest (ROIs) were also delineated in the remaining prostate PZ. This resulted in a series of 42 cancer ROIs, 49 benign but suspicious ROIs and 124 nonsuspicious benign ROIs. From the outputs of all evaluated feature selection methods on the test bench, a restrictive set of about 15 highly informative features coming from all MR sequences was discriminated, thus confirming the validity of the multiparametric approach. Quantitative evaluation of the diagnostic performance yielded a maximal area under the ROC curve (AUC) of 0.89 (0.81-0.94) for the discrimination of the malignant versus nonmalignant tissues and 0.82 (0.73-0.90) for the discrimination of the malignant versus suspicious tissues when combining the t-test feature selection approach with a SVM classifier. A preliminary comparison showed that the optimal CADx scheme mimicked, in terms of AUC, the human experts in differentiating malignant from suspicious tissues, thus demonstrating its potential for assisting cancer identification in the PZ.