Fully Automatic Segmentation of AP Pelvis X-rays via Random Forest Regression and Hierarchical Sparse Shape Composition

Knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic and robust approach for landmarking and segmentation of both pelvis and femur in a conventional AP X-ray. Our approach is based on random forest regression and hierarchical sparse shape composition. Experiments conducted on 436 clinical AP pelvis x-rays show that our approach achieves an average point-to-curve error around 1.3 mm for femur and 2.2 mm for pelvis, both with success rates around 98%. Compared to existing methods, our approach exhibits better performance in both the robustness and the accuracy.

Plotting regression coefficients and other estimates in Stata

Graphical presentation of regression results has become increasingly popular in the scientific literature, as graphs are much easier to read than tables in many cases. In Stata such plots can be produced by the -marginsplot- command. However, while -marginsplot- is very versatile and flexible, it has two major limitations: it can only process results left behind by -margins- and it can only handle one set of results at the time. In this article I introduce a new command called -coefplot- that overcomes these limitations. It plots results from any estimation command and combines results from several models into a single graph. The default behavior of -coefplot- is to plot markers for coefficients and horizontal spikes for confidence intervals. However, -coefplot- can also produce various other types of graphs. The capabilities of -coefplot- are illustrated in this article using a series of examples.

COEFPLOT: Stata module to plot regression coefficients and other results

coefplot plots results from estimation commands or Stata matrices. Results from multiple models or matrices can be combined in a single graph. The default behavior of coefplot is to draw markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce various other types of graphs.

Plasma DNA is Elevated in Patients with Deep Vein Thrombosis

OBJECTIVE To investigate if plasma DNA is elevated in patients with deep vein thrombosis (DVT) and to determine whether there is a correlation with other biomarkers of DVT. BACKGROUND Leukocytes release DNA to form extracellular traps (ETs), which have recently been linked to experimental DVT. In baboons and mice, extracellular DNA co-localized with von Willebrand factor (VWF) in the thrombus and DNA appeared in circulation at the time of thrombus formation. ETs have not been associated with clinical DVT. SETTING From December 2008 to August 2010, patients were screened through the University of Michigan Diagnostic Vascular Unit and were divided into three distinct groups: 1) the DVT positive group, consisting of patients who were symptomatic for DVT, which was confirmed by compression duplex ultrasound (n=47); 2) the DVT negative group, consisting of patients that present with swelling and leg pain but had a negative compression duplex ultrasound, (n=28); and 3) a control group of healthy non-pregnant volunteers without signs or symptoms of active or previous DVT (n=19). Patients were excluded if they were less than 18 years of age, unwillingness to consent, pregnant, on an anticoagulant therapy, or diagnosed with isolated calf vein thrombosis. METHODS Blood was collected for circulating DNA, CRP, D-dimer, VWF activity, myeloperoxidase (MPO), ADAMTS13 and VWF. The Wells score for a patient's risk of DVT was assessed. The Receiver Operating Characteristic (ROC) curve was generated to determine the strength of the relationship between circulating DNA levels and the presence of DVT. A Spearman correlation was performed to determine the relationship between the DNA levels and the biomarkers and the Wells score. Additionally the ratio of ADAMTS13/VWF was assessed. RESULTS Our results showed that circulating DNA (a surrogate marker for NETs) was significantly elevated in DVT patients, compared to both DVT negative patients (57.7±6.3 vs. 17.9±3.5ng/mL, P<.01) and controls (57.7±6.3 vs. 23.9±2.1ng/mL, P<.01). There was a strong positive correlation with CRP (P<.01), D-dimer (P<.01), VWF (P<.01), Wells score (P<.01) and myeloperoxidase (MPO) (P<.01), along with a strong negative correlation with ADAMTS13 (P<.01) and the ADAMTS13/VWF ratio. The logistic regression model showed a strong association between plasma DNA and the presence of DVT (ROC curve was determined to be 0.814). CONCLUSIONS Plasma DNA is elevated in patients with deep vein thrombosis and correlates with biomarkers of DVT. A strong correlation between circulating DNA and MPO suggests that neutrophils may be a source of plasma DNA in patients with DVT.

Regression of the inflammatory microenvironment of the peritoneal cavity in women with endometriosis by GnRHa treatment.

OBJECTIVE To investigate the effect of gonadotropin-releasing hormone analogues (GnRHa) on the peritoneal fluid microenvironment in women with endometriosis. STUDY DESIGN Peritoneal fluid was collected from 85 women with severe endometriosis (rAFS stage III and IV) during laparoscopic surgery during the proliferative phase. Prior to surgery clinical data were collected. The concentrations of specific markers for endometriosis in the peritoneal fluid were determined using an ELISA and a comparison between peritoneal fluid markers in women using GnRHa and no hormonal treatment was performed using a non-parametric Mann-Whitney U test. RESULTS The study included peritoneal fluid from 39 patients who had been administered GnRHa (Zoladex(®)) in the three months prior to surgery and 46 from women with no hormonal treatment in this period. Concentrations of IL-8, PAPP-A, glycodelin-A and midkine were significantly reduced in the GnRHa treatment group compared to women receiving no hormonal treatment. RANTES, MCP-1, ENA-78, TNF-α, OPG, IP-10 and defensin showed no significant change between the two groups. CONCLUSIONS GnRHa mediate a significant regression in the inflammatory nature of the peritoneal microenvironment in women with endometriosis.

Independent measurement of biogenic silica in sediments by FTIR spectroscopy and PLS regression

We present an independent calibration model for the determination of biogenic silica (BSi) in sediments, developed from analysis of synthetic sediment mixtures and application of Fourier transform infrared spectroscopy (FTIRS) and partial least squares regression (PLSR) modeling. In contrast to current FTIRS applications for quantifying BSi, this new calibration is independent from conventional wet-chemical techniques and their associated measurement uncertainties. This approach also removes the need for developing internal calibrations between the two methods for individual sediments records. For the independent calibration, we produced six series of different synthetic sediment mixtures using two purified diatom extracts, with one extract mixed with quartz sand, calcite, 60/40 quartz/calcite and two different natural sediments, and a second extract mixed with one of the natural sediments. A total of 306 samples—51 samples per series—yielded BSi contents ranging from 0 to 100 %. The resulting PLSR calibration model between the FTIR spectral information and the defined BSi concentration of the synthetic sediment mixtures exhibits a strong cross-validated correlation ( R2cv = 0.97) and a low root-mean square error of cross-validation (RMSECV = 4.7 %). Application of the independent calibration to natural lacustrine and marine sediments yields robust BSi reconstructions. At present, the synthetic mixtures do not include the variation in organic matter that occurs in natural samples, which may explain the somewhat lower prediction accuracy of the calibration model for organic-rich samples.

Cardiac biomarkers and clinical scores for risk stratification in elderly patients with non-high-risk pulmonary embolism.

OBJECTIVE To determine the prognostic accuracy of cardiac biomarkers alone and in combination with clinical scores in elderly patients with non-high-risk pulmonary embolism (PE). DESIGN Ancillary analysis of a Swiss multicentre prospective cohort study. SUBJECTS A total of 230 patients aged ≥65 years with non-high-risk PE. MAIN OUTCOME MEASURES The study end-point was a composite of PE-related complications, defined as PE-related death, recurrent venous thromboembolism or major bleeding during a follow-up of 30 days. The prognostic accuracy of the Pulmonary Embolism Severity Index (PESI), the Geneva Prognostic Score (GPS), the precursor of brain natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT) was determined using sensitivity, specificity, predictive values, receiver operating characteristic (ROC) curve analysis, logistic regression and reclassification statistics. RESULTS The overall complication rate during follow-up was 8.7%. hs-cTnT achieved the highest prognostic accuracy [area under the ROC curve: 0.75, 95% confidence interval (CI): 0.63-0.86, P < 0.001). At the predefined cut-off values, the negative predictive values of the biomarkers were above 95%. For levels above the cut-off, the risk of complications increased fivefold for hs-cTnT [odds ratio (OR): 5.22, 95% CI: 1.49-18.25] and 14-fold for NT-proBNP (OR: 14.21, 95% CI: 1.73-116.93) after adjustment for both clinical scores and renal function. Reclassification statistics indicated that adding hs-cTnT to the GPS or the PESI significantly improved the prognostic accuracy of both clinical scores. CONCLUSION In elderly patients with nonmassive PE, NT-proBNP or hs-cTnT could be an adequate alternative to clinical scores for identifying low-risk individuals suitable for outpatient management.

Assessment of tumor regression of esophageal adenocarcinomas after neoadjuvant chemotherapy: comparison of 2 commonly used scoring approaches.

Histopathologic determination of tumor regression provides important prognostic information for locally advanced gastroesophageal carcinomas after neoadjuvant treatment. Regression grading systems mostly refer to the amount of therapy-induced fibrosis in relation to residual tumor or the estimated percentage of residual tumor in relation to the former tumor site. Although these methods are generally accepted, currently there is no common standard for reporting tumor regression in gastroesophageal cancers. We compared the application of these 2 major principles for assessment of tumor regression: hematoxylin and eosin-stained slides from 89 resection specimens of esophageal adenocarcinomas following neoadjuvant chemotherapy were independently reviewed by 3 pathologists from different institutions. Tumor regression was determined by the 5-tiered Mandard system (fibrosis/tumor relation) and the 4-tiered Becker system (residual tumor in %). Interobserver agreement for the Becker system showed better weighted κ values compared with the Mandard system (0.78 vs. 0.62). Evaluation of the whole embedded tumor site showed improved results (Becker: 0.83; Mandard: 0.73) as compared with only 1 representative slide (Becker: 0.68; Mandard: 0.71). Modification into simplified 3-tiered systems showed comparable interobserver agreement but better prognostic stratification for both systems (log rank Becker: P=0.015; Mandard P=0.03), with independent prognostic impact for overall survival (modified Becker: P=0.011, hazard ratio=3.07; modified Mandard: P=0.023, hazard ratio=2.72). In conclusion, both systems provide substantial to excellent interobserver agreement for estimation of tumor regression after neoadjuvant chemotherapy in esophageal adenocarcinomas. A simple 3-tiered system with the estimation of residual tumor in % (complete regression/1% to 50% residual tumor/>50% residual tumor) maintains the highest reproducibility and prognostic value.

Prognostic value of histopathological regression in 850 neoadjuvantly treated oesophagogastric adenocarcinomas.

BACKGROUND Recently, histopathological tumour regression, prevalence of signet ring cells, and localisation were reported as prognostic factors in neoadjuvantly treated oesophagogastric (junctional and gastric) cancer. This exploratory retrospective study analyses independent prognostic factors within a large patient cohort after preoperative chemotherapy including clinical and histopathological factors. METHODS In all, 850 patients presenting with oesophagogastric cancer staged cT3/4 Nany cM0/x were treated with neoadjuvant chemotherapy followed by resection in two academic centres. Patient data were documented in a prospective database and retrospectively analysed. RESULTS Of all factors prognostic on univariate analysis, only clinical response, complications, ypTNM stage, and R category were independently prognostic (P<0.01) on multivariate analysis. Tumour localisation and signet ring cells were independently prognostic only when investigator-dependent clinical response evaluation was excluded from the multivariate model. Histopathological tumour regression correlates with tumour grading, Laurén classification, clinical response, ypT, ypN, and R categories but was not identified as an independent prognostic factor. Within R0-resected patients only surgical complications and ypTNM stage were independent prognostic factors. CONCLUSIONS Only established prognostic factors like ypTNM stage, R category, and complications were identified as independent prognostic factors in resected patients after neoadjuvant chemotherapy. In contrast, histopathological tumour regression was not found as an independent prognostic marker.

Plotting regression coefficients and other estimates

Graphical display of regression results has become increasingly popular in presentations and in scientific literature because graphs are often much easier to read than tables. Such plots can be produced in Stata by the marginsplot command (see [R] marginsplot). However, while marginsplot is versatile and flexible, it has two major limitations: it can only process results left behind by margins (see [R] margins), and it can handle only one set of results at a time. In this article, I introduce a new command called coefplot that overcomes these limitations. It plots results from any estimation command and combines results from several models into one graph. The default behavior of coefplot is to plot markers for coefficients and horizontal spikes for confidence intervals. However, coefplot can also produce other types of graphs. I illustrate the capabilities of coefplot by using a series of examples.