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Background: The aim of the study was to evaluate the advantage of performing planned surgery using customized three-dimensional (3D) printed models versus performing surgery without using 3D printed models in patients with craniovertebral junction (CVJ) anomalies and traumatic CVJ fractures and dislocations. Methods: Forty-two patients with CVJ anomalies, who were planned for operative intervention in the Department of Neurosurgery at SMS Hospital from March 2019 to February 2021, were randomly divided into two groups and analyzed. First group was operated after rehearsal on a customized 3D printed model whereas the second group underwent operative intervention without the rehearsal of surgery on the 3D printed model. Results: Forty-two patients were enrolled for the study. Twenty-five of these patients had developmental CVJ anomalies, 16 had post traumatic Atlantoaxial dislocation (AAD), and one had congenital AAD. Twenty-three patients underwent surgical intervention using 3D printed models and 19 without using 3D printed models. The outcome in the two groups was compared using modified Japanese orthopedic association score (mJOA), recovery rate, incidence of complications such as screw malposition, postoperative neurological deterioration, vertebral artery (VA) injury, and radiological improvement based on Atlanto-Dental interval, the distance of the tip of dens from Wackhenheims clivus canal line, and the distance of tip of dens from the Chamberlain's line. The improvement in mJOA score postoperatively was found to be statistically significant in study group (P < 0.001) as compared to control group (P = 0.06). Recovery rate was better in study group than in control group (P = 0.023). In study group, the incidence of screw malposition and VA injury was lower than control group. Three patients deteriorated neurologically postoperatively in the control group and none in the study group. The average improvements in the radiological parameters were found to be better in study group as compared to control group postoperatively. Conclusion: The authors conclude that 3D printed models are extremely helpful in analyzing joints and VA anatomy preoperatively and are helpful in unmasking any abnormal bony and vascular anatomy effectively, making the surgeon confident about the placement of the screws intraoperatively. These 3D models help in intraoperative error minimization with better neurological outcomes in postoperative period. In our opinion, these models should be included as a basic investigation tool in patients of CVJ abnormalities. The models also offer other advantages such as preoperative simulation, teaching modules, and patient education.
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Chemo-resistance is an important factor determining the response of tumor to neoadjuvant chemotherapy (NACT). Our study was aimed to determine the role of P-glycoprotein (P-glyp) expression as a predictor of response to NACT in locally advanced breast cancer (LABC) patients with special reference to molecular subtypes. Sixty cases of locally advanced breast cancer (LABC) were subjected to trucut biopsy and the tissue samples were evaluated immunohistochemically for P-glyp, ER, PR, and Her 2 neu status. Pre- and post-NACT P-glyp expression was correlated with clinical response (using WHO criteria after three cycles of CEF regimen) and molecular subtypes. The change in the P-glyp expression before (pre-) and after (post-) NACT was statistically significant with higher stage (p = 0.02), hormonal negative molecular subtypes (p = 0.01), and poor clinical response (p = 0.01). Pre-NACT-positive P-glyp expression is associated with higher stage and hormonal negative molecular subtypes and poor clinical response. The increased expression of P-glyp induced by NACT likely explains the concept of acquired chemo-resistance and may prove as an intermediate checkpoint in determining chemo-sensitivity for further treatment so that additional doses of ineffective chemotherapy may be avoided in non-responders translating into better patient safety.
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Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison of multiple results, and facilitate algorithm sensitivity studies. The sizes of images and analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present SparkGIS, a distributed, in-memory spatial data processing framework to query, retrieve, and compare large volumes of analytical image result data for algorithm evaluation. Our approach combines the in-memory distributed processing capabilities of Apache Spark and the efficient spatial query processing of Hadoop-GIS. The experimental evaluation of SparkGIS for heatmap computations used to compare nucleus segmentation results from multiple images and analysis runs shows that SparkGIS is efficient and scalable, enabling algorithm evaluation and algorithm sensitivity studies on large datasets.