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McDowell JA, Kosmacek EA, Baine MJ, Adebisi O, Zheng C, Bierman MM, Myers MS, Chatterjee A, Liermann-Wooldrik KT, Lim A, Dickinson KA, Oberley-Deegan RE. Exogenous APN protects normal tissues from radiation-induced oxidative damage and fibrosis in mice and prostate cancer patients with higher levels of APN have less radiation-induced toxicities. Redox Biol 2024; 73:103219. [PMID: 38851001 DOI: 10.1016/j.redox.2024.103219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 06/10/2024] Open
Abstract
Radiation causes damage to normal tissues that leads to increased oxidative stress, inflammation, and fibrosis, highlighting the need for the selective radioprotection of healthy tissues without hindering radiotherapy effectiveness in cancer. This study shows that adiponectin, an adipokine secreted by adipocytes, protects normal tissues from radiation damage invitro and invivo. Specifically, adiponectin (APN) reduces chronic oxidative stress and fibrosis in irradiated mice. Importantly, APN also conferred no protection from radiation to prostate cancer cells. Adipose tissue is the primary source of circulating endogenous adiponectin. However, this study shows that adipose tissue is sensitive to radiation exposure exhibiting morphological changes and persistent oxidative damage. In addition, radiation results in a significant and chronic reduction in blood APN levels from adipose tissue in mice and human prostate cancer patients exposed to pelvic irradiation. APN levels negatively correlated with bowel toxicity and overall toxicities associated with radiotherapy in prostate cancer patients. Thus, protecting, or modulating APN signaling may improve outcomes for prostate cancer patients undergoing radiotherapy.
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Affiliation(s)
- Joshua A McDowell
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Elizabeth A Kosmacek
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Michael J Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Oluwaseun Adebisi
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Cheng Zheng
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Madison M Bierman
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Molly S Myers
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Arpita Chatterjee
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Kia T Liermann-Wooldrik
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Andrew Lim
- College of Nursing, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Kristin A Dickinson
- College of Nursing, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Rebecca E Oberley-Deegan
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
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Xiao Q, Li G. Application and Challenges of Statistical Process Control in Radiation Therapy Quality Assurance. Int J Radiat Oncol Biol Phys 2024; 118:295-305. [PMID: 37604239 DOI: 10.1016/j.ijrobp.2023.08.020] [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: 05/12/2023] [Revised: 07/21/2023] [Accepted: 08/08/2023] [Indexed: 08/23/2023]
Abstract
Quality assurance (QA) is important for ensuring precision in radiation therapy. The complexity and resource-intensive nature of QA has increased with the continual evolution of equipment and techniques. An effective approach is to improve the process control technology and resource optimization. Statistical process control is an economical and efficient tool that has been widely used to monitor, control, and improve quality management processes and is now being increasingly used for radiation therapy QA. This article reviews the development and methodology of statistical process control technology, evaluates its suitability in radiation therapy QA practices, and assesses its importance and challenges in optimizing radiation therapy QA processes.
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Affiliation(s)
- Qing Xiao
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Guangjun Li
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China; Department of Radiotherapy Physics & Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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Nealon KA, Han EY, Kry SF, Nguyen C, Pham M, Reed VK, Rosenthal D, Simiele S, Court LE. Monitoring Variations in the Use of Automated Contouring Software. Pract Radiat Oncol 2024; 14:e75-e85. [PMID: 37797883 DOI: 10.1016/j.prro.2023.09.004] [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: 05/31/2023] [Revised: 08/22/2023] [Accepted: 09/16/2023] [Indexed: 10/07/2023]
Abstract
PURPOSE Our purpose was to identify variations in the clinical use of automatically generated contours that could be attributed to software error, off-label use, or automation bias. METHODS AND MATERIALS For 500 head and neck patients who were contoured by an in-house automated contouring system, Dice similarity coefficient and added path length were calculated between the contours generated by the automated system and the final contours after editing for clinical use. Statistical process control was used and control charts were generated with control limits at 3 standard deviations. Contours that exceeded the thresholds were investigated to determine the cause. Moving mean control plots were then generated to identify dosimetrists who were editing less over time, which could be indicative of automation bias. RESULTS Major contouring edits were flagged for: 1.0% brain, 3.1% brain stem, 3.5% left cochlea, 2.9% right cochlea, 4.8% esophagus, 4.1% left eye, 4.0% right eye, 2.2% left lens, 4.9% right lens, 2.5% mandible, 11% left optic nerve, 6.1% right optic nerve, 3.8% left parotid, 5.9% right parotid, and 3.0% of spinal cord contours. Identified causes of editing included unexpected patient positioning, deviation from standard clinical practice, and disagreement between dosimetrist preference and automated contouring style. A statistically significant (P < .05) difference was identified between the contour editing practice of dosimetrists, with 1 dosimetrist editing more across all organs at risk. Eighteen percent (27/150) of moving mean control plots created for 5 dosimetrists indicated the amount of contour editing was decreasing over time, possibly corresponding to automation bias. CONCLUSIONS The developed system was used to detect statistically significant edits caused by software error, unexpected clinical use, and automation bias. The increased ability to detect systematic errors that occur when editing automatically generated contours will improve the safety of the automatic treatment planning workflow.
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Affiliation(s)
- Kelly A Nealon
- Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
| | - Eun Young Han
- Department of Radiation Physics - Patient Care, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Stephen F Kry
- Radiation Physics Outreach, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Callistus Nguyen
- Department of Radiation Physics - Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Mary Pham
- Department of Radiation Physics - Research, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Valerie K Reed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David Rosenthal
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Samantha Simiele
- Department of Radiation Physics - Patient Care, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Laurence E Court
- Department of Radiation Physics - Patient Care, The University of Texas MD Anderson Cancer Center, Houston, Texas
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Li G, Xiao Q, Dai G, Wang Q, Bai L, Zhang X, Zhang X, Duan L, Zhong R, Bai S. Guaranteed performance of individual control chart used in gamma passing rate-based patient-specific quality assurance. Phys Med 2023; 109:102581. [PMID: 37084678 DOI: 10.1016/j.ejmp.2023.102581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 03/20/2023] [Accepted: 04/06/2023] [Indexed: 04/23/2023] Open
Abstract
PURPOSE To assess the effect of sampling variability on the performance of individual charts (I-charts) for PSQA and provide a robust and reliable method for unknown PSQA processes. MATERIALS AND METHODS A total of 1327 pretreatment PSQAs were analyzed. Different datasets with samples in the range of 20-1000 were used to estimate the lower control limit (LCL). Based on the iterative "Identify-Eliminate-Recalculate" and direct calculation without any outlier filtering procedures, five I-charts methods, namely the Shewhart, quantile, scaled weighted variance (SWV), weighted standard deviation (WSD), and skewness correction (SC) method, were used to compute the LCL. The average run length (ARL0) and false alarm rate (FAR0) were calculated to evaluate the performance of LCL. RESULTS The ground truth of the values of LCL, FAR0, and ARL0 obtained via in-control PSQAs were 92.31%, 0.135%, and 740.7, respectively. Further, for in-control PSQAs, the width of the 95% confidence interval of LCL values for all methods tended to decrease with the increase in sample size. In all sample ranges of in-control PSQAs, only the median LCL and ARL0 values obtained via WSD and SWV methods were close to the ground truth. For the actual unknown PSQAs, based on the "Identify-Eliminate-Recalculate" procedure, only the median LCL values obtained by the WSD method were closest to the ground truth. CONCLUSIONS Sampling variability seriously affected the I-chart performance in PSQA processes, particularly for small samples. For unknown PSQAs, the WSD method based on the implementation of the iterative "Identify-Eliminate-Recalculate" procedure exhibited sufficient robustness and reliability.
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Affiliation(s)
- Guangjun Li
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Qing Xiao
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Guyu Dai
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Qiang Wang
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Long Bai
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xiangbin Zhang
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Xiangyu Zhang
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Lian Duan
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, United States
| | - Renming Zhong
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Sen Bai
- Radiotherapy Physics & Technology Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
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Mehrens H, Douglas R, Gronberg M, Nealon K, Zhang J, Court L. Statistical process control to monitor use of a web-based autoplanning tool. J Appl Clin Med Phys 2022; 23:e13803. [PMID: 36300872 PMCID: PMC9797174 DOI: 10.1002/acm2.13803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 08/29/2022] [Accepted: 09/02/2022] [Indexed: 01/01/2023] Open
Abstract
PURPOSE To investigate the use of statistical process control (SPC) for quality assurance of an integrated web-based autoplanning tool, Radiation Planning Assistant (RPA). METHODS Automatically generated plans were downloaded and imported into two treatment planning systems (TPSs), RayStation and Eclipse, in which they were recalculated using fixed monitor units. The recalculated plans were then uploaded back to the RPA, and the mean dose differences for each contour between the original RPA and the TPSs plans were calculated. SPC was used to characterize the RPA plans in terms of two comparisons: RayStation TPS versus RPA and Eclipse TPS versus RPA for three anatomical sites, and variations in the machine parameters dosimetric leaf gap (DLG) and multileaf collimator transmission factor (MLC-TF) for two algorithms (Analytical Anisotropic Algorithm [AAA]) and Acuros in the Eclipse TPS. Overall, SPC was used to monitor the process of the RPA, while clinics would still perform their routine patient-specific QA. RESULTS For RayStation, the average mean percent dose differences across all contours were 0.65% ± 1.05%, -2.09% ± 0.56%, and 0.28% ± 0.98% and average control limit ranges were 1.89% ± 1.32%, 2.16% ± 1.31%, and 2.65% ± 1.89% for the head and neck, cervix, and chest wall, respectively. In contrast, Eclipse's average mean percent dose differences across all contours were -0.62% ± 0.34%, 0.32% ± 0.23%, and -0.91% ± 0.98%, while average control limit ranges were 1.09% ± 0.77%, 3.69% ± 2.67%, 2.73% ± 1.86%, respectively. Averaging all contours and removing outliers, a 0% dose difference corresponded with a DLG value of 0.202 ± 0.019 cm and MLC-TF value of 0.020 ± 0.001 for Acuros and a DLG value of 0.135 ± 0.031 cm and MLC-TF value of 0.015 ± 0.001 for AAA. CONCLUSIONS Differences in mean dose and control limits between RPA and two separately commissioned TPSs were determined. With varying control limits and means, SPC provides a flexible and useful process quality assurance tool for monitoring a complex automated system such as the RPA.
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Affiliation(s)
- Hunter Mehrens
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA,The University of Texas MD Anderson Graduate School of Biomedical ScienceHoustonTexasUSA
| | - Raphael Douglas
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Mary Gronberg
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA,The University of Texas MD Anderson Graduate School of Biomedical ScienceHoustonTexasUSA
| | - Kelly Nealon
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA,The University of Texas MD Anderson Graduate School of Biomedical ScienceHoustonTexasUSA
| | - Joy Zhang
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Laurence Court
- Department of Radiation PhysicsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
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Iliadou V, Kakkos I, Karaiskos P, Kouloulias V, Platoni K, Zygogianni A, Matsopoulos GK. Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach. Cancers (Basel) 2022; 14:cancers14153573. [PMID: 35892831 PMCID: PMC9331795 DOI: 10.3390/cancers14153573] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 07/14/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
Background: During RT cycles, the tumor response pattern could affect tumor coverage and may lead to organs at risk of overdose. As such, early prediction of significant volumetric changes could therefore reduce potential radiation-related adverse effects. Nevertheless, effective machine learning approaches based on the radiomic features of the clinically used CBCT images to determine the tumor volume variations due to RT not having been implemented so far. Methods: CBCT images from 40 HN cancer patients were collected weekly during RT treatment. From the obtained images, the Clinical Target Volume (CTV) and Parotid Glands (PG) regions of interest were utilized to calculate 104 delta-radiomics features. These features were fed on a feature selection and classification procedure for the early prediction of significant volumetric alterations. Results: The proposed framework was able to achieve 0.90 classification performance accuracy while detecting a small subset of discriminative characteristics from the 1st week of RT. The selected features were further analyzed regarding their effects on temporal changes in anatomy and tumor response modeling. Conclusion: The use of machine learning algorithms offers promising perspectives for fast and reliable early prediction of large volumetric deviations as a result of RT treatment, exploiting hidden patterns in the overall anatomical characteristics.
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Affiliation(s)
- Vasiliki Iliadou
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Correspondence: ; Tel.: +30-21-0772-3577
| | - Ioannis Kakkos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
- Department of Biomedical Engineering, University of West Attica, 122 43 Athens, Greece
| | - Pantelis Karaiskos
- Medical Physics Laboratory, Medical School, National and Kapodistrian University of Athens, 115 27 Athens, Greece;
| | - Vassilis Kouloulias
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Kalliopi Platoni
- 2nd Department of Radiology, Radiotherapy Unit, ATTIKON University Hospital, 124 62 Athens, Greece; (V.K.); (K.P.)
| | - Anna Zygogianni
- 1st Department of Radiology, Radiotherapy Unit, ARETAIEION University Hospital, 115 28 Athens, Greece;
| | - George K. Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, 157 73 Athens, Greece; (I.K.); (G.K.M.)
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Zhang H, Lu W, Cui H, Li Y, Yi X. Assessment of Statistical Process Control Based DVH Action Levels for Systematic Multi-Leaf Collimator Errors in Cervical Cancer RapidArc Plans. Front Oncol 2022; 12:862635. [PMID: 35664736 PMCID: PMC9157499 DOI: 10.3389/fonc.2022.862635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 04/19/2022] [Indexed: 11/18/2022] Open
Abstract
Background In the patient-specific quality assurance (QA), DVH is a critical clinically relevant parameter that is finally used to determine the safety and effectiveness of radiotherapy. However, a consensus on DVH-based action levels has not been reached yet. The aim of this study is to explore reasonable DVH-based action levels and optimal DVH metrics in detecting systematic MLC errors for cervical cancer RapidArc plans. Methods In this study, a total of 148 cervical cancer RapidArc plans were selected and measured with COMPASS 3D dosimetry system. Firstly, the patient-specific QA results of 110 RapidArc plans were retrospectively reviewed. Then, DVH-based action limits (AL) and tolerance limits (TL) were obtained by statistical process control. Secondly, systematic MLC errors were introduced in 20 RapidArc plans, generating 380 modified plans. Then, the dose difference (%DE) in DVH metrics between modified plans and original plans was extracted from measurement results. After that, the linear regression model was used to investigate the detection limits of DVH-based action levels between %DE and systematic MLC errors. Finally, a total of 180 test plans (including 162 error-introduced plans and 18 original plans) were prepared for validation. The error detection rate of DVH-based action levels was compared in different DVH metrics of 180 test plans. Results A linear correlation was found between systematic MLC errors and %DE in all DVH metrics. Based on linear regression model, the systematic MLC errors between -0.94 mm and 0.88 mm could be caught by the TL of PTV95 ([-1.54%, 1.51%]), and the systematic MLC errors between -1.00 mm and 0.80 mm could also be caught by the TL of PTVmean ([-2.06%, 0.38%]). In the validation, for original plans, PTV95 showed the minimum error detection rate of 5.56%. For error-introduced plans with systematic MLC errors more than 1mm, PTVmean showed the maximum error detection rate of 88.89%, and then was followed by PTV95 (86.67%). All the TL of DVH metrics showed a poor error detection rate in identifying error-induced plans with systematic MLC errors less than 1mm. Conclusion In 3D quality assurance of cervical cancer RapidArc plans, process-based tolerance limits showed greater advantages in distinguishing plans introduced with systematic MLC errors more than 1mm, and reasonable DVH-based action levels can be acquired through statistical process control. During DVH-based verification, main focus should be on the DVH metrics of target volume. OARs in low-dose regions were found to have a relatively higher dose sensitivity to smaller systematic MLC errors, but may be accompanied with higher false error detection rate.
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Affiliation(s)
- Hanyin Zhang
- Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wenli Lu
- Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haixia Cui
- Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ying Li
- Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xin Yi
- Department of Oncology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Tambe NS, Pires IM, Moore CS, Wieczorek A, Upadhyay S, Beavis AW. Predicting personalised and progressive adaptive dose escalation to gross tumour volume using knowledge-based planning models for inoperable advanced-stage non-small cell lung cancer patients treated with volumetric modulated arc therapy. Biomed Phys Eng Express 2022; 8. [PMID: 35189613 DOI: 10.1088/2057-1976/ac56eb] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/21/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVES Increased radiation doses could improve local control and overall survival of lung cancer patients, however, this could be challenging without exceeding organs at risk (OAR) dose constraints especially for patients with advanced-stage disease. Increasing OAR doses could reduce the therapeutic ratio and quality of life. It is therefore important to investigate methods to increase the dose to target volume without exceeding OAR dose constraints. METHODS Gross tumour volume (GTV) was contoured on synthetic computerised tomography (sCT) datasets produced using the Velocity adaptive radiotherapy software for eleven patients. The fractions where GTV volume decreased compared to that prior to radiotherapy (reference plan) were considered for personalised progressive dose escalation. The dose to the adapted GTV (GTVAdaptive) was increased until OAR doses were affected (as compared to the original clinical plan). Planning target volume (PTV) coverage was maintained for all plans. Doses were also escalated to the reference plan (GTVClinical) using the same method. Adapted, dose-escalated, plans were combined to estimate accumulated dose, D99 (dose to 99%) of GTVAdapted, PTV D99 and OAR doses and compared with those in the original clinical plans. Knowledge-based planning (KBP) model was developed to predict D99 of the adapted GTV with OAR doses and PTV coverage kept similar to the original clinical plans; prediction accuracy and model verification were performed using further data sets. RESULTS Compared to the original clinical plan, dose to GTV was significantly increased without exceeding OAR doses. Adaptive dose-escalation increased the average D99 to GTVAdaptive by 15.1Gy and 8.7Gy compared to the clinical plans. The KBP models were verified and demonstrated prediction accuracy of 0.4% and 0.7% respectively. CONCLUSION Progressive adaptive dose escalation can significantly increase the dose to GTV without increasing OAR doses or compromising dose to microscopic disease. This may increase overall survival without increasing toxicities.
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Affiliation(s)
- Nilesh S Tambe
- Radiation Physics Department, Hull University Teaching Hospitals NHS Trust, Queens Centre For Oncology And Haematology, Castle Hill Hospital, Castle Road, Cottingham, HU16 5JQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Isabel M Pires
- Biomedical Sciences, University of Hull, Cottingham Road,, Hardy Building,, Hull, Kingston upon Hull, HU6 7RX, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Craig Steven Moore
- Medical Physics, Hull University Teaching Hospitals NHS Trust, Queens Centre, Castle Hill Hospital, Cottingham, Hull, HU16 5LH, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Andrzej Wieczorek
- Hull University Teaching Hospitals NHS Trust, Department of Clinical Oncology, The Queen's Centre, Cottingham, Hull, Kingston upon Hull, HU3 2JZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Sunil Upadhyay
- Clinical Oncology Department, Hull University Teaching Hospitals NHS Trust, Castle Hill Hospital,, Queen's Centre for Oncology and Hematology, Castle Road, Cottingham, Kingston upon Hull, HU16 5JQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Andrew W Beavis
- Department of Radiotherapy Physics, Hull University Teaching Hospitals NHS Trust, Castle Hill Hospital, Hull, Kingston upon Hull, HU3 2JZ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Adaptive radiation therapy: When, how and what are the benefits that literature provides? Cancer Radiother 2021; 26:622-636. [PMID: 34688548 DOI: 10.1016/j.canrad.2021.08.023] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 08/21/2021] [Accepted: 08/24/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE To identify from the current literature when is the right time to replan and to assign thresholds for the optimum process of replanning. Nowadays, adaptive radiotherapy (ART) for head and neck cancer plays an exceptional role consisting of an evaluation procedure of the prominent anatomical and dosimetric variations. By performing complex radiotherapy methods, the credibility of the therapeutic result is crucial. Image guided radiotherapy (IGRT) was developed to ensure locoregional control and thus changes that might occur during radiotherapy be dealt with. MATERIALS AND METHODS An electronic research of articles published in PubMed/MEDLINE and Science Direct databases from January 2004 to October 2020 was performed. Among a total of 127 studies assessed for eligibility, 85 articles were ultimately retained for the review. RESULTS The most noticeable changes have been reported in the middle fraction of the treatment. Therefore, the suggested optimal time to replan is between the third and the fourth week. Anatomical deviations>1cm in the external contour, average weight loss>10%, violation in the dose coverage of the targets>5%, and violation in the dose of the peripherals were some of the thresholds that are currently used, and which lead to replanning. CONCLUSION ART may decrease toxicity and improve local-control. Whether it is beneficial or not, depends ultimately on each patient. However, more investigation of the changes should be performed in future prospective studies to obtain more accurate results.
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Tambe NS, Pires IM, Moore C, Wieczorek A, Upadhyay S, Beavis AW. Validation of in-house knowledge-based planning model for predicting change in target coverage during VMAT radiotherapy to in-operable advanced-stage NSCLC patients. Biomed Phys Eng Express 2021; 7. [PMID: 34415240 DOI: 10.1088/2057-1976/ac1f94] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 08/19/2021] [Indexed: 12/25/2022]
Abstract
Objectives. anatomical changes are inevitable during the course of radiotherapy treatments and, if significant, can severely alter expected dose distributions and affect treatment outcome. Adaptive radiotherapy (ART) is employed to maintain the planned distribution and minimise detriment to predicted treatment outcome. Typically, patients who may benefit from adaptive planning are identified via a re-planning process, i.e., re-simulation, re-contouring, re-planning and treatment plan quality assurance (QA). This time-intensive process significantly increases workload, can introduce delays and increases unnecessary stress to those patients who will not actually gain benefit. We consider it crucial to develop efficient models to predict changes to target coverage and trigger ART, without the need for re-planning.Methods.knowledge-based planning (KBP) models were developed using data for 20 patients' (400 fractions) to predict changes in PTV V95coverageΔV95PTV.Initially, this change in coverage was calculated on the synthetic computerised tomography (sCT) images produced using the Velocity adaptive radiotherapy software. Models were developed using patient (cell death bio-marker) and treatment fraction (PTV characteristic) specific parameters to predictΔV95PTVand verified using five patients (100 fractions) data.Results. three models were developed using combinations of patient and fraction specific terms. The prediction accuracy of the model developed using biomarker (PD-L1 expression) and the difference in 'planning' and 'fraction' PTV centre of the mass (characterised by mean square difference, MSD) had the higher prediction accuracy, predicting theΔV95PTVwithin ± 1.0% for 77% of the total fractions; with 59% for the model developed using, PTV size, PD-L1 and MSD and 48% PTV size and MSD respectively.Conclusion. the KBP models can predictΔV95PTVvery effectively and efficiently for advanced-stage NSCLC patients treated using volumetric modulated arc therapy and to identify patients who may benefit from adaption for a specific fraction.
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Affiliation(s)
- Nilesh S Tambe
- Radiotherapy Physics, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom.,Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, United Kingdom
| | - Isabel M Pires
- Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, United Kingdom
| | - Craig Moore
- Radiotherapy Physics, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom
| | - Andrew Wieczorek
- Clinical Oncology, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom
| | - Sunil Upadhyay
- Clinical Oncology, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom
| | - Andrew W Beavis
- Radiotherapy Physics, Queen's Centre for Oncology, Hull University Teaching Hospitals NHS Trust, Cottingham, HU16 5JQ, United Kingdom.,Faculty of Health Sciences, University of Hull, Cottingham road, Hull, HU16 7RX, United Kingdom.,Faculty of Health and Well Being, Sheffield-Hallam University, Collegiate Crescent, Sheffield, S10 2BP, United Kingdom
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Vidal M, Moignier C, Patriarca A, Sotiropoulos M, Schneider T, De Marzi L. Future technological developments in proton therapy - A predicted technological breakthrough. Cancer Radiother 2021; 25:554-564. [PMID: 34272182 DOI: 10.1016/j.canrad.2021.06.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 06/18/2021] [Indexed: 12/13/2022]
Abstract
In the current spectrum of cancer treatments, despite high costs, a lack of robust evidence based on clinical outcomes or technical and radiobiological uncertainties, particle therapy and in particular proton therapy (PT) is rapidly growing. Despite proton therapy being more than fifty years old (first proposed by Wilson in 1946) and more than 220,000 patients having been treated with in 2020, many technological challenges remain and numerous new technical developments that must be integrated into existing systems. This article presents an overview of on-going technical developments and innovations that we felt were most important today, as well as those that have the potential to significantly shape the future of proton therapy. Indeed, efforts have been done continuously to improve the efficiency of a PT system, in terms of cost, technology and delivery technics, and a number of different developments pursued in the accelerator field will first be presented. Significant developments are also underway in terms of transport and spatial resolution achievable with pencil beam scanning, or conformation of the dose to the target: we will therefore discuss beam focusing and collimation issues which are important parameters for the development of these techniques, as well as proton arc therapy. State of the art and alternative approaches to adaptive PT and the future of adaptive PT will finally be reviewed. Through these overviews, we will finally see how advances in these different areas will allow the potential for robust dose shaping in proton therapy to be maximised, probably foreshadowing a future era of maturity for the PT technique.
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Affiliation(s)
- M Vidal
- Centre Antoine-Lacassagne, Fédération Claude Lalanne, 227, avenue de la Lanterne, 06200 Nice, France
| | - C Moignier
- Centre François Baclesse, Department of Medical Physics, Centre de protonthérapie de Normandie, 14000 Caen, France
| | - A Patriarca
- Institut Curie, PSL Research University, Radiation oncology department, Centre de protonthérapie d'Orsay, Campus universitaire, bâtiment 101, 91898 Orsay, France
| | - M Sotiropoulos
- Institut Curie, Université PSL, CNRS UMR3347, Inserm U1021, Signalisation radiobiologie et cancer, 91400 Orsay, France
| | - T Schneider
- Institut Curie, Université PSL, CNRS UMR3347, Inserm U1021, Signalisation radiobiologie et cancer, 91400 Orsay, France
| | - L De Marzi
- Institut Curie, PSL Research University, Radiation oncology department, Centre de protonthérapie d'Orsay, Campus universitaire, bâtiment 101, 91898 Orsay, France; Institut Curie, PSL Research University, University Paris Saclay, Inserm LITO, Campus universitaire, 91898 Orsay, France.
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