Self-expanding metal stents in malignant colonic obstruction: experiences from Sweden

<p/> <p>Background</p> <p>Acute surgery in the management of malignant colonic obstruction is associated with high morbidity and mortality. The use of self-expanding metal stents (SEMS) is an alternative method of decompressing colonic obstruction. SEMS may allow time to optimize the patient and to perform preoperative staging, converting acute surgery into elective. SEMS is also proposed as palliative treatment in patients with contraindications to open surgery. Aim: To review our experience of SEMS focusing on clinical outcome and complications. The method used was a review of 75 consecutive trials at SEMS on 71 patients based on stent-protocols and patient charts.</p> <p>Findings</p> <p>SEMS was used for palliation in 64 (85%) cases and as a bridge to surgery in 11 (15%) cases. The majority of obstructions, 53 (71%) cases, were located in the recto-sigmoid. Technical success was achieved in 65 (87%) cases and clinical decompression was achieved in 60 (80%) cases. Reasons for technical failure were inability to cannulate the stricture in 5 (7%) cases and suboptimal SEMS placement in 3 (4%) cases. Complications included 4 (5%) procedure-related bowel perforations of which 2 (3%) patients died in junction to post operative complications. Three cases of bleeding after SEMS occurred, none of which needed invasive treatment. Five of the SEMS occluded. Two cases of stent erosion were diagnosed at the time of surgery. Average survival after palliative SEMS treatment was 6 months.</p> <p>Conclusion</p> <p>Our results correspond well to previously published data and we conclude that SEMS is a relatively safe and effective method of treating malignant colonic obstruction although the risk of SEMS-related perforations has to be taken into account.</p

New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Current practice is to perform a completion axillary lymph node dissection (ALND) for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs), although fewer than half will have non-sentinel node (NSLN) metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model.</p> <p>Methods</p> <p>We constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART. Data were compiled from a multicenter Northern California and Oregon database of 784 patients who prospectively underwent SLN biopsy and completion ALND. We compared the predictive abilities of our best model and the Memorial Sloan-Kettering Breast Cancer Nomogram (Nomogram) in our dataset and an independent dataset from Northwestern University.</p> <p>Results</p> <p>285 patients had positive SLNs, of which 213 had known angiolymphatic invasion status and 171 had complete pathologic data including hormone receptor status. 264 (93%) patients had limited SLN disease (micrometastasis, 70%, or isolated tumor cells, 23%). 101 (35%) of all SLN-positive patients had tumor-involved NSLNs. Three variables (tumor size, angiolymphatic invasion, and SLN metastasis size) predicted risk in all our models. RP-ROC and boosted CART stratified patients into four risk levels. MLR informed by CART was most accurate. Using two composite predictors calculated from three variables, MLR informed by CART was more accurate than the Nomogram computed using eight predictors. In our dataset, area under ROC curve (AUC) was 0.83/0.85 for MLR (n = 213/n = 171) and 0.77 for Nomogram (n = 171). When applied to an independent dataset (n = 77), AUC was 0.74 for our model and 0.62 for Nomogram. The composite predictors in our model were the product of angiolymphatic invasion and size of SLN metastasis, and the product of tumor size and square of SLN metastasis size.</p> <p>Conclusion</p> <p>We present a new model developed from a community-based SLN database that uses only three rather than eight variables to achieve higher accuracy than the Nomogram for predicting NSLN status in two different datasets. </p