ReIGNITE Radiation Therapy Boost: A Prospective, International Study of Radiation Oncologists' Accuracy in Contouring Prostate Tumors for Focal Radiation Therapy Boost on Conventional Magnetic Resonance Imaging Alone or With Assistance of Restriction Spectrum Imaging.


Journal

International journal of radiation oncology, biology, physics
ISSN: 1879-355X
Titre abrégé: Int J Radiat Oncol Biol Phys
Pays: United States
ID NLM: 7603616

Informations de publication

Date de publication:
01 Dec 2023
Historique:
received: 10 03 2023
revised: 27 06 2023
accepted: 02 07 2023
medline: 20 11 2023
pubmed: 16 7 2023
entrez: 15 7 2023
Statut: ppublish

Résumé

In a phase III randomized trial, adding a radiation boost to tumor(s) visible on MRI improved prostate cancer (PCa) disease-free and metastasis-free survival without additional toxicity. Radiation oncologists' ability to identify prostate tumors is critical to widely adopting intraprostatic tumor radiotherapy boost for patients. A diffusion MRI biomarker, called the Restriction Spectrum Imaging restriction score (RSIrs), has been shown to improve radiologists' identification of clinically significant PCa. We hypothesized that (1) radiation oncologists would find accurately delineating PCa tumors on conventional MRI challenging and (2) using RSIrs maps would improve radiation oncologists' accuracy for PCa tumor delineation. In this multi-institutional, international, prospective study, 44 radiation oncologists (participants) and 2 expert radiologists (experts) contoured prostate tumors on 39 total patient cases using conventional MRI with or without RSIrs maps. Participant volumes were compared to the consensus expert volumes. Contouring accuracy metrics included percent overlap with expert volume, Dice coefficient, conformal number, and maximum distance beyond expert volume. 1604 participant volumes were produced. 40 of 44 participants (91%) completely missed ≥1 expert-defined target lesion without RSIrs, compared to 13 of 44 (30%) with RSIrs maps. On conventional MRI alone, 134 of 762 contour attempts (18%) completely missed the target, compared to 18 of 842 (2%) with RSIrs maps. Use of RSIrs maps improved all contour accuracy metrics by approximately 50% or more. Mixed effects modeling confirmed that RSIrs maps were the main variable driving improvement in all metrics. System Usability Scores indicated RSIrs maps significantly improved the contouring experience (72 vs. 58, p < 0.001). Radiation oncologists struggle with accurately delineating visible PCa tumors on conventional MRI. RSIrs maps improve radiation oncologists' ability to target MRI-visible tumors for prostate tumor boost.

Identifiants

pubmed: 37453559
pii: S0360-3016(23)07629-0
doi: 10.1016/j.ijrobp.2023.07.004
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1145-1152

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Auteurs

Asona J Lui (AJ)

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California.

Karoline Kallis (K)

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California.

Allison Y Zhong (AY)

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California; UC San Diego School of Medicine, La Jolla, California.

Troy S Hussain (TS)

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California.

Christopher Conlin (C)

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California.

Leonardino A Digma (LA)

Department of Neurosciences, UC San Diego School of Medicine, La Jolla, California.

Nikki Phan (N)

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California.

Ian T Mathews (IT)

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California; UC San Diego School of Medicine, La Jolla, California.

Deondre D Do (DD)

Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California.

Mariluz Rojo Domingo (MR)

Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California.

Roshan Karunamuni (R)

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California.

Joshua Kuperman (J)

Department of Radiology, UC San Diego School of Medicine, La Jolla, California.

Anders M Dale (AM)

Department of Radiology, UC San Diego School of Medicine, La Jolla, California; Department of Neurosciences, UC San Diego School of Medicine, La Jolla, California; Halıcıoğlu Data Science Institute, UC San Diego School of Medicine, La Jolla, California.

Ahmed Shabaik (A)

Department of Pathology, UC San Diego School of Medicine, La Jolla, California.

Rebecca Rakow-Penner (R)

Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California; Department of Radiology, UC San Diego School of Medicine, La Jolla, California.

Michael E Hahn (ME)

Department of Radiology, UC San Diego School of Medicine, La Jolla, California.

Tyler M Seibert (TM)

Department of Radiation Medicine and Applied Sciences, UC San Diego School of Medicine, La Jolla, California; Department of Bioengineering, UC San Diego Jacobs School of Engineering, La Jolla, California; Department of Radiology, UC San Diego School of Medicine, La Jolla, California. Electronic address: tseibert@ucsd.edu.

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