First Clinical Investigation of Cone Beam Computed Tomography and Deformable Registration for Adaptive Proton Therapy for Lung Cancer

Long-term survival outcomes and quality of life of image-guided proton therapy for operable stage I non-small cell lung cancer: A phase 2 study

BACKGROUND AND PURPOSE

This study evaluated long-term efficacy, safety, and changes in quality of life (QOL) of patients after image-guided proton therapy (IGPT) for operable stage I non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

This single-institutional prospective phase 2 study enrolled patients with operable histologically confirmed stage IA or IB NSCLC (7th edition of UICC). The prescribed dose was 66 Gy relative biological effectiveness equivalents (GyRBE) in 10 fractions for peripheral lesions, or 72.6 GyRBE in 22 fractions for central lesions. The primary endpoint was the 3-year overall survival (OS). The secondary endpoints included disease control, toxicity, and changes in QOL score.

RESULTS

We enrolled 43 patients (median age: 68 years; range, 47-79 years) between July 2013 to January 2021, of whom 41 (95 %) had peripheral lesions and 27 (63 %) were stage IA. OS, local control, and progression-free survival rates were 95 % (95 % CI: 83-99), 95 % (82-99), and 86 % (72-94), respectively, at 3 years, and 83 % (66-92), 95 % (82-99), and 77 % (60-88), respectively, at 7 years. Four patients (9 %) developed grade 2, and one patient (2 %) developed grade 3 radiation pneumonitis. No other grade 3 or higher adverse events were observed. In the QOL analysis, global QOL remained favorable; however, approximately 40 % of patients reported dyspnea at 3 and 24 months.

CONCLUSION

Our findings suggest that IGPT provides effective disease control and survival in operable stage I NSCLC, particularly for peripheral lesions. Moreover, toxicity associated with IGPT was minimal, and patients reported favorable QOL.

Phase 2 Trial of Consolidation Pembrolizumab After Proton Reirradiation for Thoracic Recurrences of Non-Small Cell Lung Cancer

PURPOSE

Reirradiation (reRT) with proton beam therapy (PBT) may offer a chance of cure while minimizing toxicity for patients with isolated intrathoracic recurrences of non-small cell lung cancer (NSCLC). However, distant failure remains common, necessitating strategies to integrate more effective systemic therapy.

METHODS AND MATERIALS

This was a phase 2, single-arm trial (NCT03087760) of consolidation pembrolizumab after PBT reRT for locoregional recurrences of NSCLC. Four to 12 weeks after completion of 60 to 70 Gy PBT reRT, patients without progressive disease received pembrolizumab for up to 12 months. Primary endpoint was progression-free survival (PFS), measured from the start of reRT. Secondary endpoints were overall survival (OS) and National Cancer Institute Common Terminology Criteria for Adverse Events, version 5.0 toxicity.

RESULTS

Between 2017 and 2021, 22 patients received PBT reRT. Median interval from prior radiation end to reRT start was 20 months. Most recurrences (91%) were centrally located. Most patients received concurrent chemotherapy (95%) and pencil beam scanning PBT (77%), and 36% had received prior durvalumab. Fifteen patients (68%) initiated consolidation pembrolizumab on trial and received a median of 3 cycles (range, 2-17). Pembrolizumab was discontinued most commonly due to toxicity (n = 5; 2 were pembrolizumab-related), disease progression (n = 4), and completion of 1 year (n = 3). Median follow-up was 38.7 months. Median PFS and OS were 8.8 months (95% CI, 4.2-23.7) and 22.8 months (95% CI, 6.9-not reached), respectively. There was only one isolated in-field failure after reRT. Grade ≥3 toxicities occurred in 10 patients (45%); 2 were pembrolizumab-related. There were 2 grade 5 toxicities, an aorto-esophageal fistula at 6.9 months and hemoptysis at 46.8 months, both probably from reRT. The trial closed early due to widespread adoption of immunotherapy off-protocol.

CONCLUSIONS

In the first-ever prospective trial combining PBT reRT with consolidation immunotherapy, PFS was acceptable and OS favorable. Late grade 5 toxicity occurred in 2 of 22 patients. This approach may be considered in selected patients with isolated thoracic recurrences of NSCLC.

Predicting the Effect of Proton Beam Therapy Technology on Pulmonary Toxicities for Patients With Locally Advanced Lung Cancer Enrolled in the Proton Collaborative Group Prospective Clinical Trial

PURPOSE

This study aimed to predict the probability of grade ≥2 pneumonitis or dyspnea within 12 months of receiving conventionally fractionated or mildly hypofractionated proton beam therapy for locally advanced lung cancer using machine learning.

METHODS AND MATERIALS

Demographic and treatment characteristics were analyzed for 965 consecutive patients treated for lung cancer with conventionally fractionated or mildly hypofractionated (2.2-3 Gy/fraction) proton beam therapy across 12 institutions. Three machine learning models (gradient boosting, additive tree, and logistic regression with lasso regularization) were implemented to predict Common Terminology Criteria for Adverse Events version 4 grade ≥2 pulmonary toxicities using double 10-fold cross-validation for parameter hyper-tuning without leak of information. Balanced accuracy and area under the curve were calculated, and 95% confidence intervals were obtained using bootstrap sampling.

RESULTS

The median age of the patients was 70 years (range, 20-97), and they had predominantly stage IIIA or IIIB disease. They received a median dose of 60 Gy in 2 Gy/fraction, and 46.4% received concurrent chemotherapy. In total, 250 (25.9%) had grade ≥2 pulmonary toxicity. The probability of pulmonary toxicity was 0.08 for patients treated with pencil beam scanning and 0.34 for those treated with other techniques (P = 8.97e-13). Use of abdominal compression and breath hold were highly significant predictors of less toxicity (P = 2.88e-08). Higher total radiation delivered dose (P = .0182) and higher average dose to the ipsilateral lung (P = .0035) increased the likelihood of pulmonary toxicities. The gradient boosting model performed the best of the models tested, and when demographic and dosimetric features were combined, the area under the curve and balanced accuracy were 0.75 ± 0.02 and 0.67 ± 0.02, respectively. After analyzing performance versus the number of data points used for training, we observed that accuracy was limited by the number of observations.

CONCLUSIONS

In the largest analysis of prospectively enrolled patients with lung cancer assessing pulmonary toxicities from proton therapy to date, advanced machine learning methods revealed that pencil beam scanning, abdominal compression, and lower normal lung doses can lead to significantly lower probability of developing grade ≥2 pneumonitis or dyspnea.

2D antiscatter grid and scatter sampling based CBCT method for online dose calculations during CBCT guided radiation therapy of pelvis

BACKGROUND

Online dose calculations before the delivery of radiation treatments have applications in dose delivery verification, online adaptation of treatment plans, and simulation-free treatment planning. While dose calculations by directly utilizing CBCT images are desired, dosimetric accuracy can be compromised due to relatively lower HU accuracy in CBCT images.

PURPOSE

In this work, we propose a novel CBCT imaging pipeline to enhance the accuracy of CBCT-based dose calculations in the pelvis region. Our approach aims to improve the HU accuracy in CBCT images, thereby improving the overall accuracy of CBCT-based dose calculations prior to radiation treatment delivery.

METHODS

An in-house developed quantitative CBCT pipeline was implemented to address the CBCT raw data contamination problem. The pipeline combines algorithmic data correction strategies and 2D antiscatter grid-based scatter rejection to achieve high CT number accuracy. To evaluate the effect of the quantitative CBCT pipeline on CBCT-based dose calculations, phantoms mimicking pelvis anatomy were scanned using a linac-mounted CBCT system, and a gold standard multidetector CT used for treatment planning (pCT). A total of 20 intensity-modulated treatment plans were generated for five targets, using 6 and 10 MV flattening filter-free beams, and utilizing small and large pelvis phantom images. For each treatment plan, four different dose calculations were performed using pCT images and three CBCT imaging configurations: quantitative CBCT, clinical CBCT protocol, and a high-performance 1D antiscatter grid (1D ASG). Subsequently, dosimetric accuracy was evaluated for both targets and organs at risk as a function of patient size, target location, beam energy, and CBCT imaging configuration.

RESULTS

When compared to the gold-standard pCT, dosimetric errors in quantitative CBCT-based dose calculations were not significant across all phantom sizes, beam energies, and treatment sites. The largest error observed was 0.6% among all dose volume histogram metrics and evaluated dose calculations. In contrast, dosimetric errors reached up to 7% and 97% in clinical CBCT and high-performance ASG CBCT-based treatment plans, respectively. The largest dosimetric errors were observed in bony targets in the large phantom treated with 6 MV beams. The trends of dosimetric errors in organs at risk were similar to those observed in the targets.

CONCLUSIONS

The proposed quantitative CBCT pipeline has the potential to provide comparable dose calculation accuracy to the gold-standard planning CT in photon radiation therapy for the abdomen and pelvis. These robust dose calculations could eliminate the need for density overrides in CBCT images and enable direct utilization of CBCT images for dose delivery monitoring or online treatment plan adaptations before the delivery of radiation treatments.

PTCOG Gastrointestinal Subcommittee Lower Gastrointestinal Tract Malignancies Consensus Statement

Purpose

Radiotherapy delivery in the definitive management of lower gastrointestinal (LGI) tract malignancies is associated with substantial risk of acute and late gastrointestinal (GI), genitourinary, dermatologic, and hematologic toxicities. Advanced radiation therapy techniques such as proton beam therapy (PBT) offer optimal dosimetric sparing of critical organs at risk, achieving a more favorable therapeutic ratio compared with photon therapy.

Materials and Methods

The international Particle Therapy Cooperative Group GI Subcommittee conducted a systematic literature review, from which consensus recommendations were developed on the application of PBT for LGI malignancies.

Results

Eleven recommendations on clinical indications for which PBT should be considered are presented with supporting literature, and each recommendation was assessed for level of evidence and strength of recommendation. Detailed technical guidelines pertaining to simulation, treatment planning and delivery, and image guidance are also provided.

Conclusion

PBT may be of significant value in select patients with LGI malignancies. Additional clinical data are needed to further elucidate the potential benefits of PBT for patients with anal cancer and rectal cancer.

The Status and Challenges for Prostate Stereotactic Body Radiation Therapy Treatments in United States Proton Therapy Centers: An NRG Oncology Practice Survey

Purpose

To report the current practice pattern of the proton stereotactic body radiation therapy (SBRT) for prostate treatments.

Materials and Methods

A survey was designed to inquire about the practice of proton SBRT treatment for prostate cancer. The survey was distributed to all 30 proton therapy centers in the United States that participate in the National Clinical Trial Network in February, 2023. The survey focused on usage, patient selection criteria, prescriptions, target contours, dose constraints, treatment plan optimization and evaluation methods, patient-specific QA, and image-guided radiation therapy (IGRT) methods.

Results

We received responses from 25 centers (83% participation). Only 8 respondent proton centers (32%) reported performing SBRT of the prostate. The remaining 17 centers cited 3 primary reasons for not offering this treatment: no clinical need, lack of volumetric imaging, and/or lack of clinical evidence. Only 1 center cited the reduction in overall reimbursement as a concern for not offering prostate SBRT. Several common practices among the 8 centers offering SBRT for the prostate were noted, such as using Hydrogel spacers, fiducial markers, and magnetic resonance imaging (MRI) for target delineation. Most proton centers (87.5%) utilized pencil beam scanning (PBS) delivery and completed Imaging and Radiation Oncology Core (IROC) phantom credentialing. Treatment planning typically used parallel opposed lateral beams, and consistent parameters for setup and range uncertainties were used for plan optimization and robustness evaluation. Measurements-based patient-specific QA, beam delivery every other day, fiducial contours for IGRT, and total doses of 35 to 40 GyRBE were consistent across all centers. However, there was no consensus on the risk levels for patient selection.

Conclusion

Prostate SBRT is used in about 1/3 of proton centers in the US. There was a significant consistency in practices among proton centers treating with proton SBRT. It is possible that the adoption of proton SBRT may become more common if proton SBRT is more commonly offered in clinical trials.

Generation and evaluation of anatomy-preserving virtual CT for online adaptive proton therapy

BACKGROUND

Daily CTs generated by CBCT correction are required for daily replanning in online-adaptive proton therapy (APT) to effectively deal with inter-fractional changes. Out of the currently available methods, the suitability of a daily CT generation method for proton dose calculation also depends on the anatomical site.

PURPOSE

We propose an anatomy-preserving virtual CT (APvCT) method as a hybrid method of CBCT correction, which is especially suitable for large anatomy deformations. The accuracy of the hybrid method was assessed by comparison with the corrected CBCT (cCBCT) and virtual CT (vCT) methods in the context of online APT.

METHODS

Seventy-one daily CBCTs of four prostate cancer patients treated with intensity modulated proton therapy (IMPT) were converted to daily CTs using cCBCT, vCT, and the newly proposed APvCT method. In APvCT, planning CT (pCT) were mapped to CBCT geometry using deformable image registration with boundary conditions on controlling regions of interest (ROIs) created with deep learning segmentation on cCBCT. The relative frequency distribution (RFD) of HU, mass density and stopping power ratio (SPR) values were assessed and compared with the pCT. The ROIs in the APvCT and vCT were compared with cCBCT in terms of Dice similarity coefficient (DSC) and mean distance-to-agreement (mDTA). For each patient, a robustly optimized IMPT plan was created on the pCT and subsequent daily adaptive plans on daily CTs. For dose distribution comparison on the same anatomy, the daily adaptive plans on cCBCT and vCT were recalculated on the corresponding APvCT. The dose distributions were compared in terms of isodose volumes and 3D global gamma-index passing rate (GPR) at γ(2%, 2 mm) criterion.

RESULTS

For all patients, no noticeable difference in RFDs was observed amongst APvCT, vCT, and pCT except in cCBCT, which showed a noticeable difference. The minimum DSC value was 0.96 and 0.39 for contours in APvCT and vCT respectively. The average value of mDTA for APvCT was 0.01 cm for clinical target volume and ≤0.01 cm for organs at risk, which increased to 0.18 cm and ≤0.52 cm for vCT. The mean GPR value was 90.9%, 64.5%, and 67.0% for APvCT versus cCBCT, vCT versus cCBCT, and APvCT versus vCT, respectively. When recalculated on APvCT, the adaptive cCBCT and vCT plans resulted in mean GPRs of 89.5 ± 5.1% and 65.9 ± 19.1%, respectively. The mean DSC values for 80.0%, 90.0%, 95.0%, 98.0%, and 100.0% isodose volumes were 0.97, 0.97, 0.97, 0.95, and 0.91 for recalculated cCBCT plans, and 0.89, 0.88, 0.87, 0.85, and 0.81 for recalculated vCT plans. Hausdorff distance for the 100.0% isodose volume in some cases of recalculated cCBCT plans on APvCT exceeded 1.00 cm.

CONCLUSIONS

APvCT contours showed good agreement with reference contours of cCBCT which indicates anatomy preservation in APvCT. A vCT with erroneous anatomy can result in an incorrect adaptive plan. Further, slightly lower values of GPR between the APvCT and cCBCT-based adaptive plans can be explained by the difference in the cCBCT's SPR RFD from the pCT.

Evaluating the Impact of Bowel Gas Variations for Wilms' Tumor in Pediatric Proton Therapy

(1) Background: Proton therapy, a precise form of radiation treatment, can be significantly affected by variations in bowel content. The purpose was to identify the most beneficial gantry angles that minimize deviations from the treatment plan quality, thus enhancing the safety and efficacy of proton therapy for Wilms' tumor patients. (2) Methods: Thirteen patients with Wilms' tumor, enrolled in the SJWT21 clinical trial, underwent proton therapy. The variations in bowel gas were systematically monitored using daily Cone Beam Computed Tomography (CBCT) imaging. Air cavities identified in daily CBCT images were analyzed to construct daily verification plans and measure water equivalent path length (WEPL) changes. A worst-case scenario simulation was conducted to identify the safest beam angles. (3) Results: The study revealed a maximum decrease in target dose (ΔD100%) of 8.0%, which corresponded to a WEPL variation (ΔWEPL) of 11.3 mm. The average reduction in target dose, denoted as mean ΔD100%, was found to be 2.8%, with a standard deviation (SD) of 3.2%. The mean ΔWEPL was observed as 3.3 mm, with an SD of 2.7 mm. The worst-case scenario analysis suggested that gantry beam angles oriented toward the patient's right and posterior aspects from 110° to 310° were associated with minimized WEPL discrepancies. (4) Conclusions: This study comprehensively evaluated the influence of bowel gas variability on treatment plan accuracy and proton range uncertainties in pediatric proton therapy for Wilms' tumor.

Comparative Evaluation of Proton Therapy and Volumetric Modulated Arc Therapy for Brachial Plexus Sparing in the Comprehensive Reirradiation of High-Risk Recurrent Breast Cancer

Purpose

Recurrent or new primary breast cancer requiring comprehensive regional nodal irradiation after prior radiation therapy (RT) to the supraclavicular area and upper axilla is challenging due to cumulative brachial plexus (BP) dose tolerance. We assessed BP dose sparing achieved with pencil beam scanning proton therapy (PBS-PT) and photon volumetric modulated arc therapy (VMAT).

Methods and Materials

In an institutional review board-approved planning study, all patients with ipsilateral recurrent breast cancer treated with PBS-PT re-RT (PBT1) with at least partial BP overlap from prior photon RT were identified. Comparative VMAT plans (XRT1) using matched BP dose constraints were developed. A second pair of proton (PBT2) and VMAT (XRT2) plans using standardized target volumes were created, applying uniform prescription dose of 50.4 per 1.8 Gy and a maximum BP constraint <25 Gy. Incidence of brachial plexopathy was also assessed.

Results

Ten consecutive patients were identified. Median time between RT courses was 48 months (15-276). Median first, second, and cumulative RT doses were 50.4 Gy (range, 42.6-60.0), 50.4 Gy relative biologic effectiveness (RBE) (45.0-64.4), and 102.4 Gy (RBE) (95.0-120.0), respectively. Median follow-up was 15 months (5-33) and 18 months for living patients (11-33) Mean BP max was 37.5 Gy (RBE) for PBT1 and 36.9 Gy for XRT1. Target volume coverage of V85% (volume receiving 85% of prescription dose), V90%, and V95% were numerically lower for XRT1 versus PBT1. Similarly, axilla I-III and supraclavicular area coverage were significantly higher for PBT2 than XRT2 at dose levels of V55%, V65%, V75%, V85%, and V95%. Only axilla I V55% did not reach significance (P = .06) favoring PBS-PT. Two patients with high cumulative BPmax (95.2 Gy [RBE], 101.6 Gy [RBE]) developed brachial plexopathy symptoms with ulnar nerve distribution neuropathy without pain or weakness (1 of 2 had symptom resolution after 6 months without intervention).

Conclusions

PBS-PT improved BP sparing and target volume coverage versus VMAT. For patients requiring comprehensive re-RT for high-risk, nonmetastatic breast cancer recurrence with BP overlap and reasonable expectation for prolonged life expectancy, PBT may be the preferred treatment modality.

Cone beam CT-based adaptive intensity modulated proton therapy assessment using automated planning for head-and-neck cancer

BACKGROUND

To assess the feasibility of CBCT-based adaptive intensity modulated proton therapy (IMPT) using automated planning for treatment of head and neck (HN) cancers.

METHODS

Twenty HN cancer patients who received radiotherapy and had pretreatment CBCTs were included in this study. Initial IMPT plans were created using automated planning software for all patients. Synthetic CTs (sCT) were then created by deforming the planning CT (pCT) to the pretreatment CBCTs. To assess dose calculation accuracy on sCTs, repeat CTs (rCTs) were deformed to the pretreatment CBCT obtained on the same day to create deformed rCT (rCTdef), serving as gold standard. The dose recalculated on sCT and on rCTdef were compared by using Gamma analysis. The accuracy of DIR generated contours was also assessed. To explore the potential benefits of adaptive IMPT, two sets of plans were created for each patient, a non-adapted IMPT plan and an adapted IMPT plan calculated on weekly sCT images. The weekly doses for non-adaptive and adaptive IMPT plans were accumulated on the pCT, and the accumulated dosimetric parameters of two sets were compared.

RESULTS

Gamma analysis of the dose recalculated on sCT and rCTdef resulted in a passing rate of 97.9% ± 1.7% using 3 mm/3% criteria. With the physician-corrected contours on the sCT, the dose deviation range of using sCT to estimate mean dose for the most organ at risk (OARs) can be reduced to (- 2.37%, 2.19%) as compared to rCTdef, while for V95 of primary or secondary CTVs, the deviation can be controlled within (- 1.09%, 0.29%). Comparison of the accumulated doses from the adaptive planning against the non-adaptive plans reduced mean dose to constrictors (- 1.42 Gy ± 2.79 Gy) and larynx (- 2.58 Gy ± 3.09 Gy). The reductions result in statistically significant reductions in the normal tissue complication probability (NTCP) of larynx edema by 7.52% ± 13.59%. 4.5% of primary CTVs, 4.1% of secondary CTVs, and 26.8% tertiary CTVs didn't meet the V95 > 95% constraint on non-adapted IMPT plans. All adaptive plans were able to meet the coverage constraint.

CONCLUSION

sCTs can be a useful tool for accurate proton dose calculation. Adaptive IMPT resulted in better CTV coverage, OAR sparing and lower NTCP for some OARs as compared with non-adaptive IMPT.

Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy

BACKGROUND

Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning-based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT-based proton dose calculations.

PURPOSE

To compare daily CBCT-based sCT images to planning CTs (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT-based sCTs in a scenario mimicking actual clinical practice.

METHODS

Data of 56 HN cancer patients, previously treated with proton therapy was used to generate 1.962 sCT images, using a previously developed and trained deep convolutional neural network. Clinical IMPT treatment plans were recalculated on the pCT, weekly rCTs and daily sCTs. The dosimetric accuracy of sCTs was compared to same day rCTs and the initial planning CT. As a reference, rCTs were also compared to pCTs. The dose difference between sCTs and rCTs/pCT was quantified by calculating the D98 difference for target volumes and Dmean difference for organs-at-risk. To investigate the clinical relevancy of possible dose differences, NTCP values were calculated for dysphagia and xerostomia.

RESULTS

For target volumes, only minor dose differences were found for sCT versus rCT and sCT versus pCT, with dose differences mostly within ±1.5%. Larger dose differences were observed in OARs, where a general shift towards positive differences was found, with the largest difference in the left parotid gland. Delta NTCP values for grade 2 dysphagia and xerostomia were within ±2.5% for 90% of the sCTs.

CONCLUSIONS

Target doses showed high similarity between rCTs and sCTs. Further investigations are required to identify the origin of the dose differences at OAR levels and its relevance in clinical decision making.

Proton pencil beam scanning craniospinal irradiation (CSI) with a single posterior brain beam: Dosimetry and efficiency

This study explores the feasibility and potential dosimetric and time efficiency benefit of proton Pencil Beam Scanning (PBS) craniospinal irradiation with a single posterior-anterior (SPA) brain field. The SPA approach was compared to our current clinical protocol using Bilateral Posterior Oblique brain fields (BPO). Ten consecutive patients were simulated in the head-first supine position on a long BOS frame and scanned using 3 mm CT slice thickness. A customized thermoplastic mask immobilized the patient's head, neck, and shoulders. A vac-lock was used to secure the legs. PBS proton plans were robustly optimized with 3mm setup errors and 3.5% range uncertainties in the Eclipse V15.6 treatment planning system (n = 12 scenarios). In order to achieve a smooth gradient dose match at the junction area, at least 5 cm overlap region was maintained between the segments and 5 mm uncertainty along the cranial-cauda direction was applied to each segment independently as additional robust optimization scenarios. The brain doses were planned by SPA or BPO fields. All spine segments were planned with a single PA field. Dosimetric differences between the BPO and SPA approaches were compared, and the treatment efficiency was analyzed according to timestamps of beam delivery. Results: The maximum brain dose increases to 111.1 ± 2.1% for SPA vs. 109.0 ± 1.7% for BPO (p < 0.01). The dose homogeneity index (D5/D95) in brain CTV was comparable between techniques (1.037 ± 0.010 for SPA and 1.033 ± 0.008 for BPO). Lens received lower maximum doses by 2.88 ± 1.58 Gy (RBE) (left) and 2.23 ± 1.37 Gy (RBE) (right) in the SPA plans (p < 0.01). No significant cochlea dose change was observed. SPA reduced the treatment time by more than 4 minutes on average and ranged from 2 to 10 minutes, depending on the beam waiting and allocation time. SPA is dosimetrically comparable to BPO, with reduced lens doses at the cost of slightly higher dose inhomogeneity and hot spots. Implementation of SPA is feasible and can help to improve the treatment efficiency of PBS CSI treatment.

CBCT-to-CT Synthesis for Cervical Cancer Adaptive Radiotherapy via U-Net-Based Model Hierarchically Trained with Hybrid Dataset

PURPOSE

To develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and obtain accurate HU values.

MATERIALS AND METHODS

A total of 228 cervical cancer patients treated in different LINACs were enrolled. We developed an encoder-decoder architecture with residual learning and skip connections. The model was hierarchically trained and validated on 5279 paired CBCT/planning CT images and tested on 1302 paired images. The mean absolute error (MAE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) were utilized to access the quality of the synthetic CT images generated by our model.

RESULTS

The MAE between synthetic CT images generated by our model and planning CT was 10.93 HU, compared to 50.02 HU for the CBCT images. The PSNR increased from 27.79 dB to 33.91 dB, and the SSIM increased from 0.76 to 0.90. Compared with synthetic CT images generated by the convolution neural networks with residual blocks, our model had superior performance both in qualitative and quantitative aspects.

CONCLUSIONS

Our model could synthesize CT images with enhanced image quality and accurate HU values. The synthetic CT images preserved the edges of tissues well, which is important for downstream tasks in adaptive radiotherapy.

Efficient segmentation using domain adaptation for MRI-guided and CBCT-guided online adaptive radiotherapy

BACKGROUND

Delineation of regions of interest (ROIs) is important for adaptive radiotherapy (ART) but it is also time consuming and labor intensive.

AIM

This study aims to develop efficient segmentation methods for magnetic resonance imaging-guided ART (MRIgART) and cone-beam computed tomography-guided ART (CBCTgART).

MATERIALS AND METHODS

MRIgART and CBCTgART studies enrolled 242 prostate cancer patients and 530 nasopharyngeal carcinoma patients, respectively. A public dataset of CBCT from 35 pancreatic cancer patients was adopted to test the framework. We designed two domain adaption methods to learn and adapt the features from planning computed tomography (pCT) to MRI or CBCT modalities. The pCT was transformed to synthetic MRI (sMRI) for MRIgART, while CBCT was transformed to synthetic CT (sCT) for CBCTgART. Generalized segmentation models were trained with large popular data in which the inputs were sMRI for MRIgART and pCT for CBCTgART. Finally, the personalized models for each patient were established by fine-tuning the generalized model with the contours on pCT of that patient. The proposed method was compared with deformable image registration (DIR), a regular deep learning (DL) model trained on the same modality (DL-regular), and a generalized model in our framework (DL-generalized).

RESULTS

The proposed method achieved better or comparable performance. For MRIgART of the prostate cancer patients, the mean dice similarity coefficient (DSC) of four ROIs was 87.2%, 83.75%, 85.36%, and 92.20% for the DIR, DL-regular, DL-generalized, and proposed method, respectively. For CBCTgART of the nasopharyngeal carcinoma patients, the mean DSC of two target volumes were 90.81% and 91.18%, 75.17% and 58.30%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively. For CBCTgART of the pancreatic cancer patients, the mean DSC of two ROIs were 61.94% and 61.44%, 63.94% and 81.56%, for the DIR, DL-regular, DL-generalized, and the proposed method, respectively.

CONCLUSION

The proposed method utilizing personalized modeling improved the segmentation accuracy of ART.

A TOPAS model for lens-based proton radiography

Objective.Proton Radiography can be used in conjunction with proton therapy for patient positioning, real-time estimates of stopping power, and adaptive therapy in regions with motion. The modeling capability shown here can be used to evaluate lens-based radiography as an instantaneous proton-based radiographic technique. The utilization of user-friendly Monte Carlo program TOPAS enables collaborators and other users to easily conduct medical- and therapy- based simulations of the Los Alamos Neutron Science Center (LANSCE). The resulting transport model is an open-source Monte Carlo package for simulations of proton and heavy ion therapy treatments and concurrent particle imaging.Approach.The four-quadrupole, magnetic lens system of the 800-MeV proton beamline at LANSCE is modeled in TOPAS. Several imaging and contrast objects were modelled to assess transmission at energies from 230-930 MeV and different levels of particle collimation. At different proton energies, the strength of the magnetic field was scaled according toβγ,the inverse product of particle relativistic velocity and particle momentum.Main results.Materials with high atomic number, Z, (gold, gallium, bone-equivalent) generated more contrast than materials with low-Z (water, lung-equivalent, adipose-equivalent). A 5-mrad collimator was beneficial for tissue-to-contrast agent contrast, while a 10-mrad collimator was best to distinguish between different high-Z materials. Assessment with a step-wedge phantom showed water-equivalent path length did not scale directly according to predicted values but could be mapped more accurately with calibration. Poor image quality was observed at low energies (230 MeV), but improved as proton energy increased, with sub-mm resolution at 630 MeV.Significance.Proton radiography becomes viable for shallow bone structures at 330 MeV, and for deeper structures at 630 MeV. Visibility improves with use of high-Z contrast agents. This modality may be particularly viable at carbon therapy centers with accelerators capable of delivering high energy protons and could be performed with carbon therapy.

The Applications and Pitfalls of Cone-Beam Computed Tomography-Based Synthetic Computed Tomography for Adaptive Evaluation in Pencil-Beam Scanning Proton Therapy

PURPOSE

The study evaluates the efficacy of cone-beam computed tomography (CBCT)-based synthetic CTs (sCT) as a potential alternative to verification CT (vCT) for enhanced treatment monitoring and early adaptation in proton therapy.

METHODS

Seven common treatment sites were studied. Two sets of sCT per case were generated: direct-deformed (DD) sCT and image-correction (IC) sCT. The image qualities and dosimetric impact of the sCT were compared to the same-day vCT.

RESULTS

The sCT agreed with vCT in regions of homogeneous tissues such as the brain and breast; however, notable discrepancies were observed in the thorax and abdomen. The sCT outliers existed for DD sCT when there was an anatomy change and for IC sCT in low-density regions. The target coverage exhibited less than a 5% variance in most DD and IC sCT cases when compared to vCT. The Dmax of serial organ-at-risk (OAR) in sCT plans shows greater deviation from vCT than small-volume dose metrics (D0.1cc). The parallel OAR volumetric and mean doses remained consistent, with average deviations below 1.5%.

CONCLUSION

The use of sCT enables precise treatment and prompt early adaptation for proton therapy. The quality assurance of sCT is mandatory in the early stage of clinical implementation.

Advances and Challenges in Conducting Clinical Trials With Proton Beam Therapy

Advances in proton therapy have garnered much attention and speculation in recent years as the indications for proton therapy have grown beyond pediatric, prostate, spine, and ocular tumors. To achieve and maintain consistent access to this cancer treatment and to ensure the future viability and availability of proton centers in the United States, a call for evidence has been heard and answered by proton radiation oncologists. Answers provided in this review include the evolution of proton therapy research, rationale for proton clinical trial design, challenges in and barriers to the conduct of proton therapy research, and other unique considerations for the study of proton therapy.

A systematic review of volumetric image guidance in proton therapy

In recent years, proton therapy centres have begun to shift from conventional 2D-kV imaging to volumetric imaging systems for image guided proton therapy (IGPT). This is likely due to the increased commercial interest and availability of volumetric imaging systems, as well as the shift from passively scattered proton therapy to intensity modulated proton therapy. Currently, there is no standard modality for volumetric IGPT, leading to variation between different proton therapy centres. This article reviews the reported clinical use of volumetric IGPT, as available in published literature, and summarises their utilisation and workflow where possible. In addition, novel volumetric imaging systems are also briefly summarised highlighting their potential benefits for IGPT and the challenges that need to be overcome before they can be used clinically.

Monitoring of Interfractional Proton Range Verification and Dosimetric Impact Based on Daily CBCT for Pediatric Patients with Pelvic Tumors

(1) Background: Synthetic CT images of the pelvis were generated from daily CBCT images to monitor changes in water equivalent path length (WEPL) and determine the dosimetric impact of anatomy changes along the proton beam's path; (2) Methods: Ten pediatric patients with pelvic tumors treated using proton therapy with daily CBCT were included. The original planning CT was deformed to the same-day CBCT to generate synthetic CT images for WEPL comparison and dosimetric evaluation; (3) Results: WEPL changes of 20 proton fields at the distal edge of the CTV ranged from 0.1 to 12 mm with a median of 2.5 mm, and 75th percentile of 5.1 mm for (the original CT-rescanned CT) and ranged from 0.3 to 10.1 mm with a median of 2.45 mm and 75th percentile of 4.8 mm for (the original CT-synthetic CT). The dosimetric impact was due to proton range pullback or overshoot, which led to reduced coverage in CTV Dmin averaging 12.1% and 11.3% in the rescanned and synthetic CT verification plans, respectively; (4) Conclusions: The study demonstrated that synthetic CT generated by deforming the original planning CT to daily CBCT can be used to quantify proton range changes and predict adverse dosimetric scenarios without the need for excessive rescanned CT scans during large interfractional variations in adaptive proton therapy of pediatric pelvic tumors.

Scatter correction of 4D cone beam computed tomography to detect dosimetric effects due to anatomical changes in proton therapy for lung cancer

BACKGROUND

The treatment of moving tumor entities is expected to have superior clinical outcomes, using image-guided adaptive intensity-modulated proton therapy (IMPT).

PURPOSE

For 21 lung cancer patients, IMPT dose calculations were performed on scatter-corrected 4D cone beam CTs (4DCBCTcor ) to evaluate their potential for triggering treatment adaptation. Additional dose calculations were performed on corresponding planning 4DCTs and day-of-treatment 4D virtual CTs (4DvCTs).

METHODS

A 4DCBCT correction workflow, previously validated on a phantom, generates 4DvCT (CT-to-CBCT deformable registration) and 4DCBCTcor images (projection-based correction using 4DvCT as a prior) with 10 phase bins, using day-of-treatment free-breathing CBCT projections and planning 4DCT images as input. Using a research planning system, robust IMPT plans administering eight fractions of 7.5 Gy were created on a free-breathing planning CT (pCT) contoured by a physician. The internal target volume (ITV) was overridden with muscle tissue. Robustness settings for range and setup uncertainties were 3% and 6 mm, and a Monte Carlo dose engine was used. On every phase of planning 4DCT, day-of-treatment 4DvCT, and 4DCBCTcor , the dose was recalculated. For evaluation, image analysis as well as dose analysis were performed using mean error (ME) and mean absolute error (MAE) analysis, dose-volume histogram (DVH) parameters, and 2%/2-mm gamma pass rate analysis. Action levels (1.6% ITV D98 and 90% gamma pass rate) based on our previous phantom validation study were set to determine which patients had a loss of dosimetric coverage.

RESULTS

Quality enhancements of 4DvCT and 4DCBCTcor over 4DCBCT were observed. ITV D98% and bronchi D2% had its largest agreement for 4DCBCTcor -4DvCT, and the largest gamma pass rates (>94%, median 98%) were found for 4DCBCTcor -4DvCT. Deviations were larger and gamma pass rates were smaller for 4DvCT-4DCT and 4DCBCTcor -4DCT. For five patients, deviations were larger than the action levels, suggesting substantial anatomical changes between pCT and CBCT projections acquisition.

CONCLUSIONS

This retrospective study shows the feasibility of daily proton dose calculation on 4DCBCTcor for lung tumor patients. The applied method is of clinical interest as it generates up-to-date in-room images, accounting for breathing motion and anatomical changes. This information could be used to trigger replanning.

A hybrid method of correcting CBCT for proton range estimation with deep learning and deformable image registration

Objective. This study aimed to develop a novel method for generating synthetic CT (sCT) from cone-beam CT (CBCT) of the abdomen/pelvis with bowel gas pockets to facilitate estimation of proton ranges.Approach. CBCT, the same-day repeat CT, and the planning CT (pCT) of 81 pediatric patients were used for training (n= 60), validation (n= 6), and testing (n= 15) of the method. The proposed method hybridizes unsupervised deep learning (CycleGAN) and deformable image registration (DIR) of the pCT to CBCT. The CycleGAN and DIR are respectively applied to generate the geometry-weighted (high spatial-frequency) and intensity-weighted (low spatial-frequency) components of the sCT, thereby each process deals with only the component weighted toward its strength. The resultant sCT is further improved in bowel gas regions and other tissues by iteratively feeding back the sCT to adjust incorrect DIR and by increasing the contribution of the deformed pCT in regions of accurate DIR.Main results. The hybrid sCT was more accurate than deformed pCT and CycleGAN-only sCT as indicated by the smaller mean absolute error in CT numbers (28.7 ± 7.1 HU versus 38.8 ± 19.9 HU/53.2 ± 5.5 HU;P≤ 0.012) and higher Dice similarity of the internal gas regions (0.722 ± 0.088 versus 0.180 ± 0.098/0.659 ± 0.129;P≤ 0.002). Accordingly, the hybrid method resulted in more accurate proton range for the beams intersecting gas pockets (11 fields in 6 patients) than the individual methods (the 90th percentile error in 80% distal fall-off, 1.8 ± 0.6 mm versus 6.5 ± 7.8 mm/3.7 ± 1.5 mm;P≤ 0.013). The gamma passing rates also showed a significant dosimetric advantage by the hybrid method (99.7 ± 0.8% versus 98.4 ± 3.1%/98.3 ± 1.8%;P≤ 0.007).Significance. The hybrid method significantly improved the accuracy of sCT and showed promises in CBCT-based proton range verification and adaptive replanning of abdominal/pelvic proton therapy even when gas pockets are present in the beam path.

CBCT-Based Dose Monitoring and Adaptive Planning Triggers in Head and Neck PBS Proton Therapy

PURPOSE

To investigate the feasibility of using cone-beam computed tomography (CBCT)-derived synthetic CTs to monitor the daily dose and trigger a plan review for adaptive proton therapy (APT) in head and neck cancer (HNC) patients.

METHODS

For 84 HNC patients treated with proton pencil-beam scanning (PBS), same-day CBCT and verification CT (vfCT) pairs were retrospectively collected. The ground truth CT (gtCT) was created by deforming the vfCT to the same-day CBCT, and it was then used as a dosimetric baseline and for establishing plan review trigger recommendations. Two different synthetic CT algorithms were tested; the corrected CBCT (corrCBCT) was created using an iterative image correction method and the virtual CT (virtCT) was created by deforming the planning CT to the CBCT, followed by a low-density masking process. Clinical treatment plans were recalculated on the image sets for evaluation.

RESULTS

Plan review trigger criteria for adaptive therapy were established after closely reviewing the cohort data. Compared to the vfCT, the corrCBCT and virtCT reliably produced dosimetric data more similar to the gtCT. The average discrepancy in D99 for high-risk clinical target volumes (CTV) was 1.1%, 0.7%, and 0.4% and for standard-risk CTVs was 1.8%, 0.5%, and 0.5% for the vfCT, corrCBCT, and virtCT, respectively.

CONCLUSION

Streamlined APT has been achieved with the proposed plan review criteria and CBCT-based synthetic CT workflow.

Case Report: Cumulative proton dose reconstruction using CBCT-based synthetic CT for interfraction metallic port variability in breast tissue expanders

Introduction

Dose perturbation of spot-scanning proton beams passing through a dislocated metallic port (MP) of a breast tissue expander may degrade target dose coverage or deliver excess dose to the ipsilateral lung and heart. The feasibility of utilizing daily cone-beam computed tomography (CBCT)-based synthetic CTs (synCTs) for dose reconstruction was evaluated, and the fractional and cumulative dosimetric impact due to daily MP dislocation is reported.

Methods

The synCT was generated by deforming the simulation CT to daily CBCT. The MP structure template was mapped onto all CTs on the basis of daily MP position. Proton treatment plans were generated with two and three fields on the planned CT (pCT, Plan A) and the first verification CT (vCT, Plan B), respectively, for a fractional dose of 1.8 Gy(RBE). Plan A and Plan B were used alternatively, as determined by the daily MP position. The reconstructed fractional doses were calculated with corresponding plans and synCTs, and the cumulative doses were summed with the rigid or deformed fractional doses on pCT and vCT.

Results

The planned and reconstructed fractional dose demonstrated a low-dose socket around the planned MP position due to the use of field-specific targets (FSTs). Dose hot spots with >120% of the prescription due to MP dislocation were found behind the planned MP position on most reconstructed fractional doses. The reconstructed cumulative dose shows two low-dose sockets around the two planned MP positions reflecting the two plans used. The doses at the hot spots behind the planned MPs averaged out to 114% of the prescription. The cumulative D95% of the CTV_Chest Wall decreased by up to 2.4% and 4.0%, and the cumulative V20Gy(RBE) of the left lung decreased to 16.1% and 16.8% on pCT and vCT, respectively. The cumulative Dmean of the heart decreased to as low as 0.7 Gy(RBE) on pCT but increased to as high as 1.6 Gy(RBE) on vCT.

Conclusion

The robustness of proton plans using FSTs around the magnet in the MP of the tissue expander can be improved by applying multiple fields and plans, which provides forgiveness of dose heterogeneity incurred from dislocation of high-Z materials in this single case.

Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients

Background and purpose

Efficient workflows for adaptive proton therapy are of high importance. This study evaluated the possibility to replace repeat-CTs (reCTs) with synthetic CTs (sCTs), created based on cone-beam CTs (CBCTs), for flagging the need of plan adaptations in intensity-modulated proton therapy (IMPT) treatment of lung cancer patients.

Materials and methods

Forty-two IMPT patients were retrospectively included. For each patient, one CBCT and a same-day reCT were included. Two commercial sCT methods were applied; one based on CBCT number correction (Cor-sCT), and one based on deformable image registration (DIR-sCT). The clinical reCT workflow (deformable contour propagation and robust dose re-computation) was performed on the reCT as well as the two sCTs. The deformed target contours on the reCT/sCTs were checked by radiation oncologists and edited if needed. A dose-volume-histogram triggered plan adaptation method was compared between the reCT and the sCTs; patients needing a plan adaptation on the reCT but not on the sCT were denoted false negatives. As secondary evaluation, dose-volume-histogram comparison and gamma analysis (2%/2mm) were performed between the reCT and sCTs.

Results

There were five false negatives, two for Cor-sCT and three for DIR-sCT. However, three of these were only minor, and one was caused by tumour position differences between the reCT and CBCT and not by sCT quality issues. An average gamma pass rate of 93% was obtained for both sCT methods.

Conclusion

Both sCT methods were judged to be of clinical quality and valuable for reducing the amount of reCT acquisitions.

Evaluating synthetic computed tomography images for adaptive radiotherapy decision making in head and neck cancer

Background and purpose

Adaptive radiotherapy (ART) decision-making benefits from dosimetric information to supplement image inspection when assessing the significance of anatomical changes. This study evaluated a dosimetry-based clinical decision workflow for ART utilizing deformable registration of the original planning computed tomography (CT) image to the daily Cone Beam CT (CBCT) to replace the need for a replan CT for dose estimation.

Materials and methods

We used 12 retrospective Head & Neck patient cases having a ground truth - a replan CT (rCT) in response to anatomical changes apparent in the daily CBCT - to evaluate the accuracy of dosimetric assessment conducted on synthetic CTs (sCT) generated by deforming the original planning CT Hounsfield Units to the daily CBCT anatomy.The original plan was applied to the sCT and dosimetric accuracy of the sCT was assessed by analyzing plan objectives for targets and organs-at-risk compared to calculations on the ground-truth rCT. Three commercial DIR algorithms were compared.

Results

For the best-performing algorithms, the majority of dose metrics calculated on the sCTs differed by less than 4 Gy (5.7% of 70 Gy prescription dose). An uncertainty of ±2.5 Gy (3.6% of 70 Gy prescription) is recommended as a conservative tolerance when evaluating dose metrics on sCTs for head and neck.

Conclusions

Synthetic CTs present a valuable addition to the adaptive radiotherapy workflow, and synthetic CT dose estimates can be effectively used in addition to the current practice of visually inspecting the overlay of the planning CT and CBCT to assess the significance of anatomical change.

ScatterNet for projection-based 4D cone-beam computed tomography intensity correction of lung cancer patients

Background and purpose: In radiotherapy, dose calculations based on 4D cone beam CTs (4DCBCTs) require image intensity corrections. This retrospective study compared the dose calculation accuracy of a deep learning, projection-based scatter correction workflow (ScatterNet), to slower workflows: conventional 4D projection-based scatter correction (CBCTcor) and a deformable image registration (DIR)-based method (4DvCT). Materials and methods: For 26 lung cancer patients, planning CTs (pCTs), 4DCTs and CBCT projections were available. ScatterNet was trained with pairs of raw and corrected CBCT projections. Corrected projections from ScatterNet and the conventional workflow were reconstructed using MA-ROOSTER, yielding 4DCBCTSN and 4DCBCTcor. The 4DvCT was generated by 4DCT to 4DCBCT DIR, as part of the 4DCBCTcor workflow. Robust intensity modulated proton therapy treatment plans were created on free-breathing pCTs. 4DCBCTSN was compared to 4DCBCTcor and the 4DvCT in terms of image quality and dose calculation accuracy (dose-volume-histogram parameters and 3 % /3 mm gamma analysis). Results: 4DCBCTSN resulted in an average mean absolute error of 87 HU and 102 HU when compared to 4DCBCTcor and 4DvCT respectively. High agreement was observed in targets with median dose differences of 0.4 Gy (4DCBCTSN-4DCBCTcor) and 0.3 Gy (4DCBCTSN-4DvCT). The gamma analysis showed high average 3 % /3 mm pass rates of 96 % for both 4DCBCTSN vs. 4DCBCTcor and 4DCBCTSN vs. 4DvCT. Conclusions: Accurate 4D dose calculations are feasible for lung cancer patients using ScatterNet for 4DCBCT correction. Average scatter correction times could be reduced from 10 min (4DCBCTcor) to 3.9 s , showing the clinical suitability of the proposed deep learning-based method.

Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy

Objective. Adaptive radiotherapy workflows require images with the quality of computed tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve the quality of on-board cone beam CT (CBCT) images for dose calculation using deep learning.Approach. We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a challenging application due to the inter-fractional variability in bowel filling and small patient numbers. We introduced to the networks the concept of global residuals only learning and modified the cycleGAN loss function to explicitly promote structural consistency between source and synthetic images. Finally, to compensate for the anatomical variability and address the difficulties in collecting large datasets in the paediatric population, we applied a smart 2D slice selection based on the common field-of-view (abdomen) to our imaging dataset. This acted as a weakly paired data approach that allowed us to take advantage of scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training purposes. We first optimised the proposed framework and benchmarked its performance on a development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen dataset, which included calculating global image similarity metrics, segmentation-based measures and proton therapy-specific metrics.Main results. We found improved performance for our proposed method, compared to a baseline cycleGAN implementation, on image-similarity metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0 ± 16.6 HU proposed versus 58.9 ± 16.8 HU baseline). There was also a higher level of structural agreement for gastrointestinal gas between source and synthetic images measured using the dice similarity coefficient (0.872 ± 0.053 proposed versus 0.846 ± 0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for our method (3.3 ± 2.4% proposed versus 3.7 ± 2.8% baseline).Significance. Our findings indicate that our innovations to the cycleGAN framework improved the quality and structure consistency of the synthetic CTs generated.

Visual analysis of image-guided radiation therapy based on bibliometrics: A review

Radiation therapy plays an important role in tumor treatment. The development of image-guided radiation therapy (IGRT) technology provides a strong guarantee for precise radiation therapy of tumors. However, bibliometric studies on IGRT research have rarely been reported. This study uses literature collected from the Web of Science during 1987 to 2021 as a sample and uses the bibliometric method to reveal the current research status, hotspots, and development trends in IGRT. Based on 6407 papers published from the Web of Science during 1987 to 2021, we utilized Microsoft Excel 2007 and cite space software to perform statistical analysis and visualization of IGRT. A total of 6407 articles were included, this area of IGRT has gone through 4 stages: budding period, growth period, outbreak period, and stationary period. The research category is mainly distributed in Radiology Nuclear Medicine Medical Imaging, which intersects with the research categories of Materials, Physics, and Mathematics. Yin FF, Tanderup K, and Sonke JJ are highly productive scholars who are active in IGRT research, while Jaffray DA, van Herk M and Guckenberger M are authors with high impact in this field. The team of scholars has close cooperation within the team and weak cooperation among teams. The League of European Research Universities, University of Texas System, University of Toronto, and Princess Margaret Cancer are the main research institutions in this field. The United States has the most research literature, followed by China and Germany. Six thousand four hundred seven articles are distributed in 712 journals, and the top 3 journals are Med Phys, Int J Radiat Oncol, and Radiather Oncol. Precise registration, intelligence, magnetic resonance guidance, and deep learning are current research hotspots. These results demonstrate that the research in this field is relatively mature and fruitful in the past 35 years, providing a solid theoretical basis and practical experience for precision radiotherapy.

Evaluating on-board kVCT- and MVCT-based dose calculation accuracy using a thorax phantom for helical tomotherapy treatments

Purpose.To evaluate the impact of CT number calibration and imaging parameter selection on dose calculation accuracy relative to the CT planning process in thoracic treatments for on-board helical CT imaging systems used in helical tomotherapy.Methods and Materials.Direct CT number calibrations were performed with appropriate protocols for each imaging system using an electron density phantom. Large volume and SBRT treatment plans were simulated and optimized for planning CT scans of an anthropomorphic thorax phantom and transferred to registered kVCT and MVCT scans of the phantom as appropriate. Relevant DVH metrics and dose-difference maps were used to evaluate and compare dose calculation accuracy relative to the planning CT based on a variation in imaging parameters applied for the on-board systems.Results.For helical kVCT scans of the thorax phantom, median differences in DVH parameters for the large volume treatment plan were less than ±1% with dose to the target volume either over- or underestimated depending on the imaging parameters utilized for CT number calibration and thorax phantom acquisition. For the lung SBRT plan calculated on helical kVCT scans, median dose differences were up to -2.7% with a more noticeable dependence on parameter selection. For MVCT scans, median dose differences for the large volume plan were within +2% with dose to the target overestimated regardless of the imaging protocol.Conclusion.Accurate dose calculations (median errors of <±1%) using a thorax phantom simulating realistic patient geometry and scatter conditions can be achieved with images acquired with a helical kVCT system on a helical tomotherapy unit. This accuracy is considerably improved relative to that achieved with the MV-based approach. In a clinical setting, careful consideration should be made when selecting appropriate kVCT imaging parameters for this process as dose calculation accuracy was observed to vary with both parameter selection and treatment type.

Cardiac motion and its dosimetric impact during radioablation for refractory ventricular tachycardia

INTRODUCTION

Cardiac radioablation (CR) is a noninvasive treatment option for patients with refractory ventricular tachycardia (VT) during which high doses of radiation, typically 25 Gy, are delivered to myocardial scar. In this study, we investigate motion from cardiac cycle and evaluate the dosimetric impact in a cohort of patients treated with CR.

METHODS

This retrospective study included eight patients treated at our institution who had respiratory-correlated and ECG-gated 4DCT scans acquired within 2 weeks of CR. Deformable image registration was applied between maximum systole (SYS) and diastole (DIAS) CTs to assess cardiac motion. The average respiratory-correlated CT (AVG_resp ) was deformably registered to the average cardiac (AVG_cardiac ), SYS, and DIAS CTs, and contours were propagated using the deformation vector fields (DVFs). Finally, the original treatment plan was recalculated on the deformed AVG_resp CT for dosimetric assessment.

RESULTS

Motion magnitudes were measured as the mean (SD) value over the DVFs within each structure. Displacement during the cardiac cycle for all chambers was 1.4 (0.9) mm medially/laterally (ML), 1.6 (1.0) mm anteriorly/posteriorly (AP), and 3.0 (2.8) mm superiorly/inferiorly (SI). Displacement for the 12 distinct clinical target volumes (CTVs) was 1.7 (1.5) mm ML, 2.4 (1.1) mm AP, and 2.1 (1.5) SI. Displacements between the AVG_resp and AVG_cardiac scans were 4.2 (2.0) mm SI and 5.8 (1.4) mm total. Dose recalculations showed that cardiac motion may impact dosimetry, with dose to 95% of the CTV dropping from 27.0 (1.3) Gy on the AVG_resp to 20.5 (7.1) Gy as estimated on the AVG_cardiac .

CONCLUSIONS

Cardiac CTV motion in this patient cohort is on average below 3 mm, location-dependent, and when not accounted for in treatment planning may impact target coverage. Further study is needed to assess the impact of cardiac motion on clinical outcomes.

Enhancement of cone beam CT image registration by super-resolution pre-processing algorithm

In order to enhance cone-beam computed tomography (CBCT) image information and improve the registration accuracy for image-guided radiation therapy, we propose a super-resolution (SR) image enhancement method. This method uses super-resolution techniques to pre-process the CBCT prior to registration. Three rigid registration methods (rigid transformation, affine transformation, and similarity transformation) and a deep learning deformed registration (DLDR) method with and without SR were compared. The five evaluation indices, the mean squared error (MSE), mutual information, Pearson correlation coefficient (PCC), structural similarity index (SSIM), and PCC + SSIM, were used to validate the results of registration with SR. Moreover, the proposed method SR-DLDR was also compared with the VoxelMorph (VM) method. In rigid registration with SR, the registration accuracy improved by up to 6% in the PCC metric. In DLDR with SR, the registration accuracy was improved by up to 5% in PCC + SSIM. When taking the MSE as the loss function, the accuracy of SR-DLDR is equivalent to that of the VM method. In addition, when taking the SSIM as the loss function, the registration accuracy of SR-DLDR is 6% higher than that of VM. SR is a feasible method to be used in medical image registration for planning CT (pCT) and CBCT. The experimental results show that the SR algorithm can improve the accuracy and efficiency of CBCT image alignment regardless of which alignment algorithm is used.

Accuracy and Feasibility of Synthetic CT for Lung Adaptive Radiotherapy: A Phantom Study

OBJECTIVES

The respiratory variations will lead to inconsistency between the actual delivery dose and the planning dose. How the minor interfractional amplitude changes affect the geometry and dose delivery accuracy remains to be investigated in the context of lung adaptive radiotherapy.

METHODS

Planning 4-dimensional-computed tomography and kV-cone beam computed tomography were scanned based on the Computerized Imaging Reference Systems phantom, which was employed to simulate the minor interfractional amplitude variations. The corresponding synthetic computed tomography for a particular motion pattern can be generated from Velocity program. Then a clinically meaningful synthetic computed tomography was analyzed through the geometrical and dosimetric assessment.

RESULTS

The image quality of synthetic computed tomography was improved obviously compared with cone beam computed tomography. Mean absolute error was minimized when no significant interfractional motion occurs and Velocity can be qualified for dealing with the regular breathing motion patterns. The mean percent hounsfield unit difference of the synthetic hounsfield unit values per organ relative to the planning 4-dimensional-computed tomography image was 22.3%. Under the same conditions, the mean percent hounsfield unit difference of the cone beam computed tomography hounsfield unit values per organ, relative to the planning 4-dimensional-computed tomography image was 83.9%. Overall, the accuracy of hounsfield unit in synthetic computed tomography was improved obviously and the variability of the synthetic image correlates with the planning 4-dimensional-computed tomography image variability. Meanwhile, the dose-volume histograms between planning 4-dimensional-computed tomography and synthetic computed tomography almost coincided each other, which indicates that Velocity program can qualify lung adaptive radiotherapy well when there were no interfractional respiratory variations. However, for cases with obvious interfractional amplitude change, the volume covered at least by 100% of the prescription dose was only 59.6% for that synthetic image.

CONCLUSION

The synthetic computed tomography images generated from Velocity were close to the real images in anatomy and dosimetry, which can make clinical lung adaptive radiotherapy possible based on the actual patient anatomy during treatment.

Deep learning-based 4D-synthetic CTs from sparse-view CBCTs for dose calculations in adaptive proton therapy

BACKGROUND

Time-resolved 4D cone beam-computed tomography (4D-CBCT) allows a daily assessment of patient anatomy and respiratory motion. However, 4D-CBCTs suffer from imaging artifacts that affect the CT number accuracy and prevent accurate proton dose calculations. Deep learning can be used to correct CT numbers and generate synthetic CTs (sCTs) that can enable CBCT-based proton dose calculations.

PURPOSE

In this work, sparse view 4D-CBCTs were converted into 4D-sCT utilizing a deep convolutional neural network (DCNN). 4D-sCTs were evaluated in terms of image quality and dosimetric accuracy to determine if accurate proton dose calculations for adaptive proton therapy workflows of lung cancer patients are feasible.

METHODS

A dataset of 45 thoracic cancer patients was utilized to train and evaluate a DCNN to generate 4D-sCTs, based on sparse view 4D-CBCTs reconstructed from projections acquired with a 3D acquisition protocol. Mean absolute error (MAE) and mean error were used as metrics to evaluate the image quality of single phases and average 4D-sCTs against 4D-CTs acquired on the same day. The dosimetric accuracy was checked globally (gamma analysis) and locally for target volumes and organs-at-risk (OARs) (lung, heart, and esophagus). Furthermore, 4D-sCTs were also compared to 3D-sCTs. To evaluate CT number accuracy, proton radiography simulations in 4D-sCT and 4D-CTs were compared in terms of range errors. The clinical suitability of 4D-sCTs was demonstrated by performing a 4D dose reconstruction using patient specific treatment delivery log files and breathing signals.

RESULTS

4D-sCTs resulted in average MAEs of 48.1 ± 6.5 HU (single phase) and 37.7 ± 6.2 HU (average). The global dosimetric evaluation showed gamma pass ratios of 92.3% ± 3.2% (single phase) and 94.4% ± 2.1% (average). The clinical target volume showed high agreement in D₉₈ between 4D-CT and 4D-sCT, with differences below 2.4% for all patients. Larger dose differences were observed in mean doses of OARs (up to 8.4%). The comparison with 3D-sCTs showed no substantial image quality and dosimetric differences for the 4D-sCT average. Individual 4D-sCT phases showed slightly lower dosimetric accuracy. The range error evaluation revealed that lung tissues cause range errors about three times higher than the other tissues.

CONCLUSION

In this study, we have investigated the accuracy of deep learning-based 4D-sCTs for daily dose calculations in adaptive proton therapy. Despite image quality differences between 4D-sCTs and 3D-sCTs, comparable dosimetric accuracy was observed globally and locally. Further improvement of 3D and 4D lung sCTs could be achieved by increasing CT number accuracy in lung tissues.

Outcomes and toxicities after proton partial breast radiotherapy for early stage, hormone receptor positive breast cancer: 3-Year results of a phase II multi-center trial

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Proton therapy is a good treatment option for partial breast irradiation.

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Proton PBI results in excellent local tumor control and OAR sparing.

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Cosmesis and quality of life with PBT are comparable to other PBI modalities.

Purpose

Proton therapy (PT) for partial breast irradiation (PBI) in early-stage breast cancer can decrease morbidity versus photon PBI with superior organs-at-risk sparing. We report 3-year outcomes of the first prospective, multicenter, phase II trial of proton PBI.

Methods and Materials

This Proton Collaborative Group phase II trial (PCG BRE007-12) recruited women ≥ 50 years with node-negative, estrogen receptor (ER)-positive, ≤3cm, invasive ductal carcinoma (IDC) or ductal carcinoma in situ undergoing breast conserving surgery followed by proton PBI (40 Gy(RBE), 10 daily fractions). Primary endpoint was freedom from ipsilateral breast cancer recurrence. Adverse events were prospectively graded using CTCAEv4.0. Breast Cancer Treatment Outcome Scale (BCTOS) assessed patient-reported quality of life (PRQOL).

Results

Thirty-eight evaluable patients enrolled between 2/2013–11/2016. Median age was 67 years (range 50–79); 55 % had left-sided disease, and median tumor size was 0.9 cm. Treatment was delivered in ≥ 2 fields predominantly with uniform scanning PT (n = 37). At 35-month median follow-up (12–62), all patients were alive, and none had local, regional or distant disease progression. One patient developed an ER-negative contralateral IDC. Seven grade 2 adverse events occurred; no radiotherapy-related grade ≥ 3 toxicities occurred. Changes in BCTOS subdomain mean scores were maximum 0.36, indicating no meaningful change in PRQOL. Median heart volume receiving 5 Gy (V5Gy), lung V20Gy, and lung V10Gy were 0 %, 0 % and 0.19 %, respectively.

Conclusion

At 3 years, proton PBI provided 100 % cancer control for early-stage, ER-positive breast cancer. Toxicities are minimal, and PRQOL remains acceptable with continued follow-up. These findings support PT as a safe and effective PBI delivery option.

A traffic light protocol workflow for image-guided adaptive radiotherapy in lung cancer patients

BACKGROUND AND PURPOSE

Image-guided radiotherapy using cone beam-CT (CBCT) images is used to evaluate patient anatomy and positioning before radiotherapy. In this study we analyzed and optimized a traffic light protocol (TLP) used in lung cancer patients to identify patients requiring treatment adaptation.

MATERIALS AND METHODS

First, CBCT review requests of 243 lung cancer patients were retrospectively analyzed and divided into 6 pre-defined categories. Frequencies and follow-up actions were scored. Based on these results, the TLP was optimized and evaluated in the same way on 230 patients treated in 2018.

RESULTS

In the retrospective study, a total of 543 CBCT review requests were created during treatment in 193/243 patients due to changed anatomy of lung (24%), change of tumor volume (24%), review of match (18%), shift of the mediastinum (15%), shift of tumor (15%) and other (4%). The majority of requests (474, 87%) did not require further action. In 6% an adjustment of the match criteria sufficed; in 7% treatment plan adaptation was required. Plan adaptation was frequently seen in the categories changed anatomy of lung, change of tumor volume and shift of tumor outside the PTV. Shift of mediastinum outside PRV and shift of GTV outside CTV (but inside PTV) never required plan adaptation and were omitted to optimize the TLP, which reduced the CBCT review requests by 23%.

CONCLUSIONS

The original TLP selected patients that required a treatment adaptation, but with a high false positive rate. The optimized TLP reduced the amount of CBCT review requests, while still correctly identifying patients requiring adaptation.

Improving workflow for adaptive proton therapy with predictive anatomical modelling: A proof of concept

PURPOSE

To demonstrate predictive anatomical modelling for improving the clinical workflow of adaptive intensity-modulated proton therapy (IMPT) for head and neck cancer.

METHODS

10 radiotherapy patients with nasopharyngeal cancer were included in this retrospective study. Each patient had a planning CT, weekly verification CTs during radiotherapy and predicted weekly CTs from our anatomical model. Predicted CTs were used to create predicted adaptive plans in advance with the aim of maintaining clinically acceptable dosimetry. Adaption was triggered when the increase in mean dose (Dmean) to the parotid glands exceeded 3 Gy(RBE). We compared the accumulated dose of two adaptive IMPT strategies: 1) Predicted plan adaption: One adaptive plan per patient was optimised on a predicted CT triggered by replan criteria. 2) Standard replan: One adaptive plan was created reactively in response to the triggering weekly CT.

RESULTS

Statistical analysis demonstrates that the accumulated dose differences between two adaptive strategies are not significant (p > 0.05) for CTVs and OARs. We observed no meaningful differences in D95 between the accumulated dose and the planned dose for the CTVs, with mean differences to the high-risk CTV of -1.20 %, -1.23 % and -1.25 % for no adaption, standard and predicted plan adaption, respectively. The accumulated parotid Dmean using predicted plan adaption is within 3 Gy(RBE) of the planned dose and 0.31 Gy(RBE) lower than the standard replan approach on average.

CONCLUSION

Prediction-based replanning could potentially enable adaptive therapy to be delivered without treatment gaps or sub-optimal fractions, as can occur during a standard replanning strategy, though the benefit of using predicted plan adaption over the standard replan was not shown to be statistically significant with respect to accumulated dose in this study. Nonetheless, a predictive replan approach can offer advantages in improving clinical workflow efficiency.

A prediction model for dosimetric-based lung adaptive radiotherapy

PURPOSE

Anatomical changes occurred during the treatment course of radiation therapy for lung cancer patients may introduce clinically unacceptable dosimetric deviations from the planned dose. Adaptive radiotherapy (ART) can compensate these dosimetric deviations in subsequent treatments via plan adaption. Determining whether and when to trigger plan adaption during the treatment course is essential to the effectiveness and efficiency of ART. In this study, we aimed to develop a prediction model as an auxiliary decision-making tool for lung ART to identify the patients with intrathoracic anatomical changes that would potentially benefit from the plan adaptions during the treatment course.

METHODS

Seventy-one pairs of weekly cone-beam computer tomography (CBCT) and planning CT (pCT) from 17 advanced non-small cell lung cancer patients were enrolled in this study. To assess the dosimetric impacts brought by anatomical changes observed on each CBCT, dose distribution of the original treatment plan on the CBCT anatomy was calculated on a virtual CT generated by deforming the corresponding pCT to the CBCT, and compared to that of the original plan. A replan was deemed needed for the CBCT anatomy once the recalculated dose distribution violated our dosimetric-based trigger criteria. A three-dimensional region of significant anatomical changes (region of interest, ROI) between each CBCT and the corresponding pCT was identified and 16 morphological features of the ROI were extracted. Additionally, eight features from the overlapped volume histograms (OVHs) of patient anatomy were extracted for each patient to characterize the patient specific anatomy. Based on the 24 extracted features and the evaluated replanning needs of the pCT-CBCT pairs, a nonlinear supporting vector machine was used to build a prediction model to identify the anatomical changes on CBCTs that would trigger plan adaptions. The most relevant features were selected using the sequential backward selection (SBS) algorithm and a shuffling-and-splitting validation scheme was used for model evaluation.

RESULTS

Fifty-Five CBCT-pCT pairs were identified of having a ROI, among which 21 CBCT anatomies required plan adaptions. For these 21 positive cases, statistically significant improvements in the sparing of lung, esophagus and spinal cord were achieved by plan adaptions. A high model performance of 0.929 AUC and 0.851 accuracy was achieved with six selected features including five ROI shape features and one OVH feature. Without involving the OVH features in the feature selection process, the mean AUC and accuracy of the model significantly decreased to 0.826 and 0.779, respectively. Further investigation showed that poor prediction performance with AUC of 0.76 was achieved by the univariate model in solving this binary classification task.

CONCLUSION

We built a prediction model based on the features of patient anatomy and the anatomical changes captured by on-treatment CBCT imaging to trigger plan adaption for lung cancer patients. This model effectively associated the anatomical changes with the dosimetric impacts for lung ART. This model can be a promising tool to assist the clinicians in making decisions for plan adaptions during the treatment courses. This article is protected by copyright. All rights reserved.

Evaluating Proton Dose and Associated Range Uncertainty Using Daily Cone-Beam CT

Purpose

This study aimed to quantitatively evaluate the range uncertainties that arise from daily cone-beam CT (CBCT) images for proton dose calculation compared to CT using a measurement-based technique.

Methods

For head and thorax phantoms, wedge-shaped intensity-modulated proton therapy (IMPT) treatment plans were created such that the gradient of the wedge intersected and was measured with a 2D ion chamber array. The measured 2D dose distributions were compared with 2D dose planes extracted from the dose distributions using the IMPT plan calculated on CT and CBCT. Treatment plans of a thymoma cancer patient treated with breath-hold (BH) IMPT were recalculated on 28 CBCTs and 9 CTs, and the resulting dose distributions were compared.

Results

The range uncertainties for the head phantom were determined to be 1.2% with CBCT, compared to 0.5% for CT, whereas the range uncertainties for the thorax phantom were 2.1% with CBCT, compared to 0.8% for CT. The doses calculated on CBCT and CT were similar with similar anatomy changes. For the thymoma patient, the primary source of anatomy change was the BH uncertainty, which could be up to 8 mm in the superior-inferior (SI) direction.

Conclusion

We developed a measurement-based range uncertainty evaluation method with high sensitivity and used it to validate the accuracy of CBCT-based range and dose calculation. Our study demonstrated that the CBCT-based dose calculation could be used for daily dose validation in selected proton patients.

Dosimetric evaluation of cone-beam CT-based synthetic CTs in pediatric patients undergoing intensity-modulated proton therapy

PURPOSE

To evaluate dosimetric changes detected using synthetic computed tomography (sCT) derived from online cone-beam CTs (CBCT) in pediatric patients treated using intensity-modulated proton therapy (IMPT).

METHODS

Ten pediatric patients undergoing IMPT and aligned daily using proton gantry-mounted CBCT were identified for retrospective analysis with treated anatomical sites fully encompassed in the CBCT field of view. Dates were identified when the patient received both a CBCT and a quality assurance CT (qCT) for routine dosimetric evaluation. sCTs were generated based on a deformable registration between the initial plan CT (pCT) and CBCT. The clinical IMPT plans were re-computed on the same day qCT and sCT, and dosimetric changes due to tissue change or response from the initial plan were computed using each image. Linear regression analysis was performed to determine the correlation between dosimetric changes detected using the qCT and the sCT. Gamma analysis was also used to compare the dose distributions computed on the qCT and sCT.

RESULTS

The correlation coefficients (p-values) between qCTs and sCTs for changes detected in target coverage, overall maximum dose, and organ at risk dose were 0.97 (< .001), 0.84 (.002) and 0.91 (< .001), respectively. Mean ± SD gamma pass rates of the sCT-based dose compared to the qCT-based dose at 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria were 96.5%±4.5%, 93.2%±6.3%, and 91.3%±7.8%, respectively. Pass rates tended to be lower for targets near lung.

CONCLUSION

While insufficient for re-planning, sCTs provide approximate dosimetry without administering additional imaging dose in pediatric patients undergoing IMPT. Dosimetric changes detected using sCTs are correlated with changes detected using clinically-standard qCTs; however, residual differences in dosimetry remain a limitation. Further improvements in sCT image quality may both improve online dosimetric evaluation and reduce imaging dose for pediatric patients by reducing the need for routine qCTs.

A two-step method to improve image quality of CBCT with phantom-based supervised and patient-based unsupervised learning strategies

Objective.In this study, we aimed to develop deep learning framework to improve cone-beam computed tomography (CBCT) image quality for adaptive radiation therapy (ART) applications.Approach.Paired CBCT and planning CT images of 2 pelvic phantoms and 91 patients (15 patients for testing) diagnosed with prostate cancer were included in this study. First, well-matched images of rigid phantoms were used to train a U-net, which is the supervised learning strategy to reduce serious artifacts. Second, the phantom-trained U-net generated intermediate CT images from the patient CBCT images. Finally, a cycle-consistent generative adversarial network (CycleGAN) was trained with intermediate CT images and deformed planning CT images, which is the unsupervised learning strategy to learn the style of the patient images for further improvement. When testing or applying the trained model on patient CBCT images, the intermediate CT images were generated from the original CBCT image by U-net, and then the synthetic CT images were generated by the generator of CycleGAN with intermediate CT images as input. The performance was compared with conventional methods (U-net/CycleGAN alone trained with patient images) on the test set.Results.The proposed two-step method effectively improved the CBCT image quality to the level of CT scans. It outperformed conventional methods for region-of-interest contouring and HU calibration, which are important to ART applications. Compared with the U-net alone, it maintained the structure of CBCT. Compared with CycleGAN alone, our method improved the accuracy of CT number and effectively reduced the artifacts, making it more helpful for identifying the clinical target volume.Significance.This novel two-step method improves CBCT image quality by combining phantom-based supervised and patient-based unsupervised learning strategies. It has immense potential to be integrated into the ART workflow to improve radiotherapy accuracy.

Management of Motion and Anatomical Variations in Charged Particle Therapy: Past, Present, and Into the Future

The major aim of radiation therapy is to provide curative or palliative treatment to cancerous malignancies while minimizing damage to healthy tissues. Charged particle radiotherapy utilizing carbon ions or protons is uniquely suited for this task due to its ability to achieve highly conformal dose distributions around the tumor volume. For these treatment modalities, uncertainties in the localization of patient anatomy due to inter- and intra-fractional motion present a heightened risk of undesired dose delivery. A diverse range of mitigation strategies have been developed and clinically implemented in various disease sites to monitor and correct for patient motion, but much work remains. This review provides an overview of current clinical practices for inter and intra-fractional motion management in charged particle therapy, including motion control, current imaging and motion tracking modalities, as well as treatment planning and delivery techniques. We also cover progress to date on emerging technologies including particle-based radiography imaging, novel treatment delivery methods such as tumor tracking and FLASH, and artificial intelligence and discuss their potential impact towards improving or increasing the challenge of motion mitigation in charged particle therapy.

Detection of anatomical changes using two-dimensional X-ray images for head and neck adaptive radiotherapy

PURPOSE

To develop a system for detecting anatomical changes using two-dimensional (2D) X-ray images.

METHODS

Ten patients with head and neck cancer were retrospectively analyzed using 2D x-ray and cone-beam computed tomography (CBCT) images. The 2D x-ray images were acquired daily, whereas the CBCT images were acquired weekly during the treatment period. The developed system imported the 2D x-ray images obtained on the initial treatment day and on another day, and thereafter converted them into the water equivalent thickness (WET) using the conversion table. The difference between the WET images for the 1^st and other treatment days (ΔWET) was calculated as the quantitative value for anatomical changes and visualized to recognize the anatomical change location. We compared ΔWET and the difference in the lateral neck distance (ΔLND) on the corresponding CBCT images. ΔLND was used as the ground truth for anatomical changes. ΔWET and ΔLND were measured at the first cervical vertebra (C1) and the tumor center (TC). C1 and TC were selected to observe the volume changes in the parotid gland and tumor, respectively. Sensitivity and specificity were calculated to evaluate the performance of the 2D-WET system. The cutoff values of WET and LND were set to 2-10 mm. Furthermore, intensity-modulated proton therapy (IMPT) plans for six patients with rescan CT images were generated. The IMPT plans on the rescan CT images were compared to the original plans on simulation CT using the dosimetric parameters for the target and the organs at risk (OARs).

RESULTS

The mean differences between ΔWET and ΔLND for C1 and TC were -0.62 ± 1.66 mm and -0.93 ± 1.28 mm (mean ± 1SD), respectively. ΔWET in the proposed system was in good agreement with ΔLND using the CBCT images. In the sensitivity and specificity results for C1 and TC with cut-off values from 2 mm to 10 mm, the sensitivity was >85% for all cut-off values, while the specificity was > 90% at 5-10 mm and < 90% at less than 5 mm. The average ΔWET at the time of replanning was 12.8 mm which resulted in maximum dose increase in the spinal cord D1cc by 8.4 Gy, the parotid gland D50 by 26.6 Gy, and the oral cavity D50 by 23.2 Gy.

CONCLUSIONS

We developed a new system for detecting anatomical changes using 2D x-ray images. The developed system with ΔWET showed an agreement with ΔLND at C1 and TC with an average difference of less than 1 mm. ΔWET detected anatomical changes with high sensitivity and specificity with a cut-off value of 5-10 mm. This system can monitor daily anatomical changes without causing high exposure to patients and requiring any inefficient work, and it can be applied to daily online adaptive PBT and triggered adaptive radiotherapy. This article is protected by copyright. All rights reserved.

Onboard cone-beam CT-based replan evaluation for head and neck proton therapy

Purpose

Quality assurance computed tomography (QACT) is the current clinical practice in proton therapy to evaluate the needs for replan. QACT could falsely indicate replan because of setup issues that would be solved on the treatment machine. Deforming the treatment planning CT (TPCT) to the pretreatment CBCT may eliminate this issue. We investigated the performance of replan evaluation based on deformed TPCT (TPCTdir) for proton head and neck (H&N) therapy.

Methods and materials

Twenty‐eight H&N datasets along with pretreatment CBCT and QACT were used to validate the method. The changes in body volume were analyzed between the no‐replan and replan groups. The dose on the TPCTdir, the deformed QACT (QACTdir), and the QACT were calculated by applying the clinical plans to these image sets. Dosimetric parameters’ changes, including ΔD95, ΔDmean, and ΔD1 for the clinical target volumes (CTVs) were calculated. Receiver operating characteristic curves for replan evaluation based on ΔD95 on QACT and TPCTdir were calculated, using ΔD95 on QACTdir as the reference. A threshold for replan based on ΔD95 on TPCTdir is proposed. The specificities for the proposed method were calculated.

Results

The changes in the body contour were 95.8 ± 83.8 cc versus 305.0 ± 235.0 cc (p < 0.01) for the no‐replan and replan groups, respectively. The ΔD95, ΔDmean, and ΔD1 are all comparable for all the evaluations. The differences between TPCTdir and QACTdir evaluations were 0.30% ± 0.86%, 0.00 ± 0.22 Gy, and −0.17 ± 0.61 Gy for CTV ΔD95, ΔDmean, and ΔD1, respectively. The corresponding differences between the QACT and QACTdir were 0.12% ± 1.1%, 0.02 ± 0.32 Gy, and −0.01 ± 0.71 Gy. CTV ΔD95 > 2.6% in TPCTdir was chosen as the threshold to trigger QACT/replan. The corresponding specificity was 94% and 98% for the clinical practice and the proposed method, respectively.

Conclusions

The replan evaluation based on TPCTdir provides better specificity than that based on the QACT.

Artificial Intelligence in Radiation Therapy

Artificial intelligence (AI) has great potential to transform the clinical workflow of radiotherapy. Since the introduction of deep neural networks, many AI-based methods have been proposed to address challenges in different aspects of radiotherapy. Commercial vendors have started to release AI-based tools that can be readily integrated to the established clinical workflow. To show the recent progress in AI-aided radiotherapy, we have reviewed AI-based studies in five major aspects of radiotherapy including image reconstruction, image registration, image segmentation, image synthesis, and automatic treatment planning. In each section, we summarized and categorized the recently published methods, followed by a discussion of the challenges, concerns, and future development. Given the rapid development of AI-aided radiotherapy, the efficiency and effectiveness of radiotherapy in the future could be substantially improved through intelligent automation of various aspects of radiotherapy.

Evaluation of Lung Tumor Target Volume in a Large Sample: Target and Clinical Factors Influencing the Volume Derived From Four-Dimensional CT and Cone Beam CT

Background and Purpose

This study aimed to systematically evaluate the influence of target-related and clinical factors on volume differences and the similarity of targets derived from four-dimensional computed tomography (4DCT) and cone beam computed tomography (CBCT) images in lung stereotactic body radiation therapy (SBRT).

Materials and Methods

4DCT and CBCT image data of 210 tumors from 195 patients were analyzed. The internal gross target volume (IGTV) derived from the maximum intensity projection (MIP) of 4DCT (IGTV-MIP) and the IGTV from CBCT (IGTV-CBCT) were compared with the reference IGTV from 10 phases of 4DCT (IGTV-10). The target size, tumor motion, and the similarity between IGTVs were measured. The influence of target-related and clinical factors on the adequacy of IGTVs derived from 4DCT MIP and CBCT images was evaluated.

Results

The mean tumor motion amplitude in the 3D direction was 6.5 ± 5 mm. The mean size ratio of IGTV-CBCT and IGTV-MIP compared to IGTV-10 in all patients was 0.71 ± 0.21 and 0.8 ± 0.14, respectively. Female sex, greater BSA, and larger target size were protective factors, while the Karnofsky Performance Status, body mass index, and motion were risk factors for the similarity between IGTV-MIP and IGTV-10. Older age and larger target size were protective factors, while adhesion to the heart, coexistence with cardiopathy, and tumor motion were risk factors for the similarity between IGTV-CBCT and IGTV-10.

Conclusion

Clinical factors should be considered when using MIP images for defining ITV, and when using CBCT images for verifying treatment targets.

Multi-resolution residual deep neural network for improving pelvic CBCT image quality

PURPOSE

Cone-beam computed tomography (CBCT) is frequently used for accurate image guided radiation therapy (IGRT). However, the poor CBCT image quality prevents its further clinical use. Thus, it is important to improve the HU accuracy and structure preservation of CBCT images.

METHODS

In this study, we proposed a novel method to generate synthetic CT (sCT) images from CBCT images. A multi-resolution residual deep neural network (RDNN) was adopted for image regression from CBCT images to planning CT (pCT) images. At the coarse level, RDNN was first trained with a large amount of lower resolution images, which can make the network focus on coarse information and prevent overfitting problems. More fine information was obtained gradually by fine-tuning the coarse model using fewer number of higher resolution images. Our model was optimized by using aligned pCT and CBCT image pairs of a particular body region of 153 prostate cancer patients treated in our hospital (120 for training, 33 for testing). Five-fold cross-validation was used to tune the hyperparameters and the testing data were used to evaluate the performance of the final models.

RESULTS

The mean absolute error (MAE) between CBCT and pCT on the testing data was 352.56 HU, while the MAE between the sCT and pCT images was 52.18 HU for our proposed multi-resolution RDNN model, which reduced the MAE by 85.20% (p < 0.01). In addition, the average structural similarity index measure (SSIM) between the sCT and CBCT was 19.64% (p = 0.01) higher than that of pCT and CBCT.

CONCLUSIONS

The sCT images generated using our proposed multi-resolution RDNN have higher HU accuracy and structural fidelity, which may promote the further applications of CBCT images in the clinic for structure segmentation, dose calculation and adaptive radiotherapy planning. This article is protected by copyright. All rights reserved.

A Comparison Study Between CNN-Based Deformed Planning CT and CycleGAN-Based Synthetic CT Methods for Improving iCBCT Image Quality

Purpose

The aim of this study is to compare two methods for improving the image quality of the Varian Halcyon cone-beam CT (iCBCT) system through the deformed planning CT (dpCT) based on the convolutional neural network (CNN) and the synthetic CT (sCT) generation based on the cycle-consistent generative adversarial network (CycleGAN).

Methods

A total of 190 paired pelvic CT and iCBCT image datasets were included in the study, out of which 150 were used for model training and the remaining 40 were used for model testing. For the registration network, we proposed a 3D multi-stage registration network (MSnet) to deform planning CT images to agree with iCBCT images, and the contours from CT images were propagated to the corresponding iCBCT images through a deformation matrix. The overlap between the deformed contours (dpCT) and the fixed contours (iCBCT) was calculated for purposes of evaluating the registration accuracy. For the sCT generation, we trained the 2D CycleGAN using the deformation-registered CT-iCBCT slicers and generated the sCT with corresponding iCBCT image data. Then, on sCT images, physicians re-delineated the contours that were compared with contours of manually delineated iCBCT images. The organs for contour comparison included the bladder, spinal cord, femoral head left, femoral head right, and bone marrow. The dice similarity coefficient (DSC) was used to evaluate the accuracy of registration and the accuracy of sCT generation.

Results

The DSC values of the registration and sCT generation were found to be 0.769 and 0.884 for the bladder (p < 0.05), 0.765 and 0.850 for the spinal cord (p < 0.05), 0.918 and 0.923 for the femoral head left (p > 0.05), 0.916 and 0.921 for the femoral head right (p > 0.05), and 0.878 and 0.916 for the bone marrow (p < 0.05), respectively. When the bladder volume difference in planning CT and iCBCT scans was more than double, the accuracy of sCT generation was significantly better than that of registration (DSC of bladder: 0.859 vs. 0.596, p < 0.05).

Conclusion

The registration and sCT generation could both improve the iCBCT image quality effectively, and the sCT generation could achieve higher accuracy when the difference in planning CT and iCBCT was large.

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.