Is Needle Biopsy Clinically Useful in Preoperative Grading of Central Chondrosarcoma of the Pelvis and Long Bones?

BACKGROUND: Central chondrosarcoma of bone is graded on a scale of 1 to 3 according to histological criteria. Clinically, these tumors can be divided into low-grade (Grade 1) and high-grade (Grade 2, Grade 3, and dedifferentiated) chondrosarcomas. Although en bloc resection has been the most widely used treatment, it has become generally accepted that in selected patients with low-grade chondrosarcomas of long bones, curettage is safe and effective. This approach requires an accurate preoperative estimation of grade to avoid under- or overtreatment, but prior reports have indicated that both imaging and biopsy do not always give an accurate prediction of grade. QUESTIONS/PURPOSES: (1) What is the concordance of image-guided needle preoperative biopsy and postoperative grading in central (intramedullary) chondrosarcomas of long bones, and how does this compare with the concordance of image-guided needle preoperative biopsy and postoperative grading in central pelvic chondrosarcomas? (2) What is the concordance of preoperative image-guided needle biopsy and postoperative findings in differentiating low-grade from high-grade central chondrosarcomas of long bones, and how does this compare with the concordance in central pelvic chondrosarcomas? METHODS: Between 1997 and 2014, in our institution, we treated 126 patients for central chondrosarcomas located in long bones and the pelvis. Of these 126 cases, 41 were located in the pelvis and the remaining 85 cases were located in long bones. This study considers 39 (95%) and 40 (47%) of them, respectively. We included all cases in which histological information was complete regarding preoperative and postoperative tumor grading. We excluded all cases with incomplete data sets or nondiagnostic preoperative biopsies. To evaluate the needle biopsy accuracy, we compared the histological tumor grade, obtained from the preoperative biopsy, with the final histological grade obtained from the postoperative surgical specimen. The weighted and nonweighted kappa statistics were used to evaluate the agreement. RESULTS: Concordance between the preoperative biopsy and the final pathological analysis in terms of histological grade was much higher in long-bone chondrosarcoma than in pelvic chondrosarcoma (83% [33 of 40] versus 36% [14 of 39]; odds ratio, 8, 48). Likewise, the weighted kappa coefficients were higher in long-bone chondrosarcoma than in pelvic chondrosarcoma for the determination of histological grade (0.63; 95% confidence interval [CI], 0.34-0.91 versus 0.12; -0.32 to 0.57; p < 0.001). When categorizing the lesions as low grade or high grade, concordance between the preoperative biopsy and the final pathological analysis was much higher in long-bone chondrosarcoma than in pelvic chondrosarcoma (90% [36 of 40] versus 67% [26 of 39]; odds ratio, 4, 5). Likewise, the weighted kappa coefficients were higher in long-bone chondrosarcoma than in pelvic chondrosarcoma (0.73; 95% CI, 0.51-0.94 versus 0.26; 0.04-0.48; p < 0.001). CONCLUSIONS: Image-guided needle biopsy, when performed by a specialist radiologist and evaluated by an experienced bone pathologist, is a useful tool in determining the histological grade of long-bone chondrosarcomas allowing identification of true low-grade tumors. The histological grade should be correlated with imaging and the clinical presentation, but under these circumstances, experienced tumor surgeons may use this information in planning surgical treatment. The same appears not to be true for pelvic lesions, in which histological grade established by needle biopsy should be interpreted with caution. LEVEL OF EVIDENCE: Level III, diagnostic study.

What Is the Expected Learning Curve in Computer-assisted Navigation for Bone Tumor Resection?

BACKGROUND: Computer navigation during surgery can help oncologic surgeons perform more accurate resections. However, some navigation studies suggest that this tool may result in unique intraoperative problems and increased surgical time. The degree to which these problems might diminish with experience-the learning curve-has not, to our knowledge, been evaluated for navigation-assisted tumor resections. QUESTIONS/PURPOSES: (1) What intraoperative technical problems were observed during the first 2 years using navigation? (2) What was the mean time for navigation procedures and the time improvement during the learning curve? (3) Have there been any differences in the accuracy of the registration technique that occurred over time? (4) Did navigation achieve the goal of achieving a wide bone margin? METHODS: All patients who underwent preoperative virtual planning for tumor bone resections and operated on with navigation assistance from 2010 to 2012 were prospectively collected. Two surgeons (GLF, LAA-T) performed the intraoperative navigation assistance. Both surgeons had more than 5 years of experience in orthopaedic oncology with more than 60 oncology cases per year per surgeon. This study includes from the very first patients performed with navigation. Although they did not take any formal training in orthopaedic oncology navigation, both surgeons were trained in navigation for knee prostheses. Between 2010 and 2012, we performed 124 bone tumor resections; of these, 78 (63%) cases were resected using intraoperative navigation assistance. During this period, our general indications for use of navigation included pelvic and sacral tumors and those tumors that were reconstructed with massive bone allografts to obtain precise matching of the host and allograft osteotomies. Seventy-eight patients treated with this technology were included in the study. Technical problems (crashes) and time for the navigation procedure were reported after surgery. Accuracy of the registration technique was defined and the surgical margins of the removed specimen were determined by an experienced bone pathologist after the surgical procedure as intralesional, marginal, or wide margins. To obtain these data, we performed a chart review and review of operative notes. RESULTS: In four patients (of 78 [5%]), the navigation was not completed as a result of technical problems; all occurred during the first 20 cases of the utilization of this technology. The mean time for navigation procedures during the operation was 31 minutes (range, 11-61 minutes), and the early navigations took more time (the regression analysis shielded R2 = 0.35 with p < 0.001). The median registration error was 0.6 mm (range, 0.3-1.1 mm). Registration did not improve over time (the regression analysis slope estimate is -0.014, with R2 = 0.026 and p = 0.15). Histological examinations of all specimens showed a wide bone tumor margin in all patients. However, soft tissue margins were wide in 58 cases and marginal in 20. CONCLUSIONS: We conclude that navigation may be useful in achieving negative bony margins, but we cannot state that it is more effective than other means for achieving this goal. Technical difficulty precluded the use of navigation in 5% of cases in this series. Navigation time decreased with more experience in the procedure but with the numbers available, we did not improve the registration error over time. Given these observations and the increased time and expense of using navigation, larger studies are needed to substantiate the value of this technology for routine use. LEVEL OF EVIDENCE: Level IV, therapeutic study.

Virtual Planning and Allograft Preparation Guided by Navigation for Reconstructive Oncologic Surgery: A Technical Report.

INTRODUCTION: Advanced virtual simulators can be used to accurately detect the best allograft according to size and shape. STEP 1 ACQUISITION OF MEDICAL IMAGES: Obtain a multislice CT scan and a magnetic resonance imaging (MRI) scan preoperatively for each patient; however, if the time between the scans and the surgery is >1 month, consider repeating the MRI because the size of the tumor may have changed during that time. STEP 2 SELECT AN ALLOGRAFT USING VIRTUAL IMAGING TO OPTIMIZE SIZE MATCHING: Load DICOM images into a virtual simulation station (Windows 7 Service Pack 1, 64 bit, Intel Core i5/i7 or equivalent) and use mediCAS planning software ( medicas3d.com ) or equivalent (Materialise Mimics or Amira software [FEI]) for image segmentation and virtual simulation with STL (stereolithography) files. STEP 3 PLAN AND OUTLINE THE TUMOR MARGINS ON THE PREOPERATIVE IMAGING: Determine and outline the tumor margin on manually fused CT and MRI studies using the registration tool of the mediCAS planning software or equivalent (Materialise Mimics software.). STEP 4 PLAN AND OUTLINE THE SAME OSTEOTOMIES ON THE ALLOGRAFT: Determine and outline the osteotomies between host and donor using the registration tool of the mediCAS planning software or equivalent (Materialise Mimics software.). STEP 5 ASSESS THE PATIENT AND ALLOGRAFT IN A VIRTUAL SCENARIO: Be sure to consider the disintegration of bone tissue that occurs during the osteotomy and corresponds to the thickness of the blade (approximately 1.5 mm). STEP 6 NAVIGATION SETTINGS: A tool of the mediCAS planning software allows the virtual preoperative planning (STL files) to be transferred to the surgical navigation format, DICOM files. STEP 7 PATIENT AND ALLOGRAFT INTRAOPERATIVE NAVIGATION: The tumor and allograft are resected using the navigated guidelines, which were previously planned with the virtual platform. RESULTS: The 3D virtual preoperative planning and surgical navigation software are tools designed to increase the accuracy of bone tumor resection and allograft reconstruction3.

Techniques in surgical navigation of extremity tumors: state of the art.

Image-guided surgical navigation allows the orthopedic oncologist to perform adequate tumor resection based on fused images (CT, MRI, PET). Although surgical navigation was first performed in spine and pelvis, recent reports have described the use of this technique in bone tumors located in the extremities. In long bones, this technique has moved from localization or percutaneous resection of benign tumors to complex bone tumor resections and guided reconstructions (allograft or endoprostheses). In recent years, the reported series have increased from small numbers (5 to 16 patients) to larger ones (up to 130 patients). The purpose of this paper is to review recent reports regarding surgical navigation in the extremities, describing the results obtained with different kind of reconstructions when navigation is used and how the previously described problems were solved.

Virtual Microscopy Large Slide Automated Acquisition: Error Analysis and Validation.

The aim of this work is to assess and analyze the discrepancies introduced in the reconstruction of an entire tumoral bone slice from multiple field acquisitions of a large microscopy slide. The reconstruction tends to preserve the original structural information and its error is estimated by comparing the reconstructed images of eight samples against single pictures of these samples. This comparison is held using the Structural Similarity index. The measurements show that smaller samples yield better results. The detected errors are introduced by the insufficiently corrected optical distortion caused by the camera lens, which tends to accumulate along the sample. Nevertheless, the maximum error encountered does not exceed 0.39 mm, which is smaller than the maximum tolerable error for the intended application, stated in 1 mm.

3D Printed Models and Navigation for Skull Base Surgery: Case Report and Virtual Validation.

In recent years, computer-assisted surgery tools have become more versatile. Having access to a 3D printed model expands the possibility for surgeons to practice with the particular anatomy of a patient before surgery and improve their skills. Optical navigation is capable of guiding a surgeon according to a previously defined plan. These methods improve accuracy and safety at the moment of executing the operation. We intend to carry on a validation process for computed-assisted tools. The aim of this project is to propose a comparative validation method to enable physicians to evaluate differences between a virtual planned approach trajectory and a real executed course. Summarily, this project is focused on decoding data in order to obtain numerical values so as to establish the quality of surgical procedures.

Accuracy of Chest Wall Tumor Resection Guided by Navigation: Experimental Model.

Difficulty in identification wall chest tumors lead to unnecessary wide resections. Optical navigation and preoperative virtual planning are assets for surgeries that require exactness and accuracy. These tools enable physicians to study real anatomy before surgery and to follow an established pathway during procedure ensuring effectiveness. The aim of this paper is to demonstrate that Preoperative Virtual Planning is a useful tool in chest tumor interventions to define oncological margins successfully. Moreover, it is possible to use a virtual specimen in order to quantify accuracy. Optical navigation has been used in surgical procedures such as neurosurgery, orthopaedics and ENT over the last ten years. This principle is used in order to orientate the surgeon in three dimensional spaces during the surgery. Surgeons are guided intraoperatively with navigation and are able to obtain a correspondence between images acquired and processed before the surgery and the real anatomy.

Bone tumor resection: analysis about 3D preoperative planning and navigation method using a virtual specimen.

The use of three-dimensional preoperative planning and bone tumor resection guided by navigation has increased in the last ten years. However, no study to date, as far as we know, has directly provided evidence of accuracy of this method. The objective of this study was to describe a method capable of determining the accuracy of osteotomies performed for tumor resection planned and guided by navigation. We hypothesize that matching the 3D reconstructed surgical specimen is an acceptable method to determine the accuracy of virtual planning and navigation. A total of seven patients and 14 osteotomies were evaluated. After surgery, all surgical specimens were 3D reconstructed from CT images. The mean of quantitative comparisons between osteotomies planned and osteotomies obtained through the resected specimen was in a global mean of 1.56 millimeters (SD: 2.91) for all the cases. Based on our observations, a three-dimensional model obtained from the tumor surgical specimen is a useful tool to determine accuracy of 3D planning and surgical navigation.

Accuracy of 3-D planning and navigation in bone tumor resection.

Surgical precision in oncologic surgery is essential to achieve adequate margins in bone tumor resections. Three-dimensional preoperative planning and bone tumor resection by navigation have been introduced to orthopedic oncology in recent years. However, the accuracy of preoperative planning and navigation is unclear. The purpose of this study was to evaluate the accuracy of preoperative planning and the navigation system. A total of 28 patients were evaluated between May 2010 and February 2011. Tumor locations were the femur (n=17), pelvis (n=6), sacrum (n=2), tibia (n=2), and humerus (n=1). All resections were planned in a virtual scenario using computed tomography and magnetic resonance imaging fusion. A total of 61 planes or osteotomies were performed to resect the tumors. Postoperatively, computed tomography scans were obtained for all surgical specimens, and the specimens were 3-dimensionally reconstructed from the scans. Differences were determined by finding the distances between the osteotomies virtually programmed and those performed. The global mean of the quantitative comparisons between the osteotomies programmed and those obtained through the resected specimen was 2.52±2.32 mm for all patients. Differences between osteotomies virtually programmed and those achieved by navigation intraoperatively were minimal.