Cancers (Jan 2022)

TMS Seeded Diffusion Tensor Imaging Tractography Predicts Permanent Neurological Deficits

  • Matthew Muir,
  • Sarah Prinsloo,
  • Hayley Michener,
  • Jeffrey I. Traylor,
  • Rajan Patel,
  • Ron Gadot,
  • Dhiego Chaves de Almeida Bastos,
  • Vinodh A. Kumar,
  • Sherise Ferguson,
  • Sujit S. Prabhu

DOI
https://doi.org/10.3390/cancers14020340
Journal volume & issue
Vol. 14, no. 2
p. 340

Abstract

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Surgeons must optimize the onco-functional balance by maximizing the extent of resection and minimizing postoperative neurological morbidity. Optimal patient selection and surgical planning requires preoperative identification of nonresectable structures. Transcranial magnetic stimulation is a method of noninvasively mapping the cortical representations of the speech and motor systems. Despite recent promising data, its clinical relevance and appropriate role in a comprehensive mapping approach remains unknown. In this study, we aim to provide direct evidence regarding the clinical utility of transcranial magnetic stimulation by interrogating the eloquence of TMS points. Forty-two glioma patients were included in this retrospective study. We collected motor function outcomes 3 months postoperatively. We overlayed the postoperative MRI onto the preoperative MRI to visualize preoperative TMS points in the context of the surgical cavity. We then generated diffusion tensor imaging tractography to identify meaningful subsets of TMS points. We correlated the resection of preoperative imaging features with clinical outcomes. The resection of TMS-positive points was significantly predictive of permanent deficits (p = 0.05). However, four out of eight patients had TMS-positive points resected without a permanent deficit. DTI tractography at a 75% FA threshold identified which TMS points are essential and which are amenable to surgical resection. TMS combined with DTI tractography shows a significant prediction of postoperative neurological deficits with both a high positive predictive value and negative predictive value.

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