BACKGROUND AND PURPOSE: Current oral mucositis normal tissue complication probability models, based on the dose distribution to the oral cavity volume, have suboptimal predictive power. Improving the delineation of the oral mucosa is likely to improve these models, but is resource intensive. We developed and evaluated fully-automated atlas-based segmentation (ABS) of a novel delineation technique for the oral mucosal surfaces. MATERIAL AND METHODS: An atlas of mucosal surface contours (MSC) consisting of 46 patients was developed. It was applied to an independent test cohort of 10 patients for whom manual segmentation of MSC structures, by three different clinicians, and conventional outlining of oral cavity contours (OCC), by an additional clinician, were also performed. Geometric comparisons were made using the dice similarity coefficient (DSC), validation index (VI) and Hausdorff distance (HD). Dosimetric comparisons were carried out using dose-volume histograms. RESULTS: The median difference, in the DSC and HD, between automated-manual comparisons and manual-manual comparisons were small and non-significant (-0.024; p=0.33 and -0.5; p=0.88, respectively). The median VI was 0.086. The maximum normalised volume difference between automated and manual MSC structures across all of the dose levels, averaged over the test cohort, was 8%. This difference reached approximately 28% when comparing automated MSC and OCC structures. CONCLUSIONS: Fully-automated ABS of MSC is suitable for use in radiotherapy dose-response modelling.
BACKGROUND AND PURPOSE: Current oral mucositis normal tissue complication probability models, based on the dose distribution to the oral cavity volume, have suboptimal predictive power. Improving the delineation of the oral mucosa is likely to improve these models, but is resource intensive. We developed and evaluated fully-automated atlas-based segmentation (ABS) of a novel delineation technique for the oral mucosal surfaces. MATERIAL AND METHODS: An atlas of mucosal surface contours (MSC) consisting of 46 patients was developed. It was applied to an independent test cohort of 10 patients for whom manual segmentation of MSC structures, by three different clinicians, and conventional outlining of oral cavity contours (OCC), by an additional clinician, were also performed. Geometric comparisons were made using the dice similarity coefficient (DSC), validation index (VI) and Hausdorff distance (HD). Dosimetric comparisons were carried out using dose-volume histograms. RESULTS: The median difference, in the DSC and HD, between automated-manual comparisons and manual-manual comparisons were small and non-significant (-0.024; p=0.33 and -0.5; p=0.88, respectively). The median VI was 0.086. The maximum normalised volume difference between automated and manual MSC structures across all of the dose levels, averaged over the test cohort, was 8%. This difference reached approximately 28% when comparing automated MSC and OCC structures. CONCLUSIONS: Fully-automated ABS of MSC is suitable for use in radiotherapy dose-response modelling.
Authors: David L Schwartz; Katherine Hutcheson; Denise Barringer; Susan L Tucker; Merrill Kies; F Christopher Holsinger; K Kian Ang; William H Morrison; David I Rosenthal; Adam S Garden; Lei Dong; Jan S Lewin Journal: Int J Radiat Oncol Biol Phys Date: 2010-06-18 Impact factor: 7.038
Authors: Jean Bourhis; Jens Overgaard; Hélène Audry; Kian K Ang; Michele Saunders; Jacques Bernier; Jean-Claude Horiot; Aurélie Le Maître; Thomas F Pajak; Michael G Poulsen; Brian O'Sullivan; Werner Dobrowsky; Andrzej Hliniak; Krzysztof Skladowski; John H Hay; Luiz H J Pinto; Carlo Fallai; Karen K Fu; Richard Sylvester; Jean-Pierre Pignon Journal: Lancet Date: 2006-09-02 Impact factor: 79.321
Authors: Nicholas Hardcastle; Wolfgang A Tomé; Donald M Cannon; Charlotte L Brouwer; Paul W H Wittendorp; Nesrin Dogan; Matthias Guckenberger; Stéphane Allaire; Yogish Mallya; Prashant Kumar; Markus Oechsner; Anne Richter; Shiyu Song; Michael Myers; Bülent Polat; Karl Bzdusek Journal: Radiat Oncol Date: 2012-06-15 Impact factor: 3.481
Authors: Charlotte L Brouwer; Roel J H M Steenbakkers; Edwin van den Heuvel; Joop C Duppen; Arash Navran; Henk P Bijl; Olga Chouvalova; Fred R Burlage; Harm Meertens; Johannes A Langendijk; Aart A van 't Veld Journal: Radiat Oncol Date: 2012-03-13 Impact factor: 3.481
Authors: J A Dean; L C Welsh; K H Wong; A Aleksic; E Dunne; M R Islam; A Patel; P Patel; I Petkar; I Phillips; J Sham; U Schick; K L Newbold; S A Bhide; K J Harrington; C M Nutting; S L Gulliford Journal: Clin Oncol (R Coll Radiol) Date: 2017-01-03 Impact factor: 4.126
Authors: Jamie A Dean; Kee H Wong; Liam C Welsh; Ann-Britt Jones; Ulrike Schick; Kate L Newbold; Shreerang A Bhide; Kevin J Harrington; Christopher M Nutting; Sarah L Gulliford Journal: Radiother Oncol Date: 2016-05-27 Impact factor: 6.280
Authors: Wen Chen; Yimin Li; Brandon A Dyer; Xue Feng; Shyam Rao; Stanley H Benedict; Quan Chen; Yi Rong Journal: Radiat Oncol Date: 2020-07-20 Impact factor: 3.481