MASAMUNE Ken
   Department   Research Institutes and Facilities, Research Institutes and Facilities
   Position   Professor
Article types Original article
Language English
Peer review Peer reviewed
Title Reliability of Residual Tumor Estimation Based on Navigation Log.
Journal Formal name：Neurologia medico-chirurgica
Abbreviation：Neurol Med Chir (Tokyo)
ISSN code：13498029/04708105
Domestic / ForeginForegin
Volume, Issue, Page 60(9),pp.458-467
Author and coauthor YAMADA Hiroyuki†, MARUYAMA Takashi, KONISHI Yoshiyuki, MASAMUNE Ken, MURAGAKI Yoshihiro
Publication date 2020/08/15
Summary The mass of residual tumors has previously been estimated using time-series records of the position of surgical instruments acquired from neurosurgical navigation systems (navigation log). This method has been shown to be useful for rapid evaluation of residual tumors during resection. However, quantitative analysis of the method's reliability has not been sufficiently reported. The effect of poor log coverage is dominant in previous studies, in that it did not highlight other disturbance factors, such as intraoperative brain shift. We analyzed 25 patients with a high log-acquisition rate that was calculated by dividing the log-available time by the instrument-use time. We estimated the region of resection using the trajectory of surgical instrument that was extracted from the navigation log. We then calculated the residual tumor region and measured its volume as log-estimation residual tumor volume (RTV). We evaluated the correlation between the log-estimation RTV and the RTV in the post-resection magnetic resonance (MR) image. We also evaluated the accuracy of detecting the residual tumor mass using the estimated residual tumor region. The log-estimation RTV and the RTV in the post-resection MR image were significantly correlated (correlation coefficient = 0.960; P <0.001). The presence of patient-wise residual tumor mass was detected with a sensitivity of 81.8% and a specificity of 92.9%. The individual residual tumor mass was detected with a positive predictive value of 72%. Estimation of residual tumor with adequate log coverage appears to be a suitable method with a high reliability. This method can support rapid decision-making during resection.
DOI 10.2176/nmc.oa.2020-0042
Document No. U918170005<Pre 医中誌>
PMID 32801273