[Imaging diagnostics of the prostate]

Incremental learning with SVM for multimodal classification of prostatic adenocarcinoma

Robust detection of prostatic cancer is a challenge due to the multitude of variants and their representation in MR images. We propose a pattern recognition system with an incremental learning ensemble algorithm using support vector machines (SVM) tackling this problem employing multimodal MR images and a texture-based information strategy. The proposed system integrates anatomic, texture, and functional features. The data set was preprocessed using B-Spline interpolation, bias field correction and intensity standardization. First- and second-order angular independent statistical approaches and rotation invariant local phase quantization (RI-LPQ) were utilized to quantify texture information. An incremental learning ensemble SVM was implemented to suit working conditions in medical applications and to improve effectiveness and robustness of the system. The probability estimation of cancer structures was calculated using SVM and the corresponding optimization was carried out with a heuristic method together with a 3-fold cross-validation methodology. We achieved an average sensitivity of 0.844 ± 0.068 and a specificity of 0.780 ± 0.038, which yielded superior or similar performance to current state of the art using a total database of only 41 slices from twelve patients with histological confirmed information, including cancerous, unhealthy non-cancerous and healthy prostate tissue. Our results show the feasibility of an ensemble SVM being able to learn additional information from new data while preserving previously acquired knowledge and preventing unlearning. The use of texture descriptors provides more salient discriminative patterns than the functional information used. Furthermore, the system improves selection of information, efficiency and robustness of the classification. The generated probability map enables radiologists to have a lower variability in diagnosis, decrease false negative rates and reduce the time to recognize and delineate structures in the prostate.

[Multiparametric MRI, elastography, contrastenhanced TRUS. Are there indications with reliable diagnostic advantages before prostate biopsy?]

Prostate cancer (PCA) is the most common malignancy in men with an increasing incidence and is responsible for about 11,000 deaths per year in Germany. Fortunately, the mortality of PCA has decreased in recent years despite the rising incidence reflecting improvements in diagnostic methods. Many new innovations in imaging techniques for PCA are available and may be helpful in early detection of PCA. Contrast-enhanced sonography, computer-assisted sonography, elastography and multiparametric magnetic resonance imaging (MRI) seem to be the most promising methods to increase the detection rate of PCA during diagnostic work-up. The value of these new innovative techniques concerning improvement in PCA detection is reviewed.

[Multiparametric prostate MRI for follow-up monitoring after radiation therapy]

CLINICAL/METHODICAL ISSUE

Radiation therapy is a therapeutic option with curative intent for patients with prostate cancer. Monitoring of prostate-specific antigen (PSA) values is the current standard of care in the follow-up. Imaging is recommended only for symptomatic patients and/or for further therapeutic options.

STANDARD RADIOLOGICAL METHODS

For detection of local recurrence magnetic resonance imaging (MRI) of the prostate is acknowledged as the method of choice.

PERFORMANCE

Good results for primary diagnosis were found especially in combination with functional techniques, whereas in recurrent prostate cancer only few studies with heterogeneous study design are available for prostate MRI. Furthermore, changes in different MRI modalities due to radiation therapy have been insufficiently investigated to date.

PRACTICAL RECOMMENDATIONS

As the initial results were promising prostate MRI and available therapeutic options for detection of local recurrence should be considered in patients with increased PSA.