A planning study of focal dose escalations to multiparametric MRI-defined dominant intraprostatic lesions in prostate proton radiation therapy

Proton versus photon therapy for high-risk prostate cancer with dose escalation of dominant intraprostatic lesions: a preliminary planning study

Background and purpose

This study aimed to investigate the feasibility of safe-dose escalation to dominant intraprostatic lesions (DILs) and assess the clinical impact using dose-volume (DV) and biological metrics in photon and proton therapy. Biological parameters defined as late grade ≥ 2 gastrointestinal (GI) and genitourinary (GU) derived from planned (D P) and accumulated dose (D A) were utilized.

Materials and methods

In total, 10 patients with high-risk prostate cancer with multiparametric MRI-defined DILs were investigated. Each patient had two plans with a focal boost to the DILs using intensity-modulated proton therapy (IMPT) and volumetric-modulated arc therapy (VMAT). Plans were optimized to obtain DIL coverage while respecting the mandatory organ-at-risk constraints. For the planning evaluation, DV metrics, tumor control probability (TCP) for the DILs and whole prostate excluding the DILs (prostate-DILs), and normal tissue complication probability (NTCP) for the rectum and bladder were calculated. Wilcoxon signed-rank test was used for analyzing TCP and NTCP data.

Results

IMPT achieved a higher Dmean for the DILs compared to VMAT (IMPT: 68.1 GyRBE vs. VMAT: 66.6 Gy, p < 0.05). Intermediate-high rectal and bladder doses were lower for IMPT (p < 0.05), while the high-dose region (V60 Gy) remained comparable. IMPT-TCP for prostate-DIL were higher compared to VMAT (IMPT: 86%; α/β = 3, 94.3%; α/β = 1.5 vs. VMAT: 84.7%; α/β = 3, 93.9%; α/β = 1.5, p < 0.05). Likewise, IMPT obtained a moderately higher DIL TCP (IMPT: 97%; α/β = 3, 99.3%; α/β = 1.5 vs. VMAT: 95.9%; α/β = 3, 98.9%; α/β = 1.5, p < 0.05). Rectal D A-NTCP displayed the highest GI toxicity risk at 5.6%, and IMPT has a lower GI toxicity risk compared to VMAT-predicted Quantec-NTCP (p < 0.05). Bladder D P-NTCP projected a higher GU toxicity than D A-NTCP, with VMAT having the highest risk (p < 0.05).

Conclusion

Dose escalation using IMPT is able to achieve a high TCP for the DILs, with the lowest rectal and bladder DV doses at the intermediate-high-dose range. The reduction in physical dose was translated into a lower NTCP (p < 0.05) for the bladder, although rectal toxicity remained equivalent.

Achievable Dosimetric Constraints in Stereotactic Reirradiation for Recurrent Prostate Cancer

PURPOSE

Stereotactic body radiation therapy has been proposed as a salvage treatment for recurrent prostate cancer after irradiation. One crucial issue is choosing appropriate dose-volume constraints (DVCs) during planning. The objectives of this study were to (1) quantify the proportion of patients respecting the DVCs according to the Urogenital Tumor Study Group GETUG-31 trial, testing 36 Gy in six fractions, (2) explain geometrically why the DVCs could not be respected, and (3) propose the most suitable DVCs.

METHODS AND MATERIALS

This retrospective dosimetric analysis included 141 patients treated for recurrent prostate cancer with Cyberknife (Accuray), according to GETUG-31 DVCs: V95% ≥ 95% for the planning target volume (PTV), V12Gy < 20% and V27Gy < 2 cc for the rectum, and V12Gy < 15% and V27Gy < 5 cc for the bladder. The percentage of patients not respecting the DVCs was quantified. Correlations between the DVCs and anatomic structures were examined. New DVCs were proposed.

RESULTS

Only 19% of patients respected all DVCs, with a mean PTV of 18.5 cc (range, 3-48 cc), although the mean PTV was 40.5 cc (range, 3-174 cc) in the whole series. A total of 98% of the patients with a clinical target volume (CTV)/prostate ratio >0.5 could not respect the DVCs in the organs at risk. The target coverage and organ-at-risk sparing decreased significantly with increase in the values of PTV, CTV, CTV/prostate ratio, the overlapping volume between the PTV and bladder wall and between the PTV and rectal wall. Threshold values of PTV, >20 cc and 40 cc, allowed for the PTV and bladder DVCs, respectively. To improve DVC respect in case of large target volume, we proposed the following new DVCs: V12Gy < 25% and 25% and V27Gy < 2 cc and 5 cc for the rectum and bladder, respectively.

CONCLUSIONS

GETUG-31 DVCs are achievable only for small target volumes (CTV more than half of the prostate). For a larger target volume, new DVCs have been proposed.

Focal Boost in Prostate Cancer Radiotherapy: A Review of Planning Studies and Clinical Trials

BACKGROUND

Focal boost radiotherapy was developed to deliver elevated doses to functional sub-volumes within a target. Such a technique was hypothesized to improve treatment outcomes without increasing toxicity in prostate cancer treatment.

PURPOSE

To summarize and evaluate the efficacy and variability of focal boost radiotherapy by reviewing focal boost planning studies and clinical trials that have been published in the last ten years.

METHODS

Published reports of focal boost radiotherapy, that specifically incorporate dose escalation to intra-prostatic lesions (IPLs), were reviewed and summarized. Correlations between acute/late ≥G2 genitourinary (GU) or gastrointestinal (GI) toxicity and clinical factors were determined by a meta-analysis.

RESULTS

By reviewing and summarizing 34 planning studies and 35 trials, a significant dose escalation to the GTV and thus higher tumor control of focal boost radiotherapy were reported consistently by all reviewed studies. Reviewed trials reported a not significant difference in toxicity between focal boost and conventional radiotherapy. Acute ≥G2 GU and late ≥G2 GI toxicities were reported the most and least prevalent, respectively, and a negative correlation was found between the rate of toxicity and proportion of low-risk or intermediate-risk patients in the cohort.

CONCLUSION

Focal boost prostate cancer radiotherapy has the potential to be a new standard of care.

MRI-based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network

PURPOSE

Dose escalation to dominant intraprostatic lesions (DILs) is a novel treatment strategy to improve the treatment outcome of prostate radiation therapy. Treatment planning requires accurate and fast delineation of the prostate and DILs. In this study, a 3D cascaded scoring convolutional neural network is proposed to automatically segment the prostate and DILs from MRI.

METHODS AND MATERIALS

The proposed cascaded scoring convolutional neural network performs end-to-end segmentation by locating a region-of-interest (ROI), identifying the object within the ROI, and defining the target. A scoring strategy, which is learned to judge the segmentation quality of DIL, is integrated into cascaded convolutional neural network to solve the challenge of segmenting the irregular shapes of the DIL. To evaluate the proposed method, 77 patients who underwent MRI and PET/CT were retrospectively investigated. The prostate and DIL ground truth contours were delineated by experienced radiologists. The proposed method was evaluated with five-fold cross validation and holdout testing.

RESULTS

The average centroid distance, volume difference, and Dice similarity coefficient (DSC) value for prostate/DIL are 4.3±7.5mm/3.73±3.78mm, 4.5±7.9cc/0.41±0.59cc and 89.6±8.9%/84.3±11.9%, respectively. Comparable results were obtained in the holdout test. Similar or superior segmentation outcomes were seen when compared the results of the proposed method to those of competing segmentation approaches CONCLUSIONS: : The proposed automatic segmentation method can accurately and simultaneously segment both the prostate and DILs. The intended future use for this algorithm is focal boost prostate radiation therapy. This article is protected by copyright. All rights reserved.

Brachytherapy focal dose escalation using ultrasound based tissue characterization by patients with non-metastatic prostate cancer: Five-year results from single-center phase 2 trial

PURPOSE

This prospective trial investigates side effects and efficacy of focal dose escalation with brachytherapy for patients with prostate cancer.

METHODS AND MATERIALS

In the Phase II, monocentric prospective trial 101 patients with low-/intermediate- and high-risk prostate cancer were enrolled between 2011 and 2013. Patients received either PDR-/HDR-brachytherapy alone with 86-90 Gy (EQD2, α/β = 3 Gy) or PDR-/HDR-brachytherapy as boost after external beam radiation therapy up to a total dose of 91-96 Gy (EQD2, α/β = 3 Gy). Taking place brachytherapy all patients received the simultaneous integrated focal boost to the intra-prostatic tumor lesions visible in computer-aided ultrasonography (HistoScanning™) - up to a total dose of 108-119 Gy (EQD2, α/β = 3 Gy). The primary endpoint was toxicity. Secondary endpoints were cumulative freedom from local recurrence, PSA-free survival, distant metastases-free survival, and overall survival. This trial is registered with ClinicalTrials.gov, number NCT01409876.

RESULTS

Median follow-up was 65 months. Late toxicity was generally low with only four patients scoring urinary grade 3 toxicity (4/101, 4%). Occurrence of any grade of late rectal toxicities was very low. We did not register any grade ≥2 of late rectal toxicities. The cumulative 5 years local recurrence rate (LRR) for all patients was 1%. Five years- biochemical disease-free survival estimates according Kaplan-Meier were 98,1% and 81,3% for low-/intermediate-risk and high-risk patients, respectively. Five years metastases-free survival estimates according Kaplan-Meier were 98,0% and 83,3% for all patients, low-/intermediate-risk and high-risk patients, respectively.

CONCLUSIONS

The 5 years-results from this Phase II Trial show that focal dose escalation with computer-aided ultrasonography and brachytherapy for patients with non-metastatic prostate cancer is safe and effective.

Prostate and dominant intraprostatic lesion segmentation on PET/CT using cascaded regional-net

Focal boost to dominant intraprostatic lesions (DILs) has recently been proposed for prostate radiation therapy. Accurate and fast delineation of the prostate and DILs is thus required during treatment planning. In this paper, we develop a learning-based method using positron emission tomography (PET)/computed tomography (CT) images to automatically segment the prostate and its DILs. To enable end-to-end segmentation, a deep learning-based method, called cascaded regional-Net, is utilized. The first network, referred to as dual attention network, is used to segment the prostate via extracting comprehensive features from both PET and CT images. A second network, referred to as mask scoring regional convolutional neural network (MSR-CNN), is used to segment the DILs from the PET and CT within the prostate region. Scoring strategy is used to diminish the misclassification of the DILs. For DIL segmentation, the proposed cascaded regional-Net uses two steps to remove normal tissue regions, with the first step cropping images based on prostate segmentation and the second step using MSR-CNN to further locate the DILs. The binary masks of DILs and prostates of testing patients are generated on the PET/CT images by the trained model. For evaluation, we retrospectively investigated 49 prostate cancer patients with PET/CT images acquired. The prostate and DILs of each patient were contoured by radiation oncologists and set as the ground truths and targets. We used five-fold cross-validation and a hold-out test to train and evaluate our method. The mean surface distance and DSC values were 0.666 ± 0.696 mm and 0.932 ± 0.059 for the prostate and 0.814 ± 1.002 mm and 0.801 ± 0.178 for the DILs among all 49 patients. The proposed method has shown promise for facilitating prostate and DIL delineation for DIL focal boost prostate radiation therapy.

Dosimetric Uncertainties in Dominant Intraprostatic Lesion Simultaneous Boost Using Intensity Modulated Proton Therapy

Purpose

While intensity modulated proton therapy can deliver simultaneous integrated boost (SIB) to the dominant intraprostatic lesion (DIL) with high precision, it is sensitive to anatomic changes. We investigated the dosimetric effects from these changes based on pretreatment cone-beam computed tomographic (CBCT) images and identified the most important factors using a multilayer perceptron neural network (MLPNN).

Methods and Materials

DILs were contoured based on coregistered multiparametric magnetic resonance images for 25 previously treated prostate cancer patients. SIB plans were created with (1) prostate clinical target volume − V70 Gy = 98%; (2) DIL − V98 Gy > 95%; and (3) all organs at risk (OARs)"?> within clinical constraints. SIB plans were applied to daily CBCT-based deformed planning computed tomography (CT)"?>. DIL − V98 Gy, bladder/rectum maximum dose (Dmax) and volume changes, femur shifts, and the distance from DIL to organs at riskOARs"?> in both planning computed tomogramsCT"?> and CBCT were calculated. Wilcoxon signed-ranks tests were used to compare the changes. MLPNNs were used to model the change in ΔDIL − V98 Gy > 10% and bladder/rectum Dmax > 80 Gy, and the relative importance factors for the model were provided. The performances of the models were evaluated with receiver operating characteristic curves.

Results

Comparing initial plan to the average from evaluation plans, respectively, DIL − V98 Gy was 89.3% ± 19.9% versus 86.2% ± 21.3% (P = .151); bladder Dmax 71.9 ± 0.6 Gy versus 74.5 ± 2.9 Gy (P < .001); and rectum Dmax 70.1 ± 2.4 Gy versus 74.9 ± 9.1Gy (P = .007). Bladder and rectal volumes were 99.6% ± 39.5% and 112.8% ± 27.2%, respectively, of their initial volume. The femur shift was 3.16 ± 2.52 mm. In the modeling of ΔDIL V98 Gy > 10%, DIL to rectum distance changes, DIL to bladder distance changes, and rectum volume changes ratio are the 3 most important factors. The areas under the receiver operating characteristic curves were 0.89, 1.00, and 0.99 for the modeling of ΔDIL − V98 Gy > 10%, and bladder and rectum Dmax > 80 Gy, respectively.

Conclusions

Dosimetric changes in DIL SIB with intensity modulated proton therapy can be modeled and classified based on anatomic changes on pretreatment images by an MLPNN.

Proton vs. photon radiotherapy for MR-guided dose escalation of intraprostatic lesions

BACKGROUND

Dose escalation has been associated with improved biochemical control for prostate cancer. Focusing the high dose on the MRI-defined intraprostatic lesions (IL) could spare the surrounding organs at risk and hence allow further escalation. We compare treatment efficacy between state-of-the-art focally-boosted proton and photon-based radiotherapy, and investigate possible predictive guidelines regarding individualized treatment prescriptions.

MATERIAL AND METHODS

Ten prostate cancer patients with well-defined ILs were selected. Multiparametric MRI was used to delineate ILs, which were transferred to the planning CT via image registration. Pencil beam scanning proton therapy and volumetric modulated arc therapy treatment plans, were created for each patient. Each modality featured 6 plans: (1) moderately hypofractionated dose: 70 Gy to the prostate in 28 fractions, (2)-(6) plan 1 plus additional simultaneous-integrated-boost to ILs to 75.6, 81.2, 86.6, 98 and 112 Gy in 28 fractions. Equivalent dose to 2 Gy-per-fraction (EqD2) was used to calculate tumor control (TCP) and normal tissue complication probabilities (NTCP) for ILs and organs-at-risk.

RESULTS

For both modalities, the maximum necessary dose to achieve TCP > 99% was 98 Gy for very high-risk ILs. For lower risk ILs lower doses were sufficient. NTCP was <25% and 35% for protons and photons at the maximum dose escalation, respectively. For the cases and beam characteristics considered, proton therapy was dosimetrically superior when IL was >4 cc or located <2.5 mm from the rectum.

CONCLUSION

This work demonstrated the potential role for proton therapy in the setting of prostate focal dose escalation. We propose that anatomical characteristic could be used as criteria to identify patients who would benefit from proton treatment.

Catheter position prediction using deep-learning-based multi-atlas registration for high-dose rate prostate brachytherapy

PURPOSE

High-dose-rate (HDR) prostate brachytherapy involves treatment catheter placement, which is currently empirical and physician dependent. The lack of proper catheter placement guidance during the procedure has left the physicians to rely on a heuristic thinking-while-doing technique, which may cause large catheter placement variation and increased plan quality uncertainty. Therefore, the achievable dose distribution could not be quantified prior to the catheter placement. To overcome this challenge, we proposed a learning-based method to provide HDR catheter placement guidance for prostate cancer patients undergoing HDR brachytherapy.

METHODS

The proposed framework consists of deformable registration via registration network (Reg-Net), multi-atlas ranking, and catheter regression. To model the global spatial relationship among multiple organs, binary masks of the prostate and organs-at-risk are transformed into distance maps, which describe the distance of each local voxel to the organ surfaces. For a new patient, the generated distance map is used as fixed image. Reg-Net is utilized to deformably register the distance maps from multi-atlas set to match this patient's distance map and then bring catheter maps from multi-atlas to this patient via spatial transformation. Several criteria, namely prostate volume similarity, multi-organ semantic image similarity, and catheter position criteria (far from the urethra and within the partial prostate), are used for multi-atlas ranking. The top-ranked atlas' deformed catheter positions are selected as the predicted catheter positions for this patient. Finally, catheter regression is used to refine the final catheter positions. A retrospective study on 90 patients with a fivefold cross-validation scheme was used to evaluate the proposed method's feasibility. In order to investigate the impact of plan quality from the predicted catheter pattern, we optimized the source dwell position and time for both the clinical catheter pattern and predicted catheter pattern with the same optimization settings. Comparisons of clinically relevant dose volume histogram (DVH) metrics were completed.

RESULTS

For all patients, on average, both the clinical plan dose and predicted plan dose meet the common dose constraints when prostate dose coverage is kept at V100 = 95%. The plans from the predicted catheter pattern have slightly higher hotspot in terms of V150 by 5.0% and V200 by 2.9% on average. For bladder V75, rectum V75, and urethra V125, the average difference is close to zero, and the range of most patients is within ±1 cc.

CONCLUSION

We developed a new catheter placement prediction method for HDR prostate brachytherapy based on a deep-learning-based multi-atlas registration algorithm. It has great clinical potential since it can provide catheter location estimation prior to catheter placement, which could reduce the dependence on physicians' experience in catheter implantation and improve the quality of prostate HDR treatment plans. This approach merits further clinical evaluation and validation as a method of quality control for HDR prostate brachytherapy.

Influence of Urethra Sparing on Tumor Control Probability and Normal Tissue Complication Probability in Focal Dose Escalated Hypofractionated Radiotherapy: A Planning Study Based on Histopathology Reference

Purpose

Multiparametric magnetic resonance tomography (mpMRI) and prostate specific membrane antigen positron emission tomography (PSMA-PET/CT) are used to guide focal radiotherapy (RT) dose escalation concepts. Besides improvements of treatment effectiveness, maintenance of a good quality of life is essential. Therefore, this planning study investigates whether urethral sparing in moderately hypofractionated RT with focal RT dose escalation influences tumour control probability (TCP) and normal tissue complication probability (NTCP).

Patients and Methods

10 patients with primary prostate cancer (PCa), who underwent 68Ga PSMA-PET/CT and mpMRI followed by radical prostatectomy were enrolled. Intraprostatic tumour volumes (gross tumor volume, GTV) based on both imaging techniques (GTV-MRI and -PET) were contoured manually using validated contouring techniques and GTV-Union was created by summing both. For each patient three IMRT plans were generated with 60 Gy to the whole prostate and a simultaneous integrated boost up to 70 Gy to GTV-Union in 20 fractions by (Plan 1) not respecting and (Plan 2) respecting dose constraints for urethra as well as (Plan 3) respecting dose constraints for planning organ at risk volume for urethra (PRV = urethra + 2mm expansion). NTCP for urethra was calculated applying a Lyman-Kutcher-Burman model. TCP-Histo was calculated based on PCa distribution in co-registered histology (GTV-Histo). Complication free tumour control probability (P+) was calculated. Furthermore, the intrafractional movement was considered.

Results

Median overlap of GTV-Union and PRV-Urethra was 1.6% (IQR 0-7%). Median minimum distance of GTV-Histo to urethra was 3.6 mm (IQR 2 - 7 mm) and of GTV-Union to urethra was 1.8 mm (IQR 0.0 - 5.0 mm). The respective prescription doses and dose constraints were reached in all plans. Urethra-sparing in Plans 2 and 3 reached significantly lower NTCP-Urethra (p = 0.002) without significantly affecting TCP-GTV-Histo (p = p > 0.28), NTCP-Bladder (p > 0.85) or NTCP-Rectum (p = 0.85), resulting in better P+ (p = 0.006). Simulation of intrafractional movement yielded even higher P+ values for Plans 2 and 3 compared to Plan 1.

Conclusion

Urethral sparing may increase the therapeutic ratio and should be implemented in focal RT dose escalation concepts.

Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks

BACKGROUND AND PURPOSE

Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric magnetic resonance imaging (MRI) with CBCT images for improved DIL delineation, treatment planning, and dose monitoring in prostate radiotherapy.

METHODS AND MATERIALS

We proposed a novel registration framework which considers biomechanical constraint when deforming the MR to CBCT. The registration framework consists of two segmentation convolutional neural networks (CNN) for MR and CBCT prostate segmentation, and a three-dimensional (3D) point cloud (PC) matching network. Image intensity-based rigid registration was first performed to initialize the alignment between MR and CBCT prostate. The aligned prostates were then meshed into tetrahedron elements to generate volumetric PC representation of the prostate shapes. The 3D PC matching network was developed to predict a PC motion vector field which can deform the MRI prostate PC to match the CBCT prostate PC. To regularize the network's motion prediction with biomechanical constraints, finite element (FE) modeling-generated motion fields were used to train the network. MRI and CBCT images of 50 patients with intraprostatic fiducial markers were used in this study. Registration results were evaluated using three metrics including dice similarity coefficient (DSC), mean surface distance (MSD), and target registration error (TRE). In addition to spatial registration accuracy, Jacobian determinant and strain tensors were calculated to assess the physical fidelity of the deformation field.

RESULTS

The mean and standard deviation of our method were 0.93 ± 0.01, 1.66 ± 0.10 mm, and 2.68 ± 1.91 mm for DSC, MSD, and TRE, respectively. The mean TRE of the proposed method was reduced by 29.1%, 14.3%, and 11.6% as compared to image intensity-based rigid registration, coherent point drifting (CPD) nonrigid surface registration, and modality-independent neighborhood descriptor (MIND) registration, respectively.

CONCLUSION

We developed a new framework to accurately register the prostate on MRI to CBCT images for external beam radiotherapy. The proposed method could be used to aid DIL delineation on CBCT, treatment planning, dose escalation to DIL, and dose monitoring.

Cone-beam CT-derived relative stopping power map generation via deep learning for proton radiotherapy

PURPOSE

In intensity-modulated proton therapy (IMPT), protons are used to deliver highly conformal dose distributions, targeting tumors, and sparing organs-at-risk. However, due to uncertainties in both patient setup and relative stopping power (RSP) calculation, margins are added to the treatment volume during treatment planning, leading to higher doses to normal tissues. Cone-beam computed tomography (CBCT) images are taken daily before treatment; however, the poor image quality of CBCT limits the use of these images for online dose calculation. In this work, we use a deep-learning-based method to predict RSP maps from daily CBCT images, allowing for online dose calculation in a step toward adaptive radiation therapy.

METHODS

Twenty-three head-and-neck cancer patients were simulated using a Siemens TwinBeam dual-energy CT (DECT) scanner. Mixed-energy scans (equivalent to a 120 kVp single-energy CT scan) were converted to RSP maps for treatment planning. Cone-beam computed tomography images were taken on the first day of treatment, and the planning RSP maps were registered to these images. A deep learning network based on a cycle-GAN architecture, relying on a compound loss function designed for structural and contrast preservation, was then trained to create an RSP map from a CBCT image. Leave-one-out and holdout cross validations were used for evaluation, and mean absolute error (MAE), mean error (ME), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used to quantify the differences between the CT-based and CBCT-based RSP maps. The proposed method was compared to a deformable image registration-based method which was taken as the ground truth and two other deep learning methods. For one patient who underwent resimulation, the new planning RSP maps and CBCT images were used for further evaluation and validation.

RESULTS

The CBCT-based RSP generation method was evaluated on 23 head-and-neck cancer patients. From leave-one-out testing, the MAE between CT-based and CBCT-based RSP was 0.06 ± 0.01 and the ME was -0.01 ± 0.01. The proposed method statistically outperformed the comparison DL methods in terms of MAE and ME when compared to the planning CT. In terms of dose comparison, the mean gamma passing rate at 3%/3 mm was 94% when three-dimensional (3D) gamma index was calculated per plan and 96% when gamma index was calculated per field.

CONCLUSIONS

The proposed method provides sufficiently accurate RSP map generation from CBCT images, allowing for evaluation of daily dose based on CBCT and possibly allowing for CBCT-guided adaptive treatment planning for IMPT.