Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy

. A 3D-Unet for CTV segmentation trained on a large single institute cohort consisting of planning CTs and manual segmentations was built and externally validated, reaching high performance.

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Publication Details

Journal
Physics and Imaging in Radiation Oncology
Published
2025-03-28
DOI
https://doi.org/10.1016/j.phro.2025.100749
Primary Topic
Advanced Radiotherapy Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy

Antonella Del Vecchio, N. Di Muzio, G. Palazzo, Lorenzo Placidi et al.
Physics and Imaging in Radiation Oncology
Advanced Radiotherapy Techniques
article

Development and external multicentric validation of a deep learning-based clinical target volume segmentation model for whole-breast radiotherapy

Antonella Del Vecchio, N. Di Muzio, G. Palazzo, Lorenzo Placidi, E. Cagni, Roberta Castriconi, Maria Giulia Ubeira-Gabellini, Robert Jeraj, Giulia Rambaldi Guidasci, Valeria Landoni, Alessia Tudda, Aldo Mazzilli, Andrei Fodor, Luciano Rivetti, C. Fiorino, Martina Mori, Eugenia Moretti, Cecilia Riani, Caterina Oliviero
article en

Abstract

. A 3D-Unet for CTV segmentation trained on a large single institute cohort consisting of planning CTs and manual segmentations was built and externally validated, reaching high performance.

Physics and Imaging in Radiation OncologyVol. 34
University of Parma (IT), University of Udine (IT), University of Wisconsin–Madison (US), University of Ljubljana (SI), Vita-Salute San Raffaele University (IT), Agostino Gemelli University Polyclinic (IT), Azienda Sanitaria Unità Locale di Reggio Emilia (IT), Fatebenefratelli Hospital (IT), Federico II University Hospital (IT), Tumori Foundation (US), Istituti di Ricovero e Cura a Carattere Scientifico (IT), Istituto di Ricovero e Cura a Carattere Scientifico San Raffaele (IT)
Università degli Studi di Milano, Associazione Italiana per la Ricerca sul Cancro
Openalex Percentile: Top 92%
Advanced Radiotherapy Techniques
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