Literature DB >> 23127079

Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans.

Lulin Yuan1, Yaorong Ge, W Robert Lee, Fang Fang Yin, John P Kirkpatrick, Q Jackie Wu.   

Abstract

PURPOSE: The authors present an evidence-based approach to quantify the effects of an array of patient anatomical features of the planning target volumes (PTVs) and organs-at-risk (OARs) and their spatial relationships on the interpatient OAR dose sparing variation in intensity modulated radiation therapy (IMRT) plans by learning from a database of high-quality prior plans.
METHODS: The authors formulized the dependence of OAR dose volume histograms (DVHs) on patient anatomical factors into feature models which were learned from prior plans by a stepwise multiple regression method. IMRT plans for 64 prostate, 82 head-and-neck (HN) treatments were used to train the models. Two major groups of anatomical features were considered in this study: the volumetric information and the spatial information. The geometry of OARs relative to PTV is represented by the distance-to-target histogram, DTH. Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. The final models were tested by additional 24 prostate and 24 HN plans.
RESULTS: Significant patient anatomical factors contributing to OAR dose sparing in prostate and HN IMRT plans have been analyzed and identified. They are: the median distance between OAR and PTV, the portion of OAR volume within an OAR specific distance range, and the volumetric factors: the fraction of OAR volume which overlaps with PTV and the portion of OAR volume outside the primary treatment field. Overall, the determination coefficients R(2) for predicting the first principal component score (PCS1) of the OAR DVH by the above factors are above 0.68 for all the OARs and they are more than 0.53 for predicting the second principal component score (PCS2) of the OAR DVHs except brainstem and spinal cord. Thus, the above set of anatomical features combined has captured significant portions of the DVH variations for the OARs in prostate and HN plans. To test how well these features capture the interpatient organ dose sparing variations in general, the DVHs and specific dose-volume indices calculated from the regression models were compared with the actual DVHs and dose-volume indices from each patient's plan in the validation dataset. The dose-volume indices compared were V99%, V85%, and V50% for bladder and rectum in prostate plans and parotids median dose in HN plans. The authors found that for the bladder and rectum models, 17 out of 24 plans (71%) were within 6% OAR volume error and 21 plans (85%) were within 10% error; For the parotids model, the median dose values for 30 parotids out of 48 (63%) were within 6% prescription dose error and the values in 40 parotids (83%) were within 10% error.
CONCLUSIONS: Quantitative analysis of patient anatomical features and their correlation with OAR dose sparing has identified a number of important factors that explain significant amount of interpatient DVH variations in OARs. These factors can be incorporated into evidence-based learning models as effective features to provide patient-specific OAR dose sparing goals.

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Year:  2012        PMID: 23127079     DOI: 10.1118/1.4757927

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  84 in total

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7.  Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT.

Authors:  Lulin Yuan; Q Jackie Wu; Fang-Fang Yin; Yuliang Jiang; David Yoo; Yaorong Ge
Journal:  Med Phys       Date:  2014-02       Impact factor: 4.071

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

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9.  Outlier identification in radiation therapy knowledge-based planning: A study of pelvic cases.

Authors:  Yang Sheng; Yaorong Ge; Lulin Yuan; Taoran Li; Fang-Fang Yin; Qingrong Jackie Wu
Journal:  Med Phys       Date:  2017-09-30       Impact factor: 4.071

10.  Machine learning and modeling: Data, validation, communication challenges.

Authors:  Issam El Naqa; Dan Ruan; Gilmer Valdes; Andre Dekker; Todd McNutt; Yaorong Ge; Q Jackie Wu; Jung Hun Oh; Maria Thor; Wade Smith; Arvind Rao; Clifton Fuller; Ying Xiao; Frank Manion; Matthew Schipper; Charles Mayo; Jean M Moran; Randall Ten Haken
Journal:  Med Phys       Date:  2018-08-24       Impact factor: 4.071

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