Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy | Zendy

Charlotte L. Brouwer | Zendy; Djamal Boukerroui | Zendy; Jorge Oliveira | Zendy; Pádraig Looney | Zendy; Roel J.H.M. Steenbakkers | Zendy; Johannes A. Langendijk | Zendy; Stefan Both | Zendy; Mark J. Gooding | Zendy

Open Access

Assessment of manual adjustment performed in clinical practice following deep learning contouring for head and neck organs at risk in radiotherapy

Author(s) -

Charlotte L. Brouwer,

Djamal Boukerroui,

Jorge Oliveira,

Pádraig Looney,

Roel J.H.M. Steenbakkers,

Johannes A. Langendijk,

Stefan Both,

Mark J. Gooding

Publication year - 2020

Publication title -

physics and imaging in radiation oncology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.777

H-Index - 12

ISSN - 2405-6316

DOI - 10.1016/j.phro.2020.10.001

Subject(s) - contouring , medicine , context (archaeology) , clinical practice , radiation therapy , medical physics , percentile , head and neck , head and neck cancer , surgery , computer science , physical therapy , statistics , mathematics , paleontology , biology , computer graphics (images)

Auto-contouring performance has been widely studied in development and commissioning studies in radiotherapy, and its impact on clinical workflow assessed in that context. This study aimed to evaluate the manual adjustment of auto-contouring in routine clinical practice and to identify improvements regarding the auto-contouring model and clinical user interaction, to improve the efficiency of auto-contouring.

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