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Chen J, Chen K, OuYang M, Wang G, Bai P, You H. Evaluation of dose delivery based on deformed CT using a commercial software for lung cancer. Sci Rep 2024; 14:14470. [PMID: 38914766 PMCID: PMC11196743 DOI: 10.1038/s41598-024-65381-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 06/19/2024] [Indexed: 06/26/2024] Open
Abstract
This study employed a commercial software velocity to perform deformable registration and dose calculation on deformed CT images, aiming to assess the accuracy of dose delivery during the radiotherapy for lung cancers. A total of 20 patients with lung cancer were enrolled in this study. Adaptive CT (ACT) was generated by deformed the planning CT (pCT) to the CBCT of initial radiotherapy fraction, followed by contour propagation and dose recalculation. There was not significant difference between volumes of GTV and CTV calculated from the ACT and pCT. However, significant differences in dice similarity coefficient (DSC) and coverage ratio (CR) between GTV and CTV were observed, with lower values for GTV volumes below 15 cc. The mean differences in dose corresponding to 95% of the GTV, GTV-P, CTV, and CTV-P between ACT and pCT were - 0.32%, 4.52%, 2.17%, and 4.71%, respectively. For the dose corresponding to 99%, the discrepancies were - 0.18%, 8.35%, 1.92%, and 24.96%, respectively. These differences in dose primarily appeared at the edges of the target areas. Notably, a significant enhancement of dose corresponding to 1 cc for spinal cord was observed in ACT, compared with pCT. There was no statistical difference in the mean dose of lungs and heart. In general, for lung cancer patients, anatomical motion may result in both CTV and GTV moving outside the original irradiation region. The dose difference within the original target area was small, but the difference in the planning target area was considerable.
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Affiliation(s)
- Jihong Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Kaiqiang Chen
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Min OuYang
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China
| | - Guohua Wang
- School of Nuclear Science and Technology, University of South China, Hengyang, 421001, China
| | - Penggang Bai
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China
| | - Hongqiang You
- Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, Fujian, China.
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Torchia J, Velec M. Deformable image registration for composite planned doses during adaptive radiation therapy. J Med Imaging Radiat Sci 2024; 55:82-90. [PMID: 38218679 DOI: 10.1016/j.jmir.2023.12.009] [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: 10/23/2023] [Revised: 12/18/2023] [Accepted: 12/21/2023] [Indexed: 01/15/2024]
Abstract
INTRODUCTION Some patients have significant anatomic changes during radiotherapy, necessitating an adaptive repeat CT-simulation and re-planning. This yields two unique planning datasets that introduce uncertainty into total dose records. This study explored the impact of using deformable image registration (DIR) to spatially align repeat CT-simulation images and calculate total planned dose distributions. MATERIALS & METHODS Data from 5 head-and-neck, 5 lung, and 5 sarcoma patients who had unanticipated re-planning during radiotherapy were analyzed in a treatment planning system (RayStation v6.1 RaySearch Laboratories). Total planned doses to normal tissues were calculated using two methods and the previously generated manual contours defined on each CT. The first method, termed 'parameter addition', simply sums the relevant DVH metrics from the initial and re-planned distributions without spatially registering the CTs. The second, termed 'dose accumulation', uses a validated hybrid contour/intensity-based DIR algorithm to deform initial CT and dose distribution onto the repeat CT and re-planning dose distribution. DVH metrics from the summed distribution on the repeat CT are then calculated. Dose differences for organs-at-risk between parameter addition and dose accumulation ≥100 cGy were assumed to be clinically relevant. To elucidate whether relevant differences were due to registration accuracy or contouring variability between CTs, the analysis was repeated using contours on the first CT and the same contours deformed to the repeat CT with DIR. RESULTS For all patients, high overall DIR accuracy was verified visually (qualitatively) and numerically (quantitatively) using image similarity and contour-based metrics. All head-and-neck and lung patients, and one sarcoma patient (11 of 15 total) had dose differences between parameter addition and dose accumulation ≥100 cGy, with absolute mean differences of 160 cGy (range 101-436 cGy) seen in 41 of 205 total DVH criteria. In 22 of these 41 criteria, these differences were attributed to contouring variability between CTs. After correcting for contouring variations using DIR, the mean absolute differences in 7 of these 22 criteria with a relevant result (across 6 patients) was 146 cGy (range 100-502 cGy). In only 4 DVH criteria, the DIR mapped contours had higher variations than the original contours. One lung patient had a DVH criteria exceeding the clinical dose constraint by 125 cGy with parameter addition, and with accurate DIR and dose accumulation, the criteria was actually 97 cGy lower than the constraint. CONCLUSIONS The use of DIR to generate total planned dose records revealed substantial dose differences in most cases compared to commonly used clinical methods (i.e. parameter addition), and altered the planned acceptance criteria in a minority. DIR is recommended to be used for future adaptive re-plans to generate total planned dose records and facilitate accurate re-contouring. More accurate dose records may also improve our understanding of clinical outcomes.
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Affiliation(s)
- Joshua Torchia
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
| | - Michael Velec
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada; Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada.
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Jin X, Pan Y, Zhai C, Shen H, You L, Pan H. Exploration and machine learning model development for T2 NSCLC with bronchus infiltration and obstructive pneumonia/atelectasis. Sci Rep 2024; 14:4793. [PMID: 38413705 PMCID: PMC10899628 DOI: 10.1038/s41598-024-55507-6] [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: 10/25/2023] [Accepted: 02/24/2024] [Indexed: 02/29/2024] Open
Abstract
In the 8th edition of the American Joint Committee on Cancer (AJCC) staging system for Non-Small Cell Lung Cancer (NSCLC), tumors exhibiting main bronchial infiltration (MBI) near the carina and those presenting with complete lung obstructive pneumonia/atelectasis (P/ATL) have been reclassified from T3 to T2. Our investigation into the Surveillance, Epidemiology, and End Results (SEER) database, spanning from 2007 to 2015 and adjusted via Propensity Score Matching (PSM) for additional variables, disclosed a notably inferior overall survival (OS) for patients afflicted with these conditions. Specifically, individuals with P/ATL experienced a median OS of 12 months compared to 15 months (p < 0.001). In contrast, MBI patients demonstrated a slightly worse prognosis with a median OS of 22 months versus 23 months (p = 0.037), with both conditions significantly correlated with lymph node metastasis (All p < 0.001). Upon evaluating different treatment approaches for these particular T2 NSCLC variants, while adjusting for other factors, surgery emerged as the optimal therapeutic strategy. We counted those who underwent surgery and found that compared to surgery alone, the MBI/(P/ATL) group experienced a much higher proportion of preoperative induction therapy or postoperative adjuvant therapy than the non-MBI/(P/ATL) group (41.3%/54.7% vs. 36.6%). However, for MBI patients, initial surgery followed by adjuvant treatment or induction therapy succeeded in significantly enhancing prognosis, a benefit that was not replicated for P/ATL patients. Leveraging the XGBoost model for a 5-year survival forecast and treatment determination for P/ATL and MBI patients yielded Area Under the Curve (AUC) scores of 0.853 for P/ATL and 0.814 for MBI, affirming the model's efficacy in prognostication and treatment allocation for these distinct T2 NSCLC categories.
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Affiliation(s)
- Xuanhong Jin
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Yang Pan
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, China
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China
| | - Chongya Zhai
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Hangchen Shen
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Liangkun You
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
| | - Hongming Pan
- Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, Hangzhou, China.
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He Y, Cazoulat G, Wu C, Svensson S, Almodovar-Abreu L, Rigaud B, McCollum E, Peterson C, Wooten Z, Rhee DJ, Balter P, Pollard-Larkin J, Cardenas C, Court L, Liao Z, Mohan R, Brock K. Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy. Int J Radiat Oncol Biol Phys 2024; 118:231-241. [PMID: 37552151 DOI: 10.1016/j.ijrobp.2023.07.016] [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: 12/02/2022] [Revised: 06/04/2023] [Accepted: 07/14/2023] [Indexed: 08/09/2023]
Abstract
PURPOSE The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT). METHODS AND MATERIALS Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses. RESULTS Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|. CONCLUSIONS With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.
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Affiliation(s)
- Yulun He
- Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center UTHealth Houston, Houston, Texas; Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Guillaume Cazoulat
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carol Wu
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | | | - Bastien Rigaud
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Emma McCollum
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Christine Peterson
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Zachary Wooten
- Department of Statistics, Rice University, Houston, Texas
| | - Dong Joo Rhee
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Peter Balter
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Julianne Pollard-Larkin
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Carlos Cardenas
- Department of Radiation Oncology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Laurence Court
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Zhongxing Liao
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Radhe Mohan
- Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kristy Brock
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas
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A Nomogram Based on Atelectasis/Obstructive Pneumonitis Could Predict the Metastasis of Lymph Nodes and Postoperative Survival of Pathological N0 Classification in Non-small Cell Lung Cancer Patients. Biomedicines 2023; 11:biomedicines11020333. [PMID: 36830869 PMCID: PMC9953094 DOI: 10.3390/biomedicines11020333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/19/2023] [Accepted: 01/21/2023] [Indexed: 01/27/2023] Open
Abstract
The eighth TNM staging system proposal classifies lung cancer with partial or complete atelectasis/obstructive pneumonia into the T2 category. We aimed to develop nomograms to predict the possibility of lymph node metastasis (LNM) and the prognosis for NSCLC based on atelectasis and obstructive pneumonitis. METHODS NSCLC patients over 20 years old diagnosed between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. The nomograms were based on risk factors that were identified by Logistic regression. The area under the receiver operating characteristic (ROC) curve (AUC) was performed to confirm the predictive values of our nomograms. Cox proportional hazards analysis and Kaplan-Meier survival analysis were also used in this study. RESULTS A total of 470,283 patients were enrolled. Atelectasis/obstructive pneumonitis, age, gender, race, histologic types, grade, and tumor size were defined as independent predictive factors; then, these seven factors were integrated to establish nomograms of LNM. The AUC is 0.70 (95% CI: 0.694-0.704). Moreover, the Cox proportional hazards analysis and Kaplan-Meier survival analysis showed that the scores derived from the nomograms were significantly correlated with the survival of pathological N0 classification. CONCLUSION Nomograms based on atelectasis/obstructive pneumonitis were developed and validated to predict LNM and the postoperative prognosis of NSCLC.
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Guberina N, Pöttgen C, Santiago A, Levegrün S, Qamhiyeh S, Ringbaek TP, Guberina M, Lübcke W, Indenkämpen F, Stuschke M. Machine-learning-based prediction of the effectiveness of the delivered dose by exhale-gated radiotherapy for locally advanced lung cancer: The additional value of geometric over dosimetric parameters alone. Front Oncol 2023; 12:870432. [PMID: 36713497 PMCID: PMC9880443 DOI: 10.3389/fonc.2022.870432] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 12/08/2022] [Indexed: 01/15/2023] Open
Abstract
Purpose This study aimed to assess interfraction stability of the delivered dose distribution by exhale-gated volumetric modulated arc therapy (VMAT) or intensity-modulated arc therapy (IMAT) for lung cancer and to determine dominant prognostic dosimetric and geometric factors. Methods Clinical target volume (CTVPlan) from the planning CT was deformed to the exhale-gated daily CBCT scans to determine CTVi, treated by the respective dose fraction. The equivalent uniform dose of the CTVi was determined by the power law (gEUDi) and cell survival model (EUDiSF) as effectiveness measure for the delivered dose distribution. The following prognostic factors were analyzed: (I) minimum dose within the CTVi (Dmin_i), (II) Hausdorff distance (HDDi) between CTVi and CTVPlan, (III) doses and deformations at the point in CTVPlan at which the global minimum dose over all fractions per patient occurs (PDmin_global_i), and (IV) deformations at the point over all CTVi margins per patient with the largest Hausdorff distance (HDPworst). Prognostic value and generalizability of the prognostic factors were examined using cross-validated random forest or multilayer perceptron neural network (MLP) classifiers. Dose accumulation was performed using back deformation of the dose distribution from CTVi to CTVPlan. Results Altogether, 218 dose fractions (10 patients) were evaluated. There was a significant interpatient heterogeneity between the distributions of the normalized gEUDi values (p<0.0001, Kruskal-Wallis tests). Accumulated gEUD over all fractions per patient was 1.004-1.023 times of the prescribed dose. Accumulation led to tolerance of ~20% of fractions with gEUDi <93% of the prescribed dose. Normalized Dmin >60% was associated with predicted gEUD values above 95%. Dmin had the highest importance for predicting the gEUD over all analyzed prognostic parameters by out-of-bag loss reduction using the random forest procedure. Cross-validated random forest classifier based on Dmin as the sole input had the largest Pearson correlation coefficient (R=0.897) in comparison to classifiers using additional input variables. The neural network performed better than the random forest classifier, and the gEUD values predicted by the MLP classifier with Dmin as the sole input were correlated with the gEUD values characterized by R=0.933 (95% CI, 0.913-0.948). The performance of the full MLP model with all geometric input parameters was slightly better (R=0.952) than that based on Dmin (p=0.0034, Z-test). Conclusion Accumulated dose distributions over the treatment series were robust against interfraction CTV deformations using exhale gating and online image guidance. Dmin was the most important parameter for gEUD prediction for a single fraction. All other parameters did not lead to a markedly improved generalizable prediction. Dosimetric information, especially location and value of Dmin within the CTV i , are vital information for image-guided radiation treatment.
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Affiliation(s)
- Nika Guberina
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany,*Correspondence: Nika Guberina,
| | - Christoph Pöttgen
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Alina Santiago
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Sabine Levegrün
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Sima Qamhiyeh
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Toke Printz Ringbaek
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Maja Guberina
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Wolfgang Lübcke
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Frank Indenkämpen
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany
| | - Martin Stuschke
- Department of Radiation Therapy, West German Cancer Center, University Hospital Essen, University Duisburg-Essen, Essen, Germany,German Cancer Consortium (DKTK), Partner Site University Hospital Essen, Essen, Germany
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Huesa-Berral C, Juan-Cruz C, van Kranen S, Rossi M, Belderbos J, Diego Azcona J, Burguete J, Sonke JJ. Detailed dosimetric evaluation of inter-fraction and respiratory motion in lung stereotactic body radiation therapy based on daily 4D cone beam CT images. Phys Med Biol 2022; 68. [PMID: 36538287 DOI: 10.1088/1361-6560/aca94d] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022]
Abstract
Objective. Periodic respiratory motion and inter-fraction variations are sources of geometric uncertainty in stereotactic body radiation therapy (SBRT) of pulmonary lesions. This study extensively evaluates and validates the separate and combined dosimetric effect of both factors using 4D-CT and daily 4D-cone beam CT (CBCT) dose accumulation scenarios.Approach. A first cohort of twenty early stage or metastatic disease lung cancer patients were retrospectively selected to evaluate each scenario. The planned-dose (3DRef) was optimized on a 3D mid-position CT. To estimate the dosimetric impact of respiratory motion (4DRef), inter-fractional variations (3DAcc) and the combined effect of both factors (4DAcc), three dose accumulation scenarios based on 4D-CT, daily mid-cone beam CT (CBCT) position and 4D-CBCT were implemented via CT-CT/CT-CBCT deformable image registration (DIR) techniques. Each scenario was compared to 3DRef.A separate cohort of ten lung SBRT patients was selected to validate DIR techniques. The distance discordance metric (DDM) was implemented per voxel and per patient for tumor and organs at risk (OARs), and the dosimetric impact for CT-CBCT DIR geometric errors was calculated.Main results.Median and interquartile range (IQR) of the dose difference per voxel were 0.05/2.69 Gy and -0.12/2.68 Gy for3DAcc-3DRefand4DAcc-3DRef.For4DRef-3DRefthe IQR was considerably smaller -0.15/0.78 Gy. These findings were confirmed by dose volume histogram parameters calculated in tumor and OARs. For CT-CT/CT-CBCT DIR validation, DDM (95th percentile) was highest for heart (6.26 mm)/spinal cord (8.00 mm), and below 3 mm for tumor and the rest of OARs. The dosimetric impact of CT-CBCT DIR errors was below 2 Gy for tumor and OARs.Significance. The dosimetric impact of inter-fraction variations were shown to dominate those of periodic respiration in SBRT for pulmonary lesions. Therefore, treatment evaluation and dose-effect studies would benefit more from dose accumulation focusing on day-to-day changes then those that focus on respiratory motion.
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Affiliation(s)
- Carlos Huesa-Berral
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.,Physics and Applied Mathematics, School of Science, University of Navarra, E-31008 Pamplona, Navarra, Spain.,Service of Radiation Physics and Radiation Protection, University of Navarra Clinic, E-31008 Pamplona, Navarra, Spain
| | - Celia Juan-Cruz
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Simon van Kranen
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Maddalena Rossi
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - José Belderbos
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Juan Diego Azcona
- Service of Radiation Physics and Radiation Protection, University of Navarra Clinic, E-31008 Pamplona, Navarra, Spain
| | - Javier Burguete
- Physics and Applied Mathematics, School of Science, University of Navarra, E-31008 Pamplona, Navarra, Spain
| | - Jan-Jakob Sonke
- Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
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Shinde P, Jadhav A, Shankar V, Gupta KK, Dhoble NS, Dhoble SJ. Evaluation of kV-CBCT based 3D dose calculation accuracy and its validation using delivery fluence derived dose metrics in Head and Neck Cancer. Phys Med 2022; 96:32-45. [PMID: 35217498 DOI: 10.1016/j.ejmp.2022.02.014] [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: 06/19/2021] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 10/19/2022] Open
Abstract
PURPOSE The purpose of this study is to evaluate the dosimetric impact of Hounsfield unit (HU) variations in kilovoltage cone-beam computed tomography (kV-CBCT) based 3D dose calculation accuracy in the treatment planning system and its validation using measured treatment delivery dose (MTDD) derived dose metrics for Volumetric Modulated Arc Therapy (VMAT) and Intensity Modulated Radiotherapy (IMRT) plans in Head and Neck (HN) Cancer. METHODS CBCT dose calculation accuracy was evaluated for 8 VMAT plans on inhomogeneous phantom and 40 VMAT and IMRT plans of HN Cancer patients and validated using ArcCHECK diode array MTDD derived 3D dose metric on CT and CBCT. RESULTS The mean percentage dose difference between CBCT and CT in TPS (ΔD(CBCT-CT)TPS) and 3DVH (ΔD(CBCT-CT)3DVH) were compared for the corresponding evaluation dose metrics (D98%, D95%, D50%, D2%, Dmax, D1cc, D0.03cc, Dmean) of all PTVs and OARs in phantom and patients. ΔD(CBCT-CT)TPS and ΔD(CBCT-CT)3DVH for all evaluation dose points of all PTVs and OARs were less than 2.55% in phantom and 2.4% in HN patients. The Pearson correlation coefficient (r) between ΔD(CBCT-CT)TPS and ΔD(CBCT-CT)3DVH for all dose points in all PTVs and OARs showed a strong to moderate correlation in phantom and patients with p < 0.001. CONCLUSIONS This study evaluated and validated the potential feasibility of kV-CBCT for treatment plan 3D dose reconstruction in clinical decision making for Adaptive radiotherapy on CT in Head and Neck cancer.
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Affiliation(s)
- Prashantkumar Shinde
- Department of Physics, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur 440033, India
| | - Anand Jadhav
- Department of Radiation Oncology, Sir H N Reliance Foundation Hospital and Research Centre, Mumbai 400004, India
| | - V Shankar
- Department of Radiation Oncology, Apollo Cancer Center, Chennai 600035, India
| | - Karan Kumar Gupta
- Department of Chemical Engineering, National Taiwan University, Taipei, Taiwan, ROC.
| | - Nirupama S Dhoble
- Department of Chemistry, Sevadal Mahila Mahavidhyalay, Nagpur 440015, India
| | - Sanjay J Dhoble
- Department of Physics, Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur 440033, India.
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Omari EA, Zhang Y, Ahunbay E, Paulson E, Amjad A, Chen X, Liang Y, Li XA. Multi parametric magnetic resonance imaging for radiation treatment planning. Med Phys 2022; 49:2836-2845. [PMID: 35170769 DOI: 10.1002/mp.15534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/05/2021] [Accepted: 01/03/2022] [Indexed: 11/09/2022] Open
Abstract
In recent years, multi-parametric magnetic resonance imaging (MpMRI) has played a major role in radiation therapy treatment planning. The superior soft tissue contrast, functional or physiological imaging capabilities and the flexibility of site-specific image sequence development has placed MpMRI at the forefront. In this article, the present status of MpMRI for external beam radiation therapy planning is reviewed. Common MpMRI sequences, preprocessing and QA strategies are briefly discussed, and various image registration techniques and strategies are addressed. Image segmentation methods including automatic segmentation and deep learning techniques for organs at risk and target delineation are reviewed. Due to the advancement in MRI guided online adaptive radiotherapy, treatment planning considerations addressing MRI only planning are also discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Eenas A Omari
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Zhang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ergun Ahunbay
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Asma Amjad
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Xinfeng Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Liang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
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