Coverage-based treatment planning to accommodate deformable organ variations in prostate cancer treatment.

PURPOSE To compare two coverage-based planning (CP) techniques with standard fixed margin-based planning (FM), considering the dosimetric impact of interfraction deformable organ motion exclusively for high-risk prostate treatments. METHODS Nineteen prostate cancer patients with 8-13 prostate CT images of each patient were used to model patient-specific interfraction deformable organ changes. The model was based on the principal component analysis (PCA) method and was used to predict the patient geometries for virtual treatment course simulation. For each patient, an IMRT plan using zero margin on target structures, prostate (CTVprostate) and seminal vesicles (CTVSV), were created, then evaluated by simulating 1000 30-fraction virtual treatment courses. Each fraction was prostate centroid aligned. Patients whose D98 failed to achieve 95% coverage probability objective D98,95 ≥ 78 Gy (CTVprostate) or D98,95 ≥ 66 Gy (CTVSV) were replanned using planning techniques: (1) FM (PTVprostate = CTVprostate + 5 mm, PTVSV = CTVSV + 8 mm), (2) CPOM which optimized uniform PTV margins for CTVprostate and CTVSV to meet the coverage probability objective, and (3) CPCOP which directly optimized coverage probability objectives for all structures of interest. These plans were intercompared by computing probabilistic metrics, including 5% and 95% percentile DVHs (pDVH) and TCP/NTCP distributions. RESULTS All patients were replanned using FM and two CP techniques. The selected margins used in FM failed to ensure target coverage for 8/19 patients. Twelve CPOM plans and seven CPCOP plans were favored over the other plans by achieving desirable D98,95 while sparing more normal tissues. CONCLUSIONS Coverage-based treatment planning techniques can produce better plans than FM, while relative advantages of CPOM and CPCOP are patient-specific.

[1]  M Alber,et al.  Dosimetric treatment course simulation based on a statistical model of deformable organ motion , 2012, Physics in medicine and biology.

[2]  J. Siebers,et al.  Coverage-based treatment planning: optimizing the IMRT PTV to meet a CTV coverage criterion. , 2009, Medical physics.

[3]  M Goitein,et al.  Implementation of a model for estimating tumor control probability for an inhomogeneously irradiated tumor. , 1993, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[4]  J. Lyman Complication Probability as Assessed from Dose-Volume Histograms , 1985 .

[5]  Radhe Mohan,et al.  Increased risk of biochemical and local failure in patients with distended rectum on the planning CT for prostate cancer radiotherapy. , 2005, International journal of radiation oncology, biology, physics.

[6]  K. Brock,et al.  A magnetic resonance imaging study of prostate deformation relative to implanted gold fiducial markers. , 2007, International journal of radiation oncology, biology, physics.

[7]  Lei Dong,et al.  Dose-response characteristics of low- and intermediate-risk prostate cancer treated with external beam radiotherapy. , 2004, International journal of radiation oncology, biology, physics.

[8]  Fang-Fang Yin,et al.  Adaptive prostate IGRT combining online re-optimization and re-positioning: a feasibility study , 2011, Physics in medicine and biology.

[9]  Gary Luxton,et al.  Dosimetry and radiobiologic model comparison of IMRT and 3D conformal radiotherapy in treatment of carcinoma of the prostate. , 2004, International journal of radiation oncology, biology, physics.

[10]  J. Siebers,et al.  Sensitivity of postplanning target and OAR coverage estimates to dosimetric margin distribution sampling parameters. , 2011, Medical physics.

[11]  T. Byrne,et al.  A review of prostate motion with considerations for the treatment of prostate cancer. , 2005, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[12]  A. Nahum,et al.  The delta-TCP concept: a clinically useful measure of tumor control probability. , 1999, International journal of radiation oncology, biology, physics.

[13]  D. Yan,et al.  Organ sample generator for expected treatment dose construction and adaptive inverse planning optimization. , 2012, Medical physics.

[14]  A. E. Nahum,et al.  Maximizing Local Control by Customized Dose Prescription for Pelvic Tumours , 1992 .

[15]  E Weiss,et al.  Coverage optimized planning: probabilistic treatment planning based on dose coverage histogram criteria. , 2010, Medical physics.

[16]  C J Moore,et al.  A method to calculate coverage probability from uncertainties in radiotherapy via a statistical shape model , 2007, Physics in medicine and biology.

[17]  M. Herk Errors and margins in radiotherapy. , 2004 .

[18]  David Jaffray,et al.  Online image-guided intensity-modulated radiotherapy for prostate cancer: How much improvement can we expect? A theoretical assessment of clinical benefits and potential dose escalation by improving precision and accuracy of radiation delivery. , 2004, International journal of radiation oncology, biology, physics.

[19]  S Webb,et al.  A model for calculating tumour control probability in radiotherapy including the effects of inhomogeneous distributions of dose and clonogenic cell density. , 1993, Physics in medicine and biology.

[20]  B. V. K. Vijaya Kumar,et al.  Efficient Calculation of Primary Images from a Set of Images , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  S. Hui,et al.  Assessing prostate, bladder and rectal doses during image guided radiation therapy — need for plan adaptation? , 2009, Journal of applied clinical medical physics.

[22]  R Mohan,et al.  A method of incorporating organ motion uncertainties into three-dimensional conformal treatment plans. , 1996, International journal of radiation oncology, biology, physics.

[23]  Cristian Lorenz,et al.  Generation of Point-Based 3D Statistical Shape Models for Anatomical Objects , 2000, Comput. Vis. Image Underst..

[24]  M. V. van Herk,et al.  The probability of correct target dosage: dose-population histograms for deriving treatment margins in radiotherapy. , 2000, International journal of radiation oncology, biology, physics.

[25]  L Bondar,et al.  A population-based model to describe geometrical uncertainties in radiotherapy: applied to prostate cases , 2011, Physics in medicine and biology.

[26]  Marcel van Herk,et al.  Quantification of shape variation of prostate and seminal vesicles during external beam radiotherapy. , 2005, International journal of radiation oncology, biology, physics.

[27]  J C Stroom,et al.  Internal organ motion in prostate cancer patients treated in prone and supine treatment position. , 1999, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[28]  M. Goitein,et al.  Fitting of normal tissue tolerance data to an analytic function. , 1991, International journal of radiation oncology, biology, physics.

[29]  Di Yan,et al.  Adaptive radiation therapy for prostate cancer. , 2010, Seminars in radiation oncology.

[30]  Karl Bzdusek,et al.  What CTV-to-PTV margins should be applied for prostate irradiation? Four-dimensional quantitative assessment using model-based deformable image registration techniques. , 2008, International journal of radiation oncology, biology, physics.

[31]  M. V. van Herk,et al.  A model to simulate day-to-day variations in rectum shape. , 2002, International journal of radiation oncology, biology, physics.

[32]  G T Chen,et al.  Using serial imaging data to model variabilities in organ position and shape during radiotherapy. , 2001, Physics in medicine and biology.

[33]  J J W Lagendijk,et al.  Variation in target and rectum dose due to prostate deformation: an assessment by repeated MR imaging and treatment planning , 2008, Physics in medicine and biology.

[34]  D. Yan,et al.  A model to accumulate fractionated dose in a deforming organ. , 1999, International journal of radiation oncology, biology, physics.

[35]  M. Rosenblatt Remarks on Some Nonparametric Estimates of a Density Function , 1956 .

[36]  X. Li,et al.  Characterizing interfraction variations and their dosimetric effects in prostate cancer radiotherapy. , 2011, International journal of radiation oncology, biology, physics.

[37]  J. Siebers,et al.  Dose deformation-invariance in adaptive prostate radiation therapy: implication for treatment simulations. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[38]  Gary E. Christensen,et al.  Consistent image registration , 2001, IEEE Transactions on Medical Imaging.

[39]  M. Alber,et al.  Modelling individual geometric variation based on dominant eigenmodes of organ deformation: implementation and evaluation , 2005, Physics in medicine and biology.

[40]  Sean S. Park,et al.  Adaptive image-guided radiotherapy (IGRT) eliminates the risk of biochemical failure caused by the bias of rectal distension in prostate cancer treatment planning: clinical evidence. , 2012, International journal of radiation oncology, biology, physics.

[41]  Jeffrey V Siebers,et al.  A method to estimate the effect of deformable image registration uncertainties on daily dose mapping. , 2012, Medical physics.

[42]  E. Parzen On Estimation of a Probability Density Function and Mode , 1962 .

[43]  M. Hoogeman,et al.  Margin evaluation in the presence of deformation, rotation, and translation in prostate and entire seminal vesicle irradiation with daily marker-based setup corrections. , 2011, International journal of radiation oncology, biology, physics.

[44]  J V Siebers,et al.  Evaluation of dosimetric margins in prostate IMRT treatment plans. , 2008, Medical physics.

[45]  David J. Sandoz,et al.  The application of principal component analysis and kernel density estimation to enhance process monitoring , 2000 .

[46]  R Mohan,et al.  Algorithms and functionality of an intensity modulated radiotherapy optimization system. , 2000, Medical physics.