Literature DB >> 28202228

Cross-institutional knowledge-based planning (KBP) implementation and its performance comparison to Auto-Planning Engine (APE).

Binbin Wu1, Martijn Kusters2, Martina Kunze-Busch2, Tim Dijkema2, Todd McNutt3, Giuseppe Sanguineti4, Karl Bzdusek5, Anatoly Dritschilo6, Dalong Pang6.   

Abstract

BACKGROUND AND
PURPOSE: To investigate (1) whether a plan library established at one institution can be applied for another institution's knowledge-based planning (KBP); (2) the performance of cross-institutional KBP compared to Auto-Planning Engine (APE).
MATERIAL AND METHODS: Radboud University Medical Center (RUMC) provided 35 oropharyngeal cancer patients (68Gy to PTV68 and 50.3Gy to PTV50.3) with clinically-delivered and comparative APE plans. The Johns Hopkins University (JHU) contributed a three-dose-level plan library consisting of 179 clinically-delivered plans. MedStar Georgetown University Hospital (MGUH) contributed a KBP approach employing overlap-volume histogram (OVH-KBP), where the JHU library was used for guiding RUMC patients' KBP. Since clinical protocols adopted at RUMC and JHU are different and both approaches require protocol-specific planning parameters as initial input, 10 randomly selected patients from RUMC were set aside for deriving them. The finalized parameters were applied to the remaining 25 patients for OVH-KBP and APE plan generation. A Wilcoxon rank-sum test was used for statistical comparison.
RESULTS: PTV68 and PTV50.3's V95 in OVH-KBP and APE were similar (p>0.36). Cord's D0.1 cc in OVH-KBP was reduced by 5.1Gy (p=0.0001); doses to other organs were similar (p>0.2).
CONCLUSION: APE and OVH-KBP's plan quality is comparable. Institutional-protocol differences can be addressed to allow cross-institutional library sharing.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  APE; IMRT; KBP; OVH

Mesh:

Year:  2017        PMID: 28202228     DOI: 10.1016/j.radonc.2017.01.012

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  22 in total

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8.  Planning comparison of five automated treatment planning solutions for locally advanced head and neck cancer.

Authors:  J Krayenbuehl; M Zamburlini; S Ghandour; M Pachoud; S Tanadini-Lang; J Tol; M Guckenberger; W F A R Verbakel
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10.  Support Vector Machine Model Predicts Dose for Organs at Risk in High-Dose Rate Brachytherapy of Cervical Cancer.

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Journal:  Front Oncol       Date:  2021-07-15       Impact factor: 6.244

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