Literature DB >> 23957565

Automatic delineation for replanning in nasopharynx radiotherapy: what is the agreement among experts to be considered as benchmark?

Gian Carlo Mattiucci1, Luca Boldrini, Giuditta Chiloiro, Giuseppe Roberto D'Agostino, Silvia Chiesa, Fiorenza De Rose, Luigi Azario, Danilo Pasini, Maria Antonietta Gambacorta, Mario Balducci, Vincenzo Valentini.   

Abstract

BACKGROUND AND
PURPOSE: Anatomic changes during head and neck radiotherapy require replanning. The primary aim of this study is the definition of the agreement among experts in the head and neck automatic delineation frame to use as benchmark. The secondary goal is to assess the reliability of automatic delineation for nasopharynx radiotherapy and time saving.
MATERIAL AND METHODS: A computed tomography (CT) scan was acquired in 10 nasopharynx patients along intensity-modulated radiotherapy (IMRT) treatment for replanning. Deformable registration with replanning autocontouring of the structures was performed using VelocityAI 2.3© software defining Structure Set A. The optimization of these contours was obtained through revision by a skilled operator, drawing Structure Set B. An ex novo Structure Set C was segmented on the replanning CT-scan by an expert delineation team. The mean Dice's Similarity Index (mDSI) was calculated between Structure Set A and B, A and C, and between B and C for each volume. All segmentation times for organs at risk (OARs) and clinical target volume (CTV) were recorded and compared.
RESULTS: We validated the replanning autocontoured Structure Sets for 10 patients. For volumetric analysis we observed mDSI values of 0.87 for the OARs, 0.70 for nodes, 0.90 for CTV in the Structure Set A-B comparison and respectively of 0.74, 0.63 and 0.78 for the Structure Set A-C one, and 0.78, 0.78 and 0.85 for Structure Set B-C, which represents the existing expert based benchmark. We calculated a mean saved time in Structure Set B of 30 minutes.
CONCLUSIONS: Autocontouring procedures offer considerable segmentation time saving with acceptable reliability of the contours, even if an independent check procedure for their optimization is still required to increase their adherence to referential benchmark gold standard among experts, which stands at a 0.80 DSI value.

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Year:  2013        PMID: 23957565     DOI: 10.3109/0284186X.2013.813069

Source DB:  PubMed          Journal:  Acta Oncol        ISSN: 0284-186X            Impact factor:   4.089


  21 in total

1.  Assessment of a guideline-based heart substructures delineation in left-sided breast cancer patients undergoing adjuvant radiotherapy : Quality assessment within a randomized phase III trial testing a cardioprotective treatment strategy (SAFE-2014).

Authors:  Giulio Francolini; Isacco Desideri; Icro Meattini; Carlotta Becherini; Francesca Terziani; Emanuela Olmetto; Camilla Delli Paoli; Donato Pezzulla; Mauro Loi; Pierluigi Bonomo; Daniela Greto; Silvia Calusi; Marta Casati; Stefania Pallotta; Lorenzo Livi
Journal:  Strahlenther Onkol       Date:  2018-11-07       Impact factor: 3.621

2.  The utility of atlas-assisted segmentation in the male pelvis is dependent on the interobserver agreement of the structures segmented.

Authors:  K A Langmack; C Perry; C Sinstead; J Mills; D Saunders
Journal:  Br J Radiol       Date:  2014-08-29       Impact factor: 3.039

Review 3.  Vision 20/20: perspectives on automated image segmentation for radiotherapy.

Authors:  Gregory Sharp; Karl D Fritscher; Vladimir Pekar; Marta Peroni; Nadya Shusharina; Harini Veeraraghavan; Jinzhong Yang
Journal:  Med Phys       Date:  2014-05       Impact factor: 4.071

4.  Segmentation of parotid glands from registered CT and MR images.

Authors:  Domen Močnik; Bulat Ibragimov; Lei Xing; Primož Strojan; Boštjan Likar; Franjo Pernuš; Tomaž Vrtovec
Journal:  Phys Med       Date:  2018-06-19       Impact factor: 2.685

5.  Auto-segmentation of the brachial plexus assessed with TaCTICS - a software platform for rapid multiple-metric quantitative evaluation of contours.

Authors:  Musaddiq Awan; Brandon Alan Dyer; Jayashree Kalpathy-Cramer; Eva Bongers; Max Dahele; Jinzhong Yang; Gary V Walker; Nikhil G Thaker; Emma Holliday; Andrew J Bishop; Charles R Thomas; David I Rosenthal; Clifton David Fuller
Journal:  Acta Oncol       Date:  2014-10-03       Impact factor: 4.089

6.  Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Authors:  Bulat Ibragimov; Lei Xing
Journal:  Med Phys       Date:  2017-02       Impact factor: 4.071

7.  A convolutional neural network combined with positional and textural attention for the fully automatic delineation of primary nasopharyngeal carcinoma on non-contrast-enhanced MRI.

Authors:  Lun M Wong; Qi Yong H Ai; Darren M C Poon; Macy Tong; Brigette B Y Ma; Edwin P Hui; Lin Shi; Ann D King
Journal:  Quant Imaging Med Surg       Date:  2021-09

8.  External validation of deep learning-based contouring of head and neck organs at risk.

Authors:  Ellen J L Brunenberg; Isabell K Steinseifer; Sven van den Bosch; Johannes H A M Kaanders; Charlotte L Brouwer; Mark J Gooding; Wouter van Elmpt; René Monshouwer
Journal:  Phys Imaging Radiat Oncol       Date:  2020-07-10

9.  Manual versus semiautomatic segmentation of soft-tissue sarcomas on magnetic resonance imaging: evaluation of similarity and comparison of segmentation times.

Authors:  Fernando Carrasco Ferreira Dionisio; Larissa Santos Oliveira; Mateus de Andrade Hernandes; Edgard Eduard Engel; Paulo Mazzoncini de Azevedo-Marques; Marcello Henrique Nogueira-Barbosa
Journal:  Radiol Bras       Date:  2021 May-Jun

10.  Segmentation of Organs and Tumor within Brain Magnetic Resonance Images Using K-Nearest Neighbor Classification.

Authors:  S A Yoganathan; Rui Zhang
Journal:  J Med Phys       Date:  2022-03-31
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