Intraarterial thrombolysis is of value in restoring the distal run off before bypass in PA presenting as acute limb-threatening ischemia. However, the results do not justify an expectant policy for asymptomatic aneurysms.

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ObjectiveThe Union for International Cancer Control (UICC) Node (N) classification is the most common used staging method for the prognosis of gastric cancer. It demands adequate, at least 16 lymph nodes (LNs) to be dissected; therefore different staging systems were invented.MethodsBetween March 2005 and March 2010, 164 patients were evaluated at the Department of General Surgery in the Kenézy Gyula Hospital and at the Department of General, Thoracic and Vascular Surgery in the Kaposi Mór Hospital. The 6th, 7th and 8th UICC N-staging systems, the number of examined LNs, the number of harvested negative LNs, the metastatic lymph node ratio (MLR) and the log odds of positive LNs (LODDS) were determined to measure their 5-year survival rates and to compare them to each other.ResultsThe overall 5-year survival rate for all patients was 55.5% with a median overall survival time of 102 months. The tumor stage, gender, UICC N-stages, MLR and the LODDS were significant prognostic factors for the 5-year survival with univariate analysis. The 6th UICC N-stage did not follow the adequate risk in comparing N2 vs. N0 and N3 vs. N0 with multivariate investigation. Comparison of performances of the residual N classifications proved that the LODDS system was first in the prediction of prognosis during the evaluation of all patients and in cases with less than 16 harvested LNs. The MLR gave the best prognostic prediction when adequate (more than or equal to 16) lymphadenectomy was performed. ConclusionsWe suggest the application of LODDS system routinely in western patients and the usage of MLR classification in cases with extended lymphadenectomy.

Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer’s and 85% user’s Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring.

To investigate the most commonly used technique, the wire-guided localization (WGL) in non-palpable breast cancer. To analyze the effective factors on positive surgical margins in our practice and determine the surgical learning curve of this method. Prospective consecutive study was performed from January 2005 to December 2011. Inclusion criteria was a non-palpable breast lesion with malignancy on preoperative histology. All lesions were localized by ultrasound or stereotactic guided wire placement. Margins 1 mm or closer were accepted as positive margins which required re-excision. To determine the learning curve of WGL method we investigated the change in the reoperation rate after primary procedure performed by "high-volume" surgeon. Two hundred and fourteen consecutive patients were enrolled. In 23 patients (10.7%) reexcision was needed. Positive surgical margins were significantly influenced by the patient's age (p = 0.03), tumor volume (p < =0.001), proportion of tumor volume/specimen volume (p < 0.001), presence of DCIS (p < 0.001), multifocality (p = 0.03) and the learning curve (p = 0.006) with univariate analysis. Only the tumor volume, presence of DCIS and the learning curve were proved as independent prognostic factor for reoperation by multivariate analysis. The reoperation rate decreased below 20% after the fortieth operation. Results of our single institutional study suggest, that this localization technique can be performed safely with very good results after 40 procedures as a learning curve for surgeons.