Cost-effectiveness of weekly adaptive radiotherapy versus standard IMRT in head and neck cancer alongside the ARTIX trial.

BACKGROUND AND PURPOSE: We performed a cost-effectiveness analysis (CEA) comparing an adaptive radiotherapy (ART) strategy, based on weekly replanning, aiming to correct the parotid gland overdose during treatment and expecting therefore to decrease xerostomia, when compared to a standard IMRT. MATERIALS AND METHODS: We conducted the ARTIX trial, a randomized, parallel-group, multicentric study comparing a systematic weekly replanning ART to a standard IMRT. The primary endpoint was the frequency of xerostomia at 12 months, measured by stimulating salivary flow with paraffin. The CEA was designed alongside the ARTIX trial which was linked to the French national health data system (SNDS). For each patient, healthcare consumptions and costs were provided by the SNDS. The reference case analysis was based on the primary endpoint of the trial. Sensitivity and scenario analyses were performed. RESULTS: Of the 129 patients randomly assigned between 2013 and 2018, only 2 records were not linked to the SNDS, which provides a linkage proportion of 98.4%. All of the other 127 records were linked with good to very good robustness. On the intent-to-treat population at 12 months, mean total costs per patient were €41,564 (SD 23,624) and €33,063 (SD 16,886) for ART and standard IMRT arms, respectively (p = 0.033). Incremental cost effectiveness ratio (ICER) was €162,444 per xerostomia avoided. At 24 months, ICER was €194,521 per xerostomia avoided. For both progression-free and overall survival, ART was dominated by standard IMRT. CONCLUSION: The ART strategy was deemed to be not cost-effective compared with standard IMRT for patients with locally advanced oropharyngeal cancer.

Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods.

The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system. Then, the voxel-based process was applied on two sCT generation methods: a bulk-density method and a generative adversarial network. The CT and MRI pairs of 39 patients treated by radiotherapy for prostate cancer were used for sCT generation, and 26 of them with delineated structures were selected for analysis. Voxel-wise errors in sCT compared to CT were assessed for image intensities and dose calculation, and a population-based statistical test was applied to identify the regions where discrepancies were significant. The cumulative histograms of the mean absolute dose error per volume of tissue were computed to give a quantitative indication of the error for each generation method. Accurate interpatient registration was achieved, with mean Dice scores higher than 0.91 for all organs. The proposed method produces three-dimensional maps that precisely show the location of the major discrepancies for both sCT generation methods, highlighting the heterogeneity of image and dose errors for sCT generation methods from MRI across the pelvic anatomy. Hence, this method provides additional information that will assist with both sCT development and quality control for MRI-based planning radiotherapy.

Deep learning-based segmentation in prostate radiation therapy using Monte Carlo simulated cone-beam computed tomography.

PURPOSE: Segmenting organs in cone-beam CT (CBCT) images would allow to adapt the radiotherapy based on the organ deformations that may occur between treatment fractions. However, this is a difficult task because of the relative lack of contrast in CBCT images, leading to high inter-observer variability. Deformable image registration (DIR) and deep-learning based automatic segmentation approaches have shown interesting results for this task in the past years. However, they are either sensitive to large organ deformations, or require to train a convolutional neural network (CNN) from a database of delineated CBCT images, which is difficult to do without improvement of image quality. In this work, we propose an alternative approach: to train a CNN (using a deep learning-based segmentation tool called nnU-Net) from a database of artificial CBCT images simulated from planning CT, for which it is easier to obtain the organ contours. METHODS: Pseudo-CBCT (pCBCT) images were simulated from readily available segmented planning CT images, using the GATE Monte Carlo simulation. CT reference delineations were copied onto the pCBCT, resulting in a database of segmented images used to train the neural network. The studied segmentation contours were: bladder, rectum, and prostate contours. We trained multiple nnU-Net models using different training: (1) segmented real CBCT, (2) pCBCT, (3) segmented real CT and tested on pseudo-CT (pCT) generated from CBCT with cycleGAN, and (4) a combination of (2) and (3). The evaluation was performed on different datasets of segmented CBCT or pCT by comparing predicted segmentations with reference ones thanks to Dice similarity score and Hausdorff distance. A qualitative evaluation was also performed to compare DIR-based and nnU-Net-based segmentations. RESULTS: Training with pCBCT was found to lead to comparable results to using real CBCT images. When evaluated on CBCT obtained from the same hospital as the CT images used in the simulation of the pCBCT, the model trained with pCBCT scored mean DSCs of 0.92 ± 0.05, 0.87 ± 0.02, and 0.85 ± 0.04 and mean Hausdorff distance 4.67 ± 3.01, 3.91 ± 0.98, and 5.00 ± 1.32 for the bladder, rectum, and prostate contours respectively, while the model trained with real CBCT scored mean DSCs of 0.91 ± 0.06, 0.83 ± 0.07, and 0.81 ± 0.05 and mean Hausdorff distance 5.62 ± 3.24, 6.43 ± 5.11, and 6.19 ± 1.14 for the bladder, rectum, and prostate contours, respectively. It was also found to outperform models using pCT or a combination of both, except for the prostate contour when tested on a dataset from a different hospital. Moreover, the resulting segmentations demonstrated a clinical acceptability, where 78% of bladder segmentations, 98% of rectum segmentations, and 93% of prostate segmentations required minor or no corrections, and for 76% of the patients, all structures of the patient required minor or no corrections. CONCLUSION: We proposed to use simulated CBCT images to train a nnU-Net segmentation model, avoiding the need to gather complex and time-consuming reference delineations on CBCT images.

Local dose analysis to predict acute and late urinary toxicities after prostate cancer radiotherapy: Assessment of cohort and method effects.

PURPOSE: To perform bladder dose-surface map (DSM) analysis for (1) identifying symptom-related sub-surfaces (Ssurf) and evaluating their prediction capability of urinary toxicity, (2) comparing DSM with dose-volume map (DVM) (method effect), and (3) assessing the reproducibility of DSM (cohort effect). METHODS AND MATERIALS: Urinary toxicities were prospectively analyzed for 254 prostate cancer patients treated with IMRT/IGRT at 78/80 Gy. DSMs were generated by unfolding bladder surfaces in a 2D plane. Pixel-by-pixel analysis was performed to identify symptom-related Ssurf. Likewise, voxel-by-voxel DVM analysis was performed to identify sub-volumes (Svol). The prediction capability of Ssurf and Svol DVHs was assessed by logistic/Cox regression using the area under the ROC curve (AUC). The Ssurf localization and prediction capability were compared to (1) the Svol obtained by DVM analysis in the same cohort and (2) the Ssurf obtained from other DSM studies. RESULTS: Three Ssurf were identified in the bladder: posterior for acute retention (AUC = 0.64), posterior-superior for late retention (AUC = 0.68), and inferior-anterior-lateral for late dysuria (AUC = 0.73). Five Svol were identified: one in the urethra for acute incontinence and four in the posterior bladder part for acute and late retention, late dysuria, and hematuria. The overlap between Ssurf and Svol was moderate for acute retention, good for late retention, and bad for late dysuria, and AUCs ranged from 0.62 to 0.81. The prediction capabilities of Ssurf and Svol models were not significantly different. Among five symptoms comparable between cohorts, common Ssurf was found only for late dysuria, with a good spatial agreement. CONCLUSION: Spatial agreement between methods is relatively good although DVM identified more sub-regions. Reproducibility of identified Ssurf between cohorts is low.

Cost of prostate image-guided radiation therapy: results of a randomized trial.

PURPOSE: This cost analysis aimed to quantify the cost of IGRT in relation to IGRT frequency and modality with Cone Beam Computed Tomography (CBCT) or orthogonal electronic portal imaging with fiducial markers (EPI-FM). MATERIAL AND METHODS: Patients undergoing IGRT for localized prostate cancer were randomized into two prostate control frequencies (daily or weekly). Costs were calculated based on the micro-costing results according to hospitals' perspectives (in Euros, 2009) and the time horizon was radiation therapy. RESULTS: A total of 208 patients were enrolled in seven French cancer centers. A total of 6865 fractions were individually analyzed. The mean total treatment fraction duration was 21.0 min for daily CBCT and 18.3 min for daily EPI-FM. Increasing the control frequency from weekly to daily increased the mean treatment fraction duration by 7.3 min (+53%) for CBCT and 1.7 min (+10%) for EPI-FM (p ≤ 0.01). The mean additional cost per patient of daily controls compared with weekly controls was €679 and €187 for CBCT and EPI-FM, respectively (p<0.0001). CONCLUSIONS: The incremental costs due to different prostate IGRT strategies are relatively moderate, suggesting that daily IGRT combined with intensity-modulated RT (IMRT) could be administered in cases of high-dose radiation delivery to the prostate.