Literature DB >> 28711334

Fully automated, comprehensive knowledge-based planning for stereotactic radiosurgery: Preclinical validation through blinded physician review.

Benjamin P Ziemer1, Satomi Shiraishi2, Jona A Hattangadi-Gluth1, Parag Sanghvi1, Kevin Lawrence Moore3.   

Abstract

PURPOSE: As knowledge-based planning (KBP) attempts to augment and potentially supplant manual treatment planning, it is imperative to ensure any implementation maintains or improves overall plan quality in any disease site. The purpose of this study was to demonstrate the overall quality of KBP-driven automated stereotactic radiosurgery (SRS) treatment planning using blinded physician comparison and determine systematic factors predictive of physician plan preference to guide future KBP refinement. METHODS AND MATERIALS: Automated noncoplanar volume modulated arc therapy KBP routines were developed for 199 plans across 3 clinical SRS scenarios: isolated lesions (isolated), lesions closely abutting (<3 cm) organs at risk (involved), and single-isocenter multiple metastases (multimet). Overall plan quality and preference were assessed via blinded review of the plans by two SRS physicians. Quantitative quality metrics were also compared to determine systematic differences in the treatment plans. Multiple parameters were investigated as predictors of KBP plan selection.
RESULTS: For the isolated, involved, and multimet scenarios, the KBP plans were considered to be superior or equivalent to clinical plans 86.7% (91/105), 81.1% (43/53), and 78.1% (32/41) of the time, respectively. All investigated quality metrics were equivalent or indicated more sparing for all KBP plans. The only nondosimetric predictor was planning target volume in the isolated (P = .02) and involved (P = .05) groups. The dosimetric predictors for the isolated group were gradient measure and heterogeneity index (both P < .01). In the multimet category, the only significant dosimetric predictor was interlesion dose (P = .01).
CONCLUSIONS: The fully automated KBP SRS plans were equivalent or superior to previously treated plans in 83.4% (166/199) of cases. In clinical implementation, geometric features found to be predictive of KBP performance can be used to identify plans where KBP results might benefit from further refinement, whereas dosimetric predictive features could be used to further refine KBP optimization priorities.
Copyright © 2017 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Year:  2017        PMID: 28711334     DOI: 10.1016/j.prro.2017.04.011

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


  7 in total

Review 1.  Automation in intensity modulated radiotherapy treatment planning-a review of recent innovations.

Authors:  Mohammad Hussein; Ben J M Heijmen; Dirk Verellen; Andrew Nisbet
Journal:  Br J Radiol       Date:  2018-09-04       Impact factor: 3.039

2.  Determining normal tissue dose in intracranial stereotactic radiosurgery: A diameter-based predictive nomogram.

Authors:  Donal Cummins; Siobhra O'Sullivan; Mary Dunne; Ronan McDermott; Maeve Keys; David Fitzpatrick; Clare Faul; Mohsen Javadpour; Christina Skourou
Journal:  J Radiosurg SBRT       Date:  2020

3.  Clinical Acceptability of Automated Radiation Treatment Planning for Head and Neck Cancer Using the Radiation Planning Assistant.

Authors:  Adenike Olanrewaju; Laurence E Court; Lifei Zhang; Komeela Naidoo; Hester Burger; Sameera Dalvie; Julie Wetter; Jeannette Parkes; Christoph J Trauernicht; Rachel E McCarroll; Carlos Cardenas; Christine B Peterson; Kathryn R K Benson; Monique du Toit; Ricus van Reenen; Beth M Beadle
Journal:  Pract Radiat Oncol       Date:  2021-02-25

4.  Performance Comparison of Knowledge-Based Dose Prediction Techniques Based on Limited Patient Data.

Authors:  Angelia Landers; Ryan Neph; Fabien Scalzo; Dan Ruan; Ke Sheng
Journal:  Technol Cancer Res Treat       Date:  2018-01-01

Review 5.  Applications and limitations of machine learning in radiation oncology.

Authors:  Daniel Jarrett; Eleanor Stride; Katherine Vallis; Mark J Gooding
Journal:  Br J Radiol       Date:  2019-06-05       Impact factor: 3.629

6.  Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling: A Pelvic Case Study.

Authors:  Yang Sheng; Jiahan Zhang; Chunhao Wang; Fang-Fang Yin; Q Jackie Wu; Yaorong Ge
Journal:  Technol Cancer Res Treat       Date:  2019-01-01

7.  Attention-aware 3D U-Net convolutional neural network for knowledge-based planning 3D dose distribution prediction of head-and-neck cancer.

Authors:  Alexander F I Osman; Nissren M Tamam
Journal:  J Appl Clin Med Phys       Date:  2022-05-09       Impact factor: 2.243

  7 in total

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