OBJECTIVE: Size characterization of osteolytic jaw lesions (OJL), in particular of neoplastic nature, is heterogeneously performed and lacks standardization in the medical literature and clinical practice. An OJL's volume holds promise as a surrogate for treatment response and prognosis. We comparatively evaluate various methods for size characterization of odontogenic OJLs. METHODS: We retrospectively performed semiautomatic image segmentation of CBCT data sets for volume approximation of neoplastic (51) and non-neoplastic odontogenic OJLs (100). We assessed the three greatest orthogonal diameters and calculated the volume using the cuboid- and ellipsoid-formula. Image segmentation was carried out using ITK-SNAP. Image segmentation-based volume approximation served as reference. Intra- and inter-rater variability were evaluated at hand of Bland-Altman-Analysis and dice similarity coefficient (DSC). RESULTS: Concerning the intrarater variability, we found the DSC to be highest for image segmentation-based volume approximation, simultaneously showing the tightest limits of agreement and greatest reliability. The cuboid formula showed consistent overestimation of the lesion's volume with a percent mean difference of -52 % (upper and lower limits of agreement +8.57 % and -112.63%, respectively). In mean, the ellipsoid formula underestimated the lesion's volume by 10.1% (upper and lower limits of agreement +76.8% and -56.6%, respectively). Inter rater variability was higher for formula-based volume approximation. Volume and multilocularity (p = 0.001) correlate with aggressiveness and growth potential. CONCLUSIONS: Segmentation-based volume approximation holds great promise for patient individualized treatment planning and clinical management. The data suggest that maximum tumour diameter-based size characterization, especially the cuboid-formula and the maximum diameter alone, should not be recommended.
OBJECTIVE: Size characterization of osteolytic jaw lesions (OJL), in particular of neoplastic nature, is heterogeneously performed and lacks standardization in the medical literature and clinical practice. An OJL's volume holds promise as a surrogate for treatment response and prognosis. We comparatively evaluate various methods for size characterization of odontogenic OJLs. METHODS: We retrospectively performed semiautomatic image segmentation of CBCT data sets for volume approximation of neoplastic (51) and non-neoplastic odontogenic OJLs (100). We assessed the three greatest orthogonal diameters and calculated the volume using the cuboid- and ellipsoid-formula. Image segmentation was carried out using ITK-SNAP. Image segmentation-based volume approximation served as reference. Intra- and inter-rater variability were evaluated at hand of Bland-Altman-Analysis and dice similarity coefficient (DSC). RESULTS: Concerning the intrarater variability, we found the DSC to be highest for image segmentation-based volume approximation, simultaneously showing the tightest limits of agreement and greatest reliability. The cuboid formula showed consistent overestimation of the lesion's volume with a percent mean difference of -52 % (upper and lower limits of agreement +8.57 % and -112.63%, respectively). In mean, the ellipsoid formula underestimated the lesion's volume by 10.1% (upper and lower limits of agreement +76.8% and -56.6%, respectively). Inter rater variability was higher for formula-based volume approximation. Volume and multilocularity (p = 0.001) correlate with aggressiveness and growth potential. CONCLUSIONS: Segmentation-based volume approximation holds great promise for patient individualized treatment planning and clinical management. The data suggest that maximum tumour diameter-based size characterization, especially the cuboid-formula and the maximum diameter alone, should not be recommended.
Authors: Frederic J Kaye; Alison M Ivey; Walter E Drane; William M Mendenhall; Robert W Allan Journal: J Natl Cancer Inst Date: 2014-12-03 Impact factor: 13.506
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Authors: Thibaud P Coroller; Wenya Linda Bi; Elizabeth Huynh; Malak Abedalthagafi; Ayal A Aizer; Noah F Greenwald; Chintan Parmar; Vivek Narayan; Winona W Wu; Samuel Miranda de Moura; Saksham Gupta; Rameen Beroukhim; Patrick Y Wen; Ossama Al-Mefty; Ian F Dunn; Sandro Santagata; Brian M Alexander; Raymond Y Huang; Hugo J W L Aerts Journal: PLoS One Date: 2017-11-16 Impact factor: 3.240
Authors: D Dejaco; C Url; V H Schartinger; A K Haug; N Fischer; D Riedl; A Posch; H Riechelmann; G Widmann Journal: Cancer Imaging Date: 2015-09-29 Impact factor: 3.909