Clinical validation and benchmarking of knowledge-based IMRT and VMAT treatment planning in pelvic anatomy

被引:156
作者
Hussein, Mohammad [1 ]
South, Christopher P. [1 ]
Barry, Miriam A. [1 ]
Adams, Elizabeth J. [1 ]
Jordan, Tom J. [1 ]
Stewart, Alexandra J. [3 ]
Nisbet, Andrew [1 ,2 ]
机构
[1] Royal Surrey Cty Hosp NHS Fdn Trust, Dept Med Phys, Guildford, Surrey, England
[2] Univ Surrey, Ctr Nucl & Radiat Phys, Guildford GU2 5XH, Surrey, England
[3] Royal Surrey Cty Hosp NHS Fdn Trust, Dept Oncol, Guildford, Surrey, England
关键词
VMAT; IMRT; Knowledge-based treatment planning; Prostate cancer; Cervical cancer; RapidPlan; PROSTATE-CANCER; AT-RISK; THERAPY; RADIOTHERAPY; QUALITY; VOLUME; HEAD; NECK;
D O I
10.1016/j.radonc.2016.06.022
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Purpose: The aim of this work was to determine whether a commercial knowledge-based treatment planning (KBP) module can efficiently produce IMRT and VMAT plans in the pelvic region (prostate & cervical cancer), and to assess sensitivity of plan quality to training data and model parameters. Methods: Initial benchmarking of KBP was performed using prostate cancer cases. Structures and dose distributions from 40 patients previously treated using a 5-field IMRT technique were used for model training. Two types of model were created: one excluded statistical outliers (as identified by RapidPlan guidelines) and the other had no exclusions. A separate model for cervix uteri cancer cases was subsequently developed using 37 clinical patients treated for cervical cancer using RapidA (TM) VMAT, with no exclusions. The resulting models were then used to generate plans for ten patients from each patient group who had not been included in the modelling process. Comparisons of generated RapidPlans with the corresponding clinical plans were carried out to indicate the required modifications to the models. Model parameters were then iteratively adjusted until plan quality converged with that obtained by experienced planners without KBP. Results: Initial automated model generation settings led to poor conformity, coverage and efficiency compared to clinical plans. Therefore a number of changes to the initial KBP models were required. Before model optimisation, it was found that the PTV coverage was slightly reduced in the superior and inferior directions for RapidPlan compared with clinical plans and therefore Ply parameters were adjusted to improve coverage. OAR doses were similar for both RapidPlan and clinical plans (p > 0.05). Excluding outliers had little effect on plan quality (p >> 0.05). Manually fixing key optimisation objectives enabled production of clinically acceptable treatment plans without further planner intervention for 9 of 10 prostate test patients and all 10 cervix test patients. Conclusions: The Varian RapidPlan (TM) system was able to produce IMRT & VMAT treatment plans in the pelvis, in a single optimisation, that had comparable sparing and comparable or better conformity than the original clinically acceptable plans. The system allows for better consistency and efficiency in the treatment planning process and has therefore been adopted clinically within our institute with over 100 patients treated. (C) 2016 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:473 / 479
页数:7
相关论文
共 21 条
[1]  
AA.VV, 2010, 83 ICRU, V10
[2]   Correlation of phantom-based and log file patient-specific QA with complexity scores for VMAT [J].
Agnew, Christina E. ;
Irvine, Denise M. ;
McGarry, Conor K. .
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS, 2014, 15 (06) :204-216
[3]   Predicting dose-volume histograms for organs-at-risk in IMRT planning [J].
Appenzoller, Lindsey M. ;
Michalski, Jeff M. ;
Thorstad, Wade L. ;
Mutic, Sasa ;
Moore, Kevin L. .
MEDICAL PHYSICS, 2012, 39 (12) :7446-7461
[4]   FIGO staging for carcinoma of the vulva, cervix, and corpus uteri [J].
Belhadj, H. ;
Berek, J. ;
Bermudez, A. ;
Bhatla, N. ;
Cain, J. ;
Denny, L. ;
Fujiwara, K. ;
Hacker, N. ;
Avall-Lundqvist, E. ;
Mutch, D. ;
Odicino, F. ;
Pecorelli, S. ;
Prat, J. ;
Quinn, M. ;
Seoud, M. A-F. ;
Shrivastava, S. K. .
INTERNATIONAL JOURNAL OF GYNECOLOGY & OBSTETRICS, 2014, 125 (02) :97-98
[5]   Knowledge-based IMRT treatment planning for prostate cancer [J].
Chanyavanich, Vorakarn ;
Das, Shiva K. ;
Lee, William R. ;
Lo, Joseph Y. .
MEDICAL PHYSICS, 2011, 38 (05) :2515-2522
[6]   Conventional versus hypofractionated high-dose intensity-modulated radiotherapy for prostate cancer: preliminary safety results from the CHHiP randomised controlled trial [J].
Dearnaley, David ;
Syndikus, Isabel ;
Sumo, Georges ;
Bidmead, Margaret ;
Bloomfield, David ;
Clark, Catharine ;
Gao, Annie ;
Hassan, Shama ;
Horwich, Alan ;
Huddart, Robert ;
Khoo, Vincent ;
Kirkbride, Peter ;
Mayles, Helen ;
Mayles, Philip ;
Naismith, Olivia ;
Parker, Chris ;
Patterson, Helen ;
Russell, Martin ;
Scrase, Christopher ;
South, Chris ;
Staffurth, John ;
Hall, Emma .
LANCET ONCOLOGY, 2012, 13 (01) :43-54
[7]   On the pre-clinical validation of a commercial model-based optimisation engine: Application to volumetric modulated arc therapy for patients with lung or prostate cancer [J].
Fogliata, Antonella ;
Belosi, Francesca ;
Clivio, Alessandro ;
Navarria, Piera ;
Nicolini, Giorgia ;
Scorsetti, Marta ;
Vanetti, Eugenio ;
Cozzi, Luca .
RADIOTHERAPY AND ONCOLOGY, 2014, 113 (03) :385-391
[8]   A Knowledge-Based Approach to Improving and Homogenizing Intensity Modulated Radiation Therapy Planning Quality Among Treatment Centers: An Example Application to Prostate Cancer Planning [J].
Good, David ;
Lo, Joseph ;
Lee, W. Robert ;
Wu, Q. Jackie ;
Yin, Fang-Fang ;
Das, Shiva K. .
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2013, 87 (01) :176-181
[9]   The effect of 6 and 15 MV on intensity-modulated radiation therapy prostate cancer treatment: plan evaluation, tumour control probability and normal tissue complication probability analysis, and the theoretical risk of secondary induced malignancies [J].
Hussein, M. ;
Aldridge, S. ;
Urbano, T. Guerrero ;
Nisbet, A. .
BRITISH JOURNAL OF RADIOLOGY, 2012, 85 (1012) :423-432
[10]   Evaluation of an automated knowledge based treatment planning system for head and neck [J].
Krayenbuehl, Jerome ;
Norton, Ian ;
Studer, Gabriela ;
Guckenberger, Matthias .
RADIATION ONCOLOGY, 2015, 10