Literature DB >> 30166077

The implementation of RapidPlan in predicting deep inspiration breath-hold candidates with left-sided breast cancer.

Aubrie Rice1, Ian Zoller2, Kevin Kocos3, Dannyl Weller4, Dominic DiCostanzo5, Ashley Hunzeker6, Nishele Lenards7.   

Abstract

The aim of this study is to determine if RapidPlan (RP) can be used as a prediction method to determine which left-sided supine breast cancer patients would benefit from the deep inspiration breath-hold (DIBH) technique. An RP model database was created with 72 clinically approved 3D conformal radiation therapy (3D-CRT) treatment plans. This model was validated by introducing 10 new patient data sets, creating RP-generated plans and comparing the clinically approved plan for the corresponding patient. The prediction ability of the model was then tested on the free-breathing (FB) scans of patients with clinically approved DIBH plans totaling 29 patients and results were then compared to the FB clinical plan attempts. A statistical analysis performed on the data indicated a strong correlation for the mean heart dose (R2 = 0.914; p-value < 0.001) with a standard deviation of 48.6 cGy. After validating the link between physician PTV and mean heart dose, the model was tested clinically on 15 patients by inserting "Test PTV Evals" that were contoured by the researchers as a surrogate for predicting mean heart dose. Statistical analysis showed a strong correlation between the dose to 5% of the heart (D5) and the mean heart dose (R2 values of 0.913 and 0.881, respectively) with a standard deviation for the mean heart dose of 27.2 cGy. It was concluded that by using a Test PTV Eval, the RP-generated plans were able to predict mean heart doses within ± 30.0 cGy.
Copyright © 2018 American Association of Medical Dosimetrists. All rights reserved.

Entities:  

Keywords:  Deep inspiration breath-hold (DIBH); Free-breathing (FB); Left-sided breast cancer; RapidPlan (RP)

Mesh:

Year:  2018        PMID: 30166077     DOI: 10.1016/j.meddos.2018.06.007

Source DB:  PubMed          Journal:  Med Dosim        ISSN: 1873-4022            Impact factor:   1.482


  5 in total

1.  Deep-inspirational breath-hold (DIBH) technique in left-sided breast cancer: various aspects of clinical utility.

Authors:  Szilvia Gaál; Zsuzsanna Kahán; Viktor Paczona; Renáta Kószó; Rita Drencsényi; Judit Szabó; Ramóna Rónai; Tímea Antal; Bence Deák; Zoltán Varga
Journal:  Radiat Oncol       Date:  2021-05-13       Impact factor: 3.481

2.  Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition.

Authors:  April Smith; Andrew Granatowicz; Cole Stoltenberg; Shuo Wang; Xiaoying Liang; Charles A Enke; Andrew O Wahl; Sumin Zhou; Dandan Zheng
Journal:  Technol Cancer Res Treat       Date:  2019 Jan-Dec

3.  Machine learning for dose-volume histogram based clinical decision-making support system in radiation therapy plans for brain tumors.

Authors:  Pawel Siciarz; Salem Alfaifi; Eric Van Uytven; Shrinivas Rathod; Rashmi Koul; Boyd McCurdy
Journal:  Clin Transl Radiat Oncol       Date:  2021-09-15

4.  Knowledge-Based Volumetric Modulated Arc Therapy Treatment Planning for Breast Cancer.

Authors:  Oscar Abel Apaza Blanco; María José Almada; Albin Ariel Garcia Andino; Silvia Zunino; Daniel Venencia
Journal:  J Med Phys       Date:  2021-12-02

5.  A Critical Overview of Predictors of Heart Sparing by Deep-Inspiration-Breath-Hold Irradiation in Left-Sided Breast Cancer Patients.

Authors:  Gianluca Ferini; Vito Valenti; Anna Viola; Giuseppe Emmanuele Umana; Emanuele Martorana
Journal:  Cancers (Basel)       Date:  2022-07-18       Impact factor: 6.575

  5 in total

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