Literature DB >> 20231063

Dosimetric evaluation of automatic segmentation for adaptive IMRT for head-and-neck cancer.

Stuart Y Tsuji1, Andrew Hwang, Vivian Weinberg, Sue S Yom, Jeanne M Quivey, Ping Xia.   

Abstract

PURPOSE: Adaptive planning to accommodate anatomic changes during treatment requires repeat segmentation. This study uses dosimetric endpoints to assess automatically deformed contours. METHODS AND MATERIALS: Sixteen patients with head-and-neck cancer had adaptive plans because of anatomic change during radiotherapy. Contours from the initial planning computed tomography (CT) were deformed to the mid-treatment CT using an intensity-based free-form registration algorithm then compared with the manually drawn contours for the same CT using the Dice similarity coefficient and an overlap index. The automatic contours were used to create new adaptive plans. The original and automatic adaptive plans were compared based on dosimetric outcomes of the manual contours and on plan conformality.
RESULTS: Volumes from the manual and automatic segmentation were similar; only the gross tumor volume (GTV) was significantly different. Automatic plans achieved lower mean coverage for the GTV: V95: 98.6 +/- 1.9% vs. 89.9 +/- 10.1% (p = 0.004) and clinical target volume: V95: 98.4 +/- 0.8% vs. 89.8 +/- 6.2% (p < 0.001) and a higher mean maximum dose to 1 cm(3) of the spinal cord 39.9 +/- 3.7 Gy vs. 42.8 +/- 5.4 Gy (p = 0.034), but no difference for the remaining structures.
CONCLUSIONS: Automatic segmentation is not robust enough to substitute for physician-drawn volumes, particularly for the GTV. However, it generates normal structure contours of sufficient accuracy when assessed by dosimetric end points. (c) 2010 Elsevier Inc. All rights reserved.

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Year:  2010        PMID: 20231063     DOI: 10.1016/j.ijrobp.2009.06.012

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  35 in total

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Journal:  Radiology       Date:  2014-11-07       Impact factor: 11.105

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Authors:  M A Deeley; A Chen; R D Datteri; J Noble; A Cmelak; E Donnelly; A Malcolm; L Moretti; J Jaboin; K Niermann; Eddy S Yang; David S Yu; B M Dawant
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10.  Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning.

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