ICAR: endoscopic skull‐base surgery

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Electronic Health Record Intervention To Decrease Non-Guideline Imaging In Early Stage Breast Cancer

As part of the Choosing Wisely initiative to decrease low-value healthcare spending, the American Society of Clinical Oncology (ASCO) recommends against ordering PET, CT, or bone scan as part of the initial staging workup for patients with early stage breast cancer. Our two-step quality improvement project targeted to increase adherence with this guideline included in-person provider education on breast cancer imaging recommendations followed by the implementation of an electronic decision support tool (best practice advisory or BPA) in our electronic health record system. To assess impact, we compared imaging ordered for stage I/II breast cancer patients who did not receive neoadjuvant therapy and were treated in the Smilow Cancer Hospital/Yale Cancer Center health system during a pre-intervention (diagnosed 7/1/14-6/30/15) and post-intervention period (diagnosed 1/1/16-12/31/16). Cancer stage was obtained through the Yale Tumor Registry, and imaging utilization was measured for each patient during the 90-day period following diagnostic biopsy. We identified 748 women in the pre- and 764 women in the post-intervention period with stage I/II breast cancer. After the intervention, the percentage of patients for whom at least one advanced imaging test was ordered decreased from 17.2% to 13.7%. This decrease approached but did not achieve statistical significance, p = 0.060. The average number of scans ordered per patient among patients for whom imaging was ordered decreased from 1.92 (SD 0.97) to 1.70 (SD 0.71), p = 0.049. The average number of CTs ordered for patients for whom at least one CT was ordered decreased from 1.24 (SD 0.61) to 1.05 (0.22), p = 0.005. In conclusion, while the post-intervention period showed a trend toward less imaging utilization, future work will aim to identify and address remaining barriers to guideline adherence

Automated atlas-based segmentation for skull base surgical planning

Purpose: Computational surgical planning tools could help develop novel skull base surgical approaches that improve safety and patient outcomes. This defines a need for automated skull base segmentation to improve the usability of surgical planning software. The objective of this work was to design and validate an algorithm for atlas-based automated segmentation of skull base structures in individual scans in skull base surgical planning. Methods: Advanced Normalization Tools software was used to construct a synthetic CT template from 6 subject scans, and skull base structures were manually segmented to create a reference atlas. Structures were also segmented on scans from 30 additional subjects to serve as ground truth scans for accuracy testing. Landmark registration followed by Elastix deformable registration were applied to the template to register it to each of the 30 patient scans. Dice coefficient, average Hausdorff distance, and clinical usability scoring were used to compare the atlas segmentations to those of the ground truth scans. Results: The upper limit of the 95% confidence intervals for the average Hausdorff distance for all structures was less than 2 mm. For structures greater than 2.5mL in volume, the average Dice coefficient was 0.73 (range 0.59-0.82), and for structures less than 2.5 mL in volume the Dice coefficient was less than 0.7. The usability scoring survey was completed by three experts, and all structures met the criteria for acceptable effort except for the foramen spinosum, rotundum, and carotid artery, which required more than minor corrections. Conclusion: Currently available open-source algorithms, such as the Elastix deformable algorithm, can be used for automated atlas-based segmentation of skull base structures with acceptable clinical accuracy and minimal corrections with the use of the proposed atlas. The first publicly available CT template and anterior skull base segmentation atlas being released with this paper will allow for general use of automated atlas-based segmentation of the skull base. Screen reader support enabled