Literature DB >> 26825250

Establishment of practice standards in nomenclature and prescription to enable construction of software and databases for knowledge-based practice review.

Charles S Mayo1, Thomas M Pisansky2, Ivy A Petersen2, Elizabeth S Yan2, Brian J Davis2, Scott L Stafford2, Yolanda I Garces2, Robert C Miller3, James A Martenson2, Robert W Mutter2, Richard Choo2, Christopher L Hallemeier2, Nadia N Laack2, Sean S Park2, Daniel J Ma2, Kenneth R Olivier2, Sameer R Keole4, Mirek Fatyga4, Robert L Foote2, Michael G Haddock2.   

Abstract

INTRODUCTION: Establishment of standards within a practice and across disease site groups for nomenclatures, prescription formatting, and measured dose-volume histogram (DVH) metrics is a key enabling step for creating software and database solutions to make routine aggregation of dosimetric data for all patients treated in a practice, practical. A process of physician-driven, iterative dialogs coupled with development of technical tools is required to implement the cultural and procedural changes. The cumulative reward for this effort is a database that can be used for defining practice norms, benchmarking against national standards, and tracking dosimetric effects of longitudinal practice pattern changes. METHODS AND MATERIALS: A 4-year project was carried out to develop and introduce standardizations, modify processes, and develop computer-based tools for reporting, aggregation, and analysis of prescription and DVH metrics. Physician disease site groups developed 42 target and 81 normal tissue templates. From the database of 32,002 DVH metrics, benchmarking was illustrated for a subgroup of breast (281) and prostate (324) patients treated with conventional fractionation over a 16-month period. Breast patients were segregated according to prescription template used: simple (S, tangents only) vs complex (C, tangents + supraclavicular ± intramammary nodes) and left (S-L or C-L) versus right (S-R or C-R).
RESULTS: Prostate patients' median and 50% confidence intervals (CIs) for bladder, stated according to the nomenclature: the percentage of bladder volume receiving doses of ≥40 Gy (V40[%]), V65Gy[%], V70Gy[%], V75Gy[%], and V80Gy[%] were 45.5 (24.9-57.0), 15.6 (9.0-23.8), 7.6 (3.3-13.6), 2.0 (0.0-7.9), and 0.0 (0.0-1.4), respectively. Values for rectum: V50Gy[%], V60 Gy[%], V65Gy[%], V70Gy[%], and V75Gy[%] were 37.1 (27.8-43.5), 21.8 (15.6-25.5), 14.6 (9.6-18.0), 7.7 (1.9-12.3), and 1.0 (0-7.0), respectively. For breast patients, heart:mean Gray values were 1.5 (1.0-2.0), 3.1 (2.2-4.8), 0.4 (0.3-0.7), and 1.1 (0.8-2.2) for S-L, C-L, S-R, and C-R, respectively. Longitudinal, moving window plots of median, 50% CI, and 90% CI for 6-month periods demonstrated the effect of practice changes to reduce heart doses.
CONCLUSIONS: Standardization was challenging as a practice change, but has resulted in significant improvements for both our clinical and research efforts.
Copyright © 2016 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 26825250     DOI: 10.1016/j.prro.2015.11.001

Source DB:  PubMed          Journal:  Pract Radiat Oncol        ISSN: 1879-8500


  10 in total

1.  Evaluation of multiple factors affecting normal brain dose in single-isocenter multiple target radiosurgery.

Authors:  Yu Yuan; Evan M Thomas; Grant A Clark; James M Markert; John B Fiveash; Richard A Popple
Journal:  J Radiosurg SBRT       Date:  2018

2.  Application of Critical Volume-Dose Constraints for Stereotactic Body Radiation Therapy in NRG Radiation Therapy Trials.

Authors:  Timothy A Ritter; Martha Matuszak; Indrin J Chetty; Charles S Mayo; Jackie Wu; Puneeth Iyengar; Michael Weldon; Clifford Robinson; Ying Xiao; Robert D Timmerman
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-05-01       Impact factor: 7.038

3.  Interinstitutional Plan Quality Assessment of 2 Linac-Based, Single-Isocenter, Multiple Metastasis Radiosurgery Techniques.

Authors:  Haisong Liu; Evan M Thomas; Jun Li; Yan Yu; David Andrews; James M Markert; John B Fiveash; Wenyin Shi; Richard A Popple
Journal:  Adv Radiat Oncol       Date:  2019-12-02

4.  Treatment data and technical process challenges for practical big data efforts in radiation oncology.

Authors:  C S Mayo; M Phillips; T R McNutt; J Palta; A Dekker; R C Miller; Y Xiao; J M Moran; M M Matuszak; P Gabriel; A S Ayan; J Prisciandaro; M Thor; N Dixit; R Popple; J Killoran; E Kaleba; M Kantor; D Ruan; R Kapoor; M L Kessler; T S Lawrence
Journal:  Med Phys       Date:  2018-09-18       Impact factor: 4.071

Review 5.  The big data effort in radiation oncology: Data mining or data farming?

Authors:  Charles S Mayo; Marc L Kessler; Avraham Eisbruch; Grant Weyburne; Mary Feng; James A Hayman; Shruti Jolly; Issam El Naqa; Jean M Moran; Martha M Matuszak; Carlos J Anderson; Lynn P Holevinski; Daniel L McShan; Sue M Merkel; Sherry L Machnak; Theodore S Lawrence; Randall K Ten Haken
Journal:  Adv Radiat Oncol       Date:  2016-10-13

6.  Data collection of patient outcomes: one institution's experience.

Authors:  Thomas J Whitaker; Charles S Mayo; Daniel J Ma; Michael G Haddock; Robert C Miller; Kimberly S Corbin; Michelle Neben-Wittich; James L Leenstra; Nadia N Laack; Mirek Fatyga; Steven E Schild; Carlos E Vargas; Katherine S Tzou; Austin R Hadley; Steven J Buskirk; Robert L Foote
Journal:  J Radiat Res       Date:  2018-03-01       Impact factor: 2.724

7.  Development and Validation of a Comprehensive Multivariate Dosimetric Model for Predicting Late Genitourinary Toxicity Following Prostate Cancer Stereotactic Body Radiotherapy.

Authors:  Luca F Valle; Dan Ruan; Audrey Dang; Rebecca G Levin-Epstein; Ankur P Patel; Joanne B Weidhaas; Nicholas G Nickols; Percy P Lee; Daniel A Low; X Sharon Qi; Christopher R King; Michael L Steinberg; Patrick A Kupelian; Minsong Cao; Amar U Kishan
Journal:  Front Oncol       Date:  2020-05-20       Impact factor: 6.244

8.  Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges.

Authors:  Hesham Elhalawani; Timothy A Lin; Stefania Volpe; Abdallah S R Mohamed; Aubrey L White; James Zafereo; Andrew J Wong; Joel E Berends; Shady AboHashem; Bowman Williams; Jeremy M Aymard; Aasheesh Kanwar; Subha Perni; Crosby D Rock; Luke Cooksey; Shauna Campbell; Pei Yang; Khahn Nguyen; Rachel B Ger; Carlos E Cardenas; Xenia J Fave; Carlo Sansone; Gabriele Piantadosi; Stefano Marrone; Rongjie Liu; Chao Huang; Kaixian Yu; Tengfei Li; Yang Yu; Youyi Zhang; Hongtu Zhu; Jeffrey S Morris; Veerabhadran Baladandayuthapani; John W Shumway; Alakonanda Ghosh; Andrei Pöhlmann; Hady A Phoulady; Vibhas Goyal; Guadalupe Canahuate; G Elisabeta Marai; David Vock; Stephen Y Lai; Dennis S Mackin; Laurence E Court; John Freymann; Keyvan Farahani; Jayashree Kaplathy-Cramer; Clifton D Fuller
Journal:  Front Oncol       Date:  2018-08-17       Impact factor: 6.244

9.  Incorporating big data into treatment plan evaluation: Development of statistical DVH metrics and visualization dashboards.

Authors:  Charles S Mayo; John Yao; Avraham Eisbruch; James M Balter; Dale W Litzenberg; Martha M Matuszak; Marc L Kessler; Grant Weyburn; Carlos J Anderson; Dawn Owen; William C Jackson; Randall Ten Haken
Journal:  Adv Radiat Oncol       Date:  2017-04-27

10.  Big Data Readiness in Radiation Oncology: An Efficient Approach for Relabeling Radiation Therapy Structures With Their TG-263 Standard Name in Real-World Data Sets.

Authors:  Thilo Schuler; John Kipritidis; Thomas Eade; George Hruby; Andrew Kneebone; Mario Perez; Kylie Grimberg; Kylie Richardson; Sally Evill; Brooke Evans; Blanca Gallego
Journal:  Adv Radiat Oncol       Date:  2018-10-12
  10 in total

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