Intensity-Modulated Proton Therapy Adaptive Planning for Patients with Oropharyngeal Cancer

Wu, Richard; Liu, Amy; Sio, Terence T.; Blanchard, Pierre; Wages, Cody A.; Amin, M.; Gunn, G.B.; Titt, Uwe; Ye, Rong; Suzuki, Kazumichi; Gillin, Michael; Zhu, Xiaorong Ronald; Mohan, Radhe; Frank, Steven J.

doi:10.14338/ijpt-17-00010.1

Cited by 27 publications

(37 citation statements)

References 24 publications

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“…In a clinical dosimetric replanning study, Wu et al found that gross tumor volume (GTV) can be reduced by as much as 70% during the course of treatment. 24 This shrinkage, coupled with other anatomic changes such as daily sinus filling, swelling, or weight loss, can lead to differences in the planned dose and delivered dose. 25 Cone-beam CT images are useful because they provide a 3D view of the patient's anatomy; however, CBCT image quality is typically poor due to the large scatter-to-primary ratio (SPR) involved in CBCT imaging.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 AE 0.01 and the ME was −0.01 AE 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 CBCTguided adaptive treatment planning for IMPT.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Kraan et al [23] showed that treatment uncertainties in patients with oropharyngeal cancer could result in significant differences between planned and delivered IMPT doses, and the investigators recommended repeat diagnostic CT scans during treatment with replanning as indicated. Wu et al [24] demonstrated significant dose differences to CTVs and OARs when verification CT-QA scans were performed during the fourth week of treatment in 10 patients with oropharyngeal cancer treated with IMPT. Wu et al [24] showed that all 10 patients' mean CTV doses were significantly decreased and multiple OAR doses were increased between the simulation and verification CT-QA scans, which prompted replanning.…”

Section: Discussionmentioning

confidence: 99%

The Importance of Verification CT-QA Scans in Patients Treated with IMPT for Head and Neck Cancers

Evans

Harper

Petersen

et al. 2020

International Journal of Particle Therapy

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Purpose To understand how verification computed tomography-quality assurance (CT-QA) scans influenced clinical decision-making to replan patients with head and neck cancer and identify predictors for replanning to guide intensity-modulated proton therapy (IMPT) clinical practice. Patients and Methods We performed a quality-improvement study by prospectively collecting data on 160 consecutive patients with head and neck cancer treated using spot-scanning IMPT who underwent weekly verification CT-QA scans. Kaplan-Meier estimates were used to determine the cumulative probability of a replan by week. Predictors for replanning were determined with univariate (UVA) and multivariate (MVA) Cox model hazard ratios (HRs). Logistic regression was used to determine odds ratios (ORs). P < .05 was considered statistically significant. Results Of the 160 patients, 79 (49.4%) had verification CT-QA scans, which prompted a replan. The cumulative probability of a replan by week 1 was 13.7% (95% confidence interval [CI], 8.82-18.9), week 2, 25.0% (95% CI, 18.0-31.4), week 3, 33.1% (95% CI, 25.4-40.0), week 4, 45.6% (95% CI, 37.3-52.8), and week 5 and 6, 49.4% (95% CI, 41.0-56.6). Predictors for replanning were sinonasal disease site (UVA: HR, 1.82, P = .04; MVA: HR, 3.64, P = .03), advanced stage disease (UVA: HR, 4.68, P < .01; MVA: HR, 3.10, P < .05), dose > 60 Gy equivalent (GyE; relative biologic effectiveness, 1.1) (UVA: HR, 1.99, P < .01; MVA: HR, 2.20, P < .01), primary disease (UVA: HR, 2.00 versus recurrent, P = .01; MVA: HR, 2.46, P = .01), concurrent chemotherapy (UVA: HR, 2.05, P < .01; MVA: not statistically significant [NS]), definitive intent treatment (UVA: HR, 1.70 versus adjuvant, P < .02; MVA: NS), bilateral neck treatment (UVA: HR, 2.07, P = .03; MVA: NS), and greater number of beams (5 beam UVA: HR, 5.55 versus 1 or 2 beams, P < .02; MVA: NS). Maximal weight change from baseline was associated with higher odds of a replan (≥3 kg: OR, 1.97, P = .04; ≥ 5 kg: OR, 2.13, P = .02). Conclusions Weekly verification CT-QA scans frequently influenced clinical decision-making to replan. Additional studies that evaluate the practice of monitoring IMPT-treated patients with weekly CT-QA scans and whether that improves clinical outcomes are warranted.

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“…The use of CBCT for proton therapy is evolving as new proton centers are built worldwide . Verification and adaptive planning should be used during the course of proton therapy for patients with head and neck cancer to ensure that adequate dose is delivered to the planned CTVs while still respecting limits on doses to OARs . Users of CBCT should ensure that their CBCT system has adequate image quality for daily image guidance of intensity‐modulated proton therapy (IMPT).…”

Section: Discussionmentioning

confidence: 99%

“…CBCT is widely used for patient alignment and more recently for adaptive treatment planning. Targets and organs are known to change position and shape during fractionated radiotherapy . Deformable image registration (DIR) software has gained acceptance for managing contour propagation, dose tracking, and related issues over the course of such therapy.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the accuracy of deformable image registration for cone‐beam computed tomography with a physical phantom

Liu

Williamson

et al. 2019

J Applied Clin Med Phys

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Purpose: Kilo-voltage cone-beam computed tomography (CBCT) is widely used for patient alignment, contour propagation, and adaptive treatment planning in radiation therapy. In this study, we evaluated the accuracy of deformable image registration (DIR) for CBCT under various imaging protocols with different noise and patient dose levels.Methods: A physical phantom previously developed to facilitate end-to-end testing of the DIR accuracy was used with Varian Velocity v4.0 software to evaluate the performance of image registration from CT to CT, CBCT to CT, and CBCT to CBCT. The phantom is acrylic and includes several inserts that simulate different tissue shapes and properties. Deformations and anatomic changes were simulated by changing the rotations of both the phantom and the inserts. CT images (from a head and neck protocol) and CBCT images (from pelvis, head and "Image Gently" protocols) were obtained with different image noise and dose levels. Large inserts were filled with Mobil DTE oil to simulate soft tissue, and small inserts were filled with bone materials. All inserts were contoured before the DIR process to provide a ground truth contour size and shape for comparison. After the DIR process, all deformed contours were compared with the originals using Dice similarity coefficient (DSC) and mean distance to agreement (MDA). Both large and small volume of interests (VOIs) for DIR volume selection were tested by simulating a DIR process that included whole patient image volume and clinical target volumes (CTV) only (for CTVs propagation). Results:For cross-modality DIR registration (CT to CBCT), the DSC were >0.8 and the MDA were <3 mm for CBCT pelvis, and CBCT head protocols. For CBCT to CBCT and CT to CT, the DIR accuracy was improved relative to the cross-modality tests. For smaller VOIs, the DSC were >0.8 and MDA <2 mm for all modalities. Conclusions:The accuracy of DIR depends on the quality of the CBCT image at different dose and noise levels.Wu and Liu contributed equally.---

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Intensity-Modulated Proton Therapy Adaptive Planning for Patients with Oropharyngeal Cancer

Cited by 27 publications

References 24 publications

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

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

The Importance of Verification CT-QA Scans in Patients Treated with IMPT for Head and Neck Cancers

Quantifying the accuracy of deformable image registration for cone‐beam computed tomography with a physical phantom

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