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Saito Y, Suzuki R, Miyamoto N, Sutherland KL, Kanehira T, Tamura M, Mori T, Nishioka K, Hashimoto T, Aoyama H. A new predictive parameter for dose-volume metrics in intensity-modulated radiation therapy planning for prostate cancer: Initial phantom study. J Appl Clin Med Phys 2024; 25:e14250. [PMID: 38146130 PMCID: PMC11005967 DOI: 10.1002/acm2.14250] [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: 06/01/2023] [Revised: 08/10/2023] [Accepted: 11/23/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Organ-at-risk (OAR) sparing is often assessed using an overlap volume-based parameter, defined as the ratio of the volume of OAR that overlaps the planning target volume (PTV) to the whole OAR volume. However, this conventional overlap-based predictive parameter (COPP) does not consider the volume relationship between the PTV and OAR. PURPOSE We propose a new overlap-based predictive parameter that consider the PTV volume. The effectiveness of proposed overlap-based predictive parameter (POPP) is evaluated compared with COPP. METHODS We defined as POPP = (overlap volume between OAR and PTV/OAR volume) × (PTV volume/OAR volume). We generated intensity modulated radiation therapy (IMRT) based on step and shoot technique, and volumetric modulated arc therapy (VMAT) plans with the Auto-Planning module of Pinnacle3 treatment planning system (v14.0, Philips Medical Systems, Fitchburg, WI) using the American Association of Physicists in Medicine Task Group (TG119) prostate phantom. The relationship between the position and size of the prostate phantom was systematically modified to simulate various geometric arrangements. The correlation between overlap-based predictive parameters (COPP and POPP) and dose-volume metrics (mean dose, V70Gy, V60Gy, and V37.5 Gy for rectum and bladder) was investigated using linear regression analysis. RESULTS Our results indicated POPP was better than COPP in predicting intermediate-dose metrics. The bladder results showed a trend similar to that of the rectum. The correlation coefficient of POPP was significantly greater than that of COPP in < 62 Gy (82% of the prescribed dose) region for IMRT and in < 55 Gy (73% of the prescribed dose) region for VMAT regarding the rectum (p < 0.05). CONCLUSIONS POPP is superior to COPP for creating predictive models at an intermediate-dose level. Because rectal bleeding and bladder toxicity can be associated with intermediate-doses as well as high-doses, it is important to predict dose-volume metrics for various dose levels. POPP is a useful parameter for predicting dose-volume metrics and assisting the generation of treatment plans.
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Affiliation(s)
- Yuki Saito
- Graduate School of Biomedical Science and EngineeringHokkaido UniversitySapporoJapan
| | - Ryusuke Suzuki
- Graduate School of Biomedical Science and EngineeringHokkaido UniversitySapporoJapan
- Department of Medical PhysicsHokkaido University HospitalSapporoJapan
| | - Naoki Miyamoto
- Department of Medical PhysicsHokkaido University HospitalSapporoJapan
- Faculty of EngineeringHokkaido UniversitySapporoJapan
| | - Kenneth Lee Sutherland
- Global Center for Biomedical Science and EngineeringFaculty for MedicineHokkaido UniversitySapporoJapan
| | - Takahiro Kanehira
- Graduate School of Biomedical Science and EngineeringHokkaido UniversitySapporoJapan
- Department of Medical PhysicsHokkaido University HospitalSapporoJapan
| | - Masaya Tamura
- Graduate School of Biomedical Science and EngineeringHokkaido UniversitySapporoJapan
- Department of Medical PhysicsHokkaido University HospitalSapporoJapan
| | - Takashi Mori
- Department of Radiation OncologyHokkaido University HospitalSapporoJapan
| | - Kentaro Nishioka
- Global Center for Biomedical Science and EngineeringFaculty for MedicineHokkaido UniversitySapporoJapan
- Department of Radiation OncologyHokkaido University HospitalSapporoJapan
| | - Takayuki Hashimoto
- Global Center for Biomedical Science and EngineeringFaculty for MedicineHokkaido UniversitySapporoJapan
- Department of Radiation OncologyHokkaido University HospitalSapporoJapan
| | - Hidefumi Aoyama
- Department of Radiation OncologyHokkaido University HospitalSapporoJapan
- Department of Radiation OncologyFaculty of MedicineHokkaido UniversitySapporoJapan
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Bera S, Choudhury D, Roy S, Mukhopadhyay P, Sarkar S. Development of Prediction Model for Mean Parotid Dose of HNC Undergoing Radiotherapy - A Single Institutional Study. J Med Phys 2023; 48:274-280. [PMID: 37969153 PMCID: PMC10642594 DOI: 10.4103/jmp.jmp_52_23] [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: 04/20/2023] [Revised: 06/09/2023] [Accepted: 06/26/2023] [Indexed: 11/17/2023] Open
Abstract
Aim The aim of the study was to develop a simple prediction model based on previous treatment plans for head-and-neck cancer (HNC). Materials and Methods This study was conducted on 95 patients who underwent volumetric-modulated arc therapy (VMAT) with curative intent for HNC at our institute between January 2016 and December 2022 with intact bilateral parotid glands. Two simple prediction models were used: one linear regression model and one exponential model. Both models use fractional overlapping parotid volume with planning target volume (PTV) as a predictor of mean parotid dose. The fractional overlapping volume was calculated as the difference between the volume of the parotid gland minus the volume of the parotid gland outside the PTV plus a 2 mm margin, divided by the volume of the parotid gland. Statistical calculations were done using data analysis tools and Solver in Microsoft Excel (Microsoft Office 2013, Redmond, WA, USA). To enhance the accuracy of the results, outliers were excluded with residuals >2 standard deviations below and above the residuals. R2 and root-mean-square error were calculated for both models to evaluate the quality of the predictions. The normality of both models' residuals was validated using the Shapiro-Wilk test. Results Both linear and exponential prediction models exhibited strong correlation statistics, with r2 = 0.85 and 0.82, respectively. The authors found a fractional overlap of 16.4% and 18.9% in linear and exponential models that predict parotid mean dose 26 Gy. The implementation was carried out on a cohort of 12 prospective patients, demonstrating a remarkable improvement in minimizing the dose to the parotid glands. Conclusion In this single-institutional study, the authors successfully developed a prediction model for mean parotid dose in HNC patients undergoing radiotherapy. The model showed promising accuracy and has the potential to assist planners in optimizing treatment plans and minimizing radiation-related toxicity. It is possible to avoid under sparing the organs at risks in some cases and wasting time or effort on physically impossible goals in others using this prediction model. As a result, planning resources can be used much more efficiently. Future studies should focus on validating the model's performance using external datasets and exploring its integration into clinical practice.
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Affiliation(s)
- Soumen Bera
- Department of Radiation Oncology, Ruby General Hospital, Kolkata, West Bengal, India
| | - Dipika Choudhury
- Department of Radiation Oncology, Ruby General Hospital, Kolkata, West Bengal, India
| | - Sanjoy Roy
- Department of Radiation Oncology, Ruby General Hospital, Kolkata, West Bengal, India
| | - Partha Mukhopadhyay
- Department of Radiation Oncology, Ruby General Hospital, Kolkata, West Bengal, India
| | - Sandip Sarkar
- Department of Radiation Oncology, Ruby General Hospital, Kolkata, West Bengal, India
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The Role of Patient- and Treatment-Related Factors and Early Functional Imaging in Late Radiation-Induced Xerostomia in Oropharyngeal Cancer Patients. Cancers (Basel) 2021; 13:cancers13246296. [PMID: 34944916 PMCID: PMC8699504 DOI: 10.3390/cancers13246296] [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: 10/30/2021] [Revised: 12/05/2021] [Accepted: 12/10/2021] [Indexed: 11/22/2022] Open
Abstract
Simple Summary In the present prospective study, we assessed the role of various Magnetic Resonance Imaging biomarkers combined with self-assessed xerostomia questionnaires and patient- and treatment-related factors, in predicting xerostomia at 12 months after chemoradiotherapy for oropharyngeal squamous cell carcinoma. We hypothesized that the integration of pre-treatment imaging biomarkers, which addresses the tissue heterogeneity and individual variations among patients, could improve the accuracy of conventional prediction models that are based only on dose information, ultimately providing a better understanding of the pathophysiological mechanisms underlying radiation induced salivary dysfunction. The implementation of multifactorial models, driven by machine learning algorithms, may improve prediction accuracy of radiation-induced toxicity and tailor individual treatment options for patients. Abstract The advent of quantitative imaging in personalized radiotherapy (RT) has offered the opportunity for a better understanding of individual variations in intrinsic radiosensitivity. We aimed to assess the role of magnetic resonance imaging (MRI) biomarkers, patient-related factors, and treatment-related factors in predicting xerostomia 12 months after RT (XER12) in patients affected by oropharyngeal squamous cell carcinoma (OSCC). Patients with locally advanced OSCC underwent diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI at baseline; DWI was repeated at the 10th fraction of RT. The Radiation Therapy Oncology Group (RTOG) toxicity scale was used to evaluate salivary gland toxicity. Xerostomia-related questionnaires (XQs) were administered weekly during and after RT. RTOG toxicity ≥ grade 2 at XER12 was considered as endpoint to build prediction models. A Decision Tree classification learner was applied to build the prediction models following a five-fold cross-validation. Of the 89 patients enrolled, 63 were eligible for analysis. Thirty-six (57.1%) and 21 (33.3%) patients developed grade 1 and grade 2 XER12, respectively. Including only baseline variables, the model based on DCE-MRI and V65 (%) (volume of both glands receiving doses ≥ 65 Gy) had a fair accuracy (77%, 95% CI: 66.5–85.4%). The model based on V65 (%) and XQ-Intmid (integral of acute XQ scores from the start to the middle of RT) reached the best accuracy (81%, 95% CI: 71–88.7%). In conclusion, non-invasive biomarkers from DCE-MRI, in combination with dosimetric variables and self-assessed acute XQ scores during treatment may help predict grade 2 XER12 with a fair to good accuracy.
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Zhang H, Cao Y, Antone J, Riegel AC, Ghaly M, Potters L, Jamshidi A. A Model-Based Method for Assessment of Salivary Gland and Planning Target Volume Dosimetry in Volumetric-Modulated Arc Therapy Planning on Head-and-Neck Cancer. J Med Phys 2019; 44:201-206. [PMID: 31576068 PMCID: PMC6764180 DOI: 10.4103/jmp.jmp_19_19] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
This study examined the relationship of achievable mean dose and percent volumetric overlap of salivary gland with the planning target volume (PTV) in volumetric-modulated arc therapy (VMAT) plan in radiotherapy for a patient with head-and-neck cancer. The aim was to develop a model to predict the viability of planning objectives for both PTV coverage and organs-at-risk (OAR) sparing based on overlap volumes between PTVs and OARs, before the planning process. Forty patients with head-and-neck cancer were selected for this retrospective plan analysis. The patients were treated using 6 MV photons with 2-arc VMAT plan in prescriptions with simultaneous integrated boost in dose of 70 Gy, 63 Gy, and 58.1 Gy to primary tumor sites, high-risk nodal regions, and low-risk nodal regions, respectively, over 35 fractions. A VMAT plan was generated using Varian Eclipse (V13.6), in optimization with biological-based generalized equivalent uniform dose (gEUD) objective for OARs and targets. Target dose coverage (D95, Dmax, conformity index) and salivary gland dose (Dmean and Dmax) were evaluated in those plans. With a range of volume overlaps between salivary glands and PTVs and dose constraints applied, results showed that dose D95 for each PTV was adequate to satisfy D95 >95% of the prescription. Mean dose to parotid <26 Gy could be achieved with <20% volumetric overlap with PTV58 (parotid-PTV58). On an average, the Dmean was seen at 15.6 Gy, 21.1 Gy, and 24.2 Gy for the parotid-PTV58 volume at <5%, <10%, and <20%, respectively. For submandibular glands (SMGs), an average Dmean of 27.6 Gy was achieved in patients having <10% overlap with PTV58, and 36.1 Gy when <20% overlap. Mean doses on parotid and SMG were linearly correlated with overlap volume (regression R2 = 0.95 and 0.98, respectively), which were statistically significant (P < 0.0001). This linear relationship suggests that the assessment of the structural overlap might provide prospective for achievable planning objectives in the head-and-neck plan.
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Affiliation(s)
- Honglai Zhang
- Department of Radiation Medicine, Northwell Health Cancer Institute, Lake Success, New York, USA
| | - Yijian Cao
- Department of Radiation Medicine, Northwell Health Cancer Institute, Lake Success, New York, USA.,Zucker School of Medicine at Northwell/Hofstra, Hempstead, New York, USA
| | - Jeffrey Antone
- Department of Radiation Medicine, Northwell Health Cancer Institute, Lake Success, New York, USA
| | - Adam C Riegel
- Department of Radiation Medicine, Northwell Health Cancer Institute, Lake Success, New York, USA.,Zucker School of Medicine at Northwell/Hofstra, Hempstead, New York, USA
| | - Maged Ghaly
- Department of Radiation Medicine, Northwell Health Cancer Institute, Lake Success, New York, USA.,Zucker School of Medicine at Northwell/Hofstra, Hempstead, New York, USA
| | - Louis Potters
- Department of Radiation Medicine, Northwell Health Cancer Institute, Lake Success, New York, USA.,Zucker School of Medicine at Northwell/Hofstra, Hempstead, New York, USA
| | - Abolghassem Jamshidi
- Department of Radiation Medicine, Northwell Health Cancer Institute, Lake Success, New York, USA.,Zucker School of Medicine at Northwell/Hofstra, Hempstead, New York, USA
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