Contouring of prostate tumors on multiparametric MRI: Evaluation of clinical delineations in a multicenter radiotherapy trial.

PURPOSE: To date no guidelines are available for contouring prostate cancer inside the gland, as visible on multiparametric (mp-) MRI. We assessed inter-institutional differences in interpretation of mp-MRI in the multicenter phase III FLAME trial. METHODS: We analyzed clinical delineations on mp-MRI and clinical characteristics from 260 patients across three institutes. We performed a logistic regression analysis to examine each institute's weighting of T2w, ADC and Ktrans intensity maps in the delineation of the cancer. As reviewing of all delineations by an expert panel is not feasible, we made a selection based on discrepancies between a published tumor probability (TP) model and each institute's clinical delineations using Areas Under the ROC Curve (AUC) analysis. RESULTS: Regression coefficients for the three institutes were -0.07, -0.27 and -0.11 for T2w, -1.96, -0.53 and -0.65 for ADC and 0.15, 0.20 and 0.62 for Ktrans, with significant differences between institutes for ADC and Ktrans. AUC analysis showed median AUC values of 0.92, 0.80 and 0.79. Five patients with lowest AUC values were reviewed by a uroradiologist. CONCLUSION: Regression coefficients revealed considerably different interpretations of mp-MRI in tumor contouring between institutes and demonstrated the need for contouring guidelines. Based on AUC values outlying delineations could efficiently be identified for review.

Biochemical recurrence prediction after radiotherapy for prostate cancer with T2w magnetic resonance imaging radiomic features.

BACKGROUND AND PURPOSE: High-risk prostate cancer patients are frequently treated with external-beam radiotherapy (EBRT). Of all patients receiving EBRT, 15-35% will experience biochemical recurrence (BCR) within five years. Magnetic resonance imaging (MRI) is commonly acquired as part of the diagnostic procedure and imaging-derived features have shown promise in tumour characterisation and biochemical recurrence prediction. We investigated the value of imaging features extracted from pre-treatment T2w anatomical MRI to predict five year biochemical recurrence in high-risk patients treated with EBRT. MATERIALS AND METHODS: In a cohort of 120 high-risk patients, imaging features were extracted from the whole-prostate and a margin surrounding it. Intensity, shape and textural features were extracted from the original and filtered T2w-MRI scans. The minimum-redundancy maximum-relevance algorithm was used for feature selection. Random forest and logistic regression classifiers were used in our experiments. The performance of a logistic regression model using the patient's clinical features was also investigated. To assess the prediction accuracy we used stratified 10-fold cross validation and receiver operating characteristic analysis, quantified by the area under the curve (AUC). RESULTS: A logistic regression model built using whole-prostate imaging features obtained an AUC of 0.63 in the prediction of BCR, outperforming a model solely based on clinical variables (AUC = 0.51). Combining imaging and clinical features did not outperform the accuracy of imaging alone. CONCLUSIONS: These results illustrate the potential of imaging features alone to distinguish patients with an increased risk of recurrence, even in a clinically homogeneous cohort.

Magnetic resonance imaging for prostate cancer radiotherapy.

For radiotherapy of prostate cancer, MRI is used increasingly for delineation of the prostate gland. For focal treatment of low-risk prostate cancer or focal dose escalation for intermediate and high-risk cancer, delineation of the tumor is also required. While multi-parametric MRI is well established for detection of tumors and for staging of the disease, delineation of the tumor inside the prostate is not common practice. Guidelines, such as the PI-RADS classification, exist for tumor detection and staging, but no such guidelines are available for tumor delineation. Indeed, interobserver studies show substantial variation in tumor contours. Computer-aided tumor detection and delineation may help improve the robustness of the interpretation of multi-parametric MRI data. Comparing the performance of an earlier developed model for tumor segmentation with expert delineations, we found a significant correlation between tumor probability in a voxel and the number of experts identifying this voxel as tumor. This suggests that the model agrees with 'the wisdom of the crowd', and thus could serve as a reference for individual physicians in their decision making. With multi-parametric MRI it becomes feasible to revisit the GTV-CTV concept in radiotherapy of prostate cancer. While detection of index lesions is quite reliable, contouring variability and the low sensitivity to small lesions suggest that the remainder of the prostate should be treated as CTV. Clinical trials that investigate the options for dose differentiation, for example with dose escalation to the visible tumor or dose reduction to the CTV, are therefore warranted.

Prostate tumor delineation using multiparametric magnetic resonance imaging: Inter-observer variability and pathology validation.

BACKGROUND AND PURPOSE: Boosting the dose to the largest (dominant) lesion in radiotherapy of prostate cancer may improve treatment outcome. The success of this approach relies on the detection and delineation of tumors. The agreement among teams of radiation oncologists and radiologists delineating lesions on multiparametric magnetic resonance imaging (mp-MRI) was assessed by measuring the distances between observer contours. The accuracy of detection and delineation was determined using whole-mount histopathology specimens as reference. MATERIAL AND METHODS: Six observer teams delineated tumors on mp-MRI of 20 prostate cancer patients who underwent a prostatectomy. To assess the inter-observer agreement, the inter-observer standard deviation (SD) of the contours was calculated for tumor sites which were identified by all teams. RESULTS: Eighteen of 89 lesions were identified by all teams, all were dominant lesions. The median histological volume of these was 2.4cm(3). The median inter-observer SD of the delineations was 0.23cm. Sixty-six of 69 satellites were missed by all teams. CONCLUSION: Since all teams identify most dominant lesions, dose escalation to the dominant lesion is feasible. Sufficient dose to the whole prostate may need to be maintained to prevent under treatment of smaller lesions and undetected parts of larger lesions.