Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: An intertechnique and interinstitutional study

被引:83
|
作者
Lian, Jun [1 ]
Yuan, Lulin [2 ]
Ge, Yaorong [3 ]
Chera, Bhishamjit S. [1 ]
Yoo, David P. [2 ]
Chang, Sha [1 ]
Yin, FangFang [2 ]
Wu, Q. Jackie [2 ]
机构
[1] Univ N Carolina, Dept Radiat Oncol, Chapel Hill, NC 27599 USA
[2] Duke Univ, Dept Radiat Oncol, Durham, NC 27710 USA
[3] Univ N Carolina, Dept Software & Informat Syst, Charlotte, NC 28223 USA
关键词
IMRT; intertechnique; interinstitutional; prediction; INTENSITY-MODULATED RADIOTHERAPY; DOSE-VOLUME HISTOGRAMS; HELICAL TOMOTHERAPY; RADIATION-THERAPY; LUNG-CANCER; PROSTATE; DELIVERY; GEOMETRY;
D O I
10.1118/1.4828788
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Purpose: To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution. Methods: Forty-four Tomotherapy intensity modulate radiotherapy plans of HN cases (Tomo-IMRT) from Institution A were included in the study. A different patient group of 53 HN fixed gantry IMRT (FG-IMRT) plans was selected from Institution B. The analyzed OARs included the parotid, larynx, spinal cord, brainstem, and submandibular gland. Two major groups of anatomical features were considered: the volumetric information and the spatial information. The volume information includes the volume of target, OAR, and overlapped volume between target and OAR. The spatial information of OARs relative to PTVs was represented by the distance-to-target histogram (DTH). Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. Two regression models, one for Tomotherapy plan and one for IMRT plan, were built independently. The accuracy of intratreatment-modality model prediction was validated by a leave one out cross-validation method. The intertechnique and interinstitution validations were performed by using the FG-IMRT model to predict the OAR dosimetry of Tomo-IMRT plans. The dosimetry of OARs, under the same and different institutional preferences, was analyzed to examine the correlation between the model prediction and planning protocol. Results: Significant patient anatomical factors contributing to OAR dose sparing in HN Tomotherapy plans have been analyzed and identified. For all the OARs, the discrepancies of dose indices between the model predicted values and the actual plan values were within 2.1%. Similar results were obtained from the modeling of FG-IMRT plans. The parotid gland was spared in a comparable fashion during the treatment planning of two institutions. The model based on FG-IMRT plans was found to predict the median dose of the parotid of Tomotherapy plans quite well, with a mean error of 2.6%. Predictions from the FG-IMRT model suggested the median dose of the larynx, median dose of the brainstem and D2 of the brainstem could be reduced by 10.5%, 12.8%, and 20.4%, respectively, in the Tomo-IMRT plans. This was found to be correlated to the institutional differences in OAR constraint settings. Re-planning of six Tomotherapy patients confirmed the potential of optimization improvement predicted by the FG-IMRT model was correct. Conclusions: The authors established a mathematical model to correlate the anatomical features and dosimetric indexes of OARs of HN patients in Tomotherapy plans. The model can be used for the setup of patient-specific OAR dose sparing goals and quality control of planning results. The institutional clinical experience was incorporated into the model which allows the model from one institution to generate a reference plan for another institution, or another IMRT technique. (C) 2013 American Association of Physicists in Medicine.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Intertechnique and Interinstitutional Modeling of the Dosimetry of Organs-at-Risk in Head and Neck IMRT Plans
    Lian, J.
    Yuan, L.
    Ge, Y.
    Chera, B.
    Yoo, D.
    Chang, S.
    Yin, F.
    Wu, Q.
    [J]. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2013, 87 (02): : S56 - S56
  • [2] Developing Head-and-Neck IMRT Organ-at-risk Sparing Decisions Support System using Model Tree
    Sheng, Y.
    Wu, Q. J.
    Zhang, J.
    Xie, T.
    Yin, F. F.
    Ge, Y.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2018, 127 : S1215 - S1215
  • [3] Organ-at-risk sparing in head and neck radiotherapy with dynamic trajectory radiotherapy
    Bertholet, J.
    Mackeprang, P.
    Mueller, S.
    Volken, W.
    Frei, D.
    Elicin, O.
    Aebersold, D. M.
    Fix, M. K.
    Manser, P.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2021, 161 : S276 - S277
  • [4] Study the Effect of Grid Size On Head and Neck IMRT Dosimetry
    Srivastava, S.
    Das, I.
    Cheng, C.
    Nohadani, O.
    [J]. MEDICAL PHYSICS, 2013, 40 (06)
  • [5] Dental radiation dosimetry maps from IMRT planning for head and neck cancers
    Emile, G.
    Polce, S.
    Antone, J.
    Frank, D.
    Segal, J.
    Potters, L.
    Parashar, B.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2020, 152 : S744 - S745
  • [6] Simulation of Realistic Organ-At-Risk Delineation Variability in Head and Neck Radiation Therapy
    Choi, W.
    Aliotta, E.
    Nourzadeh, H.
    Siebers, J.
    [J]. MEDICAL PHYSICS, 2019, 46 (06) : E186 - E187
  • [7] HaN-Seg: The head and neck organ-at-risk CT and MR segmentation dataset
    Podobnik, Gasper
    Strojan, Primoz
    Peterlin, Primoz
    Ibragimov, Bulat
    Vrtovec, Tomaz
    [J]. MEDICAL PHYSICS, 2023, 50 (03) : 1917 - 1927
  • [8] A TLD and film dosimetry study of surface dose for head and neck IMRT
    Mitchell, M. J.
    Ramsey, C. R.
    [J]. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2006, 66 (03): : S714 - S715
  • [9] HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge
    Podobnik, Gasper
    Ibragimov, Bulat
    Tappeiner, Elias
    Lee, Chanwoong
    Kim, Jin Sung
    Mesbah, Zacharia
    Modzelewski, Romain
    Ma, Yihao
    Yang, Fan
    Rudecki, Mikoaj
    Wodzinski, Marek
    Peterlin, Primoz
    Strojan, Primoz
    Vrtovec, Tomaz
    [J]. RADIOTHERAPY AND ONCOLOGY, 2024, 198
  • [10] Prospective randomized double-blind study of atlas-based organ-at-risk autosegmentation-assisted radiation planning in head and neck cancer
    Walker, Gary V.
    Awan, Musaddiq
    Tao, Randa
    Koay, Eugene J.
    Boehling, Nicholas S.
    Grant, Jonathan D.
    Sittig, Dean F.
    Gunn, Gary Brandon
    Garden, Adam S.
    Jack Phan
    Morrison, William H.
    Rosenthal, David I.
    Mohamed, Abdallah Sherif Radwan
    Fuller, Clifton David
    [J]. RADIOTHERAPY AND ONCOLOGY, 2014, 112 (03) : 321 - 325