Literature DB >> 31345672

Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: Time, geometrical, and dosimetric analysis.

Nelson Tsz Cheong Fung1, Wai Man Hung2, Chun Kin Sze2, Michael Chi Hang Lee3, Wai Tong Ng2.   

Abstract

The aim of this study was to quantify the geometrical differences between manual contours and autocontours, the dosimetric impacts, and the time gain of using autosegmentation in adaptive nasopharyngeal carcinoma (NPC) intensity-modulated radiotherapy (IMRT) for a commercial system. A total of 20 consecutive Stages I to III NPC patients who had undergone adaptive radiation therapy (ART) re planning for IMRT treatment were retrospectively studied. Manually delineated organs at risks (OARs) on the replanning computed tomography (CT) were compared with the autocontours generated by VelocityAI using deformable registration from the original planning CT. Dice similarity coefficients and distance-to-agreements (DTAs) were used to quantify their geometric differences. IMRT test plans were generated with the assistance of RapidPlan based on the autocontours of OARs and manually segmented target volumes. The dose distributions were applied on the manually delineated OARs, their dose volume histograms and dose constraints compliances were analyzed. Times spent on target, OAR contouring, and IMRT replanning were recorded, and the total time of replanning using manual contouring and autocontouring were compared. The averaged mean DTA of all structures included in the study were less than 2 mm, and 90% of the patients fulfilled the mean distance agreement tolerance recommended by AAPM 132.1 The averaged maximum DTA for brainstem, cord, optic chiasm, and optic nerves were all less than 4 mm, whereas temporal lobes and parotids have larger average maximum DTA of 4.7 mm and 6.8 mm, respectively. It was found that large contour discrepancies in temporal lobes and parotids were often associated with large magnitude of deformation (warp distance) in image registrations. The resultant maximum dose of manually segmented brainstem, cord, and temporal lobe and the median dose of manually segmented parotids were found to be statistically higher than those to their autocontoured counter parts in test plans. Dose constraints of the manually segmented OARs in test plans were only met in 15% of the cases. The average time of manual contouring and autocontouring were 108 and 10 minutes, respectively (p < 0.001). More than 30% of the total replanning time would be spent in manual OAR contouring. Manual OAR delineation takes up a significant portion of time spent in ART replanning and OAR autocontouring could considerably enhance ART workflow efficiency. Geometrical discrepancies between auto- and manual contours in head and neck OARs were comparable to typical interobserver variation suggested in various literatures; however, some of the corresponding dosimetric differences were substantial, making it essential to carefully review the autocontours.
Copyright © 2019 American Association of Medical Dosimetrists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Adaptive planning; Automatic segmentation; Head and neck cancer; Radiotherapy

Mesh:

Year:  2019        PMID: 31345672     DOI: 10.1016/j.meddos.2019.06.002

Source DB:  PubMed          Journal:  Med Dosim        ISSN: 1873-4022            Impact factor:   1.482


  8 in total

1.  A Feasibility Study of Deep Learning-Based Auto-Segmentation Directly Used in VMAT Planning Design and Optimization for Cervical Cancer.

Authors:  Along Chen; Fei Chen; Xiaofang Li; Yazhi Zhang; Li Chen; Lixin Chen; Jinhan Zhu
Journal:  Front Oncol       Date:  2022-06-01       Impact factor: 5.738

Review 2.  Nasopharyngeal carcinoma: an evolving paradigm.

Authors:  Kenneth C W Wong; Edwin P Hui; Kwok-Wai Lo; Wai Kei Jacky Lam; David Johnson; Lili Li; Qian Tao; Kwan Chee Allen Chan; Ka-Fai To; Ann D King; Brigette B Y Ma; Anthony T C Chan
Journal:  Nat Rev Clin Oncol       Date:  2021-06-30       Impact factor: 66.675

Review 3.  Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review.

Authors:  Michael V Sherer; Diana Lin; Sharif Elguindi; Simon Duke; Li-Tee Tan; Jon Cacicedo; Max Dahele; Erin F Gillespie
Journal:  Radiother Oncol       Date:  2021-05-11       Impact factor: 6.901

4.  Knowledge-based planning using pseudo-structures for volumetric modulated arc therapy (VMAT) of postoperative uterine cervical cancer: a multi-institutional study.

Authors:  Tatsuya Kamima; Yoshihiro Ueda; Jun-Ichi Fukunaga; Mikoto Tamura; Yumiko Shimizu; Yuta Muraki; Yasuo Yoshioka; Nozomi Kitamura; Yuya Nitta; Masakazu Otsuka; Hajime Monzen
Journal:  Rep Pract Oncol Radiother       Date:  2021-12-30

5.  Deep learning tools for the cancer clinic: an open-source framework with head and neck contour validation.

Authors:  John C Asbach; Anurag K Singh; L Shawn Matott; Anh H Le
Journal:  Radiat Oncol       Date:  2022-02-08       Impact factor: 3.481

6.  Evaluating principal component analysis models for representing anatomical changes in head and neck radiotherapy.

Authors:  Raul Argota-Perez; Jennifer Robbins; Andrew Green; Marcel van Herk; Stine Korreman; Eliana Vásquez-Osorio
Journal:  Phys Imaging Radiat Oncol       Date:  2022-04-13

7.  Effect of Body Size Change on Off-Center Cervical Point and Face Doses in Patients Undergoing Nasopharyngeal Carcinoma Radiotherapy.

Authors:  Meifang Fang; Lu Xu; Xianxiang Wu; Zhen Cui; Zelai He; Haoxuan Zhang; Hao Jin
Journal:  Dis Markers       Date:  2022-04-25       Impact factor: 3.434

8.  Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.

Authors:  Yuka Urago; Hiroyuki Okamoto; Tomoya Kaneda; Naoya Murakami; Tairo Kashihara; Mihiro Takemori; Hiroki Nakayama; Kotaro Iijima; Takahito Chiba; Junichi Kuwahara; Shouichi Katsuta; Satoshi Nakamura; Weishan Chang; Hidetoshi Saitoh; Hiroshi Igaki
Journal:  Radiat Oncol       Date:  2021-09-09       Impact factor: 3.481

  8 in total

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