MRI to CT Prostate Registration for Improved Targeting in Cancer External Beam Radiotherapy.

External radiotherapy is a major clinical treatment for localized prostate cancer. Currently, computed tomography (CT) is used to delineate the prostate and to plan the radiotherapy treatment. However, CT images suffer from a poor soft-tissue contrast and do not allow an accurate organ delineation. On the contrary, magnetic resonance imaging (MRI) provides rich details and high soft-tissue contrast, allowing tumor detection. Thus, the intraindividual propagation of MRI delineations toward the planning CT may improve tumor targeting. In this paper, we introduce a new method to propagate MRI prostate delineations to the planning CT. In the first step, a random forest classification is performed to coarsely detect the prostate in the CT images, yielding a prostate probability membership for each voxel and a prostate hard segmentation. Then, the registration is performed using a new similarity metric which maximizes the probability and the collinearity between the normals of the manual registration (MR) existing contour and the contour resulting from the CT classification. The first study on synthetic data was performed to analyze the influence of the metric parameters with different levels of noise. Then, the method was also evaluated on real MR-CT data using manual alignments and intraprostatic fiducial markers and compared to a classically used mutual information (MI) approach. The proposed metric outperformed MI by 7% in terms of Dice score coefficient, by 3.14 mm the Hausdorff distance, and 2.13 mm the markers position errors. Finally, the impact of registration uncertainties on the treatment planning was evaluated, demonstrating the potential advantage of the proposed approach in a clinical setup to define a precise target.

A new parameter computed with independent component analysis to predict rectal toxicity following prostate cancer radiotherapy.

The main challenge in prostate cancer radiotherapy is to deliver the prescribed dose to the clinical target while minimizing the dose to the neighboring organs at risk and thus avoiding subsequent toxicity-related events. With the aim of improving toxicity prediction following prostate cancer radiotherapy, the goal of our work is to propose a new predictive variable computed with independent component analysis to predict late rectal toxicity, and to compare its performance to other models (logistic regression, normal tissue complication probability model and recent principal component analysis approach). Clinical data and dose-volume histograms were collected from 216 patients having received 3D conformal radiation for prostate cancer with at least two years of follow-up. Independent component analysis was trained to predict the risk of 3-year rectal bleeding Grade ≥ 2. The performance of all the models was assessed by computing the area under the receiving operating characteristic curve. Clinical parameters combined with the new variable were found to be predictors of rectal bleeding. The mean area under the receiving operating curve for our proposed approach was 0:75. The AUC values for the logistic regression, the Lyman-Kutcher-Burman model and the recent principal component analysis approach were 0:62, 0:53 and 0:62, respectively. Our proposed new variable may be an useful new tool in predicting late rectal toxicity. It appears as a strong predictive variable to improve classical models.

Nomograms to predict late urinary toxicity after prostate cancer radiotherapy.

OBJECTIVE: To analyze late urinary toxicity after prostate cancer radiotherapy (RT): symptom description and identification of patient characteristics or treatment parameters allowing for the generation of nomograms. METHODS: Nine hundred and sixty-five patients underwent RT in seventeen French centers for localized prostate cancer. Median total dose was 70 Gy (range, 65-80 Gy), using different fractionations (2 or 2.5 Gy/day) and techniques. Late urinary toxicity and the corresponding symptoms (urinary frequency, incontinence, dysuria/decreased stream, and hematuria) were prospectively assessed in half of the patients using the LENT-SOMA classification. Univariate and multivariate Cox regression models addressed patient or treatment-related predictors of late urinary toxicity (≥grade 2). Nomograms were built up, and their performance was assessed. RESULTS: The median follow-up was 61 months. The 5-year (≥grade 2) global urinary toxicity, urinary frequency, hematuria, dysuria, and urinary incontinence rates were 15, 10, 5, 3 and 1 %, respectively. The 5-year (≥grade 3) urinary toxicity rate was 3 %. The following parameters significantly increased the 5-year risk of global urinary toxicity (≥grade 2): anticoagulant treatment (RR = 2.35), total dose (RR = 1.09), and age (RR = 1.06). Urinary frequency was increased by the total dose (RR = 1.07) and diabetes (RR = 4). Hematuria was increased by anticoagulant treatment (RR = 2.9). Dysuria was increased by the total dose (RR = 1.1). Corresponding nomograms and their calibration plots were generated. Nomogram performance should be validated with external data. CONCLUSIONS: The first nomograms to predict late urinary toxicity but also specific urinary symptoms after prostate RT were generated, contributing to prostate cancer treatment decision.