Research Article MRI-Based Assessment of Safe Margins in Tumor Surgery Laura Bellanova, 1 Thomas Schubert, 1 Olivier Cartiaux, 1 Frédéric Lecouvet, 2 Christine Galant, 3 Xavier Banse, 1 and Pierre-Louis Docquier 1 1 Computer Assisted and Robotic Surgery (CARS), Institut de Recherche Exp´ erimentale et Clinique (IREC), Universit´ e catholique de Louvain Tour Pasteur +4, Avenue Mounier, 53, 1200 Brussels, Belgium 2 D´ epartement D’imagerie M´ edicale, Cliniques Universitaires Saint-Luc 10, Avenue Hippocrate, 1200 Brussels, Belgium 3 D´ epartement de Pathologie, Cliniques Universitaires Saint-Luc 10, Avenue Hippocrate, 1200 Brussels, Belgium Correspondence should be addressed to Pierre-Louis Docquier; pierre-louis.docquier@uclouvain.be Received 17 November 2013; Accepted 19 January 2014; Published 20 February 2014 Academic Editor: Luca Sangiorgi Copyright © 2014 Laura Bellanova et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Introduction. In surgical oncology, histological analysis of excised tumor specimen is the conventional method to assess the safety of the resection margins. We tested the feasibility of using MRI to assess the resection margins of freshly explanted tumor specimens in rats. Materials and Methods. Fourteen specimen of sarcoma were resected in rats and analysed both with MRI and histologically. Slicing of the specimen was identical for the two methods and corresponding slices were paired. 498 margins were measured in length and classified using the UICC classification (R0, R1, and R2). Results. e mean difference between the 498 margins measured both with histology and MRI was 0.3 mm (SD 1.0 mm). e agreement interval of the two measurement methods was [−1.7 mm; 2.2 mm]. In terms of the UICC classification, a strict correlation was observed between MRI- and histology-based classifications ( = 0.84,  < 0.05). Discussion. is experimental study showed the feasibility to use MRI images of excised tumor specimen to assess the resection margins with the same degree of accuracy as the conventional histopathological analysis. When completed, MRI acquisition of resected tumors may alert the surgeon in case of inadequate margin and help advantageously the histopathological analysis. 1. Introduction Limb-salvage surgery is nowadays the ideal treatment for bone and soſt tissue sarcoma [1]. Although histological grade and tumor size are important prognostic factors, inadequate resection margins remain one of the most significant predic- tors of local recurrence for bone and soſt tissue sarcomas, even in the presence of adjuvant therapies [2–4]. A local recurrence usually impairs limb preservation and functional outcomes, but it is also correlated with an increased risk of metastatic disease development [5, 6]. Identified causes of local recurrence are insufficient resection margins, unde- tected metastasis, for instance in the lymph nodes, and tumor venous emboli. While other causes cannot be treated by surgery alone and require adjuvant treatment, insufficient resection margins can be avoided with a careful dissection and safe resection margins [7, 8]. Gross extemporaneous macroscopical analysis of the excised tumor specimen by the surgeon, followed by delayed histopathological analysis, is the conventional method to evaluate the safety of the resection margins. In a histopatho- logical study, Picci et al. correlated local recurrence with insufficient resection margins [9]. Histologic assessment of margin status was shown useful for predicting local recurrence of cutaneous malignant tumors in dogs and cats treated by means of excision alone [10]. Several prognostic classifications have been published to histologically evaluate surgical margins and identify high-/low-risk groups for local recurrence aſter limb salvage surgery. A standardized classi- fication was created by the Union for International Cancer Control (UICC). It distinguishes R0 as in sano resection R1 as possible microscopic residuals (margin between 0 and 1 mm) and R2 as macroscopic residual disease [11]. Magnetic resonance imaging (MRI) is widely used for oncological diagnosis, disease extension assessment, surgical planning, and postoperative followup [12]. Current available resolution of preoperative MRI images enables accurate delineation of the tumor boundaries for surgical planning Hindawi Publishing Corporation Sarcoma Volume 2014, Article ID 686790, 5 pages http://dx.doi.org/10.1155/2014/686790