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Liu W, Feng H, Taylor PA, Kang M, Shen J, Saini J, Zhou J, Giap HB, Yu NY, Sio TS, Mohindra P, Chang JY, Bradley JD, Xiao Y, Simone CB, Lin L. NRG Oncology and Particle Therapy Co-Operative Group Patterns of Practice Survey and Consensus Recommendations on Pencil-Beam Scanning Proton Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies. Int J Radiat Oncol Biol Phys 2024; 119:1208-1221. [PMID: 38395086 PMCID: PMC11209785 DOI: 10.1016/j.ijrobp.2024.01.216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 11/25/2023] [Accepted: 01/28/2024] [Indexed: 02/25/2024]
Abstract
Stereotactic body radiation therapy (SBRT) and hypofractionation using pencil-beam scanning (PBS) proton therapy (PBSPT) is an attractive option for thoracic malignancies. Combining the advantages of target coverage conformity and critical organ sparing from both PBSPT and SBRT, this new delivery technique has great potential to improve the therapeutic ratio, particularly for tumors near critical organs. Safe and effective implementation of PBSPT SBRT/hypofractionation to treat thoracic malignancies is more challenging than the conventionally fractionated PBSPT because of concerns of amplified uncertainties at the larger dose per fraction. The NRG Oncology and Particle Therapy Cooperative Group Thoracic Subcommittee surveyed proton centers in the United States to identify practice patterns of thoracic PBSPT SBRT/hypofractionation. From these patterns, we present recommendations for future technical development of proton SBRT/hypofractionation for thoracic treatment. Among other points, the recommendations highlight the need for volumetric image guidance and multiple computed tomography-based robust optimization and robustness tools to minimize further the effect of uncertainties associated with respiratory motion. Advances in direct motion analysis techniques are urgently needed to supplement current motion management techniques.
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Affiliation(s)
- Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona.
| | - Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona; College of Mechanical and Power Engineering, China Three Gorges University, Yichang, Hubei, China; Department of Radiation Oncology, Guangzhou Concord Cancer Center, Guangzhou, Guangdong, China
| | - Paige A Taylor
- Imaging and Radiation Oncology Core Houston Quality Assurance Center, University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona
| | - Jatinder Saini
- Seattle Cancer Care Alliance Proton Therapy Center and Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Huan B Giap
- Department of Radiation Oncology, Medical University of South Carolina, Charleston, South Carolina
| | - Nathan Y Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona
| | - Terence S Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona
| | - Pranshu Mohindra
- Department of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Joe Y Chang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jeffrey D Bradley
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
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Han C, Qiu J, Bai L, Liu T, Chen J, Wang H, Dang J. Pneumonitis Risk After Chemoradiotherapy With and Without Immunotherapy in Patients With Locally Advanced Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis. Int J Radiat Oncol Biol Phys 2024; 119:1179-1207. [PMID: 38360117 DOI: 10.1016/j.ijrobp.2024.01.217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 12/28/2023] [Accepted: 01/28/2024] [Indexed: 02/17/2024]
Abstract
PURPOSE Chemoradiotherapy (CRT) combined with immune checkpoint inhibitors (ICIs) is the standard of care for patients with unresectable and locally advanced non-small cell lung cancer. This study aimed to determine whether the addition of ICIs to CRT is associated with an increased risk of pneumonitis. METHODS AND MATERIALS The PubMed, Embase, Cochrane Library, and Web of Science databases were searched for eligible studies published between January 1, 2015, and July 31, 2023. The outcome of interest was the incidence rate of pneumonitis. A random-effects model was used for statistical analysis. RESULTS A total of 185 studies with 24,527 patients were included. The pooled rate of grade ≥2 pneumonitis for CRT plus ICIs was significantly higher than that for CRT alone (29.6%; 95% CI, 25.7%-33.6% vs 20.2%; 95% CI, 17.7%-22.8%; P < .0001) but not that of grade ≥3 (5.7%; 95% CI, 4.8%-6.6% vs 5.6%; 95% CI, 4.7%-6.5%; P = .64) or grade 5 (0.1%; 95% CI, 0.0%-0.2% vs 0.3%; 95% CI, 0.1%-0.4%; P = .68). The results from the subgroup analyses of prospective studies, retrospective studies, Asian and non-Asian studies, concurrent CRT (cCRT), and durvalumab consolidation were comparable to the overall results. However, CRT or cCRT plus PD-1 inhibitors not only significantly increased the incidence of grade ≥2 but also that of grade ≥3 pneumonitis compared to CRT alone or cCRT plus PD-L1 inhibitors. CONCLUSIONS Compared with CRT alone, durvalumab consolidation after CRT appears to be associated with a higher incidence of moderate pneumonitis and CRT plus PD-1 inhibitors with an increased risk of severe pneumonitis. Nevertheless, these findings are based on observational studies and need to be validated in future large head-to-head studies.
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Affiliation(s)
- Chong Han
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China
| | - Jingping Qiu
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China
| | - Lu Bai
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China
| | - Tingting Liu
- Department of Radiation Oncology, Anshan Cancer Hospital, Anshan, China
| | - Jun Chen
- Department of Radiation Oncology, Shenyang Tenth People's Hospital, Shenyang, China
| | - He Wang
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China
| | - Jun Dang
- Department of Radiation Oncology, First Hospital of China Medical University, Shenyang, China.
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Zhang L, Holmes JM, Liu Z, Vora SA, Sio TT, Vargas CE, Yu NY, Keole SR, Schild SE, Bues M, Li S, Liu T, Shen J, Wong WW, Liu W. Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy. Med Phys 2024; 51:1484-1498. [PMID: 37748037 DOI: 10.1002/mp.16758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 08/28/2023] [Accepted: 09/11/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities. PURPOSE To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS PBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs-at-risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. RESULTS Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s. CONCLUSIONS An accurate and efficient deep learning-augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.
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Affiliation(s)
- Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Jason M Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Zhengliang Liu
- School of Computing, University of Georgia, Athens, Georgia, USA
| | - Sujay A Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Terence T Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Carlos E Vargas
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Nathan Y Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Sameer R Keole
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Sheng Li
- School of Data Science, University of Virginia, Charlottesville, Virginia, USA
| | - Tianming Liu
- School of Computing, University of Georgia, Athens, Georgia, USA
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - William W Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
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Liu W, Feng H, Taylor PA, Kang M, Shen J, Saini J, Zhou J, Giap HB, Yu NY, Sio TS, Mohindra P, Chang JY, Bradley JD, Xiao Y, Simone CB, Lin L. Proton Pencil-Beam Scanning Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies: Patterns of Practice Survey and Recommendations for Future Development from NRG Oncology and PTCOG. ARXIV 2024:arXiv:2402.00489v1. [PMID: 38351927 PMCID: PMC10862926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Stereotactic body radiation therapy (SBRT) and hypofractionation using pencil-beam scanning (PBS) proton therapy (PBSPT) is an attractive option for thoracic malignancies. Combining the advantages of target coverage conformity and critical organ sparing from both PBSPT and SBRT, this new delivery technique has great potential to improve the therapeutic ratio, particularly for tumors near critical organs. Safe and effective implementation of PBSPT SBRT/hypofractionation to treat thoracic malignancies is more challenging than the conventionally-fractionated PBSPT due to concerns of amplified uncertainties at the larger dose per fraction. NRG Oncology and Particle Therapy Cooperative Group (PTCOG) Thoracic Subcommittee surveyed US proton centers to identify practice patterns of thoracic PBSPT SBRT/hypofractionation. From these patterns, we present recommendations for future technical development of proton SBRT/hypofractionation for thoracic treatment. Amongst other points, the recommendations highlight the need for volumetric image guidance and multiple CT-based robust optimization and robustness tools to minimize further the impact of uncertainties associated with respiratory motion. Advances in direct motion analysis techniques are urgently needed to supplement current motion management techniques.
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Affiliation(s)
- Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Paige A. Taylor
- The Imaging and Radiation Oncology Core Houston Quality Assurance Center, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Minglei Kang
- New York Proton Center, New York City, New York, USA
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Jatinder Saini
- Seattle Cancer Care Alliance Proton Therapy Center and Department of Radiation Oncology, University of Washington School of Medicine, Seattle, WA 98195, USA
| | - Jun Zhou
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
| | - Huan B. Giap
- Department of Radiation Oncology, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Nathan Y. Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Terence S. Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Pranshu Mohindra
- Department of Radiation Oncology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Joe Y. Chang
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston
| | - Jeffrey D. Bradley
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Liyong Lin
- Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, Georgia
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Liu C, Liu Z, Holmes J, Zhang L, Zhang L, Ding Y, Shu P, Wu Z, Dai H, Li Y, Shen D, Liu N, Li Q, Li X, Zhu D, Liu T, Liu W. Artificial general intelligence for radiation oncology. META-RADIOLOGY 2023; 1:100045. [PMID: 38344271 PMCID: PMC10857824 DOI: 10.1016/j.metrad.2023.100045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale.
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Affiliation(s)
- Chenbin Liu
- Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, Guangdong, China
| | | | - Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, USA
| | - Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Yuzhen Ding
- Department of Radiation Oncology, Mayo Clinic, USA
| | - Peng Shu
- School of Computing, University of Georgia, USA
| | - Zihao Wu
- School of Computing, University of Georgia, USA
| | - Haixing Dai
- School of Computing, University of Georgia, USA
| | - Yiwei Li
- School of Computing, University of Georgia, USA
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, China
- Shanghai United Imaging Intelligence Co., Ltd, China
- Shanghai Clinical Research and Trial Center, China
| | - Ninghao Liu
- School of Computing, University of Georgia, USA
| | - Quanzheng Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Xiang Li
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, USA
| | | | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, USA
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Ding Y, Feng H, Yang Y, Holmes J, Liu Z, Liu D, Wong WW, Yu NY, Sio TT, Schild SE, Li B, Liu W. Deep-learning based fast and accurate 3D CT deformable image registration in lung cancer. Med Phys 2023; 50:6864-6880. [PMID: 37289193 PMCID: PMC10704004 DOI: 10.1002/mp.16548] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 04/20/2023] [Accepted: 05/24/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Deformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application. PURPOSE A deep-learning-based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc. METHODS: A deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2 weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre-trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per-voxel absolute CT-number-difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3 mm/3%/10% and 2 mm/2%/10%, respectively. RESULTS The two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190 ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per-voxel absolute CT-number-difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5 , within ±0.06% for total lung V5 , ≤1.5cGy[RBE] for heart and esophagus Dmean , and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (> 96% for 3 mm/3%/10% and > 94% for 2 mm/2%/10%, respectively) were also observed. CONCLUSION A deep neural network-based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.
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Affiliation(s)
- Yuzhen Ding
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Yunze Yang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Zhengliang Liu
- Department of Computer Science, University of Georgia, Athens, GA 30602, USA
| | - David Liu
- Athens Academy, Athens, GA 30602, USA
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Nathan Y. Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Terence T. Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Steven E. Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Baoxin Li
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA 85281
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
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Liu T, Li S, Ding S, Qiu J, Ren C, Chen J, Wang H, Wang X, Li G, He Z, Dang J. Comparison of post-chemoradiotherapy pneumonitis between Asian and non-Asian patients with locally advanced non-small cell lung cancer: a systematic review and meta-analysis. EClinicalMedicine 2023; 64:102246. [PMID: 37781162 PMCID: PMC10539643 DOI: 10.1016/j.eclinm.2023.102246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 08/28/2023] [Accepted: 09/13/2023] [Indexed: 10/03/2023] Open
Abstract
Background Pneumonitis is a common complication for patients with locally advanced non-small cell lung cancer undergoing definitive chemoradiotherapy (CRT). It remains unclear whether there is ethnic difference in the incidence of post-CRT pneumonitis. Methods PubMed, Embase, Cochrane Library, and Web of Science were searched for eligible studies from January 1, 2000 to April 30, 2023. The outcomes of interest were incidence rates of pneumonitis. The random-effect model was used for statistical analysis. This meta-analysis was registered with PROSPERO (CRD42023416490). Findings A total of 248 studies involving 28,267 patients were included. Among studies of CRT without immunotherapy, the pooled rates of pneumonitis for Asian patients were significantly higher than that for non-Asian patients (all grade: 66.8%, 95% CI: 59.2%-73.9% vs. 28.1%, 95% CI: 20.4%-36.4%; P < 0.0001; grade ≥2: 25.1%, 95% CI: 22.9%-27.3% vs. 14.9%, 95% CI: 12.0%-18.0%; P < 0.0001; grade ≥3: 6.5%, 95% CI: 5.6%-7.3% vs. 4.6%, 95% CI: 3.4%-5.9%; P = 0.015; grade 5: 0.6%, 95% CI: 0.3%-0.9% vs. 0.1%, 95% CI: 0.0%-0.2%; P < 0.0001). Regarding studies of CRT plus immunotherapy, Asian patients had higher rates of all-grade (74.8%, 95% CI: 63.7%-84.5% vs. 34.3%, 95% CI: 28.7%-40.2%; P < 0.0001) and grade ≥2 (34.0%, 95% CI: 30.7%-37.3% vs. 24.6%, 95% CI: 19.9%-29.3%; P = 0.001) pneumonitis than non-Asian patients, but with no significant differences in the rates of grade ≥3 and grade 5 pneumonitis. Results from subgroup analyses were generally similar to that from the all studies. In addition, the pooled median/mean of lung volume receiving ≥20 Gy and mean lung dose were relatively low in Asian studies compared to that in non-Asian studies. Interpretation Asian patients are likely to have a higher incidence of pneumonitis than non-Asian patients, which appears to be due to the poor tolerance of lung to radiation. Nevertheless, these findings are based on observational studies and with significant heterogeneity, and need to be validated in future large prospective studies focusing on the subject. Funding None.
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Affiliation(s)
- Tingting Liu
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
- Department of Radiation Oncology, Anshan Cancer Hospital, Anshan, China
| | - Sihan Li
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Silu Ding
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Jingping Qiu
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Chengbo Ren
- Department of Radiation Oncology, The First Affiliated Hospital of Hebei North University, Zhangjiakou, Hebei, China
| | - Jun Chen
- Department of Radiation Oncology, Shenyang Tenth People's Hospital, Shenyang, China
| | - He Wang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Xiaoling Wang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Guang Li
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Zheng He
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
| | - Jun Dang
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, China
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Zhang L, Holmes JM, Liu Z, Vora SA, Sio TT, Vargas CE, Yu NY, Keole SR, Schild SE, Bues M, Li S, Liu T, Shen J, Wong WW, Liu W. Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy. ARXIV 2023:arXiv:2305.18572v1. [PMID: 37396612 PMCID: PMC10312803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
PURPOSE To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning. METHODS PBSPT plans of 103 prostate cancer patients and 83 lung cancer patients previously treated at our institution were included in the study, each with CTs, structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine. For the ablation study, we designed three experiments corresponding to the following three methods: 1) Experiment 1, the conventional region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. 3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency. RESULTS Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices. For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method can improve the passing rates in these regions and the sliding window method further improved them. A similar trend was also observed for the dice coefficients. In fact, this trend was especially remarkable for relatively low prescription isodose lines. The dose predictions for all the testing cases were completed within 0.25s.
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Affiliation(s)
- Lian Zhang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Jason M. Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Zhengliang Liu
- Department of Computer Science, University of Georgia, Athens, GA 30602, USA
| | - Sujay A. Vora
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Terence T. Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Carlos E. Vargas
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Nathan Y. Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Sameer R. Keole
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Steven E. Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Sheng Li
- Department of Data Science, University of Virginia, Charlottesville, VA 22903, USA
| | - Tianming Liu
- Department of Computer Science, University of Georgia, Athens, GA 30602, USA
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
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9
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Tatebe H, Harada H, Mori K, Iwata H, Akimoto T, Murakami M, Waki T, Ogino T, Nakamura M, Taguchi H, Nakayama H, Satouchi M, Aoyama H. Clinical results of proton beam radiotherapy for inoperable stage III non-small cell lung cancer: a Japanese national registry study. JOURNAL OF RADIATION RESEARCH 2023:7150736. [PMID: 37137157 DOI: 10.1093/jrr/rrad017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/19/2023] [Indexed: 05/05/2023]
Abstract
This study presents the first data of a Japanese nationwide multi-institutional cohort and compares them with the findings of systematic literature reviews on radiation therapies and inoperable stage III non-small cell lung cancer (NSCLC) conducted by the Lung Cancer Working Group in the Particle Beam Therapy (PBT) Committee and Subcommittee at Japanese Society for Radiation Oncology. The Lung Cancer Working Group extracted eight reports and compared their data with those of the PBT registry from May 2016 to June 2018. All the analyzed 75 patients aged ≤80 years underwent proton therapy (PT) with concurrent chemotherapy for inoperable stage III NSCLC. The median follow-up period of the surviving patients was 39.5 (range, 1.6-55.6) months. The 2- and 3-year overall survival (OS) and progression-free survival rates were 73.6%/64.7% and 28.9%/25.1%, respectively. During the follow-up period, six patients (8.0%) had adverse events of Grade ≥ 3, excluding abnormal laboratory values. These included esophagitis in four patients, dermatitis in one and pneumonitis in one. Adverse events of Grade ≥ 4 were not observed. The results of these PBT registry data in patients with inoperable stage III NSCLC suggest that the OS rate was at least equivalent to that of radiation therapy using X-rays and that the incidence of severe radiation pneumonitis was low. PT may be an effective treatment to reduce toxicities of healthy tissues, including the lungs and heart, in patients with inoperable stage III NSCLC.
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Affiliation(s)
- Hitoshi Tatebe
- Proton Therapy Center, Fukui Prefectural Hospital, 2-8-1 Yotsui, Fukui 910-0846, Japan
| | - Hideyuki Harada
- Radiation and Proton Therapy Center, Shizuoka Cancer Center, 1007 Shimonagamubo, Nagaizumi-cho, Sunto-Gun, Shizuoka 411-8777, Japan
| | - Keita Mori
- Department of Biostatistics, Clinical Research Center, Shizuoka Cancer Center, 1007 Shimonagamubo, Nagaizumi-cho, Sunto-Gun, Shizuoka 411-8777, Japan
| | - Hiromitsu Iwata
- Department of Radiation Oncology, Nagoya Proton Therapy Center, Nagoya City University West Medical Center, 1-1-1 Hirate-cho, Kita-ku, Nagoya 462-8508, Japan
| | - Tetsuo Akimoto
- Division of Radiation Oncology and Particle Therapy, National Cancer Center Hospital East, Kashiwa 277-0882, Japan
| | - Masao Murakami
- Southern Tohoku Proton Therapy Center, 7-172, Yatsuyamada, Fukushima, Koriyama 963-8052, Japan
| | - Takahiro Waki
- Department of Radiology, Tsuyama Chuo Hospital, 1756 Kawasaki, Tsuyama City, Okayama 708-0841, Japan
| | - Takashi Ogino
- Medipolis Proton Therapy and Research Center, 4423 Higashikata, Ibusuki City, Kagoshima 891-0304, Japan
| | - Masatoshi Nakamura
- Department of Radiation Oncology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Ibaraki, Tsukuba 305-8577, Japan
| | - Hiroshi Taguchi
- Department of Radiation Oncology, Hokkaido University Hospital, Kita 15, Nishi 7, Kita-ku, Sapporo, Hokkaido 060-0808, Japan
| | - Haruhiko Nakayama
- Department of Thoracic Surgery, Kanagawa Cancer Center, 2-3-2 Nakao, Asahi-ku, Yokohama City, Kanagawa 241-8515, Japan
| | - Miyako Satouchi
- Department of Thoracic Oncology, Hyogo Cancer Center, 13-70 kitaojicho, Akashi, Hyogo 673-8558, Japan
| | - Hidefumi Aoyama
- Department of Radiation Oncology, Hokkaido University Faculty of Medicine, Hokkaido 060-0808, Japan
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10
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Ding Y, Feng H, Yang Y, Holmes J, Liu Z, Liu D, Wong WW, Yu NY, Sio TT, Schild SE, Li B, Liu W. Deep-Learning-based Fast and Accurate 3D CT Deformable Image Registration in Lung Cancer. ARXIV 2023:arXiv:2304.11135v1. [PMID: 37131881 PMCID: PMC10153353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
PURPOSE In some proton therapy facilities, patient alignment relies on two 2D orthogonal kV images, taken at fixed, oblique angles, as no 3D on-the-bed imaging is available. The visibility of the tumor in kV images is limited since the patient's 3D anatomy is projected onto a 2D plane, especially when the tumor is behind high-density structures such as bones. This can lead to large patient setup errors. A solution is to reconstruct the 3D CT image from the kV images obtained at the treatment isocenter in the treatment position. METHODS An asymmetric autoencoder-like network built with vision-transformer blocks was developed. The data was collected from 1 head and neck patient: 2 orthogonal kV images (1024x1024 voxels), 1 3D CT with padding (512x512x512) acquired from the in-room CT-on-rails before kVs were taken and 2 digitally-reconstructed-radiograph (DRR) images (512x512) based on the CT. We resampled kV images every 8 voxels and DRR and CT every 4 voxels, thus formed a dataset consisting of 262,144 samples, in which the images have a dimension of 128 for each direction. In training, both kV and DRR images were utilized, and the encoder was encouraged to learn the jointed feature map from both kV and DRR images. In testing, only independent kV images were used. The full-size synthetic CT (sCT) was achieved by concatenating the sCTs generated by the model according to their spatial information. The image quality of the synthetic CT (sCT) was evaluated using mean absolute error (MAE) and per-voxel-absolute-CT-number-difference volume histogram (CDVH). RESULTS The model achieved a speed of 2.1s and a MAE of <40HU. The CDVH showed that <5% of the voxels had a per-voxel-absolute-CT-number-difference larger than 185 HU. CONCLUSION A patient-specific vision-transformer-based network was developed and shown to be accurate and efficient to reconstruct 3D CT images from kV images.
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Affiliation(s)
- Yuzhen Ding
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Hongying Feng
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Yunze Yang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Zhengliang Liu
- Department of Computer Science, University of Georgia, Athens, GA 30602, USA
| | - David Liu
- Athens Academy, Athens, GA 30602, USA
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Nathan Y. Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Terence T. Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Steven E. Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
| | - Baoxin Li
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, Arizona, USA 85281
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ 85054, USA
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Chen Z, Dominello MM, Joiner MC, Burmeister JW. Proton versus photon radiation therapy: A clinical review. Front Oncol 2023; 13:1133909. [PMID: 37064131 PMCID: PMC10091462 DOI: 10.3389/fonc.2023.1133909] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 03/13/2023] [Indexed: 03/31/2023] Open
Abstract
While proton radiation therapy offers substantially better dose distribution characteristics than photon radiation therapy in certain clinical applications, data demonstrating a quantifiable clinical advantage is still needed for many treatment sites. Unfortunately, the number of patients treated with proton radiation therapy is still comparatively small, in some part due to the lack of evidence of clear benefits over lower-cost photon-based treatments. This review is designed to present the comparative clinical outcomes between proton and photon therapies, and to provide an overview of the current state of knowledge regarding the effectiveness of proton radiation therapy.
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Affiliation(s)
- Zhe Chen
- School of Medicine, Wayne State University, Detroit, MI, United States
- *Correspondence: Zhe Chen,
| | - Michael M. Dominello
- Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, Detroit, MI, United States
| | - Michael C. Joiner
- Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, Detroit, MI, United States
| | - Jay W. Burmeister
- Karmanos Cancer Institute, Department of Oncology, Wayne State University School of Medicine, Detroit, MI, United States
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12
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McMillan MT, Kang M, Shepherd AF, Liu W, Lin L, Lin H, Simone CB. Stereotactic body proton therapy for non-small cell lung cancer: Clinical indications and recommendations. JOURNAL OF RADIOSURGERY AND SBRT 2023; 9:17-32. [PMID: 38029014 PMCID: PMC10681144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/11/2023] [Indexed: 12/01/2023]
Abstract
Stereotactic body radiation therapy (SBRT) has emerged as a standard treatment approach for early-stage lung cancer and intrathoracic oligometastatic or oligoprogressive disease. While local control is often excellent with this modality when delivered with photon therapy, toxicities for select patients can be significant. Proton therapy offers a unique opportunity to widen the therapeutic window when treating patients with thoracic malignancies requiring or benefitting from ultra-high doses per fraction. Thoracic proton SBRT may be particularly beneficial in cases requiring dose escalation, for tumors >5 cm, for central or ultra-central tumors, for reirradiation, in patients with interstitial lung diseases, and when combining radiation with immunotherapy. These clinical indications are detailed, along with supporting literature and clinical recommendations. Other considerations, future directions and potential benefits of proton SBRT, including sparing lymphocytes, when delivered as intensity-modulated proton therapy or as FLASH, and for the treatment of locally advanced non-small cell lung cancer or in patients with homologous recombination repair deficiencies, are also discussed.
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Affiliation(s)
- Matthew T. McMillan
- Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, New York, NY, USA
| | | | - Annemarie F. Shepherd
- Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, New York, NY, USA
- New York Proton Center, New York, NY, USA
| | - Wei Liu
- Mayo Clinic, Department of Radiation Oncology, Phoenix, AZ, USA
| | - Liyong Lin
- Emory University, Department of Radiation Oncology, Atlanta, GA, USA
| | - Haibo Lin
- Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, New York, NY, USA
- New York Proton Center, New York, NY, USA
| | - Charles B. Simone
- Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, New York, NY, USA
- New York Proton Center, New York, NY, USA
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13
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Holmes J, Shen J, Patel SH, Wong WW, Foote RL, Bues M, Liu W. Collimating individual beamlets in pencil beam scanning proton therapy, a dosimetric investigation. Front Oncol 2022; 12:1031340. [PMID: 36439436 PMCID: PMC9692234 DOI: 10.3389/fonc.2022.1031340] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 10/27/2022] [Indexed: 03/26/2024] Open
Abstract
The purpose of this work is to investigate collimating individual proton beamlets from a dosimetric perspective and to introduce a new device concept, the spot scanning aperture (SSA). The SSA consists of a thin aperture with a small cylindrical opening attached to a robotics system, which allows the aperture to follow and align with individual beamlets during spot delivery. Additionally, a range shifter is incorporated (source-side) for treating shallow depths. Since the SSA trims beamlets spot by spot, the patient-facing portion of the device only needs to be large enough to trim a single proton beamlet. The SSA has been modelled in an open-source Monte-Carlo-based dose engine (MCsquare) to characterize its dosimetric properties in water at depths between 0 and 10 cm while varying the following parameters: the aperture material, thickness, distance to the water phantom, distance between the aperture and attached range shifter, and the aperture opening radius. Overall, the SSA greatly reduced spot sizes for all the aperture opening radii that were tested (1 - 4 mm), especially in comparison with the extended range shifter (ranger shifter placed at 30 cm from patient); greater than 50% when placed less than 10 cm away from the patient at depths in water less than 50 mm. The peak to entrance dose ratio and linear energy transfer was found to depend on the thickness of the aperture and therefore the aperture material. Neutron production rates were also investigated and discussed.
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Affiliation(s)
- Jason Holmes
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - Samir H. Patel
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - William W. Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - Robert L. Foote
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, United States
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14
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Yang Y, Patel SH, Bridhikitti J, Wong WW, Halyard MY, McGee LA, Rwigema JCM, Schild SE, Vora SA, Liu T, Bues M, Fatyga M, Foote RL, Liu W. Exploratory study of seed spots analysis to characterize dose and linear energy transfer effect in adverse event initialization of pencil beam scanning proton therapy. Med Phys 2022; 49:6237-6252. [PMID: 35820062 DOI: 10.1002/mp.15859] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 06/20/2022] [Accepted: 07/06/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Both dose and linear-energy-transfer (LET) could play a substantial role in adverse event (AE) initialization of cancer patients treated with pencil-beam-scanning proton therapy (PBS). However, not all the voxels within the AE regions are directly induced from the dose and LET effect. It is important to study the synergistic effect of dose and LET in AE initialization by only including a subset of voxels that are dosimetrically important. PURPOSE To perform exploratory investigation of the dose and LET effects upon AE initialization in PBS using seed spots analysis. METHODS 113 head and neck (H&N) cancer patients receiving curative PBS were included. Among them, 20 patients experienced unanticipated CTCAEv4.0 grade≥3 AEs (AE group) and 93 patients did not (control group). Within the AE group, 13 AE patients were included in the seed spot analysis to derive the descriptive features of AE initialization and the remaining 7 mandible osteoradionecrosis patients and 93 control patients were used to derive the feature-based volume constraint of mandible osteoradionecrosis. The AE regions were contoured and the corresponding dose-LET volume histograms (DLVHs) of AE regions were generated for all patients in the AE group. We selected high LET voxels (the highest 5% of each dose bin) with a range of moderate to high dose (≥∼40 Gy[RBE]) as critical voxels. Critical voxels which were contiguous with each other were grouped into clusters. Each cluster was considered as a potential independent seed spot for AE initialization. Seed spots were displayed in a 2D dose-LET plane based on their mean dose and LET to derive the descriptive features of AE initialization. A volume constraint of mandible osteoradionecrosis was then established based on the extracted features using a receiver operating characteristic curve. RESULTS The product of dose and LET (xBD) was found to be a descriptive feature of seed spots leading to AE initialization in this preliminary study. The derived xBD volume constraint for mandible osteoradionecrosis showed good performance with an area-under-curve of 0.87 (sensitivity of 0.714 and specificity of 0.807 in the leave-one-out cross validation) for the very limited patient data included in this study. CONCLUSION Our exploratory study showed that both dose and LET were observed to be important in AE initializations. The derived xBD volume constraint could predict mandible osteoradionecrosis reasonably well in the very limited H&N cancer patient data treated with PBS included in this study. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yunze Yang
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Samir H Patel
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Jidapa Bridhikitti
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - William W Wong
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Michele Y Halyard
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Lisa A McGee
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | | | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Sujay A Vora
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Tianming Liu
- Department of Computer Science, the University of Georgia, Athens, Georgia, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Mirek Fatyga
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
| | - Robert L Foote
- Department of Radiation Oncology, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, Arizona, USA
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15
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Algranati C, Strigari L. Imaging Strategies in Proton Therapy for Thoracic Tumors: A Mini Review. Front Oncol 2022; 12:833364. [PMID: 35515119 PMCID: PMC9063639 DOI: 10.3389/fonc.2022.833364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/09/2022] [Indexed: 11/13/2022] Open
Abstract
Proton beam therapy (PBT) is often more attractive for its high gradient dose distributions than other treatment modalities with external photon beams. However, in thoracic lesions treated particularly with pencil beam scanning (PBS) proton beams, several dosimetric issues are addressed. The PBS approach may lead to large hot or cold spots in dose distributions delivered to the patients, potentially affecting the tumor control and/or increasing normal tissue side effects. This delivery method particularly benefits image-guided approaches. Our paper aims at reviewing imaging strategies and their technological trends for PBT in thoracic lesions. The focus is on the use of imaging strategies in simulation, planning, positioning, adaptation, monitoring, and delivery of treatment and how changes in the anatomy of thoracic tumors are handled with the available tools and devices in PBT. Starting from bibliographic research over the past 5 years, retrieving 174 papers, major key questions, and implemented solutions were identified and discussed; the results aggregated and presented following the methodology of analysis of expert interviews.
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Affiliation(s)
- Carlo Algranati
- Proton Therapy Department, Azienda Provinciale per i Servizi Sanitari (APSS), Trento, Italy
- Dipartimento di Medicina Specialistica, Diagnostica e Sperimentale (DIMES), University of Bologna, Bologna, Italy
| | - Lidia Strigari
- Department of Medical Physics, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
- *Correspondence: Lidia Strigari,
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16
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Clinical Outcomes of Pencil Beam Scanning Proton Therapy in Locally Advanced Non-Small Cell Lung Cancer: Propensity Score Analysis. Cancers (Basel) 2021; 13:cancers13143497. [PMID: 34298711 PMCID: PMC8307066 DOI: 10.3390/cancers13143497] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/07/2021] [Accepted: 07/12/2021] [Indexed: 12/25/2022] Open
Abstract
This study compared the efficacy and safety of pencil beam scanning proton therapy (PBSPT) versus intensity-modulated (photon) radiotherapy (IMRT) in patients with stage III non-small cell lung cancer (NSCLC). We retrospectively reviewed 219 patients with stage III NSCLC who received definitive concurrent chemoradiotherapy between November 2016 and December 2018. Twenty-five patients (11.4%) underwent PBSPT (23 with single-field optimization) and 194 patients (88.6%) underwent IMRT. Rates of locoregional control (LRC), overall survival, and acute/late toxicities were compared between the groups using propensity score-adjusted analyses. Patients treated with PBSPT were older (median: 67 vs. 62 years) and had worse pulmonary function at baseline (both FEV1 and DLCO) compared to those treated with IMRT. With comparable target coverage, PBSPT exhibited superior sparing of the lung, heart, and spinal cord to radiation exposure compared to IMRT. At a median follow-up of 21.7 (interquartile range: 16.8-26.8) months, the 2-year LRC rates were 72.1% and 84.1% in the IMRT and PBSPT groups, respectively (p = 0.287). The rates of grade ≥ 3 esophagitis were 8.2% and 20.0% after IMRT and PBSPT (p = 0.073), respectively, while corresponding rates of grade ≥ 2 radiation pneumonitis were 28.9% and 16.0%, respectively (p = 0.263). PBSPT appears to be an effective and safe treatment technique even for patients with poor lung function, and it does not jeopardize LRC.
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17
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Younkin JE, Morales DH, Shen J, Ding X, Stoker JB, Yu NY, Sio TT, Daniels TB, Bues M, Fatyga M, Schild SE, Liu W. Technical Note: Multiple energy extraction techniques for synchrotron-based proton delivery systems may exacerbate motion interplay effects in lung cancer treatments. Med Phys 2021; 48:4812-4823. [PMID: 34174087 DOI: 10.1002/mp.15056] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 03/12/2021] [Accepted: 06/09/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The multiple energy extraction (MEE) delivery technique for synchrotron-based proton delivery systems reduces beam delivery time by decelerating the beam multiple times during one accelerator spill, but this might cause additional plan quality degradation due to intrafractional motion. We seek to determine whether MEE causes significantly different plan quality degradation compared to single energy extraction (SEE) for lung cancer treatments due to the interplay effect. METHODS Ten lung cancer patients treated with IMPT at our institution were nonrandomly sampled based on a representative range of tumor motion amplitudes, tumor volumes, and respiratory periods. Dose-volume histogram (DVH) indices from single-fraction SEE and MEE four-dimensional (4D) dynamic dose distributions were compared using the Wilcoxon signed-rank test. Distributions of monitor units (MU) to breathing phases were investigated for features associated with plan quality degradation. SEE and MEE DVH indices were compared in fractionated deliveries of the worst-case patient treatment scenario to evaluate the impact of fractionation. RESULTS There were no clinically significant differences in target mean dose, target dose conformity, or dose to organs-at-risk between SEE and MEE in single-fraction delivery. Three patients had significantly worse dose homogeneity with MEE compared to SEE (single-fraction mean D5% -D95% increased by up to 9.6% of prescription dose), and plots of MU distribution to breathing phases showed synchronization patterns with MEE but not SEE. However, after 30 fractions the patient in the worst-case scenario had clinically acceptable target dose homogeneity and coverage with MEE (mean D5% -D95% increased by 1% compared to SEE). CONCLUSIONS For some patients with breathing periods close to the mean spill duration, MEE resulted in significantly worse single-fraction target dose homogeneity compared to SEE due to the interplay effect. However, this was mitigated by fractionation, and target dose homogeneity and coverage were clinically acceptable after 30 fractions with MEE.
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Affiliation(s)
- James E Younkin
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | | | - Jiajian Shen
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Xiaoning Ding
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Joshua B Stoker
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Nathan Y Yu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Terence T Sio
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Thomas B Daniels
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Mirek Fatyga
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA
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18
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Chiang JS, Yu NY, Daniels TB, Liu W, Schild SE, Sio TT. Proton beam radiotherapy for patients with early-stage and advanced lung cancer: a narrative review with contemporary clinical recommendations. J Thorac Dis 2021; 13:1270-1285. [PMID: 33717598 PMCID: PMC7947490 DOI: 10.21037/jtd-20-2501] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Although lung cancer rates are decreasing nationally, lung cancer remains the leading cause of cancer related death. Despite advancements in treatment and technology, overall survival (OS) for lung cancer remains poor. Proton beam therapy (PBT) is an advanced radiation therapy (RT) modality for treatment of lung cancer with the potential to achieve dose escalation to tumor while sparing critical structures due to higher target conformality. In early and late-stage non-small cell lung cancer (NSCLC), dosimetric studies demonstrated reduced doses to organs at risk (OARs) such as the lung, spinal cord, and heart, and clinical studies report limited toxicities with PBT, including hypofractionated regimens. In limited-stage SCLC, studies showed that regimens chemo RT including PBT were well tolerated, which may help optimize clinical outcomes. Improved toxicity profiles may be beneficial in post-operative radiotherapy, for which initial dosimetric and clinical data are encouraging. Sparing of OARs may also increase the proportion of patients able to complete reirradiation for recurrent disease. However, there are various challenges of using PBT including a higher financial burden on healthcare and limited data supporting its cost-effectiveness. Further studies are needed to identify subgroups that benefit from PBT based on prognostic factors, and to evaluate PBT combined with immunotherapy, in order to elucidate the benefit that PBT may offer future lung cancer patients.
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Affiliation(s)
- Jennifer S Chiang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Nathan Y Yu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Thomas B Daniels
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Terence T Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
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Kim N, Myoung Noh J, Lee W, Park B, Park H, Young Park J, Pyo H. Proton beam therapy reduces the risk of severe radiation-induced lymphopenia during chemoradiotherapy for locally advanced non-small cell lung cancer: A comparative analysis of proton versus photon therapy. Radiother Oncol 2020; 156:166-173. [PMID: 33359267 DOI: 10.1016/j.radonc.2020.12.019] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 12/09/2020] [Accepted: 12/11/2020] [Indexed: 12/23/2022]
Abstract
HYPOTHESIS We investigated differences in severe radiation-induced lymphopenia (SRL) after pencil beam scanning proton therapy (PBSPT) or intensity-modulated (photon) radiotherapy (IMRT) for patients with locally advanced non-small cell lung cancer. METHODS We retrospectively reviewed 223 patients who received definitive concurrent chemoradiotherapy with PBSPT (n = 29) or IMRT (n = 194). SRL was defined when ≥2 events of absolute lymphocyte counts (ALCs) of <200 cells/μL were observed in weekly laboratory tests conducted during treatment. Stepwise multivariate logistic regression with 10-fold cross-validation was performed to identify predictive values of SRL. Furthermore, 1:2 propensity score matching (PSM) analysis was performed between the PBSPT and IMRT groups. RESULTS Baseline ALC was comparable between the PBSPT and IMRT groups (median, 2130 vs. 2040 cells/μL; p = 0.983). Lung volumes receiving ≥ 5-20 GyE and the mean dose were significantly lower in patients receiving PBSPT than those receiving IMRT (p < 0.001). Among 72 (32.3%) patients with SRL; 69 (95.8%) and 3 (4.2%) patients were treated with IMRT and PBSPT, respectively. After multivariable analysis, PBSPT reduced SRL compared to IMRT (odds ratio [OR] 0.13, p = 0.003). Specifically, lung V5Gy were identified as the strongest predictor of SRL before (OR 1.11) and after PSM (OR, 1.07) (p < 0.05). With a median follow-up of 23.0 months, the 2-year overall survival in patients with SRL was worse than that those without SRL (63.4% vs. 79.9%; p = 0.003). CONCLUSIONS Reduced irradiated lung volumes of PBSPT consequently reduced SRL. In addition, lung V5Gy contributed to the SRL. Reduction of SRL through the optimized RT might be essential to improve the outcomes.
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Affiliation(s)
- Nalee Kim
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jae Myoung Noh
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Woojin Lee
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Byoungsuk Park
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Heejoo Park
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ji Young Park
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hongryull Pyo
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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20
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Owen D, Sio TT. Stereotactic body radiotherapy (SBRT) for central and ultracentral node-negative lung tumors. J Thorac Dis 2020; 12:7024-7031. [PMID: 33282407 PMCID: PMC7711409 DOI: 10.21037/jtd-2019-cptn-01] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Advancements in imaging and radiotherapy (RT) techniques have allowed for remarkably precise delivery of high radiation dose per treatment fraction to intrathoracic targets. As a non-invasive therapeutic modality (compared to surgery), stereotactic body radiotherapy (SBRT) is an attractive option for patients with early-stage non-small cell lung cancers and oligometastases, especially for older patients with significant comorbidities and pre-existing pulmonary dysfunction. However, the outcomes and side effect profile of SBRT are highly dependent on tumor location, especially if the tumor is located centrally (within 2 cm of the proximal bronchial tree (PBT)] or ultracentrally (touching or within 1 cm of the mediastinum, esophagus, and PBT). In this focused review, we will examine the contemporary practice and principles of using hypofractionated RT or SBRT for central and ultracentral thoracic tumors. We will identify future directions on how this practice may be incorporated into the increasingly complicated modern paradigm of lung cancer treatments which now include immunotherapy along with proton beam radiotherapy.
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Affiliation(s)
- Dawn Owen
- Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA
| | - Terence T Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
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21
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Shan J, Yang Y, Schild SE, Daniels TB, Wong WW, Fatyga M, Bues M, Sio TT, Liu W. Intensity-modulated proton therapy (IMPT) interplay effect evaluation of asymmetric breathing with simultaneous uncertainty considerations in patients with non-small cell lung cancer. Med Phys 2020; 47:5428-5440. [PMID: 32964474 DOI: 10.1002/mp.14491] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 07/14/2020] [Accepted: 09/11/2020] [Indexed: 12/15/2022] Open
Abstract
PURPOSE Intensity-modulated proton therapy (IMPT) is sensitive to uncertainties from patient setup and proton beam range, as well as interplay effect. In addition, respiratory motion may vary from cycle to cycle, and also from day to day. These uncertainties can severely degrade the original plan quality and potentially affect patient's outcome. In this work, we developed a new tool to comprehensively consider the impact of all these uncertainties and provide plan robustness evaluation under them. METHODS We developed a comprehensive plan robustness evaluation tool that considered both uncertainties from patient setup and proton beam range, as well as respiratory motion simultaneously. To mimic patients' respiratory motion, the time spent in each phase was randomly sampled based on patient-specific breathing pattern parameters as acquired during the four-dimensional (4D)-computed tomography (CT) simulation. Spots were then assigned to one specific phase according to the temporal relationship between spot delivery sequence and patients' respiratory motion. Dose in each phase was calculated by summing contributions from all the spots delivered in that phase. The final 4D dynamic dose was obtained by deforming all doses in each phase to the maximum exhalation phase. Three hundred (300) scenarios (10 different breathing patterns with 30 different setup and range uncertainty scenario combinations) were calculated for each plan. The dose-volume histograms (DVHs) band method was used to assess plan robustness. Benchmarking the tool as an application's example, we compared plan robustness under both three-dimensional (3D) and 4D robustly optimized IMPT plans for 10 nonrandomly selected patients with non-small cell lung cancer. RESULTS The developed comprehensive plan robustness tool had been successfully applied to compare the plan robustness between 3D and 4D robustly optimized IMPT plans for 10 lung cancer patients. In the presence of interplay effect with uncertainties considered simultaneously, 4D robustly optimized plans provided significantly better CTV coverage (D95% , P = 0.002), CTV homogeneity (D5% -D95% , P = 0.002) with less target hot spots (D5% , P = 0.002), and target coverage robustness (CTV D95% bandwidth, P = 0.004) compared to 3D robustly optimized plans. Superior dose sparing of normal lung (lung Dmean , P = 0.020) favoring 4D plans and comparable normal tissue sparing including esophagus, heart, and spinal cord for both 3D and 4D plans were observed. The calculation time for all patients included in this study was 11.4 ± 2.6 min. CONCLUSION A comprehensive plan robustness evaluation tool was successfully developed and benchmarked for plan robustness evaluation in the presence of interplay effect, setup and range uncertainties. The very high efficiency of this tool marks its clinical adaptation, highly practical and versatile nature, including possible real-time intra-fractional interplay effect evaluation as a potential application for future use.
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Affiliation(s)
- Jie Shan
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Yunze Yang
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Steven E Schild
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Thomas B Daniels
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - William W Wong
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Mirek Fatyga
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Martin Bues
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Terence T Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, 85054, USA
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22
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Diwanji T, Sawant A, Sio TT, Patel NV, Mohindra P. Proton stereotactic body radiation therapy for non-small cell lung cancer. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:1198. [PMID: 33241047 PMCID: PMC7576051 DOI: 10.21037/atm-20-2975] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Tejan Diwanji
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL, USA
| | - Amit Sawant
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.,Maryland Proton Treatment Center Baltimore, MD, USA
| | - Terence T Sio
- Department of Radiation Oncology, Mayo Clinic, Phoenix, Arizona, USA
| | - Nirav V Patel
- Department of Radiation Oncology, University of Miami Miller School of Medicine, Sylvester Comprehensive Cancer Center, Miami, FL, USA
| | - Pranshu Mohindra
- Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.,Maryland Proton Treatment Center Baltimore, MD, USA
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23
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Zou Z, Bowen SR, Thomas HMT, Sasidharan BK, Rengan R, Zeng J. Scanning Beam Proton Therapy versus Photon IMRT for Stage III Lung Cancer: Comparison of Dosimetry, Toxicity, and Outcomes. Adv Radiat Oncol 2020; 5:434-443. [PMID: 32529138 PMCID: PMC7276696 DOI: 10.1016/j.adro.2020.03.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 02/03/2020] [Accepted: 03/10/2020] [Indexed: 02/06/2023] Open
Abstract
Purpose There are limited clinical data on scanning-beam proton therapy (SPT) in treating locally advanced lung cancer, as most published studies have used passive-scatter technology. There is increasing interest in whether the dosimetric advantages of SPT compared with photon therapy can translate into superior clinical outcomes. We present our experience of SPT and photon intensity modulated radiation therapy (IMRT) with clinical dosimetry and outcomes in patients with stage III lung cancer. Methods and Materials Patients with stage III lung cancer treated at our center between 2013 and May 2018 were identified in compliance with our institutional review board (64 patients = 34 SPT + 30 IMRT). Most proton patients were treated with pencil beam scanning (28 of 34), and 6 of 34 were treated with uniform scanning. Fisher exact test, χ2 test, and Mann-Whitney test were used to compare groups. All tests were 2-sided. Results Patient characteristics were similar between the IMRT and SPT patients, except for worse lung function in the IMRT group. Mean dose to lung, heart, and esophagus was lower in the SPT group, with most benefit in the low-dose region (lungs, 9.7 Gy vs 15.7 Gy for SPT vs IMRT, respectively [P = .004]; heart, 7 Gy vs 14 Gy [P = .001]; esophagus, 28.2 Gy vs 30.9 Gy [P = .023]). Esophagitis and dermatitis grades were not different between the 2 groups. Grade 2+ pneumonitis was 21% in the SPT group and 40% in the IMRT group (P = .107). Changes in blood counts were not different between the 2 groups. Overall survival and progression-free survival were not different between SPT and IMRT (median overall survival, 41.6 vs 30.7 months, respectively [P = .52]; median progression-free survival, 19.5 vs 14.6 months [P = .50]). Conclusions We report our experience with SPT and IMRT in stage III lung cancer. Our cohort of patients treated with SPT had lower doses to normal organs (lungs, heart, esophagus) than our IMRT cohort. There was no statistically significant difference in toxicity rates or survival, although there may have been a trend toward lower rates of pneumonitis.
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Affiliation(s)
- Zhenwei Zou
- Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Stephen R Bowen
- Departments of Radiation Oncology, Seattle, Washington.,Radiology, University of Washington, Seattle, Washington
| | | | | | - Ramesh Rengan
- Departments of Radiation Oncology, Seattle, Washington
| | - Jing Zeng
- Departments of Radiation Oncology, Seattle, Washington
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