Treatment times for delivering adjuvant breast radiotherapy at a cancer centre in Ontario, Canada.


Journal

Journal of medical imaging and radiation sciences
ISSN: 1876-7982
Titre abrégé: J Med Imaging Radiat Sci
Pays: United States
ID NLM: 101469694

Informations de publication

Date de publication:
Dec 2023
Historique:
received: 04 03 2023
revised: 12 07 2023
accepted: 17 07 2023
medline: 27 11 2023
pubmed: 18 9 2023
entrez: 17 9 2023
Statut: ppublish

Résumé

Time is a valuable commodity that impacts hospital flow, patient experience and economic resources. This study aims to identify factors that affect daily treatment time over a course of radiation therapy (RT) in patients who underwent adjuvant breast RT. In all adjuvant breast/chestwall RT patients treated from October 2017 to May 2018, daily set-up, beam delivery time, and overall treatment times were collected. A multivariable linear regression analysis was conducted to identify significant predictive factors related to treatment time. A general linear regression model was used to determine whether there was a learning curve effect throughout the course of treatment that decreased treatment time as patient and staff familiarity with the treatment procedure increased. A total of 567 patients were included with a median age of 61 years. The average overall treatment time for 2-field and 4-field RT was 8.3 (SD 2.4) and 13.1 (SD 5.6) minutes, respectively. Factors that significantly increased overall treatment times in patients prescribed 2-field RT were: bilateral techniques, breath-hold (BH) techniques, prone techniques (PR), reverse decubitus techniques (RD), wide tangents techniques, the use of bolus and number of segments delivered. (p < 0.05). Factors that significantly increased overall treatment times in patients who received 3-field and 4-field RT were: wide tangents volumes, a higher number of monitor units (MUs), bilateral techniques and BH techniques (p < 0.05). Older patients (≥60) who underwent 3-field and 4-field RT demonstrated a statistically significant increase in set-up time (p < 0.0001). Overall treatment time decreased from 10.0 to 9.3 min over the course of treatment, suggesting a minor learning curve (p = 0.009). The use of bilateral RT, BH, PR, RD, wide tangents, bolus, increasing treatment volumes, and increasing plan complexity were associated with increased treatment times. Future research should quantify the impact of other factors (BMI, mobility, patient care assessments, and imaging protocols) and utility of technological tools (time-predicting models, machine learning tools, and operations research models) on treatment time to optimize RT scheduling and improve resource management.

Sections du résumé

BACKGROUND BACKGROUND
Time is a valuable commodity that impacts hospital flow, patient experience and economic resources. This study aims to identify factors that affect daily treatment time over a course of radiation therapy (RT) in patients who underwent adjuvant breast RT.
METHODS METHODS
In all adjuvant breast/chestwall RT patients treated from October 2017 to May 2018, daily set-up, beam delivery time, and overall treatment times were collected. A multivariable linear regression analysis was conducted to identify significant predictive factors related to treatment time. A general linear regression model was used to determine whether there was a learning curve effect throughout the course of treatment that decreased treatment time as patient and staff familiarity with the treatment procedure increased.
RESULTS RESULTS
A total of 567 patients were included with a median age of 61 years. The average overall treatment time for 2-field and 4-field RT was 8.3 (SD 2.4) and 13.1 (SD 5.6) minutes, respectively. Factors that significantly increased overall treatment times in patients prescribed 2-field RT were: bilateral techniques, breath-hold (BH) techniques, prone techniques (PR), reverse decubitus techniques (RD), wide tangents techniques, the use of bolus and number of segments delivered. (p < 0.05). Factors that significantly increased overall treatment times in patients who received 3-field and 4-field RT were: wide tangents volumes, a higher number of monitor units (MUs), bilateral techniques and BH techniques (p < 0.05). Older patients (≥60) who underwent 3-field and 4-field RT demonstrated a statistically significant increase in set-up time (p < 0.0001). Overall treatment time decreased from 10.0 to 9.3 min over the course of treatment, suggesting a minor learning curve (p = 0.009).
CONCLUSION CONCLUSIONS
The use of bilateral RT, BH, PR, RD, wide tangents, bolus, increasing treatment volumes, and increasing plan complexity were associated with increased treatment times. Future research should quantify the impact of other factors (BMI, mobility, patient care assessments, and imaging protocols) and utility of technological tools (time-predicting models, machine learning tools, and operations research models) on treatment time to optimize RT scheduling and improve resource management.

Identifiants

pubmed: 37718151
pii: S1939-8654(23)01806-4
doi: 10.1016/j.jmir.2023.07.011
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

611-619

Informations de copyright

Copyright © 2023. Published by Elsevier Inc.

Auteurs

Stephanie Chan (S)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Scott MacKenzie (S)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Sandi Bosnic (S)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Gina Wong (G)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Liying Zhang (L)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Sam Finkelstein (S)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Emily Lam (E)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Steve Russell (S)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Francois Gallant (F)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Matt Wronski (M)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Eric Leung (E)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Edward Chow (E)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada.

Lee Chin (L)

Odette Cancer Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada. Electronic address: lee.chin@sunnybrook.ca.

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Classifications MeSH