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Using preoperative imaging for intraoperative guidance: a case of mistaken identity
Author(s) -
HughesHallett Archie,
Pratt Philip,
Mayer Erik,
Clark Martin,
Vale Justin,
Darzi Ara
Publication year - 2016
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1654
Subject(s) - segmentation , gold standard (test) , interpretation (philosophy) , medicine , predictive value , radiology , computer science , artificial intelligence , medical physics , surgery , programming language
Abstract Background Surgical image guidance systems to date have tended to rely on reconstructions of preoperative datasets. This paper assesses the accuracy of these reconstructions to establish whether they are appropriate for use in image guidance platforms. Methods Nine raters (two experts in image interpretation and preparation, three in image interpretation, and four in neither interpretation nor preparation) were asked to perform a segmentation of ten renal tumours (four cystic and six solid tumours). These segmentations were compared with a gold standard consensus segmentation generated using a previously validated algorithm. Results Average sensitivity and positive predictive value (PPV) were 0.902 and 0.891, respectively. When assessing for variability between raters, significant differences were seen in the PPV, sensitivity and incursions and excursions from consensus tumour boundary. Conclusions This paper has demonstrated that the interpretation required for the segmentation of preoperative imaging of renal tumours introduces significant inconsistency and inaccuracy. Copyright © 2015 John Wiley & Sons, Ltd.