Detection of local recurrence with 3-tesla MRI after radical prostatectomy: A useful method for radiation treatment planning?

Background/Aim: Salvage radiotherapy improves biochemical control in patients with recurrence of prostate cancer after prostatectomy. Radiotherapy target volumes of the prostatic fossa are based on empirical data and differ between different guidelines. Localization of recurrence with multiparametric magnetic resonance imaging (MRI) might be a feasible approach to localize recurrent lesions. Patients and Methods: Twenty-one patients with biochemical recurrence after radical prostatectomy were included (median prostate-specific antigen (PSA) =0.17 ng/ml). Multi-parametric MRI was performed using a 3-T MR system. Results: Lesions were detected in seven patients with a median PSA of 0.86 ng/ml (minimum= 0.31 ng/ml). Patients without detectable recurrence had a median PSA of 0.12 ng/ml. All patients with detectable lesions responded to radiotherapy. Eleven out of 14 patients without detectable recurrence also responded. Plasma flow in suspicious lesions was correlated with PSA level. Conclusion: Detection of recurrence at the prostatic fossa with our approach was possible in a minority of patients with a low PSA level. Clinical relevance of plasma flow in suspicious lesions should be further investigated

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

Framework Workflow.

<p>Sequence of the system components and overall organization for the estimation of prostate cancer.</p

Classification results using an ensemble 1C-SVM.

<p>The performance of the three models (A. T2, B. T2DCE, C.T2Tex ) employing different metrics (TPR = Sensitivity, SPC = Specificity, PPV = Positive Predictive Value, AUC-ROC = Area under the receiver operating characteristic curve, AUC-PPV/TPR = Area under the Precision-Recall curve) is shown.</p

Classifier’s performance for an ensemble 1C-SVM.

<p>Three different combinations of features T2, T2DCE, T2Tex were assessed using the metrics TPR = Sensitivity, SPC = Specificity, PPV = Positive Predictive Value, AUC-ROC = Area under the receiver operating characteristic curve and AUC-PPV/TPR = Area under the Precision-Recall curve. The computational time given in minutes was the time required for the training and validation phase.</p

Classifier’s performance for different sizes of cancer structures.

<p>Two models were compared T2 and T2Tex using the metrics: TPR = Sensitivity, SPC = Specificity, PPV = Positive Predictive Value, AUC-ROC = Area under the receiver operating characteristic curve and AUC-PPV/TPR = Area under the Precision-Recall curve. Region A corresponds to cancer structures between and region B corresponds to cancer structures between . Five patients per region were evaluated.</p

Summary of current studies and our system results.

<p>The computer-aided studies from Artan <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093600#pone.0093600-Artan1" target="_blank">[25]</a>, Niaf <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093600#pone.0093600-Niaf1" target="_blank">[26]</a> used a leave-one-out (LOO) CV methodology, and from Tiwari <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0093600#pone.0093600-Tiwari1" target="_blank">[15]</a> which, similar to this paper, used three-fold CV/LOO. Our results are reported for both methods three-fold CV/LOO. The metrics used are: TPR = Sensitivity, SPC = Specificity, PPV = Positive Predictive Value, AUC-ROC = Area under the receiver operating characteristic curve and AUC-PPV/TPR = Area under the Precision-Recall curve.</p

Recognition of cancer structures for two clinical patients.

<p>Five slices corresponding to the MRI volume of each patient are illustrated. The probability map was superimposed over the T2-weighted images. The background represents the gray values of the T2-weighted images for the whole prostate tissue. In the foreground the probability estimation of cancer is shown using a color map only over the PZ for the corresponding slices. The probability of cancer is ranged on a color scale: red ( probability), yellow-green ( probability), without color (lower than probability of cancer). The white marks highlight the ground truth regions.</p

Classifier’s performance for a single 1C-SVM with a processed training dataset.

<p>Three different combinations of features T2, T2DCE, T2Tex were assessed using the metrics TPR = Sensitivity, SPC = Specificity, PPV = Positive Predictive Value, AUC-ROC = Area under the receiver operating characteristic curve and AUC-PPV/TPR = Area under the Precision-Recall curve. The computational time given in minutes was the time required for the training and validation phase.</p

Classifier’s performance for a single 1C-SVM.

<p>Three different combinations of features T2, T2DCE, T2Tex were assessed using the metrics TPR = Sensitivity, SPC = Specificity, PPV = Positive Predictive Value, AUC-ROC = Area under the receiver operating characteristic curve and AUC-PPV/TPR = Area under the Precision-Recall curve. The computational time given in minutes was the time required for the training and validation phase.</p